A method, system, embedded EMS and medium for autonomous energy regulation in a power station area

Abstract

The present application provides an autonomous method, system, embedded EMS and medium for energy regulation in a substation, and the method includes: obtaining historical load power consumption, characteristic parameters of power generation elements of power generation equipment and attribute parameters of energy storage equipment, and then determining predicted load power and predicted power generation power based on historical load power consumption, characteristic parameters of power generation elements and convolutional neural network. Determine the power control strategy for the energy storage equipment in a preset future time period based on the aforementioned data, and calculate the adjustable power of the distribution substation based on the predicted load power and the power control strategy for the energy storage equipment, and feed back the adjustable power to the upper power grid, so that the upper power grid can issue power dispatching instructions based on the adjustable power, and finally control the charging and discharging power of the energy storage equipment in the future time period according to the power control strategy. In this way, the substation can determine the energy regulation strategy through the embedded EMS, so that it has certain intelligent analysis and autonomous operation functions in energy management and scheduling.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an autonomous method, system, embedded EMS and medium for energy regulation in a substation. Background Art

[0002] The current new energy power supply field often has a variety of energy types (such as wind power, photovoltaic power, and hydropower), and its energy supply has the characteristics of randomness and intermittency. For the current distribution substations, due to the increase in energy types, the distribution substations need more precise control in energy management and power dispatch. However, the current distribution substations lack intelligent analysis and autonomous operation functions in energy management and dispatch, making it difficult to handle energy management and dispatch issues in new energy scenarios. Summary of the invention

[0003] Based on the above problems, in order to enable the distribution station area to have certain intelligent analysis and autonomous operation functions in energy management and scheduling, the embodiments of the present application provide a substation area energy regulation and autonomous method, system, embedded EMS and medium.

[0004] The embodiments of the present application disclose the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides an autonomous method for energy regulation in a substation area, which is applied to an embedded EMS in a distribution substation area, wherein the distribution substation area includes energy storage equipment and power generation equipment, one side of the distribution substation area is connected to a superior power grid, and the other side of the distribution substation area is connected to a load end, and the method includes:

[0006] Obtaining historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period;

[0007] According to the historical load power consumption and the convolutional neural network, the predicted load power of the load end in a preset future period is predicted, and according to the characteristic parameters of the power generation element and the convolutional neural network, the predicted power generation power of the power generation equipment in the preset future period is predicted;

[0008] Determine a power control strategy for the energy storage device within the preset future time period according to the preset electricity price configuration data within the preset future time period, the energy storage device attribute parameters, the predicted load power and the predicted generated power;

[0009] Based on the predicted load power and the power control strategy for the energy storage device, the adjustable power of the distribution station area is calculated, and the adjustable power is fed back to the upper power grid, so that the upper power grid issues a power dispatching instruction according to the adjustable power;

[0010] According to the power control strategy, the charging and discharging power of the energy storage device in the preset future time period is controlled.

[0011] In a possible implementation, the energy storage device attribute parameters include: charge and discharge unit loss, SOC limit, charge and discharge power limit, and rated capacity of the energy storage device;

[0012] The step of determining a power control strategy for the energy storage device within the preset future time period according to the preset electricity price configuration data within the preset future time period, the energy storage device attribute parameters, the predicted load power, and the predicted generated power includes:

[0013] Calculating a power control constraint condition according to the SOC limit, the charge and discharge power limit, and the rated capacity;

[0014] When the predicted power generation power is greater than the predicted load power, an output power control penalty factor is calculated, and the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, the charge and discharge unit loss, and the output power control penalty factor;

[0015] When the predicted generated power is not greater than the predicted load power, the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, and the charging and discharging unit loss.

[0016] In a possible implementation, the load end includes a variable load; the predicted load power includes: the predicted load power of the variable load; the adjustable power includes: the variable load adjustable power and the energy storage device adjustable power, and the energy storage device adjustable power includes: the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power;

[0017] The step of calculating the adjustable power of the distribution station area based on the predicted load power and the power control strategy for the energy storage device includes:

[0018] Calculating the adjustable power of the variable load according to the predicted load power of the variable load;

[0019] Calculating the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power according to the charge and discharge power limit, the rated capacity and the power control strategy;

[0020] The variable load adjustable power, the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power are summed to obtain the adjustable power of the distribution station area.

[0021] In a possible implementation, the method further includes:

[0022] In response to the power scheduling instruction, power is adjusted for at least one of the variable load and the energy storage device according to the instruction type of the power scheduling instruction, the variable load adjustable power, and the energy storage device adjustable power.

[0023] In a possible implementation, the instruction type includes: a power reduction instruction and a power increase instruction; the power scheduling instruction includes: a target scheduling power;

[0024] The step of adjusting the power of at least one of the variable load and the energy storage device according to the instruction type of the power dispatch instruction, the variable load adjustable power, and the energy storage device adjustable power comprises:

[0025] When the instruction type is the power raising instruction, determining in sequence whether the variable load adjustable power or the energy storage device adjustable power can meet the target dispatching power;

[0026] If both the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power raising instruction;

[0027] When the instruction type is the power reduction instruction, determining in sequence whether the adjustable power of the energy storage device or the adjustable power of the variable load can meet the target dispatching power;

[0028] If both the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power reduction instruction.

