Micro-grid energy scheduling method, device, equipment, storage medium and program product

Abstract

The invention provides a micro-grid energy scheduling method and device, equipment, a storage medium and a program product, and relates to the technical field of electric digital data processing, and the method comprises the steps: generating an initial solution set, and taking the initial solution set as a current solution set; determining the fitness of the current solution set; determining a local optimal solution set and a global optimal solution set based on the fitness; based on the local optimal solution set and the global optimal solution set, updating the current solution set to generate an updated solution set; taking the updated solution set as a current solution set, and returning to the step of determining the fitness of the current solution set until an iteration stop condition is met, thereby obtaining an optimal solution set; and performing energy scheduling on each micro-grid subsystem in a future preset time period based on the optimal power value of each micro-grid subsystem. By means of the mode, it is guaranteed that the generated optimal solution set is the optimal energy scheduling strategy of the micro-grid, the energy scheduling efficiency of the micro-grid can be effectively improved, and the operation stability and economical efficiency of the micro-grid are prevented from being affected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric digital data processing, and in particular to a micro-grid energy scheduling method, device, equipment, storage medium and program product. BACKGROUND

[0002] At present, the intelligent micro-grid industry is developing rapidly. Based on this, how to reasonably schedule the energy of the micro-grid to improve the operation stability and economy of the micro-grid has become a problem to be solved.

[0003] In some related technologies, heuristic algorithms are mostly selected to formulate the energy scheduling strategy of the micro-grid from the perspective of peak-valley arbitrage. However, the iteration initial value of the heuristic algorithm often has strong randomness, which can easily reduce the calculation efficiency of the algorithm, and even cause the iteration process to fail to converge or fall into a local optimal solution instead of a global optimal solution.

[0004] Therefore, the energy scheduling strategy generated by using the heuristic algorithm may not be the optimal energy scheduling strategy under the current power market conditions, which can cause the energy scheduling efficiency of the micro-grid to be not high, and can easily affect the operation stability and economy of the micro-grid. SUMMARY

[0005] The present application provides a micro-grid energy scheduling method, device, equipment, storage medium and program product to solve the defects of the existing micro-grid energy scheduling method that the energy scheduling efficiency of the micro-grid is not high, and the operation stability and economy of the micro-grid are easily affected.

[0006] The present application provides a micro-grid energy scheduling method applied to a micro-grid, the micro-grid includes a plurality of micro-grid subsystems, and the micro-grid energy scheduling method includes: generating an initial solution set and taking the initial solution set as a current solution set; the initial solution set includes power initial values of each micro-grid subsystem in a future preset time period, and each power initial value is randomly generated by using a logarithmic distribution under the power constraint condition of a micro-grid subsystem; determining the fitness of the current solution set; the fitness is determined based on the total revenue of the micro-grid; based on the fitness, determining a local optimal solution set and a global optimal solution set; based on the local optimal solution set and the global optimal solution set, updating each power initial value in the current solution set to generate an updated solution set; taking the updated solution set as the current solution set, and returning to the step of determining the fitness of the current solution set until the iteration stop condition is met to obtain an optimal solution set; the optimal solution set is a solution set with the highest total revenue, and the optimal solution set includes optimal power values of each micro-grid subsystem; and based on the optimal power values of each micro-grid subsystem, scheduling the energy of each micro-grid subsystem in the future preset time period.

[0007] The micro-grid energy scheduling method provided by the application comprises the following steps: determining a plurality of power instances of the micro-grid in a preset time period in the future; each power instance comprises a power consumer, a power producer, a power use starting time, a power use ending time, a power value and a power use type, and the power producer is any micro-grid subsystem; determining the cost of each power instance and the income of each power instance based on each power consumer, each power producer, each power use starting time, each power use ending time, each power value and each power use type; and determining the total income based on the cost of each power instance and the income of each power instance.

[0008] The micro-grid energy scheduling method provided by the application comprises the following steps: determining a plurality of power instances of the micro-grid in a preset time period in the future; each power instance comprises a power consumer, a power producer, a power use starting time, a power use ending time, a power value and a power use type, and the power producer is any micro-grid subsystem; determining the cost of each power instance and the income of each power instance based on each power consumer, each power producer, each power use starting time, each power use ending time, each power value and each power use type; and determining the total income based on the cost of each power instance and the income of each power instance.

[0009] The multi-task time sequence prediction method provided by the application comprises the following steps: determining a power value based on a power generation coefficient of a micro-grid subsystem; and determining a power generation coefficient based on a line loss weight correction method.

[0010] The micro-grid energy scheduling method provided by the application comprises the following steps: determining a plurality of power instances of the micro-grid in a preset time period in the future; each power instance comprises a power consumer, a power producer, a power use starting time, a power use ending time, a power value and a power use type, and the power producer is any micro-grid subsystem; determining the cost of each power instance and the income of each power instance based on each power consumer, each power producer, each power use starting time, each power use ending time, each power value and each power use type; and determining the total income based on the cost of each power instance and the income of each power instance.

[0011] The micro-grid energy scheduling method provided by the application comprises the following steps: determining a plurality of power instances of the micro-grid in a preset time period in the future; each power instance comprises a power consumer, a power producer, a power use starting time, a power use ending time, a power value and a power use type, and the power producer is any micro-grid subsystem; determining the cost of each power instance and the income of each power instance based on each power consumer, each power producer, each power use starting time, each power use ending time, each power value and each power use type; and determining the total income based on the cost of each power instance and the income of each power instance.

