Congestion prediction device and congestion prediction method

Abstract

To facilitate power generation control exerted on a non-firm connected power generation facility.SOLUTION: A congestion prediction device predicts the congestion of a utility grid. The congestion prediction device comprises: a weather prediction data input unit that acquires weather prediction data concerning the weather a prescribed number of days after the present day as a point of reference; a utility grid data input unit that acquires demand data concerning electric power demands a prescribed number of days after the present day as a point of reference, and acquires grid data concerning the utility grid a prescribed number of days after the present day as a point of reference; and a grid congestion prediction unit that predicts the congestion point and power generation control variable of the utility grid a prescribed number of days after the present day as a point of reference.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0001] The present invention relates to a congestion prediction device and a congestion prediction method.

Background Art

[0002] In a power system, it is necessary to balance demand and supply within a certain area. For example, Patent Document 1 describes a technique in which an energy operation system acquires information including current and future weather conditions and social environment condition patterns, predicts future energy demand and power generation amounts based on this information, and controls supply and demand within the management area based on the prediction results.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the large-scale introduction of renewable energy such as solar power generation and wind power generation, when the power generation amount of renewable energy increases, power congestion occurs in power transmission and transformation equipment such as power transmission lines and substation equipment in the power system, where the amount of power passing through the equipment exceeds the operating capacity of the power transmission and transformation equipment. As a means of promoting the introduction of renewable energy while avoiding this power congestion, a power generation equipment system connection method called non-firm connection has been introduced.

[0005] Non-firm connection means connecting power generation equipment to the power system on the premise of suppressing power generation output when there is no room left in the operating capacity of the power transmission and transformation equipment. In non-firm connection, when congestion occurs in the power transmission and transformation equipment, the operator of the power system instructs the power generation equipment to suppress the generated power, and the operator of the power generation equipment avoids power congestion by suppressing the generated power. This instruction is called generation control.

[0006] The timing and extent of power generation control depend on the power generated by all power generation facilities connected to the power grid, the power consumption of the load (or consumer), and the physical configuration of the power grid's transmission and distribution equipment. Therefore, it is difficult for operators or businesses of non-firmly connected power generation facilities to predict when and how much power generation control will occur. As a result, there has been a problem of the amount of power generation control (hereinafter referred to as "power generation control amount") being large as a safety precaution.

[0007] However, Patent Document 1 only considers the balance of power supply and demand, and does not focus on the relationship between the supply and demand balance and the constraints such as the operating capacity of power transmission and distribution equipment in the power grid, which changes moment by moment, and on controlling power generation in response to these changes. For operators or businesses of non-firm-connected power generation facilities, the technology in Patent Document 1 is not easy to use. Therefore, the present invention aims to facilitate power generation control for power generation equipment with non-firm connections. [Means for solving the problem]

[0008] The congestion prediction device of the present invention is a congestion prediction device for predicting congestion in a power grid, comprising: a weather forecast data input unit that acquires weather forecast data relating to the weather a predetermined number of days from the present; a power grid data input unit that acquires demand data relating to electricity demand a predetermined number of days from the present, and also acquires grid data relating to the power grid a predetermined number of days from the present; and a grid congestion prediction unit that predicts the congested locations and power generation control amounts of the power grid a predetermined number of days from the present based on the weather forecast data, the demand data, and the grid data. The system comprises a power generation plan data input unit that acquires power generation plans from power generators or aggregators that operate power generation facilities, loads, or energy storage facilities in a bundle, and the grid congestion prediction unit creates an operation plan for facilities within the power system based on the predicted congestion locations or the predicted power generation control amount, distributes the operation plan among the multiple aggregators if there are multiple aggregators within the same power system, and predicts congestion locations or power generation control amounts in the power system based on the acquired power generation plans. It is characterized by the following. Other means will be described within the descriptions of embodiments for carrying out the invention. [Effects of the Invention]

[0009] According to the present invention, power generation control for non-firm connection power generation equipment can be easily facilitated. [Brief explanation of the drawing]