[0029] In a possible implementation, the method further includes:

[0030] Obtaining the load rate of the distribution station area in real time;

[0031] When the load rate of the station area is greater than a preset first threshold, determining whether the SOC value of the energy storage device is in a preset first interval;

[0032] If the SOC value of the energy storage device is within the preset range, the discharge power of the energy storage device and the power generation device is increased, and the variable load adjustable power at the load end is reduced;

[0033] If the SOC value of the energy storage device is not within the preset first interval, a load alarm signal is sent to the upper-level power grid.

[0034] In a possible implementation, the method further includes:

[0035] Acquire the grid connection point voltage of the power generation equipment in real time;

[0036] When the grid connection point voltage is not in the preset second interval, the power generation factor of the power generation equipment is adaptively adjusted in parameters.

[0037] In the second aspect, the embodiment of the present application provides an autonomous system for energy regulation in a substation area, which is applied to an embedded EMS in a distribution substation area, wherein the distribution substation area includes energy storage equipment and power generation equipment, one side of the distribution substation area is connected to a superior power grid, and the other side of the distribution substation area is connected to a load end, and the system includes:

[0038] An acquisition module, used to acquire historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period;

[0039] A prediction module, configured to predict the predicted load power of the load end in a preset future period according to the historical load power consumption and the convolutional neural network, and to predict the predicted power generation power of the power generation equipment in the preset future period according to the characteristic parameters of the power generation elements and the convolutional neural network;

[0040] A determination module, configured to determine a power control strategy for the energy storage device within the preset future period according to the preset electricity price configuration data within the preset future period, the energy storage device attribute parameters, the predicted load power and the predicted generated power;

[0041] A calculation module, configured to calculate the adjustable power of the distribution station area based on the predicted load power and the power control strategy for the energy storage device, and feed back the adjustable power to the upper-level power grid so that the upper-level power grid can issue a power dispatching instruction according to the adjustable power;

[0042] The control module is used to control the charging and discharging power of the energy storage device in the preset future time period according to the power control strategy.

[0043] In the third aspect, an embodiment of the present application provides an embedded EMS, wherein the network structure of the embedded EMS is an event-driven AOE network structure, and the embedded EMS implements any possible autonomous method of energy regulation in the substation in the first aspect by executing an AOE network configuration file in a calibration format.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any possible autonomous method for energy regulation in an area in the first aspect.

[0045] Compared with the prior art, the present application has the following beneficial effects: The embodiments of the present application provide an autonomous method, system, embedded EMS and medium for energy regulation in a substation, and the method is applied to an embedded EMS set in a distribution substation. First, it is necessary to obtain the historical load power consumption of the load end, the characteristic parameters of the power generation elements of the power generation equipment and the attribute parameters of the energy storage equipment, and use the convolutional neural network to predict the predicted load power and predicted power generation power of the load end and the power generation equipment in the preset future time period. Subsequently, further based on the preset electricity price configuration data, energy storage equipment attribute parameters, predicted load power and predicted power generation power in the preset future time period, determine the power control strategy for the energy storage equipment in the preset future time period, so that the energy storage equipment and the power generation equipment can perform energy storage peak shaving and valley filling according to the actual electricity price situation while meeting the load demand of the load end, and obtain a power control strategy that can take into account both the electricity price and the actual load demand, and use the distribution substation to have a certain intelligent analysis function. Furthermore, it is also necessary to feed back the adjustable power of the distribution station to the grid side based on the predicted load power and power control strategy, so that the upper grid can issue power dispatch instructions according to the actual load conditions. In this way, when the distribution station is performing power dispatch and energy management related tasks, the energy regulation strategy can be determined through the embedded EMS set up inside it, so that the distribution station has certain intelligent analysis and autonomous operation functions in energy management and dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art 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 drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A schematic diagram of a flow chart of an autonomous method for energy regulation in a substation area provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a flow chart of a method for determining a power control strategy provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of a curve of changes in charging and discharging power of an energy storage device provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of a power change curve of a power generation device provided in an embodiment of the present application;

[0051] Figure 5 A schematic diagram of a change curve of a load end after being controlled by a power control strategy provided in an embodiment of the present application;

[0052] Figure 6 A schematic diagram of a flow chart of a method for calculating adjustable power in a distribution station area provided in an embodiment of the present application;

[0053] Figure 7 A schematic diagram of a flow chart of a power scheduling instruction response method provided in an embodiment of the present application;

[0054] Figure 8 A schematic diagram of the structure of an autonomous system for energy regulation in a substation area provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following is a further detailed description of this application in combination with specific embodiments and with reference to the accompanying drawings. It should be noted that the embodiments described in the embodiments of this application are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0056] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "include" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0057] As described above, the current renewable energy power supply field often has a variety of energy types (such as wind power, photovoltaic power, and hydropower), and its energy supply has the characteristics of randomness and intermittency. For the current distribution substations, due to the increase in energy types, the distribution substations need more precise control in energy management and power dispatch. However, the current distribution substations lack intelligent analysis and autonomous operation functions in energy management and dispatch, making it difficult to handle energy management and dispatch issues in new energy scenarios.

