Wind-solar-storage-coordinated power distribution network optimization scheduling method, computer equipment and wind-solar-storage-coordinated power distribution network optimization scheduling system

Abstract

The invention relates to a wind-solar-storage-coordinated power distribution network optimization scheduling method, computer equipment and a wind-solar-storage-coordinated power distribution network optimization scheduling system. The method comprises the steps of determining intra-day predicted operation cost of the power distribution network according to intra-day wind and light cost parameters; obtaining the load side power consumption demand quantity and the source side power generation quantity in the day; according to the intra-day predicted operation cost, the intra-day load-side power consumption demand and the intra-day source-side power generation amount, determining the intra-day source-side planned power supply amount; when it is determined that power supply of the power distribution network is insufficient according to the source side planned power supply amount in the day and the source side actual power supply amount in the day, power utilization scheduling is carried out by adopting B-type excitation type demand response, so that load balance and peak load shifting of the power distribution network are achieved; and when it is determined that power supply of the power distribution network is excessive according to the source side planned power supply amount in the day and the source side actual power supply amount in the day, redundant electric energy is stored. By adopting the method, both load balance and utilization efficiency of power resources can be considered.

Description

Technical Field

[0001] The present application relates to the technical field of wind-solar-storage collaboration, and in particular to a distribution network optimization dispatching method, computer equipment, and a distribution network optimization dispatching system for wind-solar-storage collaboration. Background Art

[0002] At present, the overall capacity of the distribution network system is increasing due to the widespread grid connection of renewable energy such as wind power and photovoltaics. This trend not only improves the power supply capacity of the power grid, but also enhances its flexibility in the use of renewable energy. However, the characteristics of wind and photovoltaic energy determine that they have greater uncertainty and intermittency, which leads to load imbalance and resource waste in the operation and scheduling of the distribution network system. Summary of the invention

[0003] Based on this, it is necessary to provide a wind-solar-storage coordinated distribution network optimization scheduling method, computer equipment and wind-solar-storage coordinated distribution network optimization scheduling system that can improve the prediction accuracy of distribution network operation scheduling in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a method for optimizing the dispatching of a distribution network coordinated by wind, solar and storage, comprising:

[0005] Determine the daily forecast operation cost of the distribution network based on the daily wind and solar cost parameters;

[0006] Obtain the load-side electricity demand and source-side power generation within the day;

[0007] Determine the planned power supply on the source side for the day based on the daily predicted operating cost, the daily load-side power demand and the source-side power generation;

[0008] When the distribution network is determined to be underpowered based on the planned power supply on the source side and the actual power supply on the source side during the day, a Class B incentive demand response is adopted to dispatch electricity to achieve load balancing and peak-shaving of the distribution network;

[0009] When the distribution network has excess power supply according to the planned power supply on the source side and the actual power supply on the source side during the day, the redundant electric energy is stored.

[0010] In one embodiment, the step of determining the amount of power generated at the source side includes:

[0011] Obtain and stratify and sample wind power generation parameters and photovoltaic power generation parameters respectively;

[0012] Determine the predicted wind power generation power according to the sampled wind power generation parameters and the weights corresponding to the wind power generation parameters;

[0013] Determine the predicted photovoltaic power generation power according to the sampled photovoltaic power generation parameters and the weights corresponding to the photovoltaic power generation parameters;

[0014] The power generation on the source side is determined based on the predicted wind power generation and the predicted photovoltaic power generation.

[0015] In one embodiment, after the step of determining the intraday predicted operating cost of the distribution network according to the intraday wind and solar cost parameters, and before the step of determining the intraday source side planned power supply according to the intraday predicted operating cost, the load side power demand and the source side power generation in the day, the method further includes:

[0016] The intraday forecast operating cost is processed based on the chaotic mapping particle swarm algorithm to obtain the updated intraday forecast operating cost.

[0017] In one of the embodiments, a preset time window within a day is configured, and the preset time window is divided into a plurality of sub-windows of the same length;

[0018] In each of the sub-windows, the steps of "determining the predicted daily operation cost of the distribution network according to the intra-day wind and solar cost parameters" to "determining that the distribution network is underpowered according to the planned power supply on the source side and the actual power supply on the source side during the day, adopting Class B incentive demand response to perform power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network" are executed; or,

[0019] In each of the sub-windows, the steps of “determining the predicted daily operation cost of the distribution network according to the daily wind and solar cost parameters” to “storing redundant electric energy when determining that the distribution network has excess power supply according to the planned power supply on the source side and the actual power supply on the source side on the day” are executed;

[0020] The preset time window has a length of less than 24 hours.

[0021] In one embodiment, the method further comprises:

[0022] Determine the day-ahead forecast operation cost of the distribution network based on the day-ahead wind and solar cost parameters;

[0023] Obtain the load-side electricity demand and source-side power generation on the previous day;

[0024] Determine the planned power supply on the source side on the day before based on the day-ahead predicted operating cost, the day-ahead load-side power demand, and the day-ahead power generation on the source side;

[0025] When the distribution network is determined to be underpowered based on the planned power supply on the source side and the actual power supply on the source side the day before, Class A incentive-based demand response and price-based demand response are used to dispatch electricity to achieve load balancing and peak-shaving in the distribution network;

[0026] When the distribution network has excess power supply, the redundant electric energy is stored according to the planned power supply on the source side the day before and the actual power supply on the source side the day before.