[0010] [Figure 1] This is a diagram showing the configuration of the first embodiment. [Figure 2] This is a flowchart of the processing procedure in the first embodiment. [Figure 3] This is a flowchart detailing step S131. [Figure 4] This is a detailed flowchart of step S132. [Figure 5] This is a detailed flowchart of step S1324. [Figure 6] This is a detailed flowchart of step S1325. [Figure 7] This is an example of a power grid diagram showing congested areas. [Figure 8] This is an example of a time-series graph showing the changes in power generation control amounts. [Figure 9] This diagram shows the configuration of a power system and the flow of signals. [Figure 10] This is a flowchart of the processing procedure in the second embodiment. [Figure 11] This is a diagram showing an example of a driving plan. [Figure 12A] This figure shows the power output of the power generation equipment, the power consumption of the load, and the charge level of the energy storage equipment in a time-series manner when the present invention is not applied. [Figure 12B] This figure shows the power output of the power generation equipment, the power consumption of the load, and the charge level of the energy storage equipment in time series when the operation plan of the present invention is applied. [Figure 13] This is a flowchart of the processing procedure in the third embodiment. [Figure 14] This figure shows the configuration of the fourth embodiment. [Modes for carrying out the invention]

[0011] Hereinafter, four embodiments will be described with reference to the drawings. The first embodiment is an example where the congestion prediction device distributes the prediction result of grid congestion. The second embodiment is an example where the congestion prediction device creates an operation plan for electrical equipment. The third embodiment is an example where the congestion prediction device calculates and outputs the electricity price. The fourth embodiment is an example where the congestion prediction device acquires a power generation plan from a power generation company or the like.

[0012] <First Embodiment> FIG. 1 is a diagram showing the configuration of the first embodiment. The congestion prediction device 1 is connected to the operator 3 of the power grid, the weather prediction company 4, the power generation company 51, and the aggregator 52 via the network 2 (connected to each computer). The aggregator 52 is a entity that bundles and integrally manages and operates power generation facilities, loads, energy storage facilities, etc. connected to the power grid, and is a entity that transmits an operation plan to the power generation facilities, etc. that it manages and operates (details will be described later).

[0013] The congestion prediction device \(1\) is a general computer and includes a central control device, an input device, an output device, a main memory device, an auxiliary memory device, a communication device, etc. (not shown). And the central control device realizes the functions of the program by reading the program from the auxiliary memory device into the main memory device. The power grid data input unit 11, the weather prediction data input unit 12, and the grid congestion prediction unit 13 in FIG. 1 are programs.

[0014] The power grid data input unit 11 of the congestion prediction device 1 acquires the power generation data 31, the demand data 32, and the grid data 33 from the operator 3 of the power grid via the network 2. The power generation data 31 stores the location of the power generation facilities and the rated capacity of the power generation facilities that can output power. The rated capacity may be stored in a future time series. The demand data 32 stores the location of the consumers who consume electricity and the consumed electricity. The consumed electricity may be stored in a time series as the future electricity consumption plan of the consumers. The demand data 32 may be an electricity consumption plan that summarizes many consumers in a regional unit, or may be an electricity consumption plan of a plurality of specific large consumers. System data 33 stores location information, topology information, and impedance information of transmission lines and transformers that constitute the power system.

[0015] It should be noted that these data are merely examples, and the data acquired by the power system data input unit 11 is not limited to these. The processing performed by the power system data input unit 11 may be fully automated, manually performed by a user using a keyboard or the like, or a combination of both.

[0016] The weather forecast data input unit 12 of the congestion prediction device 1 acquires weather forecast data 41 from a weather forecasting company 4 via the network 2. The weather forecast data 41 stores predicted values ​​for future solar radiation, wind speed, wind direction, temperature, etc. The weather forecast data 41 is data with geographical resolution; for example, it is defined on each mesh or grid point after dividing a map into a grid. The weather forecast data 41 is also data with temporal resolution, storing predicted values ​​for a predetermined number of days (from several days to several months) from the present as future prediction values.

[0017] The grid congestion prediction unit 13 of the congestion prediction device 1 transmits (distributes) the predicted congestion locations and times (see Figure 7) to the power generator 51 and the aggregator 52 via the network 2. Furthermore, the grid congestion prediction unit 13 transmits the predicted power generation control amount (see Figure 8) to the power generator 51 and the aggregator 52.

[0018] Figure 2 is a flowchart of the processing procedure in the first embodiment. In step S130, the system congestion prediction unit 13 of the congestion prediction device 1 accepts input (selection) from the user via an input device of the area (prediction target area) and the period (prediction target period) for which system congestion will be predicted. The prediction target area and prediction target period may be predetermined, or they may be input externally each time congestion prediction is needed. The prediction target period is usually a period that starts from the present and ends at a predetermined number of days later.

[0019] In step S131, the power system data input unit 11, weather forecast data input unit 12, and grid congestion forecast unit 13 of the congestion forecasting device 1 prepare the input data. Details of step S131 will be shown later in Figure 3. In step S132, the power grid data input unit 11 and the grid congestion prediction unit 13 of the congestion prediction device 1 calculate the power generation control amount. Details of step S132 will be described later in Figures 4, 5, and 6.

