Demand and supply plan development support device and demand and supply plan development support method

Abstract

To provide a supply and demand plan development support device that is capable of developing a supply and demand plan that takes into account the uncertainty of renewable energy power sources and demand, and a lead time for countermeasures.SOLUTION: A supply and demand planning support device 100 includes: a scenario creation unit 21 that creates a forecast error scenario including time-series data of a forecast error and an occurrence probability thereof, using forecast error data, which is a difference between forecast values and actual values of demand and renewable energy power sources for a past specified period; a reserve margin evaluation unit 22 that evaluates a supply reserve margin using the forecast error scenario created by the scenario creation unit 21 and power generation plan data; and an additional supply plan development unit 23 that develops an additional supply plan using the evaluation result of the supply reserve margin by the reserve margin evaluation unit 22 and data on candidate additional supply measures to compensate for supply capacity shortages.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a supply and demand plan formulation support device and a supply and demand plan formulation support method. [Background technology]

[0002] Power transmission system operators use the reserve margin (the margin of supply capacity relative to demand) as an indicator and ensure sufficient reserve margins to achieve stable supply and demand operations. Meanwhile, in recent years, renewable energy sources (hereafter referred to as "renewable energy") such as solar and wind power have been increasingly introduced, and as the amount of electricity generated by renewable energy depends on the weather, the uncertainty of supply capacity is increasing. In addition, an increase in electricity demand is expected due to the promotion of electrification, but as electricity demand is also correlated with temperature and weather, the uncertainty of electricity demand is also increasing. This increase in uncertainty has led to a decrease in the stability of supply and demand operations, and there have been cases of supply and demand tightness (a situation in which the reserve margin falls below a certain value).

[0003] Patent Document 1 proposes an operation plan creation device and a program capable of creating a plan including output suppression of renewable energy while taking into account parameters having uncertainty. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-33625 Summary of the Invention [Problem to be solved by the invention]

[0005] When tight supply and demand is anticipated, transmission system operators will take measures such as adding supply capacity to improve the reserve margin, but because each measure has a lead time, supply and demand planning on a weekly basis is necessary. However, the longer the demand and renewable energy forecast period (the time difference between the forecast point and the forecast horizon) used in the supply and demand plan, the greater the uncertainty in demand and renewable energy will be, and the larger the forecast error will be. In addition, when securing surplus supply capacity to take uncertainty into account, supply and demand operation costs will increase, so from an economic standpoint, it is necessary to keep the secured supply capacity to a minimum.

[0006] In order to achieve stable and economical supply and demand operations, it is necessary to formulate supply and demand plans that take into account the uncertainties of renewable energy and demand, as well as the lead times for countermeasures.

[0007] Patent Document 1 mentions supply and demand planning that includes output suppression taking into account uncertainties in demand and renewable energy, but does not mention the formulation of supply and demand planning that can respond to a decrease in the reserve margin while taking into account the lead time for measures.

[0008] An object of the present invention is to provide a supply and demand plan formulation support device and a supply and demand plan formulation support method that are capable of formulating a supply and demand plan that takes into account the uncertainty of renewable energy and demand and the lead time of measures. [Means for solving the problem]

[0009] In order to achieve the above object, the supply and demand plan formulation support device of the present invention is characterized by comprising: a scenario creation unit that creates a forecast error scenario including time-series data of forecast errors and their occurrence probability using forecast error data that is the difference between forecast values ​​and actual values ​​for a predetermined past period of time for demand and renewable energy power sources, a reserve margin evaluation unit that evaluates a supply reserve margin using the forecast error scenario created by the scenario creation unit and power generation plan data, and an additional supply plan formulation unit that formulates an additional supply plan using the evaluation result of the supply reserve margin by the reserve margin evaluation unit and data on additional supply measure candidates that compensate for a supply capacity shortage. Other aspects of the present invention will be described in the embodiments described later. Effect of the Invention