[0027] In one embodiment, the method further comprises:

[0028] Determine the real-time predicted operating cost of the distribution network based on the real-time wind and solar cost parameters;

[0029] Obtain real-time load-side electricity demand and source-side power generation;

[0030] Determine the real-time planned power supply on the source side based on the real-time predicted operating cost, the real-time load-side power demand and the source-side power generation;

[0031] When the distribution network is determined to be underpowered based on the real-time planned power supply and the real-time actual power supply, a Class C incentive demand response is adopted to dispatch electricity to achieve load balancing and peak-shaving in the distribution network.

[0032] When the distribution network is overpowered according to the real-time planned power supply on the source side and the real-time actual power supply on the source side, the redundant electric energy is stored.

[0033] In a second aspect, the present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any method step of the above-mentioned wind, solar and storage coordinated distribution network optimization scheduling method.

[0034] In a third aspect, the present application also provides a wind-solar-storage coordinated distribution network optimization dispatching system, including:

[0035] A wind-solar power generation module, wherein the output end of the wind-solar power generation module is used to connect a load;

[0036] An energy storage module, wherein the input end of the energy storage module is connected to the output end of the wind-solar power generation module, and the output end of the energy storage module is used to connect to the load;

[0037] A controller, wherein the controller is connected to the wind and solar power generation module and the energy storage module respectively, and the controller is also used to connect the load, and the controller is used to execute the steps of any one of the methods of claims 1 to 6.

[0038] In one embodiment, the energy storage module includes a plurality of current chambers connected in series;

[0039] The controller is also used to collect the voltage of each current chamber and generate an alarm message when the voltage of the current chamber exceeds a voltage threshold.

[0040] In one embodiment, a proton exchange membrane is disposed between the cathode and the anode of the current chamber.

[0041] The above-mentioned wind-solar-storage coordinated distribution network optimization dispatching method, computer equipment and wind-solar-storage coordinated distribution network optimization dispatching system can determine the daily predicted operation cost of the distribution network based on the wind-solar cost parameters within the day, and determine the planned power supply on the source side within the day in combination with the load-side power demand and the source-side power generation within the day; according to the planned power supply on the source side within the day and the actual power supply on the source side within the day, the power supply situation of the distribution network can be determined. When the power supply is insufficient, the Class B incentive demand response can be adopted for power dispatching, and when the power supply is excessive, the redundant electric energy can be stored. The power dispatching means adopted by determining the planned power supply on the source side within the day and the actual power supply on the source side within the day can reduce the peak power demand and balance the load; at the same time, taking into account the uncertainty and intermittency of wind energy and photovoltaic energy, the redundant electric energy is stored, thereby improving the energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 This is an application environment diagram of a distribution network optimization dispatching method for wind, solar and storage collaboration in one embodiment;

[0044] Figure 2 A schematic diagram of a flow chart of a method for optimizing the dispatching of a distribution network with wind, solar and storage collaboration in one embodiment;

[0045] Figure 3 A response coefficient query table for incentive-based demand response in one embodiment;

[0046] Figure 4 It is a structural block diagram of a distribution network optimization dispatching device for wind, solar and storage collaboration in one embodiment;

[0047] Figure 5 is an internal structure diagram of a computer device in one embodiment;

[0048] Figure 6 This is a structural block diagram of a distribution network optimization and dispatching system with wind, solar and storage collaboration in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The wind-solar-storage coordinated distribution network optimization scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the dispatch center can determine the daily predicted operation cost of the distribution network based on the daily wind and solar cost parameters, and can determine the daily planned power supply on the source side in combination with the daily source-side power generation of the wind and solar power generation modules and the load-side power demand of the load; thus, the dispatch center can determine the power supply situation of the distribution network based on the daily planned power supply on the source side and the daily actual power supply on the source side, and when the power supply of the distribution network is insufficient, adopt Class B incentive demand response for power dispatching, and when the power supply of the distribution network is excessive, store the redundant electric energy in the energy storage module.

[0051] In an exemplary embodiment, Figure 2 As shown in the figure, a wind-solar-storage coordinated distribution network optimization scheduling method is provided. Figure 1 The following is an example of the dispatch center in Figure 1.

[0052] S202, determining the daily predicted operation cost of the distribution network according to the daily wind and solar cost parameters.

[0053] The intraday wind and solar cost parameters include the penalty costs for wind and solar curtailment within the day, the distribution network operation costs, the demand response dispatch costs paid by the distribution network to users, the energy purchase costs of the distribution network, and the compensation costs of the energy storage modules participating in demand response and electricity dispatching.