[0020] In step S133, the grid congestion prediction unit 13 of the congestion prediction device 1 outputs (transmits) the location of congestion, the time of congestion, and the amount of power generation control. Specifically, the grid congestion prediction unit 13 outputs the power grid diagram in Figure 7 and the time-series transition diagram in Figure 8 to the power generation company 51 and the aggregator 52. After that, the processing procedure ends.

[0021] Figure 3 is a flowchart detailing step S131. In step S1311, the power grid data input unit 11 of the congestion prediction device 1 extracts coordinate data (location data) of renewable energy sources, such as wind power generation facilities and solar power generation facilities, whose output is determined by weather conditions, from the grid data 33. After that, the process branches into two paths.

[0022] In step S1312, the weather forecast data input unit 12 of the congestion forecasting device 1 acquires wind speed data for a location or nearby location that matches the coordinate data of the wind power generation facility from the weather forecast data 41 which has geographical resolution. The wind speed data acquired here is weather data for a predetermined number of days from the present.

[0023] In step S1313, the grid congestion prediction unit 13 of the congestion prediction device 1 first obtains a power curve representing the relationship between the output and wind speed of a wind power generation facility from an arbitrary facility. The grid congestion prediction unit 13 may create the power curve using methods such as regression analysis based on past output and wind speed data. Secondly, the grid congestion prediction unit 13 uses the power curve to calculate the predicted power generation value of the wind power generation equipment related to the power curve obtained from the wind speed data (predicted value).

[0024] In step S1314, the weather forecast data input unit 12 of the congestion forecasting device 1 acquires solar radiation data (predicted values) for a location or nearby location that matches the coordinate data of the solar power generation facility from the weather forecast data 41 which has geographical resolution. The solar radiation data acquired here is also data relating to the weather a predetermined number of days later, based on the present.

[0025] In step S1315, the grid congestion prediction unit 13 of the congestion prediction device 1 first obtains a power curve representing the relationship between the output and solar radiation of a photovoltaic power generation facility from an arbitrary facility. The grid congestion prediction unit 13 may create the power curve using methods such as regression analysis based on past data of output and solar radiation. Secondly, the grid congestion prediction unit 13 uses the power curve to calculate the predicted power generation value of the solar power generation equipment related to the power curve obtained from the solar radiation data (predicted value).

[0026] Once steps S1313 and S1315 are completed, the grid congestion prediction unit 13 will have time-series data of predicted power generation values ​​for the prediction period in the prediction area. Then, the process proceeds to step S132.

[0027] Figure 4 is a flowchart detailing step S132. In step S1321, the grid congestion prediction unit 13 of the congestion prediction device 1 calculates the initial power flow cross-section of the power grid within the prediction area during the prediction period from the predicted generation values ​​of renewable energy and demand data 32. The method for calculating the initial power flow cross-section is the power flow calculation method commonly used in grid analysis.

[0028] In step S1322, the grid congestion prediction unit 13 assigns an initial value of "1" to time T. Time T then changes to 2, 3, 4, ... In the process described later, the amount of power generation control will be calculated for each time T within the prediction period. The calculation of the power generation control amount is repeated, for example, every hour.

[0029] In step S1323, the power system data input unit 11 of the congestion prediction device 1 acquires power generation data 31 and demand data 32 at time T. Here, time T is a predetermined number of days after the present. In step S1324, the grid congestion prediction unit 13 calculates the grid constraint amount at time T. Details of step S1324 will be shown later in Figure 5. In step S1325, the grid congestion prediction unit 13 calculates the supply and demand constraint amount at time T. Details of step S1325 will be shown later in Figure 6.

[0030] In step S1326, the grid congestion prediction unit 13 determines whether time T has reached the last time of the prediction period. Specifically, if time T has reached the last time of the prediction period (step S1326 "Yes"), the grid congestion prediction unit 13 proceeds to step S133; otherwise (step S1326 "No"), it proceeds to step S1327.

[0031] In step S1327, the system congestion prediction unit 13 adds "1" to time T and then returns to step S1323.

[0032] Figure 5 is a flowchart detailing step S1324 (calculation of systematic constraints at time T). In step S1324a, the grid congestion prediction unit 13 of the congestion prediction device 1 performs power flow calculations for the power grid in the area to be predicted and identifies the presence or absence of congested areas. Congested areas are locations where the transmitted power exceeds the operating capacity of the transmission and distribution equipment, such as transmission lines and substations. Grid congestion occurs in congested areas.