[0058] In order to solve the above problems, the embodiments of the present application provide an autonomous method, system, embedded EMS and medium for energy regulation in a substation area, and the method is applied to an embedded EMS set in a distribution substation area. First, it is necessary to obtain the historical load power consumption of the load end, the characteristic parameters of the power generation elements of the power generation equipment, and the attribute parameters of the energy storage equipment, and use this to predict the predicted load power and predicted power generation power of the load end and the power generation equipment in the preset future time period through a convolutional neural network. Subsequently, further based on the preset electricity price configuration data, energy storage equipment attribute parameters, predicted load power and predicted power generation power in the preset future time period, determine the power control strategy for the energy storage equipment in the preset future time period, so that the energy storage equipment and the power generation equipment can perform energy storage peak shaving and valley filling according to the actual electricity price situation while meeting the load demand of the load end, and obtain a power control strategy that can take into account both the electricity price and the actual load demand, and use the distribution substation area with certain intelligent analysis functions. Furthermore, it is also necessary to feed back the adjustable power of the distribution station to the grid side based on the predicted load power and power control strategy, so that the upper grid can issue power dispatch instructions according to the actual load conditions. In this way, when the distribution station is performing power dispatch and energy management related tasks, the energy regulation strategy can be determined through the embedded EMS set up inside it, so that the distribution station has certain intelligent analysis and autonomous operation functions in energy management and dispatch.

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] See also Figure 1 , which is a flow chart of an autonomous method for energy regulation in a substation area provided in an embodiment of the present application, and specifically includes the following steps:

[0061] S101: Acquire historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period.

[0062] The autonomous energy regulation method provided in the embodiment of the present application is applied to the embedded EMS (Energy Management System) in the distribution station area. The embedded EMS can perform intelligent analysis based on a variety of energy data to determine a specific energy regulation plan. For the distribution station area in the embodiment of the present application, one side of the area is connected to the upper-level power grid, so as to obtain the electric energy that needs to be transmitted to the lower-level load from the upper-level power grid, and receive the power dispatching instructions from the power grid side in real time. The other side of the area is connected to the load end, and the area transmits electricity to the downstream load end through the internally arranged energy storage equipment and power generation equipment, so as to realize the supply of electricity.

[0063] In order to realize the autonomous operation of energy regulation, it is first necessary to obtain the historical load power consumption of the load end, the characteristic parameters of the power generation elements of the power generation equipment, and the attribute parameters of the energy storage equipment itself. Among them, the historical load power consumption is used to represent the power data of the load within the historical time end, and the historical time end can be selected according to the length of the predicted time. For example, if you need to predict the power situation of the load end in the distribution station area in the next day, the power data of the load end in the past two weeks can be used as the historical load power consumption, and divided according to a certain time granularity (15min).

[0064] On the other hand, a part of power generation equipment is often installed in a conventional power distribution area. When necessary, the power supply of the load end can be temporarily supplied by the power generation equipment. Among them, the power generation element characteristic parameter of the power generation equipment is related to the equipment type of the power generation equipment itself. For example, when the power generation equipment is a photovoltaic power generation equipment, its corresponding power generation element characteristic parameter can be the illuminance. When the power generation equipment is a hydropower generation equipment, its corresponding power generation element characteristic parameter can be the hydrological flow.

[0065] Energy storage device attribute parameters are used to characterize the device limitations of energy storage devices during charging and discharging, such as the unit loss of energy storage devices during charging and discharging, SOC limit (such as setting 10%-90%), charging and discharging power limit (10%-90% of rated power) and rated capacity. These data can be used as constraints during energy regulation to ensure that the power regulation method developed is supported by the energy storage device.

[0066] S102: Predicting the load power of the load end in a preset future time period based on the historical load power consumption and the convolutional neural network, and predicting the predicted power generation power of the power generation equipment in the preset future time period based on the characteristic parameters of the power generation elements and the convolutional neural network.

[0067] In this step, it is necessary to predict the load power of the load end in the preset future time period and the predicted power generation power of the power generation equipment in the same preset future time period based on the historical load power consumption and the characteristic parameters of the power generation elements, so as to determine whether the production capacity of the power generation equipment in the future time period can meet the load demand of the load end. Among them, for the preset future time period, it can be divided according to a certain granularity. For example, if it is necessary to calculate the predicted load power and predicted power generation power in the next day, the next day can be divided into 15 minutes as the minimum unit to calculate the predicted load power and predicted power generation power every 15 minutes.

[0068] In one possible implementation, when determining the predicted load power based on a convolutional neural network, time series features (such as daily periodicity, weekly periodicity, etc.) can be extracted from the historical load power consumption, and a new feature vector can be constructed by combining external weather factors and load power consumption to assist the convolutional neural network model in making better predictions. In addition, for the calculation of predicted power generation, in addition to the characteristic parameters of the power generation elements of the power generation equipment itself, calculations can also be made in combination with future external weather factors (such as whether there will be cloudy days, etc.).