[0012] The application further provides a micro-grid energy scheduling device applied to a micro-grid, wherein the micro-grid comprises a plurality of micro-grid subsystems, and the micro-grid energy scheduling device comprises: an initialization module configured to generate an initial solution set and take the initial solution set as a current solution set; the initial solution set comprises power initial values of each micro-grid subsystem in a future preset time period, and each power initial value is randomly generated by using a logarithmic distribution under a power constraint condition of a micro-grid subsystem; an iteration module configured to determine fitness of the current solution set; determine a local optimal solution set and a global optimal solution set based on the fitness; update each power initial value in the current solution set based on the local optimal solution set and the global optimal solution set to generate an updated solution set; take the updated solution set as the current solution set, and return to the step of determining the fitness of the current solution set until an iteration stop condition is met to obtain an optimal solution set; the fitness is determined based on total revenue of the micro-grid, the optimal solution set is a solution set with the highest total revenue, and the optimal solution set comprises optimal power values of each micro-grid subsystem; and an energy scheduling module configured to perform energy scheduling on each micro-grid subsystem in the future preset time period based on the optimal power value of each micro-grid subsystem.

[0013] The application further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements any of the above micro-grid energy scheduling methods when executing the computer program.

[0014] The application further provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program implements any of the above micro-grid energy scheduling methods when executed by a processor.

[0015] The application further provides a computer program product comprising a computer program, and the computer program implements any of the above micro-grid energy scheduling methods when executed by a processor.

[0016] The micro-grid energy scheduling method, device, equipment, storage medium and program product provided by the present application introduce a future search algorithm based on constraint conditions and logarithmic distribution in the energy scheduling of the micro-grid. The initial value of the power of each micro-grid subsystem in a future preset time period is randomly generated in the power constraint condition of each micro-grid subsystem by using the logarithmic distribution as an initial solution set. Then, the initial solution set is taken as a current solution set, the fitness of the current solution set is determined, the fitness is determined based on the total income of the micro-grid, the local optimal solution set and the global optimal solution set are determined based on the fitness, and the current solution set is iteratively updated according to the local optimal solution set and the global optimal solution set until the optimal solution set with the highest total income of the micro-grid is obtained. Since the iterative updating process of the current solution set fully considers the influence of the local optimal solution set and the global optimal solution set, it can effectively avoid the possibility that the iterative updating process falls into local optimization and cannot realize global optimization, thereby being conducive to ensuring that the generated optimal solution set is the optimal energy scheduling strategy of the micro-grid. Finally, the energy of each micro-grid subsystem is scheduled in the future preset time period according to the optimal power value of each micro-grid subsystem in the optimal solution set, which can effectively improve the energy scheduling efficiency of the micro-grid and avoid affecting the operation stability and economy of the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a flowchart of the micro-grid energy scheduling method provided by the present application.

[0019] Figure 2 is a structural schematic diagram of the micro-grid energy scheduling device provided by the present application.

[0020] Figure 3 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] Please refer to Figure 1 , Figure 1is a flowchart of a micro-grid energy scheduling method provided by the present application. As shown in Figure 1 In the embodiment, the micro-grid energy scheduling method is applied to a micro-grid, the micro-grid includes a plurality of micro-grid subsystems, one of the micro-grid subsystems is any one of a photovoltaic system, a wind power system, an energy storage system, a charging pile and a load device, the micro-grid energy scheduling method includes steps S110 to S160, and each step is specifically as follows: S110: generating an initial solution set and taking the initial solution set as a current solution set.

[0023] The initial solution set includes a power initial value of each micro-grid subsystem in a future preset time period, and one power initial value is randomly generated in a power constraint condition of one micro-grid subsystem by using a logarithmic distribution.

[0024] Specifically, the main wiring diagram of the micro-grid and the static parameters of each micro-grid subsystem are obtained.

[0025] Further, according to the topological information shown by the main wiring diagram of the micro-grid, a metering table is configured at each topological key node of the micro-grid, for collecting information such as power, power, voltage and the like of the topological key node.

[0026] Among them, the metering table should be able to collect at least forward active power, reverse active power, power factor, forward active power, reverse active power and the like.

[0027] Optionally, the topological key nodes are mainly divided into the following three categories: (1) the power settlement node of the micro-grid and the superior large grid, such as the high-voltage metering cabinet, the total outgoing line on the transformer side and the total outgoing line on the low-voltage side, etc.

[0028] (2) the grid-connected node and the power metering node of each micro-grid subsystem, such as the grid-connected cabinet of the wind power system, the grid-connected cabinet of the photovoltaic system, the metering box of the load device, the grid-connected cabinet of the energy storage system, etc.

[0029] (3) the convergence point of the distribution line, such as the high-voltage metering cabinet of the distribution room, the incoming line cabinet of the ring network box, the incoming line of the branch box, the high-voltage side of the transformer, etc.

[0030] It should be noted that each topological key node can jointly constitute a tree structure, wherein the power settlement node of the micro-grid and the superior large grid can be regarded as the root node of the tree structure, each micro-grid subsystem can be regarded as the leaf node (i.e. the end node) of the tree structure, and the remaining topological key nodes can be regarded as the intermediate nodes between the root node and the leaf node.

[0031] Further, according to the information collected by the metering table configured at each topological key node, a grid constraint vector corresponding to each node in the tree structure is determined.