[0054] Based on the above intraday wind and solar cost parameters and the following formula, the intraday forecast operation cost of the distribution network can be determined: :

[0055]

[0056] in, For the day The length of time for which wind and solar power are abandoned; For the day The distribution network operation costs over time; For the day The length of time the distribution network pays the user for demand response dispatch costs; For the day The cost of purchasing energy for the distribution network over a certain period of time; For the day The compensation cost of energy storage modules participating in demand response and electricity dispatch; The unit power compensation cost of the energy storage module within a day; The power consumption of the energy storage module participating in demand response and electricity scheduling during the day.

[0057] Furthermore, the intraday forecasted operating cost can be constrained according to the power generation requirements, output power balance requirements and load demand adjustment requirements of the power generation equipment in the distribution network in actual applications to determine the minimum intraday forecasted operating cost, thereby saving resources.

[0058] S204, obtaining the load-side electricity demand and source-side power generation within the day.

[0059] The electricity demand on the load side refers to the amount of electricity required by the load connected to the distribution network; the power generation on the source side refers to the amount of electricity generated by the wind and solar power generation modules on the input side of the distribution network.

[0060] S206, determining the planned power supply on the source side for the day according to the daily predicted operating cost, the daily power demand on the load side and the power generation on the source side.

[0061] S208, when it is determined that the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side during the day, a Class B incentive demand response is adopted to perform power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network.

[0062] The actual power supply at the source side during the day can be determined based on real-time monitoring and monitoring of the output power of the power generation equipment. Exemplarily, the output power of the power generation equipment can be recorded in real time by sensors or other monitoring equipment.

[0063] S210, when it is determined that the distribution network has excess power supply according to the planned power supply on the source side and the actual power supply on the source side during the day, the redundant electric energy is stored.

[0064] The power supply situation of the distribution network can be determined based on the demand relationship of the power supply of the distribution network. When the actual power supply on the source side during the day is less than the planned power supply on the source side during the day, the distribution network is in a state of insufficient power supply; when the actual power supply on the source side during the day is greater than the planned power supply on the source side during the day, the distribution network is in a state of oversupply.

[0065] Specifically, the power supply situation of the distribution network (including but not limited to the load deviation power and the unit deviation cost of the load output) can be determined according to the following formula:

[0066]

[0067] in, is the load deviation power, is the actual power supply on the source side during the day, is the planned power supply on the source side within the day, is the unit deviation cost of load output, It is the penalty floating coefficient.

[0068] when When , it indicates that the distribution network is underpowered; when When , it is characterized by excess power supply in the distribution network.

[0069] Incentive demand response refers to the use of economic incentives to encourage power users to adjust their electricity consumption patterns, thereby helping the power system balance supply and demand. Incentive demand response is usually divided into Class A, Class B and Class C. Among them, Class A incentive demand response can respond quickly, can respond continuously for a short time, and can achieve rapid adjustment in emergency situations; Class B incentive demand response has a slower response speed than Class A incentive demand response, and is used to achieve daily load balancing and peak-shaving in the distribution network; Class C incentive demand response has the slowest response speed among the three types of incentive demand response, can respond continuously for a long time, and can be used to achieve preset load adjustment of the distribution network.

[0070] In the case of insufficient power supply in the distribution network, Class B incentive demand response can be used to encourage large power users to reduce production or use of high-energy-consuming equipment during peak hours; it can also enable power companies to remotely control the opening and closing of certain equipment of large power users who have signed agreements with power companies through direct load control means. Therefore, using Class B incentive demand response for power dispatch can achieve load balancing and peak-shaving and valley-filling in the distribution network.

[0071] When there is excess power supply in the distribution network, redundant electric energy can be stored to avoid power waste.

[0072] In the above-mentioned wind, solar and storage coordinated distribution network optimization dispatching method, the daily predicted operation cost of the distribution network can be determined according to the wind and solar cost parameters within the day, and the planned power supply on the source side can be determined in combination with the daily load-side power demand and the source-side power generation; the power supply situation of the distribution network can be determined according to the planned power supply on the source side within the day and the actual power supply on the source side within the day. When the power supply is insufficient, the Class B incentive demand response can be adopted for power dispatching, and when the power supply is excessive, the redundant electric energy can be stored. The power dispatching means determined by the planned power supply on the source side within the day and the actual power supply on the source side within the day can reduce the peak power demand and balance the load; at the same time, taking into account the uncertainty and intermittency of wind energy and photovoltaic energy, the redundant electric energy is stored, thereby improving the energy utilization efficiency.

[0073] In an exemplary embodiment, the step of determining the amount of power generated at the source side includes:

[0074] The wind power generation parameters and photovoltaic power generation parameters are obtained and stratified and sampled respectively.

[0075] Wind power generation parameters include wind speed, season, weather and time; photovoltaic power generation parameters include sunshine intensity, season, weather and time. Wind speed and sunshine intensity are different in different seasons, different weather and different time periods of the day. Therefore, wind power generation parameters and photovoltaic power generation parameters are stratified and sampled according to the above parameter types, so that the sampled wind power generation parameters and photovoltaic power generation parameters can reflect the characteristics of wind power generation and photovoltaic power generation in general and reduce errors.

[0076] The predicted wind power generation power is determined based on the sampled wind power generation parameters and the weights corresponding to the wind power generation parameters.