[0069] S103: Determine a power control strategy for the energy storage device within the preset future time period according to the preset electricity price configuration data within the preset future time period, the energy storage device attribute parameters, the predicted load power and the predicted generated power.

[0070] Subsequently, the optimal power control strategy is determined by combining the predicted load power and predicted power generation power in the preset future time period, referring to the actual preset electricity price configuration data in the future time period and the attribute parameters that characterize the energy dispatching capacity of the energy storage equipment. In this way, the power regulation strategy is determined in combination with the actual electricity price situation of the power grid and the actual load demand at the load end, thereby maximizing the autonomous operation effect of the substation.

[0071] The preset electricity price configuration data is a preset electricity price information table for a preset future period, and the specific example can be found in Table 1:

[0072] Table 1

[0073]

[0074] Next, the process of determining the power control strategy in step S103 will be described in conjunction with the accompanying drawings of specific embodiments. Figure 2 , which is a flow chart of a method for determining a power control strategy provided by an embodiment of the present application, and specifically includes the following steps:

[0075] S1031: Calculate power control constraints according to the SOC limit, the charge and discharge power limit, and the rated capacity.

[0076] As can be seen from the previous text, the attribute parameters of the energy storage device are used to characterize the device limitations of the energy storage device during charging and discharging. Therefore, in the preliminary stage of determining the power control strategy, it is necessary to calculate the constraints in the power control process based on the attribute parameters of the energy storage device to ensure that the power control strategy for the energy storage device is supported by the device itself.

[0077] Specifically, the energy storage device attribute parameters include the charge and discharge unit loss, SOC limit, charge and discharge power limit and rated capacity of the energy storage device. The calculation method of the power control constraint condition is as follows:

[0078]

[0079] In the formula, Q Rate is the rated capacity, initial capacity Q 0 = Q Rate *SOC 0 , S SOC,min Minimum discharge SOC (corresponding to SOC limit), S SOC,max The maximum SOC for charging is: P c,max is the maximum charging power (corresponding to the charging and discharging power limit), P f,max is the maximum discharge power, u i For the moment i The energy storage discharge power, v i For the moment i The energy storage charging power, Indicates the scheduling calculation interval.

[0080] In the subsequent process of determining the power control strategy, all calculation processes need to refer to the power control constraints determined in this step to ensure the perfect operation of the entire power control process.

[0081] S1032: When predicting the generated power and the predicted load power, calculate the output power control penalty factor, and determine the power control strategy according to the power control constraint, the preset electricity price configuration data, the charge and discharge unit loss, and the output power control penalty factor.

[0082] As mentioned in the above step S102, by calculating the predicted load power at the load end and the predicted power generation power of the power generation equipment, it is possible to effectively determine whether the power supply performance of the power generation equipment in the distribution station area can meet the load demand at the load end. When the predicted power generation power is greater than the predicted load power, the energy storage device needs to be charged as much as possible to prevent power backflow. Therefore, it is necessary to introduce a penalty factor for the output power of the energy storage device to reduce the discharge of the energy storage device. Specifically, the calculation formula of the power control strategy under this method is as follows:

[0083]

[0084] In the formula, u i For the moment i The energy storage discharge power, v i For the moment i The energy storage charging power, C elce,i For the moment i The electricity price, C loss,i is the unit loss of charging and discharging of the energy storage device, C punish,j Represents the output power control penalty factor.

[0085] S1033: When the predicted generated power is not greater than the predicted load power, the power control strategy is determined according to the power control constraint, the preset electricity price configuration data, and the charging and discharging unit loss.

[0086] Correspondingly, when the predicted power generation of the power generation equipment is not greater than the predicted load power at the load end, there is no need to introduce the output power control penalty factor in the above formula. The penalty factor in step 1032 can be eliminated and the formula will not be repeated here.

[0087] In this way, by combining the actual electricity price situation on the grid side and the predicted power of the load and power generation equipment for power control scheduling, the control task of energy storage peak shaving and valley filling can be effectively completed, thereby improving the economy of autonomous operation of the substation. According to this control strategy, the substation is optimized and coordinated for a week, and a power control scheduling is set up with 15 minutes as the fine granularity. The power control effects of energy storage, power generation equipment and load end in the substation within a week can be referred to in sequence. Figure 3-Figure 5 .in, Figure 3 A schematic diagram of a curve showing a change in charging and discharging power of an energy storage device provided in an embodiment of the present application. Figure 4 A schematic diagram of a power change curve of a power generation device provided in an embodiment of the present application, Figure 5 This is a schematic diagram of the change curve of the load end after being controlled by the power control strategy. Figure 3 In the table, Storage Active Power indicates storage active power. Figure 4 In the table, Photovoltaic Active Power refers to the photovoltaic active power. Figure 5 In the table, Load Power indicates the load power. Figure 3-Figure 5 In the above example, index indicates the index of a time point.

[0088] The above is an introduction to the process of determining the power control strategy of the energy storage device in step S103. Figure 1 The embodiments of the present application are introduced.

[0089] S104: Based on the predicted load power and the power control strategy for the energy storage device, the adjustable power of the distribution station area is calculated, and the adjustable power is fed back to the upper power grid, so that the upper power grid issues a power dispatching instruction according to the adjustable power.