[0032] Optionally, the power value can be expressed as: [node ID of the current node, node ID of the superior node of the current node, maximum power value of the current node, minimum power value of the current node, adjustment direction]. The node ID of the current node refers to the node identification of the current node, the node ID of the superior node of the current node refers to the node identification of the superior node of the current node, the maximum power value of the current node refers to the maximum power value of the current node, the minimum power value of the current node refers to the minimum power value of the current node, and the adjustment direction refers to the direction of power value adjustment.

[0033] Optionally, the superior node of each current node can be determined according to the main wiring diagram.

[0034] Optionally, the maximum power value is expressed as follows: ; Wherein, all the subordinate nodes of each current node can be determined according to the main wiring diagram; is a maximum value function; is a summation function.

[0035] Optionally, if the node is a line, the maximum power value of the node is the rated transmission power of the line.

[0036] Optionally, if the node is a transformer, the maximum power value of the node is the rated power of the transformer.

[0037] Optionally, if the node is a bus, the maximum power value of the node is the maximum power value of the superior node.

[0038] Optionally, if the node is a charging pile, a photovoltaic system or a wind power system, the maximum power value of the node is the installed rated power.

[0039] Optionally, if the node is an energy storage system, the expression of the maximum power value of the node is: upper limit of SOC rated power; Wherein, SOC (State of Charge) refers to the proportion of the remaining available power of the energy storage system to its maximum capacity, and the value range is 0 to 1.

[0040] Optionally, if the node is a load device, the maximum power value of the node is the historical maximum actual power of the node.

[0041] Optionally, the expression of the minimum power value is as follows: .

[0042] Optionally, if the node is a line, a photovoltaic system or a wind power system, the minimum power value of the node is 0.

[0043] Optionally, if the node is a transformer, the minimum power value of the node is the no-load loss of the node.

[0044] Optionally, if the node is a load device or a charging pile, the minimum power of the node is a non-adjustable part of the total power.

[0045] Optionally, if the node is an energy storage system, the minimum power of the node is 0 or a lower limit of SOC The specific values of the rated power and the minimum power can be determined according to the charging state and the discharging state of the energy storage system.

[0046] Optionally, the adjustment direction being positive indicates a power value decrease, the adjustment direction being negative indicates a power value increase, and 0 indicates no adjustment of the power value.

[0047] For each microgrid subsystem, the power constraint condition of the microgrid subsystem can be determined according to the network constraint vector thereof, and the power constraint condition includes a maximum power, a minimum power and an adjustment direction. The power of the microgrid subsystem cannot be higher than the maximum power, the power of the microgrid subsystem cannot be lower than the minimum power, and the power of the microgrid subsystem needs to be adjusted according to the adjustment direction.

[0048] Further, the powers of all microgrid subsystems are regarded as variables. For each microgrid subsystem, within the power constraint condition of the microgrid subsystem, according to historical acquisition data of the microgrid subsystem and meteorological information in a future preset time period, a logarithmic distribution is used to randomly generate an initial power value of the microgrid subsystem in the future preset time period.

[0049] For example, assuming that the future preset time period is one day and the microgrid subsystem is a load device, a plurality of power consumption prediction values of the load device in the future one day can be generated. If 15 minutes is taken as one time point, 96 power consumption prediction values can be generated, and the average value of the 96 power consumption prediction values or any one power consumption prediction value can be taken as the initial power value of the load device in the future one day.

[0050] For example, assuming that the future preset time period is one day and the microgrid subsystem is a photovoltaic system or a wind power system, a plurality of power generation prediction values of the microgrid subsystem in the future one day can be generated. If 15 minutes is taken as one time point, 96 power generation prediction values can be generated, and the average value of the 96 power generation prediction values or any one power generation prediction value can be taken as the initial power value of the microgrid subsystem in the future one day.

[0051] Optionally, for the microgrid subsystem participating in the electricity market, the purchase price and the on-grid price of the microgrid subsystem can also be predicted.

[0052] Optionally, for each microgrid subsystem, the predicted value of the electricity quantity of the microgrid subsystem in the future preset time period can be obtained by integrating the initial power value thereof.

[0053] Optionally, the first Initial power values ​​of a microgrid subsystem within a predetermined time period in the future. The expression is as follows: ; in, Indicates the first Lower power limit of a microgrid subsystem; Indicates the first Power limit of a microgrid subsystem; It represents a random number between 1 and e (where e is a natural constant).

[0054] Furthermore, assuming the microgrid includes ( Given (a positive integer) microgrid subsystems, after obtaining the initial power value of each microgrid subsystem within a preset future time period, all initial power values ​​can be used as the initial solution set. Then we have: .

[0055] Furthermore, the initial solution set As the current solution set, it is ready for iterative updates.

[0056] S120: Determine the fitness of the current solution set.

[0057] Fitness is determined based on the total revenue of the microgrid.

[0058] Specifically, since the total revenue of a microgrid is related to the power of each microgrid subsystem, it can be determined based on the initial solution set. Calculate the total revenue of the microgrid under the current conditions, and use the total revenue as the fitness of the current solution set. .

[0059] S130: Determine the local optimal solution set and the global optimal solution set based on fitness.

[0060] Specifically, for each iteration, the local optimum goal of this iteration is to maximize the fitness. Maximize the fitness in a single iteration The largest solution set is the local optimum. For all iteration rounds, up to the current iteration, select the fitness from all iteration rounds. The maximum value, and fitness The solution set corresponding to the maximum value is taken as the global optimal solution set. .