[0033] The operating capacity of power transmission and distribution equipment is determined not only by the thermal capacity of the equipment but also by the constraints necessary for the stable operation of the power system. For example, when an accident such as a lightning strike occurs in the power system, the grid congestion prediction unit 13 defines the upper limit of the transmitted power necessary for the stable operation of the power system from the viewpoint of voltage stability, frequency stability, etc., as the operating capacity for each power transmission and distribution facility. The grid data 33 (Figure 1) also contains data on the constraints of the power system, including the operating capacity.

[0034] In step S1324b, the system congestion prediction unit 13 determines whether or not there are congested areas. Specifically, if there are congested areas (step S1324b "Yes"), the system congestion prediction unit 13 proceeds to step S1324c; otherwise (step S1324b "No"), it proceeds to step S1325.

[0035] In step S1324c, the system congestion prediction unit 13 extracts congested areas. In step S1324d, the grid congestion prediction unit 13 calculates a grid constraint amount ΔPa, which is a positive value obtained by subtracting the operating capacity of the transmission and distribution equipment from the predicted power generation value. The grid constraint amount ΔPa can also be described as the amount of excessive load (overload) on the transmission and distribution equipment. In step S1324e, the grid congestion prediction unit 13 decides to reduce the output of the power generation equipment using the non-firm connection type by ΔPa at time T. Then, the process returns to step S1324a.

[0036] The method for handling predicted grid congestion varies depending on the region and the timing of the system's application. Figure 5 shows the power generation control method for power generation facilities to which non-firm connection is applied. Note that the method for handling grid congestion is not limited to this method; the effects of the present invention can be obtained even if other systems are followed. As described above, non-firm connection is the connection of power generation facilities to the power grid on the premise that power output will be suppressed (power generation control) when the operational capacity of the transmission and distribution facilities runs out.

[0037] The grid congestion prediction unit 13 identifies power generation facilities to which non-firm connections are applied that are likely to cause grid congestion based on the direction of the power flow in the power flow calculation results, and proportionally allocates the grid constraint amount ΔPa to all identified power generation facilities according to their rated capacity, etc. Each identified power generation facility is expected to have its output suppressed (power generation controlled) according to the allocated grid constraint amount ΔPa. The grid congestion prediction unit 13 repeats this power generation control process until there are no more congested areas.

[0038] Figure 6 is a flowchart detailing step S1325 (calculation of supply and demand constraints at time T). In step S1325a, the grid congestion prediction unit 13 of the congestion prediction device 1 determines whether the predicted power generation value for the prediction target area exceeds the predicted demand value. Specifically, firstly, the grid congestion prediction unit 13 compares the sum of the predicted power generation values ​​for the prediction target area with the sum of the predicted demand amounts. Secondly, the grid congestion prediction unit 13 proceeds to step S1325b if the sum of the predicted power generation values ​​is greater (step S1325a "Yes"), and to step S1326 otherwise (step S1325a "No").

[0039] In step S1325b, the grid congestion prediction unit 13 calculates the supply and demand constraint ΔPb, which is the result of subtracting the sum of the predicted demand from the sum of the predicted power generation values ​​in the area to be predicted. The supply and demand constraint ΔPb can also be said to be the amount of power generation exceeding the demand. The specific method for calculating the supply and demand constraint ΔPb varies depending on the region and the timing of the application of the system.

[0040] In step S1325c, the grid congestion prediction unit 13 calculates the power generation control amount ΔPab for each power generation facility. Specifically, firstly, the grid congestion prediction unit 13 proportionally allocates the supply and demand constraint amount ΔPb to all the identified power generation facilities according to their rated capacity, etc. Secondly, the grid congestion prediction unit 13 calculates a power generation control amount ΔPab for each power generation facility. The power generation control amount ΔPab is the sum of a proportionally allocated grid constraint amount ΔPa and a proportionally allocated supply and demand constraint amount ΔPb.

[0041] The grid congestion prediction unit 13, for example, prioritizes power generation control for thermal power generation facilities, etc., according to priority power supply rules, and finally controls power generation control for renewable energy. The grid congestion prediction unit 13 repeatedly performs power generation control processing according to priority power supply rules until the sum of the predicted power generation values ​​in the target area matches the sum of the predicted demand. Note that the processing method during supply and demand constraints is not limited to this priority power supply rule, and the effects of the present invention can be obtained even if a method that complies with other systems is used.