[0090] Adjustable power refers to the ability of power generation equipment or load equipment to adjust its output or consumption power level according to actual needs, which is particularly important for ensuring the operating stability of the overall power system.

[0091] In actual distribution substation application scenarios, the upper power grid sometimes sends power dispatching instructions to the distribution substation to control the charging and discharging power of the substation. In order to prevent the power dispatching instructions sent by the grid from exceeding the current power dispatching capacity of the distribution substation, after determining the control strategy for the energy storage device, it is necessary to calculate the adjustable power of the distribution substation under the power control strategy based on the predicted load power calculated on the load side and the actual power control strategy of the energy storage device, and feed back the adjustable power to the upper power grid, so that the upper power grid can issue corresponding dispatching instructions based on the adjustable power of the distribution substation, ensure that the energy storage equipment in the distribution substation can normally respond to the power dispatching instructions on the grid side, and realize the autonomous operation of the distribution substation.

[0092] The adjustable power of the distribution station area includes the adjustable power for the energy storage device and the adjustable power at the load end. Next, the calculation process of the adjustable power in step S104 will be introduced in conjunction with the specific embodiment drawings. Figure 6 , which is a flow chart of a method for calculating adjustable power in a distribution station area provided in an embodiment of the present application, and specifically includes the following steps:

[0093] S1041: Calculate the variable load adjustable power according to the predicted load power of the variable load.

[0094] Variable load refers to load equipment that can dynamically adjust its power consumption according to external conditions or user needs during operation. At the load end, load equipment belonging to variable load can respond to power dispatch instructions from the substation or the power grid, thereby adjusting its own load power.

[0095] In the process of calculating the adjustable power based on the predicted load power of the variable load, multiple factors such as the expected operating state of the equipment, the adjustment range, the response time, and the external conditions can be referred to. For example, taking the air conditioner as an example of a variable load, the operating power and the inlet and outlet water temperature of the air conditioner in normal operation in the future period can be estimated, and the operating power when the inlet and outlet water temperature is within a reasonable range can be obtained. The adjustable power is calculated within the set adjustment range, and 15% of its normal operating power can be set as the adjustable power of the air conditioner (variable load).

[0096] S1042: Calculating the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power according to the charge and discharge power limit, the rated capacity, and the power control strategy;

[0097] S1043: Sum the variable load adjustable power, the energy storage peak shaving adjustable power, and the energy storage valley filling adjustable power to obtain the adjustable power of the distribution station area.

[0098] The adjustable power of energy storage equipment needs to be further divided into energy storage peak shaving adjustable power and energy storage valley filling adjustable power. The energy storage peak shaving and energy storage valley filling are determined according to the electricity price of the corresponding period. When the electricity price of the period is at the peak, the corresponding adjustable power is the energy storage peak shaving adjustable power, and when the electricity price is at the valley, the corresponding adjustable power is the energy storage valley filling adjustable power.

[0099] The adjustable power of the energy storage device needs to be calculated based on the attribute parameters of the energy storage device. For details, please refer to the following formula:

[0100]

[0101] In the formula, P xf,i For the i The energy storage peak shaving adjustable power in each period, P tg,i For the i The energy storage valley filling adjustable power in each period, Q i For the i The energy storage capacity of each period, t For the i The length of time of a period, Q Rate is the rated capacity, Pc,max is the maximum charging power, P f,max is the maximum discharge power.

[0102] Therefore, after determining the adjustable power of the energy storage device and the adjustable power of the variable load at the load end, the two are summed to obtain the adjustable power of the distribution station area. That is, the adjustable power of the distribution station area = the adjustable power of the variable load + the adjustable power of the energy storage device.

[0103] S105: According to the power control strategy, control the charging and discharging power of the energy storage device in the preset future time period.

[0104] Finally, according to the power control strategy determined in step S103, the charging and discharging power of the energy storage device in the preset future time period is set according to the set power for the energy storage device at each moment in the control strategy, so as to accurately control the charging and discharging power of the energy storage device in combination with the electricity price situation and the actual load demand, realize the autonomous operation of the distribution station area, and enable the distribution station area to have certain intelligent analysis and autonomous operation functions in energy management and scheduling.

[0105] The above is a basic introduction to the autonomous method for energy regulation in the substation area provided in the embodiment of the present application. As mentioned in the description of step S104, the embedded EMS needs to calculate the adjustable power of the distribution substation area and feed the adjustable power back to the upper-level power grid, so as to facilitate the power grid to send power dispatch instructions to the distribution substation area. In actual application scenarios, in order to ensure the maximum utilization of energy, in the method provided in the embodiment of the present application, when the substation responds to the power dispatch instruction from the power grid, it is necessary to determine the power dispatch order or dispatch method for the energy storage device and the variable load based on the instruction type of the dispatch instruction. Next, this process will be introduced in conjunction with the specific process example drawings.

[0106] See also Figure 7 , which is a flow chart of a power scheduling instruction response method provided in an embodiment of the present application, and specifically includes the following steps:

[0107] S201: Determine the instruction type of the power scheduling instruction;

[0108] S202: When the instruction type is a power increase instruction, determining in sequence whether the variable load adjustable power or the energy storage device adjustable power can meet the target dispatching power;

[0109] S203: Adjusting the adjustable power that can meet the target dispatching power;

[0110] S204: If the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power increase instruction.