[0077] The predicted photovoltaic power generation power is determined according to the sampled photovoltaic power generation parameters and the weights corresponding to the photovoltaic power generation parameters.

[0078] The weights can reflect the importance of wind power generation parameters and photovoltaic power generation parameters in wind power generation and photovoltaic power generation, so as to obtain reliable predicted wind power generation power and predicted photovoltaic power generation power.

[0079] The power generation on the source side is determined based on the predicted wind power generation and the predicted photovoltaic power generation.

[0080] For example, based on the sampled wind power generation parameters and photovoltaic power generation parameters, multiple independent Monte Carlo simulations can be performed to simulate wind speed and light intensity under various conditions, thereby determining the predicted wind power generation and predicted photovoltaic power generation. Data analysis of the predicted wind power generation and predicted photovoltaic power generation can determine the peak value of the source-side power generation and the time when the source-side power generation is insufficient, so that power dispatching measures can be taken at the corresponding time to achieve load balancing and peak-shaving of the distribution network.

[0081] Furthermore, the determined source-side power generation may be compared with the historical source-side power generation to verify the accuracy of the predicted source-side power generation, thereby correcting the above-mentioned weights to obtain a more accurate source-side power generation.

[0082] The source-side power generation determined according to wind power generation parameters and photovoltaic power generation parameters can improve the accuracy of determining the source-side power generation, thereby improving the accuracy of the distribution network optimization scheduling method with wind, photovoltaic and storage coordination.

[0083] In an exemplary embodiment, after the step of determining the intraday predicted operating cost of the distribution network according to the intraday wind and solar cost parameters, and before the step of determining the intraday source-side planned power supply according to the intraday predicted operating cost, the intraday load-side power demand and the source-side power generation, the method further includes:

[0084] The intraday forecast operating cost is processed based on the chaotic mapping particle swarm algorithm to obtain the updated intraday forecast operating cost.

[0085] Based on the intraday forecast running cost, the particle swarm with the initial position and speed of the chaotic mapping particle swarm algorithm is determined, and a random sequence is generated. The global search ability of the particle swarm is enhanced by chaotic mapping. The position and speed of the particle swarm are updated according to the intraday forecast running cost and the update formula of the chaotic mapping particle swarm algorithm to determine the historical optimal position and historical optimal speed of the particles until the preset number of iterations is reached or the updated intraday forecast running cost converges.

[0086] Based on the chaotic mapping particle swarm algorithm, the intraday forecast operation cost is processed to prevent the intraday forecast operation cost from falling into the local optimal solution, thereby obtaining the minimum intraday forecast operation cost in the global range and saving energy.

[0087] In an exemplary embodiment, a preset time window within a day is configured, and the preset time window is divided into a plurality of sub-windows of the same length.

[0088] In each sub-window, execute steps "determine the intraday forecast operation cost of the distribution network based on the intraday wind and solar cost parameters" to step "determine that when the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side during the day, adopt Class B incentive demand response to perform power dispatch to achieve load balancing and peak shaving and valley filling of the distribution network."

[0089] Or, in each sub-window, execute steps "determine the daily predicted operation cost of the distribution network based on the daily wind and solar cost parameters" to step "determine the storage of redundant electric energy when the distribution network has excess power supply based on the daily planned power supply on the source side and the daily actual power supply on the source side".

[0090] The preset time window is less than 24 hours.

[0091] In the preset time window, determine the power dispatching plan corresponding to the preset time window time length, so as to achieve load balancing and peak shaving and valley filling of the distribution network within the day as a whole; in each sub-window, determine a more detailed power dispatching plan for the sub-window time length, so as to achieve more detailed load balancing and peak shaving and valley filling of the distribution network in each sub-window.

[0092] Exemplarily, the preset time window may be 4 hours, and the sub-window may be 15 minutes.

[0093] In an exemplary embodiment, the method further comprises:

[0094] According to the day-ahead wind and solar cost parameters, the day-ahead predicted operating cost of the distribution network is determined.

[0095] Obtain the load-side electricity demand and source-side power generation for the day before.

[0096] The planned power supply on the source side for the day before is determined based on the day-ahead predicted operating cost, the day-ahead load-side electricity demand and the day-ahead source-side power generation.

[0097] When it is determined that the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side the day before, Class A incentive-based demand response and price-based demand response are adopted to carry out power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network.

[0098] Class A incentive demand response is based on an agreement or contract signed with the power company, where large commercial or industrial power users promise to reduce their power usage during peak hours in order to enjoy preferential prices. In the event of a sudden increase in load or a sharp decrease in power supply, Class A incentive demand response can be used first to quickly dispatch power through economic incentives to ensure the stability of the power system.

[0099] Furthermore, the interruptible load after adopting the type A incentive demand response can be determined according to the following formula:

[0100]

[0101] in, is the interruptible load in period t, Load and load before implementing Type A incentive demand response It is the minimum electricity consumption after implementing Class A incentive demand response.

[0102] Price-based demand response is a method in which residential electricity users or small commercial electricity users choose to increase electricity consumption during periods of lower electricity prices and reduce electricity consumption during periods of higher electricity prices based on real-time electricity prices or dynamic electricity prices, thereby improving electricity consumption flexibility.