[0061] S140: Based on the local optimal solution set and the global optimal solution set, update the initial power value of each power in the current solution set to generate an updated solution set.

[0062] Specifically, the update solution set The generation process can be represented as: ; ; ; wherein, represents the current solution set; represents the local optimal solution set of the current iteration; represents a random number between 1 and e (e is a natural constant); and are intermediate variables participating in the calculation; represents the global optimal solution set of the current iteration; represents the updated solution set.

[0063] Optionally, for the first iteration, the update process of the initial solution set can be represented as: .

[0064] S150: Take the updated solution set as the current solution set, and return to the step of determining the fitness of the current solution set until the iteration stopping condition is met, and obtain the optimal solution set.

[0065] The optimal solution set is the solution set with the highest total revenue, and the optimal solution set includes the optimal power value of each micro-grid subsystem.

[0066] Specifically, after obtaining the updated solution set , the updated solution set is taken as the current solution set, and the step of determining the fitness of the current solution set is returned until the iteration stopping condition is met, and the optimal solution set is obtained, which is the optimal energy scheduling strategy of each micro-grid subsystem.

[0067] Optionally, the iteration stopping condition is that the number of iterations reaches a preset number threshold .

[0068] Optionally, the iteration stopping condition is that after multiple consecutive iterations, the change difference of the global optimal solution set is less than a preset change threshold .

[0069] S160: Based on the optimal power value of each micro-grid subsystem, energy scheduling is performed on each micro-grid subsystem within a future preset time period.

[0070] The micro-grid energy scheduling method provided by the embodiment introduces a future search algorithm based on constraint conditions and a logarithmic distribution in the energy scheduling of the micro-grid. The power initial value of each micro-grid subsystem in a future preset time period is randomly generated in the power constraint condition of each micro-grid subsystem by using the logarithmic distribution as an initial solution set. The initial solution set is taken as a current solution set, the fitness of the current solution set is determined, the fitness is determined based on the total income of the micro-grid, the local optimal solution set and the global optimal solution set are determined based on the fitness, and the current solution set is iteratively updated according to the local optimal solution set and the global optimal solution set until the optimal solution set with the highest total income of the micro-grid is obtained. Since the iterative updating process of the current solution set fully considers the influence of the local optimal solution set and the global optimal solution set, the possibility that the iterative updating process falls into local optimization and cannot achieve global optimization can be effectively avoided, thereby being beneficial to ensuring that the generated optimal solution set is the optimal energy scheduling strategy of the micro-grid. Finally, the energy of each micro-grid subsystem is scheduled in the future preset time period according to the optimal power value of each micro-grid subsystem in the optimal solution set, which can effectively improve the energy scheduling efficiency of the micro-grid and avoid affecting the operation stability and economy of the micro-grid.

[0071] In some embodiments, each micro-grid subsystem satisfies the up-regulation point constraint, the down-regulation point constraint, and the non-regulation point constraint. The up-regulation point constraint is the energy scheduling constraint of the micro-grid subsystem at the up-regulation point, the down-regulation point constraint is the energy scheduling constraint of the micro-grid subsystem at the down-regulation point, and the non-regulation point constraint is the energy scheduling constraint of the micro-grid subsystem at the non-regulation point. The up-regulation point is a time point at which the price of purchasing electricity from the power grid is less than a first preset threshold in the future preset time period, the down-regulation point is a time point at which the price of purchasing electricity from the power grid is greater than a second preset threshold in the future preset time period, and the non-regulation point is a time point other than the up-regulation point and the down-regulation point in the future preset time period.

[0072] Specifically, the up-regulation point is a time point at which the price of purchasing electricity from the power grid is less than a first preset threshold in the future preset time period. Since the price of purchasing electricity from the power grid is low, the micro-grid subsystem can be encouraged to use more electricity, that is, the power of the micro-grid subsystem can be increased at the up-regulation point.

[0073] Optionally, the price of purchasing electricity from the power grid at a plurality of time points in the future preset time period can be predicted first, and then a plurality of time points (for example, 6 time points) with the lowest price of purchasing electricity from the power grid are selected as the up-regulation points.

[0074] Optionally, the up-regulation point constraint is specifically that if the micro-grid subsystem is an energy storage system, only the energy storage system is allowed to charge, and the adjustment direction is negative; if the micro-grid subsystem is a photovoltaic system or a wind power system, the photovoltaic system or the wind power system is allowed to adjust the power, and the adjustment direction is negative; if the micro-grid subsystem is a load device, the adjustment direction is negative.

[0075] Similarly, the down-regulation point is a time point in the future preset time period in which the grid electricity purchase price is greater than the second preset threshold. Since the grid electricity purchase price is high, the micro-grid subsystem can be restricted to use less electricity, i.e., the power of the micro-grid subsystem can be reduced at the down-regulation point.

[0076] Optionally, the grid electricity purchase prices at a plurality of time points in the future preset time period can be predicted first, and then a plurality of time points (e.g., 6 time points) with the highest grid electricity purchase prices are selected as the down-regulation points.

[0077] Optionally, the down-regulation point constraint is specifically: if the micro-grid subsystem is an energy storage system, only the energy storage system is allowed to discharge, and the adjustment direction is positive; if the micro-grid subsystem is a photovoltaic system or a wind power system, the photovoltaic system or the wind power system is not allowed to increase power; and if the micro-grid subsystem is a load device, the adjustment direction is positive.