[0111] The power dispatching instructions from the power grid side can be divided into power raising instructions and power lowering instructions. When the instruction type is a power raising instruction, in order to reduce the frequent charging and discharging adjustments of the energy storage device, it is necessary to first determine whether the variable load adjustable power at the load end can meet the target dispatching power. If it does, the variable load at the load end can be directly adjusted without adjusting the power of the energy storage device. Correspondingly, if it is determined that neither the single variable load adjustable power nor the energy storage device adjustable power can meet the target dispatching power, the variable load adjustable power and the energy storage device adjustable power are adjusted at the same time to respond to the power dispatching instruction.

[0112] In this way, the power condition response speed and accuracy of the distribution station area can be effectively improved, ineffective adjustments and conflicts between resources can be avoided, and the operating efficiency of the power system can be improved.

[0113] S205: When the instruction type is a power reduction instruction, determining in sequence whether the adjustable power of the energy storage device or the adjustable power of the variable load can meet the target dispatching power;

[0114] S206: Adjusting the adjustable power that can meet the target dispatching power;

[0115] S207: If the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power reduction instruction.

[0116] Similar to the above logic, when the instruction type of the power dispatch instruction is a power reduction instruction, since it is necessary to first ensure that the power at the load end does not drop, it is necessary to first determine whether the adjustable power of the energy storage device can meet the target dispatch power. When it is confirmed that the energy storage device cannot meet the target dispatch power, the variable load adjustable power at the load end can be adjusted. Similarly, when it is confirmed that both the adjustable power of the energy storage device and the adjustable power of the variable load cannot meet the target dispatch power, the power of the variable load and the energy storage device are adjusted together according to the target dispatch power indicated in the power reduction instruction, thereby responding to the power dispatch instruction from the power grid side.

[0117] In one possible implementation, the response to the power dispatch instruction may also refer to the adjustable power of the power generation equipment. When the adjustable power of the energy storage device and the adjustable power of the variable load cannot meet the target dispatch power, the power of the power generation equipment may be adjusted in response.

[0118] In another possible implementation, the embedded EMS in the substation can also monitor the load rate of the substation in real time. When the load rate of the substation is greater than a preset first threshold, it determines whether the SOC value of the energy storage device is within a preset first interval, so as to determine whether the energy storage device can currently respond to power control.

[0119] When it is confirmed that the SOC value of the energy storage device is not within the preset first interval, it indicates that the current energy storage situation of the energy storage device cannot meet the current load demand of the substation, and a load alarm signal is sent to the superior power grid.

[0120] When it is confirmed that the SOC value of the energy storage device is within the preset first interval, the discharge power of the energy storage device and the power generation device is increased in turn, and the variable load adjustable power at the load end is reduced, so as to provide a stronger power supply to the load end and reduce the load pressure of the substation. When the load rate of the substation returns to normal, this control logic can be exited and the normal control logic can be restored.

[0121] In another possible implementation, the embedded EMS can also monitor the grid connection point voltage between the energy storage device and the power generation equipment in real time. When the grid connection point voltage is not in the preset second interval, it is determined that the current grid connection point voltage is at risk of entering a high-low penetration state. At this time, a series of power generation factors of the power generation equipment can be adjusted to prevent the grid connection point voltage from entering a high-low penetration state.

[0122] The embodiments of the present application provide an autonomous method, system, embedded EMS and medium for energy regulation in a substation area, and the method is applied to an embedded EMS set in a distribution substation area. First, it is necessary to obtain the historical load power consumption of the load end, the characteristic parameters of the power generation elements of the power generation equipment, and the attribute parameters of the energy storage equipment, and use a convolutional neural network to predict the predicted load power and predicted power generation power of the load end and the power generation equipment in a preset future time period. Subsequently, based on the preset electricity price configuration data, energy storage equipment attribute parameters, predicted load power and predicted power generation power in the preset future time period, the power control strategy for the energy storage equipment in the preset future time period is determined, so that the energy storage equipment and the power generation equipment can perform energy storage peak shaving and valley filling according to the actual electricity price situation while meeting the load demand of the load end, and obtain a power control strategy that can take into account both the electricity price and the actual load demand, and use the distribution substation area with certain intelligent analysis functions. Furthermore, it is also necessary to feed back the adjustable power of the distribution station to the grid side based on the predicted load power and power control strategy, so that the upper grid can issue power dispatch instructions according to the actual load conditions. In this way, when the distribution station is performing power dispatch and energy management related tasks, the energy regulation strategy can be determined through the embedded EMS set up inside it, so that the distribution station has certain intelligent analysis and autonomous operation functions in energy management and dispatch.

[0123] The following is an introduction to an autonomous system for energy regulation in a substation area provided in an embodiment of the present application. The autonomous system for energy regulation in a substation area described below and the autonomous method for energy regulation in a substation area described above can be referenced to each other.