[0042] Figure 7 is an example of a power system diagram showing congested areas. In step S133, the system congestion prediction unit 13 of the congestion prediction device 1 outputs the location of the system congestion (congested area) and the time of the system congestion (congested time) to the outside. In the example in Figure 7, the system congestion prediction unit 13 highlights the congested areas at the time of congestion with thick lines on the system diagram, which consists of substations (○) and transmission lines (straight lines) in the prediction target area. The method of outputting the congested areas and congestion times is not limited to the example in Figure 7, and may be, for example, outputting the congested areas as text data along with a transmission line identification number and the congestion time, or by other methods.

[0043] Figure 8 shows an example of a time-series graph of the power generation control amount. In step S133, the grid congestion prediction unit 13 of the congestion prediction device 1 outputs a time-series graph of the power generation control amount to the outside. In the example in Figure 8, the grid congestion prediction unit 13 illustrates the power generation control amount for a certain power generation facility in a time series as the difference between the predicted output before power generation control and the predicted output after power generation control (height of the white portion of the bar graph). As mentioned above, the power generation control amount is caused by both grid congestion and the balance of power supply and demand (power generation control amount ΔPab = ΔPa + ΔPb).

[0044] The method for outputting the power generation control amount is not limited to the example in Figure 8. For example, it may be a method of outputting the power generation control amount as text data along with a number that identifies the power generation equipment and the time of congestion, or by other methods.

[0045] As shown in Figure 1, the congestion prediction device 1 predicts grid congestion and transmits the prediction results to the power generator 51 and the aggregator 52. The power generator 51 and the aggregator 52 reflect the received grid congestion prediction results in the operation plans of the power generation facilities, loads, energy storage facilities, etc. that they manage. Based on these grid congestion prediction results, the power generator 51 and the aggregator 52 can suppress the amount of power generation control required for power generation facilities during grid congestion by creating, for example, an operation plan that increases the amount of power consumed by the load or an operation plan that charges the energy storage facilities during grid congestion. As a result of the effects of the present invention, owners and operators of power generation facilities can create operation plans that reduce the amount of power generation control required, thereby realizing the effective use of renewable energy generated power.

[0046] <Second Example> In the second embodiment, the congestion prediction device 1 in the first embodiment creates an operation plan for the electrical equipment (described immediately below) based on the predicted congestion locations and power generation control amounts.

[0047] Figure 9 is a diagram showing the configuration of the power system and the flow of signals. In Figure 2, the renewable energy generation equipment (also simply called "renewable energy") 62, load 63, and energy storage equipment 64 constitute the electrical equipment managed by the aggregator 52. The aggregator 52 receives an operation plan from the congestion prediction device 1. The operation plan received here pertains to the entire electrical equipment, including the renewable energy 62, load 63, and energy storage equipment 64 (see Figure 11). The aggregator 52 transmits the operation plan to each of these electrical pieces of equipment. The operation plans transmitted here pertain to the renewable energy 62, load 63, and energy storage equipment 64, respectively (individual commands). Note that reference numeral 61 indicates a transformer.

[0048] The electrical equipment operates according to the received operating plan. Figure 9 shows an example in which the aggregator 52 manages renewable energy 62, loads 63, and energy storage equipment 64. However, the electrical equipment managed by the aggregator 52 is not limited to these; the effects of the present invention can be obtained with any combination of equipment that generates electricity, equipment that consumes electricity, and equipment that stores and discharges electricity.

[0049] Figure 10 is a flowchart of the processing procedure in the second embodiment. Steps S130 to S132 in Figure 10 are the same as steps S130 to S132 in Figure 1. In step S134, the grid congestion prediction unit 13 of the congestion prediction device 1 creates an operating plan for the electrical equipment. Specifically, based on the predicted congestion locations and power generation control amounts, the grid congestion prediction unit 13 creates an operating plan for each electrical piece of equipment under its jurisdiction to maximize the profitability of that equipment. The grid congestion prediction unit 13 may apply an optimization algorithm such as linear programming to create the operating plan, or it may apply techniques that are common in this field.

[0050] In step S135, the grid congestion prediction unit 13 outputs the operation plan for the electrical equipment (Figure 11) to the power company 51 and the aggregator 52.

[0051] Figure 11 shows an example of an operation plan. Prior to the time when power generation control is expected to occur, the grid congestion prediction unit 13 transmits an operation plan to each of the renewable energy sources 62, loads 63, and energy storage equipment 64 via the aggregator 52. The operation plan for load 63 is, for example, an instruction to perform a start-up operation in advance in order to consume power in accordance with the occurrence of power generation control. The operation plan for energy storage equipment 64 is an instruction to perform a discharge operation in advance in order to store power in accordance with the occurrence of power generation control. The operation plan for renewable energy sources 62 is, for example, an instruction to reduce the amount of power generation control (minimize the reduction amount) when performing power generation operation.