[0078] Understandably, the non-regulation point is a time point in the future preset time period other than the up-regulation point and the down-regulation point, and the non-regulation point constraint is specifically: the power of the micro-grid subsystem is not adjusted at the non-regulation point, and the adjustment direction is 0.

[0079] For example, at the non-regulation point, the power of the photovoltaic system, the wind power system, the energy storage system, the charging pile, and the load device are all in an unadjustable state.

[0080] In some embodiments, the total income is determined based on the following steps: determining a plurality of electricity instances of the micro-grid in a future preset time period; each electricity instance includes an electricity consumer, an electricity producer, an electricity use start time, an electricity use end time, an electricity value, and an electricity use type, and the electricity producer is any micro-grid subsystem; determining the cost of each electricity instance and the income of each electricity instance based on each electricity consumer, each electricity producer, each electricity use start time, each electricity use end time, each electricity value, and each electricity use type; and determining the total income based on the cost of each electricity instance and the income of each electricity instance.

[0081] The format of the electricity instance is defined, and the electricity instance should have uniqueness, and the parameter objid can be used as the unique identifier of the electricity instance.

[0082] Optionally, each electricity instance can be represented as: [objid, electricity consumer, electricity producer, electricity use start time, electricity use end time, electricity value, electricity use type].

[0083] Wherein, the electricity consumer refers to the electricity user; the electricity producer refers to the electricity source; the electricity use starting time refers to the electricity metering time starting point; the electricity use ending time refers to the electricity metering time ending point; the electricity value refers to the corrected electricity value; and the electricity use type includes power purchase, online, energy storage system charging, charging pile electricity, load equipment electricity, etc.

[0084] Specifically, a plurality of initial electricity instances of the micro-grid in the future preset time period are generated first.

[0085] For example, the plurality of initial electricity instances in the future preset time period can be generated according to the electricity use type, and the initial electricity instance is shown in Table 1.

[0086] Table 1

[0087] Further, each initial electricity instance is split and refined to supplement information such as electricity consumers and electricity producers, and a plurality of electricity instances of the micro-grid in the future preset time period are generated.

[0088] Specifically, whether all micro-grid subsystems have power generation behavior or discharge behavior in the future preset time period is predicted and judged first, if a micro-grid subsystem has power generation behavior or discharge behavior in the future preset time period, the micro-grid subsystem is recorded, and according to the electricity use type of the micro-grid subsystem, a plurality of electricity instances of the micro-grid in the future preset time period are generated, and the electricity instance is shown in Table 2.

[0089] Table 2 Wherein, the charging pile is a V2G charging pile (‌Vehicle-to-Grid Charging Station, electric vehicle and power grid two-way interactive charging).

[0090] It should be noted that each electricity instance not only includes the electricity consumer, the electricity producer and the electricity value, but also includes the electricity use starting time, the electricity use ending time and the electricity use type, so as to calculate the total income of the micro-grid subsequently.

[0091] Further, based on the electricity consumer, the electricity producer, the electricity use starting time, the electricity use ending time, the electricity value and the electricity use type in each electricity instance, the cost of each electricity instance and the income of each electricity instance are determined respectively, and based on the cost of each electricity instance and the income of each electricity instance, the total income is determined.

[0092] In the actual operation of the micro-grid, the micro-grid subsystems such as photovoltaic systems, energy storage systems, load devices, charging piles, etc. may not be all operated and managed by the micro-grid operator, and some of the micro-grid subsystems in the micro-grid may be operated and managed by a third-party operator. For example, the micro-grid operator can sign an energy contract with the third-party operator to entrust the third-party operator to manage some of the micro-grid subsystems in a centralized manner, and this operation mode will also affect the calculation of the total income of the micro-grid.

[0093] Specifically, for the price of commercial power, if the micro-grid subsystem is operated by the micro-grid operator, the price of commercial power is the purchase price of electricity between the micro-grid and the power grid, which can be determined based on the power market or local regulations, for example, determined based on one-part electricity price, two-part electricity price or peak-valley electricity price, etc.; if the micro-grid subsystem is operated by the third-party operator, the price of commercial power is the electricity price agreed in the energy contract.

[0094] Optionally, the purchase price of electricity determined based on the power market is an equivalent value, and the calculation formula is as follows: ; wherein, represents the proportion of medium and long-term market purchase of electricity, generally taking 0.8-0.9; represents the proportion of day-ahead spot market purchase of electricity, generally taking 0.8-1.2; represents the purchase price of electricity in the medium and long-term market; represents the purchase price of electricity in the day-ahead spot market; represents the purchase price of electricity in the real-time spot market.

[0095] Optionally, the actual purchase price of commercial power of the micro-grid operator is related to the signed energy contract, and the calculation formula is as follows: ; wherein, represents the purchase price of commercial power; represents a correction coefficient; represents the discount of on-grid link loss cost; represents government funds and surcharges; represents the discount of system operation cost of the micro-grid; represents the price of kilowatt-hour transmission and distribution.

[0096] Specifically, for the on-grid price, if the micro-grid subsystem is operated by the micro-grid operator, the on-grid price is the settlement on-grid price between the micro-grid and the power grid, which is determined by the mechanism price and the power market price; if the micro-grid subsystem is operated by the third-party operator, the on-grid price is the purchase price agreed in the energy contract.

[0097] In addition, other costs need to be considered in the calculation of the total revenue of the micro-grid, including the loss of the power producer, the additional income of the power producer and the additional cost of the power producer.