[0124] See also Figure 8 , which is a schematic diagram of the structure of an autonomous energy regulation system for a substation area provided in an embodiment of the present application, and specifically includes the following modules:

[0125] The acquisition module 100 is used to acquire historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period;

[0126] A prediction module 200, configured to predict the predicted load power of the load end in a preset future period according to the historical load power consumption and the convolutional neural network, and to predict the predicted power generation power of the power generation equipment in the preset future period according to the characteristic parameters of the power generation element and the convolutional neural network;

[0127] A determination module 300, configured to determine a power control strategy for the energy storage device within the preset future period according to the preset electricity price configuration data within the preset future period, the energy storage device attribute parameters, the predicted load power and the predicted generated power;

[0128] A calculation module 400, configured to calculate the adjustable power of the distribution station area based on the predicted load power and the power control strategy for the energy storage device, and feed back the adjustable power to the upper power grid so that the upper power grid issues a power dispatching instruction according to the adjustable power;

[0129] The control module 500 is used to control the charging and discharging power of the energy storage device in the preset future time period according to the power control strategy.

[0130] The embodiment of the present application also provides an embedded EMS, the network structure of which is an event-driven AOE network structure. The embedded EMS implements the autonomous method for energy regulation in the substation described in any of the above embodiments by executing an AOE network configuration file in a calibration format.

[0131] AOE network (Activity On Edge Network) is a directed acyclic graph. Each node in the graph represents an event, each edge represents an action, and the direction of the edge represents the progressive and migration relationship between the events represented by the nodes. This relationship is determined by the action represented by the edge.

[0132] In the embodiment of the present application, according to the definition of events (nodes) and actions (edges), the above-mentioned autonomous method for energy regulation in the substation is divided into several actions (steps) according to the order of execution, and the execution trigger conditions of each action and the logical connection relationship between the actions are determined by nodes (events). An event is a state, which is used here to describe the controlled object. It can be used as a condition for the execution of an action, and it can also be used as a sign that the execution of the action is completed; and an action is a behavior, which is used here to express the specific strategy of the terminal execution. Events can be refined according to the control object and environmental change state in the above-mentioned control logic, which can be the update of the measurement point value, the measurement point value threshold exceeding the set threshold, the change of the variable flag bit, etc. The steps of the control strategy can be determined according to the control instructions taken by the above-mentioned control logic for the control object, and the specific designation can be variable assignment, power calculation, control switch opening and closing, transformer gear adjustment, and execution of optimization calculation instructions, etc.

[0133] Here, events are divided into conditional nodes and branch nodes. Conditional nodes are event-triggered. If the expression of the node is true, the node event is triggered, and all actions triggered by the node (that is, all branches issued by the node) are executed in parallel; branch nodes are branch logic judgments. The node connects two branches and uses the node expression as the logical judgment. When the expression is true, the edge with the node number 1 performs the action; when the expression is false, the edge with the node number 2 performs the action.

[0134] In this way, an AOE network configuration file in a standardized format can be defined, and the basic information, variables, time, actions, and channel configurations of the control network can be defined so that the configuration file can be imported into the embedded EMS to describe the AOE network constructed by the above-mentioned substation energy control autonomous method.

[0135] Exemplarily, the AOE network configuration files may refer to the following Table 2 and Table 3:

[0136] Table 2

[0137]

[0138] Table 3

[0139]

[0140] In this way, the corresponding configuration files are created through the AOE network without writing specific codes. There is no need to design customized code files for the energy regulation autonomy of the substation, which effectively reduces the cost of autonomous operation of the substation.

[0141] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the autonomous method for energy regulation of the substation area as described in any of the above embodiments.

[0142] The computer-readable media of the embodiments of the present application include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0143] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the autonomous method for energy regulation in the substation area as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0144] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for methods, systems, embedded EMS and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The methods, systems, embedded EMS and media described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for autonomous energy regulation in a substation, characterized in that: Applied to an embedded EMS in a power distribution substation, the power distribution substation includes energy storage equipment and power generation equipment, one side of the power distribution substation is connected to a superior power grid, and the other side of the power distribution substation is connected to a load end, the method includes: Obtaining historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period; According to the historical load power consumption and the convolutional neural network, the predicted load power of the load end in a preset future period is predicted, and according to the characteristic parameters of the power generation element and the convolutional neural network, the predicted power generation power of the power generation equipment in the preset future period is predicted; Determine a power control strategy for the energy storage device within the preset future time period according to the preset electricity price configuration data within the preset future time period, the energy storage device attribute parameters, the predicted load power and the predicted generated power; Based on the predicted load power and the power control strategy for the energy storage device, the adjustable power of the distribution station area is calculated, and the adjustable power is fed back to the upper power grid, so that the upper power grid issues a power dispatching instruction according to the adjustable power; According to the power control strategy, controlling the charging and discharging power of the energy storage device in the preset future time period; The energy storage device attribute parameters include: charge and discharge unit loss, SOC limit, charge and discharge power limit and rated capacity of the energy storage device; The step of determining a power control strategy for the energy storage device within the preset future time period according to the preset electricity price configuration data within the preset future time period, the energy storage device attribute parameters, the predicted load power, and the predicted generated power includes: Calculating a power control constraint condition according to the SOC limit, the charge and discharge power limit, and the rated capacity; When the predicted power generation power is greater than the predicted load power, an output power control penalty factor is calculated, and the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, the charge and discharge unit loss, and the output power control penalty factor; When the predicted generated power is not greater than the predicted load power, the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, and the charging and discharging unit loss.