[0052] Ultimately, if the renewable energy source 62 is forced to reduce its power output, the grid congestion prediction unit 13 transmits an operating plan to the load 62 and the energy storage equipment 64 that absorbs as much power output as possible, after giving them sufficient preparation time, thereby minimizing power generation control for the renewable energy source 62.

[0053] Figure 11 shows the following: (1) The congestion prediction device 1 predicts in advance that grid congestion will occur during peak hours, that is, that power generation control for renewable energy 62 will be necessary. Generally, peak hours are a predetermined number of days (several days or several months) after the present. (2) The congestion prediction device 1 predicts this two days before the time of congestion. In other words, in the example in Figure 11, the time of congestion is two days from the present.

[0054] (3) The congestion prediction device 1 transmits the following operating plan to the load 63 via the aggregator 52. (3-1) Load 63 will be shut down until one day before the peak hours. (3-2) 24 hours before or immediately before peak hours, load 63 will be started (prepared). (3-3) During peak hours, load 63 will operate with increased power consumption.

[0055] (4) The congestion prediction device 1 transmits the following operating plan to the energy storage equipment 64 via the aggregator 52. (4-1) The energy storage equipment 64 will discharge until two hours before the peak hours. (4-2) The energy storage equipment 64 will be charged one hour before or immediately before the peak hours. (4-3) During peak hours, the energy storage equipment 64 will be charged.

[0056] (5) The congestion prediction device 1 transmits the following operating plan to the renewable energy 62 via the aggregator 52. (5-1) Renewable Energy 62 will operate under normal power generation conditions until just before peak hours. (5-2) During peak hours, renewable energy 62 will operate with reduced (minimized) power generation control levels.

[0057] As is clear from Figure 11, the operation plan reflects the congestion locations, congestion times, and power generation control amounts predicted by the grid congestion prediction unit 13.

[0058] Figure 12 illustrates the effects of the present invention. Figure 12A shows the power output of the power generation equipment, the power consumption of the load, and the charge level of the energy storage equipment 64 in time series when the present invention is not applied. Figure 12B shows the power output of the power generation equipment, the power consumption of the load, and the charge level of the energy storage equipment 64 in time series when the operation plan of the present invention is applied.

[0059] In Figures 12A and 12B, the use of the electricity generated by renewable energy 62 is one of the following: consumption by load 63 (hatched area), storage by energy storage equipment 64 (hatched area), or output to the power grid (shaded area). Here, "output to the power grid" means consumption by loads other than those covered by the operation plan, or outflow to other power grids.

[0060] Looking at Figure 12A, we can see the following: Load 63 continues to operate at a constant rate, and its power consumption remains constant. • The energy storage equipment 64 remains shut down (neither discharging nor storing energy) until the time when the renewable energy generation power 62 increases ("1 hour before"). • At the time when the power generated by renewable energy 62 increases ("1 hour before"), the energy storage equipment 64 starts charging and reduces the power generated and output to the power grid, thereby alleviating congestion in the power grid. However, there is a limit to the amount of electricity that the energy storage equipment 64 can charge, and charging must be stopped when the charge rate approaches 100%. Therefore, if power cannot be transmitted due to grid congestion, it is necessary to suppress (control) the power generated by renewable energy 62 after the time of "power generation control occurrence".

[0061] Looking at Figure 12B, we can see the following: • To prepare for future grid congestion, load 63 shifts its operating time. In other words, it consumes power intensively only after the time when renewable energy 62's power generation is likely to increase and grid congestion may occur ("1 hour prior"), thereby avoiding the control of renewable energy 62's power generation. • In preparation for future grid congestion during charging, the energy storage equipment 64 is discharged in advance ("1 day to 1 hour before") to reduce its charge level. • At times when the power generated by renewable energy 62 increases and grid congestion may occur (from "1 hour prior"), the energy storage equipment 64 starts charging to avoid controlling the power generation of renewable energy 62.

[0062] As is clear from Figures 11 and 12, the grid congestion prediction unit 13 of the congestion prediction device 1 creates an operation plan for the load or energy storage equipment so that the amount of power generation control for the power generation equipment is small, that is, so that the amount of power generation suppression is small.

[0063] As an effect of the second embodiment, the amount of renewable energy 62 power generation control is reduced, enabling the effective use of renewable energy with low carbon dioxide emissions. In addition, consumers with power-consuming loads 63 may find that purchasing electricity generated by renewable energy 62 is cheaper than purchasing electricity through retailers, thus minimizing electricity purchase costs. Furthermore, businesses operating energy storage facilities 64 can maximize their profits by efficiently operating the energy storage facilities 64.