[0098] The loss of the power producer is borne by the power producer and is not included in the income of the power producer. The additional income of the power producer includes charging service fees, parking space fees, green electricity fees, operation and maintenance service fees, etc. The additional income of the power producer can be determined according to the contract or according to the relevant local regulations. The additional cost of the power producer includes the consumption rate assessment fee, the power factor assessment fee, the cost of the demand electricity fee, etc. The additional cost of the power producer can be determined according to the relevant local regulations.

[0099] Further, after understanding the above price regulations, all electricity instances can be classified according to the revenue items of the micro-grid subsystem, the electricity consumers and the electricity producers in each electricity instance. The revenue items of the micro-grid subsystem are shown in Table 3.

[0100] Table 3 Further, after classifying all electricity instances according to the revenue items of the micro-grid subsystem, the electricity consumers and the electricity producers in each electricity instance, the cost of each electricity instance and the income of each electricity instance can be determined according to the electricity use start time, the electricity use end time, the electricity value and the electricity use type in each electricity instance.

[0101] Further, the total cost of the micro-grid is calculated according to the costs of all electricity instances. The total cost of the micro-grid is the sum of the costs of all electricity instances and the additional costs, and the calculation formula of the total cost is as follows: ; The main cost refers to the costs of all electricity instances. The additional cost refers to the additional costs of all electricity instances.

[0102] The main cost of the electricity instance is borne by its electricity consumer. The additional costs such as the consumption rate assessment fee, the power factor assessment fee and the cost of the demand electricity fee are borne by the micro-grid operator.

[0103] Similarly, the total revenue of the micro-grid is calculated according to the incomes of all electricity instances. The total revenue of the micro-grid is the sum of the incomes of all electricity instances and the additional incomes, and the calculation formula of the total revenue is as follows: ; The main income refers to the incomes of all electricity instances. The additional income refers to the additional incomes of all electricity instances, which are obtained by the micro-grid operator.

[0104] Further, the total income of the micro-grid is calculated according to the total cost of the micro-grid and the total income of the micro-grid, and the total income of the micro-grid is calculated according to the total cost of the micro-grid and the total income of the micro-grid The calculation formula of the total income of the micro-grid is as follows: .

[0105] In some embodiments, the power value is determined based on a power generation coefficient of a micro-grid subsystem, and the power generation coefficient is calculated based on a line loss weight correction method.

[0106] Please continue to refer to Table 2. As shown in Table 2, the power value is determined based on a power generation coefficient of a micro-grid subsystem.

[0107] Preferably, the power generation coefficient is calculated based on a line loss weight correction method.

[0108] In some embodiments, the power generation coefficient is determined based on the following steps: determining the total power generation of the micro-grid and the total power consumption of the micro-grid; determining the total line loss rate of the micro-grid based on the total power generation and the total power consumption; determining the line loss weight of a micro-grid subsystem based on the load moment of the micro-grid subsystem; determining the weighted line loss of a micro-grid subsystem based on the total line loss rate and the line loss weight; and determining the power generation coefficient of a micro-grid subsystem based on the weighted line loss and the power generation of the micro-grid subsystem.

[0109] Specifically, the total power generation of the micro-grid and the total power consumption of the micro-grid are first determined, and the total line loss rate of the micro-grid is determined based on the total power generation and the total power consumption, and the calculation formula of the total line loss rate is as follows: .

[0110] Further, the line loss weight of a micro-grid subsystem is determined based on the load moment of the micro-grid subsystem.

[0111] Specifically, it is first predicted and determined whether all micro-grid subsystems have power generation behavior or power discharge behavior in a future preset time period, and if a micro-grid subsystem has power generation behavior or power discharge behavior in the future preset time period, the micro-grid subsystem is recorded.

[0112] For each recorded micro-grid subsystem , the load moment of the micro-grid subsystem is first calculated, and the calculation formula of the load moment is as follows: ; Wherein, the load moment of the micro-grid subsystem refers to the load moment of the micro-grid subsystem ; the active power refers to the active power of the micro-grid subsystem ; and the reactive power total resistance to the computing node; power factor refers to the power factor of the micro-grid subsystem .

[0113] Optionally, the computing node is a superior node of the micro-grid subsystem in the tree structure.

[0114] Further, according to the load moment of the micro-grid subsystem , a line loss weight of the micro-grid subsystem is calculated, and the calculation formula of the line loss weight is as follows: ; wherein, the line loss weight refers to the line loss weight of the micro-grid subsystem ; refers to the sum of the load moments of all recorded micro-grid subsystems.

[0115] Further, for each recorded micro-grid subsystem , a weight line loss of the micro-grid subsystem is determined based on the total line loss rate and the line loss weight of the micro-grid subsystem , and the calculation formula of the weight line loss is as follows: ; wherein, the weight line loss refers to the weight line loss of the micro-grid subsystem .

[0116] Further, for each recorded micro-grid subsystem , a power generation coefficient of the micro-grid subsystem is determined based on the weight line loss of the micro-grid subsystem and the power generation amount of the micro-grid subsystem , and the calculation formula of the power generation coefficient is as follows: ; wherein, the power generation coefficient refers to the power generation coefficient of the micro-grid subsystem ; refers to the sum of the differences of all recorded micro-grid subsystems, and the difference refers to the difference between the power generation amount and the weight line loss.