2. The method according to claim 1, characterized in that The load end includes a variable load; the predicted load power includes: the predicted load power of the variable load; the adjustable power includes: the variable load adjustable power and the energy storage device adjustable power, and the energy storage device adjustable power includes: the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power; The step of calculating the adjustable power of the distribution station area based on the predicted load power and the power control strategy for the energy storage device includes: Calculating the adjustable power of the variable load according to the predicted load power of the variable load; Calculating the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power according to the charge and discharge power limit, the rated capacity and the power control strategy; The variable load adjustable power, the energy storage peak shaving adjustable power and the energy storage valley filling adjustable power are summed to obtain the adjustable power of the distribution station area.

3. The method according to claim 2, characterized in that The method further comprises: In response to the power scheduling instruction, power is adjusted for at least one of the variable load and the energy storage device according to the instruction type of the power scheduling instruction, the variable load adjustable power, and the energy storage device adjustable power.

4. The method according to claim 3, characterized in that The instruction types include: power down instruction and power up instruction; the power scheduling instruction includes: target scheduling power; The step of adjusting the power of at least one of the variable load and the energy storage device according to the instruction type of the power dispatch instruction, the variable load adjustable power, and the energy storage device adjustable power includes: When the instruction type is the power raising instruction, determining in sequence whether the variable load adjustable power or the energy storage device adjustable power can meet the target dispatching power; If both the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power raising instruction; When the instruction type is the power reduction instruction, determining in sequence whether the adjustable power of the energy storage device or the adjustable power of the variable load can meet the target dispatching power; If both the adjustable power of the variable load and the adjustable power of the energy storage device cannot meet the target dispatching power, the power of the variable load and the energy storage device are adjusted together according to the power reduction instruction.

5. The method according to claim 2, characterized in that: The method further comprises: Obtaining the load rate of the distribution station area in real time; When the load rate of the station area is greater than a preset first threshold, determining whether the SOC value of the energy storage device is in a preset first interval; If the SOC value of the energy storage device is within the preset first interval, the discharge power of the energy storage device and the power generation device is increased, and the variable load adjustable power at the load end is reduced; If the SOC value of the energy storage device is not within the preset first interval, a load alarm signal is sent to the upper-level power grid.

6. The method according to claim 1, characterized in that The method further comprises: Acquire the grid connection point voltage of the power generation equipment in real time; When the grid connection point voltage is not in the preset second interval, the power generation factor of the power generation equipment is adaptively adjusted in parameters.

7. An autonomous energy control system for a substation, characterized in that: Applied to an embedded EMS in a distribution substation, the distribution substation includes energy storage equipment and power generation equipment, one side of the distribution substation is connected to the upper power grid, and the other side of the distribution substation is connected to the load end. The system includes: An acquisition module, used to acquire historical load power consumption, characteristic parameters of power generation elements of the power generation equipment, and attribute parameters of energy storage equipment; the historical load power consumption is used to characterize the power situation of the load end in the historical period; A prediction module, configured to predict the predicted load power of the load end in a preset future period according to the historical load power consumption and the convolutional neural network, and to predict the predicted power generation power of the power generation equipment in the preset future period according to the characteristic parameters of the power generation elements and the convolutional neural network; A determination module, configured to determine a power control strategy for the energy storage device within the preset future period according to the preset electricity price configuration data within the preset future period, the energy storage device attribute parameters, the predicted load power and the predicted generated power; A calculation module, configured to calculate the adjustable power of the distribution station area based on the predicted load power and the power control strategy for the energy storage device, and feed back the adjustable power to the upper-level power grid so that the upper-level power grid can issue a power dispatching instruction according to the adjustable power; A control module, used to control the charging and discharging power of the energy storage device in the preset future time period according to the power control strategy; The energy storage device attribute parameters include: the charge and discharge unit loss, SOC limit, charge and discharge power limit and rated capacity of the energy storage device; The step of determining a power control strategy for the energy storage device in the preset future time period according to the preset electricity price configuration data in the preset future time period, the energy storage device attribute parameters, the predicted load power and the predicted generated power includes: Calculating a power control constraint condition according to the SOC limit, the charge and discharge power limit, and the rated capacity; When the predicted power generation power is greater than the predicted load power, an output power control penalty factor is calculated, and the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, the charge and discharge unit loss, and the output power control penalty factor; When the predicted generated power is not greater than the predicted load power, the power control strategy is determined according to the power control constraint condition, the preset electricity price configuration data, and the charging and discharging unit loss.

8. An embedded EMS, characterized in that: The network structure of the embedded EMS is an event-driven AOE network structure. The embedded EMS implements the autonomous method for energy regulation in the substation area as described in any one of claims 1 to 6 by executing the AOE network configuration file in a calibration format.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the autonomous method for energy regulation in an area described in any one of claims 1 to 6 is implemented.

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