[0064] <Third Embodiment> In the third embodiment, the congestion prediction device 1 in the first embodiment calculates and outputs weather forecast data 41, demand data 32, and grid data electricity price.

[0065] Market-driven methods such as zone systems and nodal systems exist for managing congestion in the power grid. These methods alleviate congestion by differentiating electricity prices by region or location. Specifically, in areas or locations where congestion occurs, lowering electricity prices prevents power generation facilities with high costs from securing contracts in the electricity market, resulting in reduced power generation. On the other hand, raising electricity prices in areas or locations where congestion does not occur encourages increased power output from power generation facilities, leading to increased contracts in the market and alternative consumption of electricity that is being suppressed in congested areas or locations. Through such market-mediated price signals, grid congestion can be resolved.

[0066] Figure 13 is a flowchart of the processing procedure for the third embodiment. In the third embodiment, the grid congestion prediction unit 13 of the congestion prediction device 1 calculates the electricity price in step S136, replacing steps S132 and S133 in the first embodiment, in a form corresponding to a market-driven congestion management method. Then, in step S137, the grid congestion prediction unit 13 outputs the electricity price to the power company 51 and the aggregator 52. In other words, the grid congestion prediction unit 13 predicts the electricity price for each region or location. For example, the grid congestion prediction unit 13 predicts a lower electricity price for a region or location if the predicted amount of power generation control is large. The grid congestion prediction unit 13 may use a zone system or a nodal system, which are commonly used as methods for predicting electricity prices.

[0067] In step S131 (step S1311) of Figure 13, the power grid data input unit 11 also acquires demand data 32. The grid congestion prediction unit 13 can also calculate the electricity price by comparing the predicted generation value with the demand data 32.

[0068] The power generator 51 and the aggregator 52 obtain predicted electricity prices from the congestion prediction device 1 and reflect these predicted electricity prices in the operation plans of the power generation facilities, loads, and energy storage facilities they manage. As an effect of the third embodiment, even in a market-driven congestion management system, owners and operators of electrical facilities can create operation plans that minimize the amount of power generation controlled, thereby enabling the effective use of renewable energy generation.

[0069] <Fourth Embodiment> In the fourth embodiment, the congestion prediction device 1 in the first embodiment obtains power generation plans from the power generation company 51 and the aggregator 52 and uses them to calculate the amount of power generation to be controlled.

[0070] Figure 14 shows the configuration of the fourth embodiment. In the fourth embodiment, similar to the first embodiment, the power generator 51a and the aggregator 52a obtain predicted values ​​of power generation control amounts from the congestion prediction device 1 via the network 2. In addition, in the fourth embodiment, the congestion prediction device 1 includes a power generation plan data input unit 14 as a program. The power generation plan data input unit 14 obtains power generation plans from other power generators 51b and other aggregators 52b.

[0071] In step S132 of Figure 2, the power generation plan data input unit 14 of the congestion prediction device 1 uses power generation plans obtained from power generators 51b and aggregators 52b when extracting congested areas by power flow calculation and calculating the amount of power generation to be controlled. By using the power generation plans created by power generators 51b and aggregators 52b, the accuracy of power flow calculations is improved and the accuracy of power generation control amount predictions is improved compared to using only the power generation data 31 published by the power system operator 3.

[0072] In Figure 14, the power generator 51a and aggregator 52a that acquire (receive) predicted values ​​of power generation control, and the power generator 51b and aggregator 52b that transmit the power generation plan exist separately. However, the effects of the present invention can also be realized even if one power generator 51 or one aggregator 52 receives the predicted values ​​of power generation control and transmits the power generation plan. As a result of the effects of the present invention, owners and operators of electrical equipment can create operation plans that reduce the amount of power generation control, thereby realizing the effective use of renewable energy generated power.

[0073] If multiple aggregators 52 exist within the same power grid, the grid congestion prediction unit 13 of the congestion prediction device 1 may distribute a single created operation plan among the multiple aggregators according to predetermined rules. For example, if the power generation control amount in the operation plan is x, the grid congestion prediction unit 13 may distribute x to each aggregator according to the rated capacity of the renewable energy 62 under the jurisdiction of each aggregator. The grid congestion prediction unit 13 may also distribute to each aggregator 52 the number of loads or the amount of loads to be prepared for startup according to the number or scale of loads 63 under the jurisdiction of each aggregator. Furthermore, the grid congestion prediction unit 13 may distribute to each aggregator 52 the number of energy storage facilities to be discharged and stored or the amount of discharged and stored according to the number or scale of energy storage facilities 64 under the jurisdiction of each aggregator. More generally, the grid congestion prediction unit 13 may distribute the operation plan among multiple aggregators.