[0103] When the distribution network has excess power supply, the redundant electric energy is stored according to the planned power supply on the source side the day before and the actual power supply on the source side the day before.

[0104] Similar to intraday power dispatch, comprehensive power dispatch of the distribution network can be performed from the day-ahead dimension. The day-ahead can be any time window greater than or equal to 24 hours, which can be denoted as t.

[0105] According to the day-ahead wind and solar cost parameters and the following formula, the day-ahead predicted operating cost of the distribution network can be determined: :

[0106]

[0107] in, For the day before The penalty cost of wind and solar power abandonment in different time periods; For the day before Distribution network operation costs during the period; For the day before The demand response dispatching costs paid by the distribution network to users during the time period; For the day before The energy purchase cost of the distribution network during the period.

[0108] Specifically, the wind and solar cost parameters on each day can be determined according to the following formula:

[0109]

[0110] in, is the unit wind curtailment cost, is the unit abandonment penalty cost, For the day before The amount of wind power abandoned during the period, For the day before The amount of abandoned photovoltaic power in the time period.

[0111]

[0112] in, is the unit operating cost of the energy storage module, is the charging power of the energy storage module in the distribution network during time period t, is the discharge power of the energy storage module in the distribution network during time period t, is the unit operating cost of the electrolyzer, is the electrolyzer power in the distribution network during time period t, is the unit operating cost of the hydrogen storage system, is the amount of hydrogen charged in the hydrogen storage system during the distribution network period t, is the amount of hydrogen released by the hydrogen storage system in the distribution network during time period t, is the unit operating cost of the gas turbine, is the gas turbine power in the distribution network during time period t.

[0113]

[0114] in, For the day before The call compensation cost of the incentive-based demand response of type A during the period; For the day before The amount of Class A incentive demand response during the period.

[0115]

[0116] in, Time-of-use electricity price, is the price of natural gas; For the day before The amount of electricity purchased from the upper-level power grid during the period; For the day before The amount of gas purchased from natural gas pipeline companies during a period.

[0117] In an exemplary embodiment, the method further comprises:

[0118] Based on the real-time wind and solar cost parameters, the real-time predicted operating cost of the distribution network is determined.

[0119] Obtain real-time load-side electricity demand and source-side power generation.

[0120] Determine the real-time planned power supply on the source side based on the real-time predicted operating cost, the real-time load-side electricity demand and the source-side power generation.

[0121] When it is determined that the distribution network is underpowered based on the real-time planned power supply on the source side and the real-time actual power supply on the source side, Class C incentive demand response is adopted to carry out power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network.

[0122] Class C incentive-based demand response refers to large-scale centralized management of loads. Specifically, it can be expressed as integrating the power demand responses of multiple power users through a centralized dispatching and management system to dynamically coordinate the load as a whole. Therefore, Class C incentive-based demand response can be a long-term means of power dispatching.

[0123] When the distribution network is overpowered according to the real-time planned power supply on the source side and the real-time actual power supply on the source side, the redundant electric energy is stored.

[0124] Similar to the intraday power dispatch, the distribution network can be dispatched in a timely manner from a real-time dimension. The time window for real-time power dispatch can be a time window that is less than or equal to the sub-window of the day-ahead power dispatch. For example, a 15-minute time window can be configured and divided into 5-minute sub-windows to complete a complete real-time power dispatch in one sub-window, so as to timely correct the power dispatch means and improve the reliability of the wind, solar and storage coordinated distribution network optimization dispatch method.

[0125] The real-time predicted operating cost can be determined based on the real-time wind and solar cost parameters and the following formula :

[0126]

[0127] in, For real-time The length of time for which wind and solar power are abandoned; For real-time The distribution network operation costs over time; For real-time The length of time the distribution network pays the user for demand response dispatch costs; For real-time The cost of purchasing energy from the distribution network over a certain period of time; For real-time The compensation cost of energy storage modules participating in demand response and electricity dispatch; is the unit deviation cost of load output; is the deviation power of the load.

[0128] Furthermore, users can voluntarily schedule electricity consumption based on real-time electricity prices or real-time electricity supply and demand conditions, thereby balancing the load of the distribution network and shaving peak loads.

[0129] Specifically, the following formula can be used to determine the rebound amount of the flexible transfer load reduction in the j-type t0 period n periods before the flexible transfer load reduction in the t0 period after the user voluntarily performs electricity scheduling: :

[0130]

[0131] in,{ =B, C} means flexible transfer load reduction type B or flexible transfer load reduction type C; , , , is the rebound coefficient; The actual transfer reduction amount of flexible transfer load reduction class j before the t0 period; It is the planned transfer reduction amount of flexible transfer load reduction class j before t0 period.

[0132] The compensation cost coefficient of incentive-based demand response can be determined by the following formula:

[0133]

[0134] in, is the actual compensation cost of k-type incentive demand response, k={A, B, C}; Compensate costs for the base unit; is the response time coefficient of the corresponding incentive demand response, is the response speed coefficient of the corresponding incentive-based demand response, is the importance coefficient of the corresponding incentive-based demand response. , and Can be based on Figure 3 The form is determined.