[0117] In some embodiments, one micro-grid subsystem is any one of a photovoltaic system, a wind power system, an energy storage system, a charging pile and a load device; the total power generation amount is determined based on the power generation amount of the photovoltaic system, the power generation amount of the wind power system, the discharge amount of the energy storage system, the discharge amount of the charging pile and the power purchase amount of the commercial power; and the total power consumption amount is determined based on the power feeding amount, the power consumption amount of the charging pile, the power consumption amount of the load device and the charging amount of the energy storage system.

[0118] Specifically, the micro-grid includes a plurality of micro-grid subsystems, one of which is any one of a photovoltaic system, a wind power system, an energy storage system, a charging pile and a load device.

[0119] Optionally, the total power generation is calculated according to the following formula: .

[0120] Wherein, the total power generation is the total power generation in the future preset time period.

[0121] Optionally, the future preset time period is divided into a plurality of time segments (for example, 30 seconds for a time segment, 1 minute for a time segment or 5 minutes for a time segment, etc.), and the power generation of the micro-grid in each time segment is calculated respectively, and the sum of the power generation of the micro-grid in all time segments is taken as the total power generation.

[0122] Similarly, the total power consumption is calculated according to the following formula: ; Wherein, the total power consumption is the total power consumption in the future preset time period.

[0123] The micro-grid energy scheduling method provided by the embodiment can support various power consumption prices, grid trading prices and contract prices, and can meet the energy operation scheduling under the self-operation mode and the third-party operation mode of the micro-grid. Secondly, it can support flexible combination of various types of micro-grid subsystems, including photovoltaic systems, wind power systems, energy storage systems, charging piles and load devices, and can meet the operation needs of different micro-grids. In addition, a future search algorithm based on reinforcement constraints and logarithmic distribution is introduced in the energy scheduling of the micro-grid. The algorithm has high calculation efficiency and fast convergence, can avoid the situation that the algorithm iteration falls into local optimum, can quickly generate the energy scheduling strategy for intraday adjustment, and is beneficial to improve the operation stability and economy of the micro-grid.

[0124] The application further provides a micro-grid energy scheduling device. Figure 2 , Figure 2 is a structural schematic diagram of the micro-grid energy scheduling device provided by the application. In the embodiment, the micro-grid energy scheduling device includes an initialization module 210, an iteration module 220 and an energy scheduling module 230.

[0125] The initialization module 210 is used to generate an initial solution set and take the initial solution set as a current solution set.

[0126] The initial solution set includes a power initial value of each micro-grid subsystem in the future preset time period, and one power initial value is randomly generated in the power constraint condition of one micro-grid subsystem by using logarithmic distribution.

[0127] The iteration module 220 is configured to determine the fitness of the current solution set; determine a local optimal solution set and a global optimal solution set based on the fitness; update each power initial value in the current solution set based on the local optimal solution set and the global optimal solution set to generate an updated solution set; and take the updated solution set as the current solution set and return to the step of determining the fitness of the current solution set until an iteration stop condition is met, and obtain the optimal solution set.

[0128] The fitness is determined based on the total revenue of the micro-grid, and the optimal solution set is a solution set with the highest total revenue, and the optimal solution set includes optimal power values of each micro-grid subsystem.

[0129] The energy scheduling module 230 is configured to perform energy scheduling on each micro-grid subsystem in a future preset time period based on the optimal power value of each micro-grid subsystem.

[0130] In some embodiments, each micro-grid subsystem satisfies an up-regulation point constraint, a down-regulation point constraint and a non-regulation point constraint; the up-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at an up-regulation point, the down-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at a down-regulation point, and the non-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at a non-regulation point; the up-regulation point is a time point at which a price of purchasing power from a power grid is less than a first preset threshold in the future preset time period, the down-regulation point is a time point at which the price of purchasing power from the power grid is greater than a second preset threshold in the future preset time period, and the non-regulation point is a time point other than the up-regulation point and the down-regulation point in the future preset time period.

[0131] In some embodiments, the total revenue is determined based on the following steps: determining a plurality of power instances of the micro-grid in the future preset time period; each power instance includes a power consumer, a power producer, a power use start time, a power use end time, a power value and a power use type, and the power producer is any micro-grid subsystem; determining a cost of each power instance and a revenue of each power instance based on each power consumer, each power producer, each power use start time, each power use end time, each power value and each power use type; and determining the total revenue based on the cost of each power instance and the revenue of each power instance.

[0132] In some embodiments, one power value is determined based on a power generation coefficient of one micro-grid subsystem, and one power generation coefficient is calculated based on a line loss weight correction method.

[0133] In some embodiments, the power generation coefficient of one micro-grid subsystem is determined based on the following steps: determining the total power generation of the micro-grid and the total power consumption of the micro-grid; determining the total line loss rate of the micro-grid based on the total power generation and the total power consumption; determining the line loss weight of one micro-grid subsystem based on the load moment of the micro-grid subsystem; determining the weighted line loss of one micro-grid subsystem based on the total line loss rate and the line loss weight; and determining the power generation coefficient of one micro-grid subsystem based on the weighted line loss and the power generation of one micro-grid subsystem.

[0134] In some embodiments, the micro-grid subsystem is any one of a photovoltaic system, a wind power system, an energy storage system, a charging pile and a load device; the total power generation is determined based on the power generation of the photovoltaic system, the power generation of the wind power system, the discharge power of the energy storage system, the discharge power of the charging pile and the power purchased from the power grid; and the total power consumption is determined based on the power fed into the power grid, the power consumption of the charging pile, the power consumption of the load device and the charging power of the energy storage system.