[0074] The congestion prediction device of this embodiment provides the following effects. (1) The congestion prediction device can predict the congested areas and the amount of power generation control in the power grid to which non-firm connection is applied. (2) The congestion prediction device can predict congested areas in the power grid and the amount of power generation to be controlled, based on the constraints of the power grid, including the operational capacity. (3) The congestion prediction device can output the predicted congestion locations and power generation control amounts to an aggregator or the like. (4) The congestion prediction device can create an operation plan.

[0075] (5) The congestion prediction device can predict electricity prices. (6) The congestion forecasting device can acquire demand data as a power consumption plan for large consumers. (7) The congestion prediction device can create an operating plan for power generation equipment, loads, and energy storage equipment. (8) The congestion prediction device can distribute the operation plan among multiple aggregators. (9) The congestion prediction device can obtain power generation plans from aggregators, etc. [Explanation of symbols]

[0076] 1. Congestion prediction device 2 Network 3. Operators of the power grid 4 Weather forecasting companies 11 Power System Data Input Section 12 Weather forecast data input section 13 System Congestion Prediction Section 14. Power generation plan data input section 51 Power Generation Companies 52 Aggregators 62 Renewable energy 63 load 64 Energy Storage Systems

Claims

1. A congestion prediction device for predicting congestion in the power grid, A weather forecast data input unit that acquires weather forecast data for a predetermined number of days in the future, based on the current weather. A power system data input unit that acquires demand data for electricity demand a predetermined number of days later, based on the present, and also acquires system data for the aforementioned power system a predetermined number of days later, based on the present. A grid congestion prediction unit predicts the locations of congestion in the power grid and the amount of power generation control required a predetermined number of days after the present, based on the weather forecast data, the demand data, and the grid data. A power generation plan data input unit that acquires power generation plans from power generation operators or aggregators that manage power generation facilities, loads, or energy storage facilities in a bundled manner, Equipped with, The aforementioned system congestion prediction unit, Based on the predicted congestion locations or the predicted power generation control amount, an operation plan for the equipment within the power system is created. If multiple aggregators exist within the same power system, the operation plan is distributed among the multiple aggregators. Based on the acquired power generation plan, predict the congested areas or the amount of power generation to be controlled in the power grid. A congestion prediction device characterized by the following.

2. The aforementioned system data is The data concerns the constraints of the power system, including its operating capacity. The congestion prediction device according to claim 1, characterized by the following:

3. The aforementioned system congestion prediction unit, Output the predicted congestion location or the predicted power generation control amount. The congestion prediction device according to claim 1, characterized by the following:

4. The aforementioned system congestion prediction unit, Based on the aforementioned weather forecast data, demand data, and grid data, predict the electricity price for each region or location within the power grid. The congestion prediction device according to claim 1, characterized by the following:

5. The aforementioned power system data input unit is To acquire the aforementioned demand data as a power consumption plan for multiple specific large consumers, The congestion prediction device according to claim 1, characterized by the following:

6. The equipment within the aforementioned power system is It includes at least one of the following: power generation equipment, loads, and energy storage equipment. The aforementioned system congestion prediction unit, To create an operating plan for the load or the energy storage equipment such that the amount of power generation control for the power generation equipment is reduced, The congestion prediction device according to claim 1, characterized by the following:

7. A congestion prediction method using a congestion prediction device that predicts congestion in a power grid, The weather forecast data input unit of the aforementioned congestion forecasting device is: Obtain weather forecast data for a predetermined number of days from the present, The power system data input unit of the aforementioned congestion prediction device is: Demand data for electricity demand a predetermined number of days later, based on the present, is acquired, and system data for the aforementioned power grid a predetermined number of days later, based on the present, The system congestion prediction unit of the aforementioned congestion prediction device is Based on the aforementioned weather forecast data, demand data, and grid data, the congestion locations and power generation control amounts for a predetermined number of days after the present are predicted. The power generation plan data input unit of the aforementioned congestion prediction device is: We obtain power generation plans from power generation companies or aggregators that manage power generation facilities, loads, or energy storage facilities in a bundled manner. The aforementioned system congestion prediction unit, Based on the predicted congestion locations or the predicted power generation control amount, an operation plan for the equipment within the power system is created. If multiple aggregators exist within the same power system, the operation plan is distributed among the multiple aggregators. Based on the acquired power generation plan, predict the congested areas or the amount of power generation to be controlled in the power grid. A congestion prediction method characterized by the following.

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