[0135] Therefore, electricity dispatch can be carried out jointly through flexible load transfer reduction and incentive-based demand response, thereby improving the dispatch flexibility of the distribution network optimization dispatch method with wind, solar and storage collaboration.

[0136] Specifically, at night, such as 0:00-5:00 and 23:00-24:00, the power of wind power generators is large, but the load demand is small. At this time, part of the load can be transferred to the low-power consumption period by flexible transfer load reduction. At the same time, the redundant power generation can be stored to avoid the abandonment of wind power during the low-power consumption period. During the day, as the load demand increases, the power of wind and solar power generators may not meet the power demand. At this time, the load balance and peak-shaving of the distribution network can be achieved through incentive demand response. At the same time, the stored electric energy can be released for use. Therefore, by jointly dispatching electricity through flexible transfer load reduction and incentive demand response, the fluctuation of wind and solar power generation output can be effectively controlled and the power demand in multiple time periods can be balanced.

[0137] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a wind-solar-storage coordinated distribution network optimization dispatching device for implementing the above-mentioned wind-solar-storage coordinated distribution network optimization dispatching method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more wind-solar-storage coordinated distribution network optimization dispatching device embodiments provided below can refer to the limitations of the wind-solar-storage coordinated distribution network optimization dispatching method above, and will not be repeated here.

[0139] In an exemplary embodiment, Figure 4 As shown, a wind-solar-storage coordinated distribution network optimization and scheduling device 300 is provided, including: a daily predicted operating cost determination module 302, a daily data acquisition module 304, a daily source-side planned power supply determination module 306, a first measure module 308 and a second measure module 310.

[0140] The intraday forecasted operation cost determination module 302 is used to determine the intraday forecasted operation cost of the distribution network according to the intraday wind and solar cost parameters.

[0141] The intraday data acquisition module 304 is used to obtain the load-side electricity demand and source-side power generation within the day.

[0142] The daily source-side planned power supply amount determination module 306 is used to determine the daily source-side planned power supply amount according to the daily predicted operating cost, the daily load-side power demand and the source-side power generation.

[0143] The first measure module 308 is used to determine that when the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side during the day, adopt Class B incentive demand response to perform power scheduling to achieve load balancing and peak shaving in the distribution network.

[0144] The second measure module 310 is used to store redundant electric energy when it is determined that the distribution network has excess power supply according to the planned power supply on the source side and the actual power supply on the source side during the day.

[0145] In an exemplary embodiment, the intraday data acquisition module 304 includes: a stratified sampling module, a predicted wind power generation determination module, a predicted photovoltaic power generation determination module and a source-side power generation determination module.

[0146] Among them, the stratified sampling module is used to obtain and stratify and sample wind power generation parameters and photovoltaic power generation parameters respectively.

[0147] The predicted wind power generation determination module is used to determine the predicted wind power generation power according to the sampled wind power generation parameters and the weights corresponding to the wind power generation parameters.

[0148] The predicted photovoltaic power generation determination module is used to determine the predicted photovoltaic power generation power according to the sampled photovoltaic power generation parameters and the weights corresponding to the photovoltaic power generation parameters.

[0149] The source side power generation determination module is used to determine the source side power generation according to the predicted wind power generation power and the predicted photovoltaic power generation power.

[0150] In an exemplary embodiment, the wind-solar-storage coordinated distribution network optimization and dispatching device 300 also includes an update module.

[0151] The updating module is used to process the intraday forecast operating cost based on the chaotic mapping particle swarm algorithm to obtain the updated intraday forecast operating cost.

[0152] In an exemplary embodiment, a preset time window within a day is configured, and the preset time window is divided into multiple sub-windows of the same length, and the intra-day predicted operating cost determination module 302 to the first measure module 308 are run within each sub-window, or the intra-day predicted operating cost determination module 302 to the second measure module 310 are run within each sub-window; wherein the time length of the preset time window is less than 24 hours.

[0153] In an exemplary embodiment, the wind-solar-storage coordinated distribution network optimization and dispatching device 300 also includes: a day-ahead predicted operating cost determination module, a day-ahead data acquisition module, a day-ahead source-side planned power supply determination module, a third measure module and a fourth measure module.

[0154] Among them, the day-ahead predicted operating cost determination module is used to determine the day-ahead predicted operating cost of the distribution network based on the day-ahead wind and solar cost parameters.

[0155] The day-ahead data acquisition module is used to obtain the day-ahead load-side electricity demand and source-side power generation.

[0156] The module for determining the planned power supply on the source side the day before is used to determine the planned power supply on the source side the day before according to the predicted operating cost the day before, the power demand on the load side the day before and the power generation on the source side.

[0157] The third measure module is used to determine that when the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side the day before, Class A incentive demand response and price demand response are used for power dispatching to achieve load balancing and peak shaving and valley filling in the distribution network.

[0158] The fourth measure module is used to store redundant electric energy when determining that the distribution network has excess power supply based on the planned power supply on the source side the day before and the actual power supply on the source side the day before.