[0135] The present application also provides an electronic device. Figure 3 is a structural schematic diagram of the electronic device provided by the present application, as Figure 3 shown, the electronic device can include a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 complete mutual communication through the communication bus 340. The processor 310 can invoke the logical instructions in the memory 330 to execute the micro-grid energy scheduling method.

[0136] In addition, the logical instructions in the memory 330 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product stored in a storage medium includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk and various program code storage media.

[0137] The present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the micro-grid energy scheduling method provided by the above-mentioned methods.

[0138] The present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the microgrid energy dispatching methods provided by the above methods.

[0139] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0140] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microgrid energy scheduling method, characterized in that, The method is applied to a micro-grid, and the micro-grid comprises a plurality of micro-grid subsystems. An initial solution set is generated, and the initial solution set is taken as a current solution set; the initial solution set comprises power initial values of each of the micro-grid subsystems in a preset future time period; one of the power initial values is randomly generated by using a logarithmic distribution within a power constraint condition of one of the micro-grid subsystems; An adaptability of the current solution set is determined; the adaptability is determined based on a total benefit of the micro-grid; Based on the adaptability, a local optimal solution set and a global optimal solution set are determined; Based on the local optimal solution set and the global optimal solution set, each of the power initial values in the current solution set is updated to generate an updated solution set; The updated solution set is taken as the current solution set, and the step of determining the adaptability of the current solution set is returned to until an iteration stop condition is met to obtain an optimal solution set; the optimal solution set is a solution set with the highest total benefit, and the optimal solution set comprises optimal power values of each of the micro-grid subsystems; Based on the optimal power values of each of the micro-grid subsystems, energy scheduling is performed on each of the micro-grid subsystems in the preset future time period.

2. The microgrid energy scheduling method of claim 1, wherein, Each of the micro-grid subsystems satisfies an up-regulation point constraint, a down-regulation point constraint and a non-regulation point constraint; The up-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at an up-regulation point, the down-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at a down-regulation point, and the non-regulation point constraint is an energy scheduling constraint of the micro-grid subsystem at a non-regulation point; The up-regulation point is a time point at which a price of a commercial power purchase is less than a first preset threshold in the preset future time period, the down-regulation point is a time point at which the price of the commercial power purchase is greater than a second preset threshold in the preset future time period, and the non-regulation point is a time point other than the up-regulation point and the down-regulation point in the preset future time period.

3. The microgrid energy scheduling method of claim 1, wherein, The total benefit is determined based on the following steps: A plurality of power instances of the micro-grid in the preset future time period are determined; each of the power instances comprises a power consumer, a power producer, a power use start time, a power use end time, a power value and a power use type, and the power producer is any of the micro-grid subsystems; Based on each of the power consumers, each of the power producers, each of the power use start times, each of the power use end times, each of the power values and each of the power use types, a cost of each of the power instances and an income of each of the power instances are respectively determined; Based on the cost of each of the power instances and the income of each of the power instances, the total benefit is determined.

4. The microgrid energy scheduling method of claim 3, wherein, One of the power values is determined based on a power generation coefficient of one of the micro-grid subsystems, and one of the power generation coefficients is calculated based on a line loss weight correction method.

5. The microgrid energy scheduling method of claim 4, wherein, One of the power generation coefficients is determined based on the following steps: A total power generation of the micro-grid and a total power consumption of the micro-grid are determined; Based on the total power generation and the total power consumption, a total line loss rate of the micro-grid is determined; determine a line loss weight of the micro-grid subsystem based on a load moment of the micro-grid subsystem; determine a weighted line loss of the micro-grid subsystem based on the total bus loss rate and the line loss weight; determine the generation coefficient of the micro-grid subsystem based on the weighted line loss and a generation amount of the micro-grid subsystem.

6. The microgrid energy scheduling method of claim 5, wherein, the micro-grid subsystem is any one of a photovoltaic system, a wind power system, an energy storage system, a charging pile, and a load device; the total generation amount is determined based on a generation amount of the photovoltaic system, a generation amount of the wind power system, a discharge amount of the energy storage system, a discharge amount of the charging pile, and a power purchase amount of a commercial power; the total power consumption amount is determined based on a power feeding amount, a power consumption amount of the charging pile, a power consumption amount of the load device, and a charging amount of the energy storage system.

7. A microgrid energy scheduling device, characterized by, The micro-grid energy scheduling device is applied to a micro-grid including a plurality of micro-grid subsystems, and includes: an initialization module configured to generate an initial solution set and take the initial solution set as a current solution set, wherein the initial solution set includes power initial values of each micro-grid subsystem in a future preset time period, and each power initial value is randomly generated in a power constraint condition of a micro-grid subsystem by using a logarithmic distribution; an iteration module configured to determine an adaptability of the current solution set, determine a local optimal solution set and a global optimal solution set based on the adaptability, update each power initial value in the current solution set based on the local optimal solution set and the global optimal solution set to generate an updated solution set, take the updated solution set as the current solution set, and return to the step of determining the adaptability of the current solution set until an iteration stop condition is met to obtain an optimal solution set, wherein the adaptability is determined based on a total income of the micro-grid, the optimal solution set is a solution set with the highest total income, and the optimal solution set includes optimal power values of each micro-grid subsystem; an energy scheduling module configured to perform energy scheduling on each micro-grid subsystem in the future preset time period based on the optimal power values of each micro-grid subsystem.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor executes the computer program to implement the micro-grid energy scheduling method according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the micro-grid energy scheduling method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the micro-grid energy scheduling method according to any one of claims 1 to 6.

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