[0159] In an exemplary embodiment, the wind-solar-storage coordinated distribution network optimization and scheduling device 300 also includes: a real-time predicted operating cost determination module, a real-time data acquisition module, a real-time source-side planned power supply determination module, a fifth measure module and a sixth measure module.

[0160] Among them, the real-time predicted operating cost determination module is used to determine the real-time predicted operating cost of the distribution network based on the real-time wind and solar cost parameters.

[0161] The real-time data acquisition module is used to obtain the real-time load-side electricity demand and source-side power generation.

[0162] The real-time source-side planned power supply determination module is used to determine the real-time source-side planned power supply according to the real-time predicted operating cost, the real-time load-side power demand and the source-side power generation.

[0163] The fifth measure module is used to determine that when the distribution network is underpowered based on the real-time planned power supply on the source side and the real-time actual power supply on the source side, a Class C incentive demand response is adopted to perform power dispatching to achieve load balancing and peak shaving of the distribution network.

[0164] The sixth measure module is used to store redundant electric energy when it is determined that the distribution network has excess power supply according to the real-time planned power supply on the source side and the real-time actual power supply on the source side.

[0165] Each module in the above-mentioned wind, solar and storage coordinated distribution network optimization dispatching device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the predicted operating cost within the day, the load-side electricity demand within the day, the source-side power generation within the day, the source-side planned power supply within the day, and the source-side actual power supply within the day. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution network optimization scheduling method for wind, light and storage collaboration is realized.

[0167] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any method step of the above-mentioned wind-solar-storage coordinated distribution network optimization scheduling method is implemented.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any method step of the above-mentioned wind-solar-storage coordinated distribution network optimization scheduling method is implemented.

[0170] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements any method step of the above-mentioned wind-solar-storage collaborative distribution network optimization scheduling method.

[0171] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0172] In one embodiment, Figure 6 As shown, a wind-solar-storage coordinated distribution network optimization scheduling system 400 is provided, including: a wind-solar power generation module 402, an energy storage module 404 and a controller 406.

[0173] Among them, the output end of the wind-solar power generation module 402 is used to connect the load 500; the input end of the energy storage module 404 is connected to the output end of the wind-solar power generation module 402, and the output end of the energy storage module 404 is used to connect the load 500; the controller 406 is respectively connected to the wind-solar power generation module 402 and the energy storage module 404, and the controller 406 is also used to connect the load 500, and the controller 406 is used to execute any method step of the above-mentioned wind-solar-storage coordinated distribution network optimization scheduling method.

[0174] In one embodiment, the energy storage module includes a plurality of current chambers connected in series; the controller is further used to collect the voltage of each current chamber and generate an alarm message when the voltage of the current chamber exceeds a voltage threshold.

[0175] The voltage threshold includes an overcharge threshold and an over-discharge threshold. When the voltage of the current chamber is less than the over-discharge threshold, it indicates that the current chamber is in an over-discharge state; when the voltage of the current chamber is greater than the overcharge threshold, it indicates that the current chamber is in an over-charge state. The controller can generate corresponding alarm information to remind the staff to take corresponding remedial measures in time.

[0176] The generated alarm information can be displayed through sound and light modules such as buzzers and signal lights, and can also be displayed through connected terminal devices.

[0177] Among them, the relationship between the voltage of a single current chamber and the electrolysis current density of the current chamber can be expressed by the following formula:

[0178]

[0179] in, is the voltage of a single current chamber, is the reversible voltage of a single current chamber, is an ideal constant, is the Faraday constant, is the operating temperature of a single current chamber, is the cathode partial voltage of a single current chamber, is the anode voltage division of a single current chamber, is the activity of water in a single current chamber, is the internal ohmic resistance of a single current chamber, is the electrolysis current density of a single current chamber, is the charge transfer amount of a single current chamber anode, is the charge transfer amount of a single current chamber cathode, is the current exchange density of a single current chamber anode, is the current exchange density of the cathode of a single current chamber.

[0180] By adjusting parameters such as the electrolysis current density of the current chamber, the operating temperature of the current chamber, and the activity of water in the current chamber, the voltage of the current chamber can be adjusted accordingly to control the voltage of the current chamber.

[0181] By monitoring the voltage of each current chamber in the energy storage module, its working status can be monitored, so as to intuitively determine the charging and discharging status and remaining energy of the energy storage module, avoid overcharging, over-discharging or short circuit, and enhance the safety of the distribution network optimization and dispatching system coordinated by wind, solar and storage.

[0182] Furthermore, the number of series current chambers can be set according to the actual needs of the distribution network optimization dispatching system for wind, solar and energy storage collaboration, so as to adaptively increase or decrease the energy storage capacity.

[0183] Furthermore, the relationship between the input power and power consumption of the energy storage module (including but not limited to the output power of the energy storage module and the mass of hydrogen generated in the time period t) can be determined by the following formula:

[0184]

[0185]

[0186]

[0187] in, is the output power of the energy storage module; It is the indicator variable of the start / stop status of the energy storage module (0-1 variable); and is the unit conversion factor; is the active area of ​​each unit in the energy storage module; is the mass of hydrogen produced in time period t; is the energy storage conversion efficiency coefficient.

[0188] In one embodiment, a proton exchange membrane is disposed between the cathode and the anode of the current chamber.

[0189] The proton exchange membrane can make the current chamber respond quickly to charging or discharging, thereby improving the sensitivity and response speed of the energy storage module and achieving rapid start and stop actions.

[0190] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0191] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A wind-solar-storage coordinated distribution network optimization dispatching method, characterized in that: The method comprises: Determine the daily forecast operation cost of the distribution network based on the daily wind and solar cost parameters; Obtain the load-side electricity demand and source-side power generation within the day; Determine the planned power supply on the source side for the day based on the predicted operating cost for the day, the load-side power demand for the day, and the power generation on the source side; When it is determined that the distribution network is underpowered based on the planned power supply on the source side and the actual power supply on the source side during the day, a Class B incentive demand response is adopted to perform power dispatching to achieve load balancing and peak load shifting of the distribution network; When it is determined that the distribution network has excess power supply based on the planned power supply on the source side during the day and the actual power supply on the source side during the day, the redundant electric energy is stored.

2. The method according to claim 1, characterized in that The step of determining the amount of power generated at the source side comprises: Obtain and stratify and sample wind power generation parameters and photovoltaic power generation parameters respectively; Determining predicted wind power generation power according to the sampled wind power generation parameters and the weights corresponding to the wind power generation parameters; Determining predicted photovoltaic power generation power according to the sampled photovoltaic power generation parameters and weights corresponding to the photovoltaic power generation parameters; The source-side power generation is determined according to the predicted wind power generation and the predicted photovoltaic power generation.

3. The method according to claim 1, characterized in that After the step of determining the intraday predicted operation cost of the distribution network according to the intraday wind and solar cost parameters, and before the step of determining the intraday source-side planned power supply according to the intraday predicted operation cost, the load-side power demand and the source-side power generation, the method further includes: The intraday predicted operating cost is processed based on a chaotic mapping particle swarm algorithm to obtain an updated intraday predicted operating cost.

4. The method according to claim 1, characterized in that: A preset time window within a day is configured, and the preset time window is divided into multiple sub-windows of the same length; In each of the sub-windows, the steps of "determining the predicted daily operation cost of the distribution network according to the wind and solar cost parameters during the day" to "determining that the distribution network is underpowered according to the planned power supply on the source side during the day and the actual power supply on the source side during the day, adopting a Class B incentive demand response to perform power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network" are executed; or, In each of the sub-windows, the steps of "determining the predicted daily operation cost of the distribution network according to the daily wind and solar cost parameters" to "storing redundant electric energy when determining that the distribution network has excess power supply according to the planned power supply on the source side and the actual power supply on the source side during the day" are executed; The preset time window has a length of less than 24 hours.

5. The method according to claim 1, characterized in that The method further comprises: Determine the day-ahead forecast operation cost of the distribution network based on the day-ahead wind and solar cost parameters; Obtain the load-side electricity demand and source-side power generation on the previous day; Determine the planned power supply on the source side on the day before according to the predicted operating cost on the day before, the load-side power demand on the day before and the power generation on the source side on the day before; When it is determined that the power supply of the distribution network is insufficient based on the planned power supply on the source side and the actual power supply on the source side the day before, Class A incentive demand response and price demand response are adopted to perform power dispatching to achieve load balancing and peak shaving and valley filling of the distribution network; When it is determined that the distribution network has excess power supply according to the planned power supply on the source side the day before and the actual power supply on the source side the day before, the redundant electric energy is stored.

6. The method according to claim 1, characterized in that The method further comprises: Determine the real-time predicted operating cost of the distribution network based on the real-time wind and solar cost parameters; Obtain real-time load-side electricity demand and source-side power generation; Determine the real-time source-side planned power supply according to the real-time predicted operating cost, the real-time load-side power demand and the source-side power generation; When it is determined that the distribution network is underpowered according to the real-time planned power supply on the source side and the real-time actual power supply on the source side, a Class C incentive demand response is adopted to perform power dispatching to achieve load balancing and peak-shaving and valley-filling of the distribution network; When it is determined that the distribution network has excess power supply according to the real-time source-side planned power supply and the real-time source-side actual power supply, the redundant electric energy is stored.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A wind, solar and storage-coordinated distribution network optimization dispatching system, characterized in that: include: A wind-solar power generation module, wherein the output end of the wind-solar power generation module is used to connect a load; An energy storage module, wherein the input end of the energy storage module is connected to the output end of the wind-solar power generation module, and the output end of the energy storage module is used to connect to the load; A controller, wherein the controller is connected to the wind and solar power generation module and the energy storage module respectively, and the controller is also used to connect the load, and the controller is used to execute the steps of the method described in any one of claims 1 to 6.

9. The system according to claim 8, characterized in that The energy storage module includes a plurality of current chambers connected in series; The controller is also used to collect the voltage of each current chamber and generate alarm information when the voltage of the current chamber exceeds a voltage threshold.

10. The system according to claim 9, characterized in that A proton exchange membrane is arranged between the cathode and the anode of the current chamber.

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