[0010] According to the present invention, it is possible to formulate a supply and demand plan that takes into account the uncertainty of renewable energy and demand and the lead time of measures. Other effects will be made clear through the embodiments. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating an example of a hardware configuration of a supply and demand plan development support device according to a first embodiment. [Diagram 2] 1 is a diagram illustrating an example of a device configuration of a supply and demand plan development support device according to a first embodiment. [Diagram 3] FIG. 2 is a diagram illustrating an example of a demand forecast database according to the first embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of a renewable energy forecast database according to the first embodiment. [Diagram 5] FIG. 2 is a diagram illustrating an example of a demand record database according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a renewable energy performance database according to the first embodiment. [Figure 7] FIG. 2 is a diagram showing an example of a power generation plan database according to the first embodiment. [Figure 8] FIG. 4 is a diagram showing an example of an additional supply measure candidate database according to the first embodiment. [Figure 9] FIG. 4 is a diagram illustrating an example of a forecast error scenario database according to the first embodiment. [Figure 10] FIG. 4 is a diagram illustrating an example of a reserve margin evaluation result database according to the first embodiment. [Figure 11] FIG. 4 is a diagram showing an example of a supplementary supply plan database according to the first embodiment. [Figure 12] 4 is a flowchart showing a scenario creation process of a scenario creation unit according to the first embodiment. [Figure 13] 5 is a flowchart showing a reserve rate evaluation process by a reserve rate evaluation unit according to the first embodiment. [Figure 14] 10 is a flowchart showing an additional supply plan development process of an additional supply plan development unit according to the first embodiment. [Figure 15A] FIG. 4 is a diagram showing an example of a forecast error scenario created by the supply and demand plan formulation unit according to the first embodiment. [Figure 15B] FIG. 4 is a diagram showing an example of a reserve margin evaluation of each forecast error scenario by the supply and demand plan formulation unit according to the first embodiment. [Figure 15C] 4 is a diagram showing an example of a supplementary supply plan created by a supply and demand plan creating unit according to the first embodiment. FIG. [Figure 16] FIG. 2 is a diagram showing an example of a display unit according to the first embodiment. [Figure 17] FIG. 11 is a diagram illustrating an example of a hardware configuration of a supply and demand plan development support device according to a second embodiment. [Figure 18] FIG. 11 is a diagram illustrating an example of a device configuration of a supply and demand plan development support device according to a second embodiment. [Figure 19] 10 is a flowchart showing a weather sensitivity calculation process of a weather sensitivity calculation unit according to the second embodiment. [Figure 20] FIG. 11 is a diagram illustrating an example of processing by a weather sensitivity calculation unit and a scenario creation unit according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Preferred embodiments of the present invention will now be described. Note that the following is merely an embodiment, and the invention itself is not intended to be limited to the specific contents described below.

[0013] <Embodiment 1> The configuration of a supply and demand plan development support device according to the first embodiment will be described with reference to FIGS. FIG. 1 is a diagram showing an example of a hardware configuration of a supply and demand plan development support device 100 according to the first embodiment. The supply and demand plan development support device 10 includes an input unit 101, a display unit 102, a communication unit 103, a processor 104, a memory 105, a bus 106, a demand forecast database DB1, a renewable energy forecast database DB2, a demand record database DB3, a renewable energy record database DB4, a power generation plan database DB5, an additional supply measure candidate database DB6, a forecast error scenario database DB7, a reserve rate evaluation result database DB8, an additional supply plan database DB9, and a program database DB10. These elements are connected via the bus 106. The communication unit 103 communicates with measuring instruments and control terminals of the power system 1 via a communication network 107.

[0014] The demand forecast database DB1 stores forecast values ​​of demand in a target power system. The renewable energy prediction database DB2 stores predicted values ​​of renewable energy in the target power system. The demand record database DB3 stores the record values ​​of demand in the target power system. The renewable energy performance database DB4 stores the renewable energy performance values ​​in the target power grid. The power generation plan database DB5 stores power generation plan values ​​of power generation facilities in the target power system. The additional supply measure candidate database DB6 stores information on additional supply measures. The forecast error scenario database DB7 stores the results of creating forecast error scenarios. The reserve margin evaluation result database DB8 stores the evaluation results of the reserve margin for each scenario. The additional supply plan database DB9 stores the results of planning additional supply measures. The program database DB10 stores various execution programs of the supply and demand plan formulation support device 10.

[0015] The input unit 101 is a means for inputting information to the system via, for example, a keyboard, a mouse, or a Universal Serial Bus (USB).

[0016] The display unit 102 is, for example, a monitor, a smart watch, etc. Input / output data of the system may be output as an image, sound, etc.

[0017] The communication unit 103 connects the computer to, for example, an Ethernet, a telephone line, wireless communication, etc. The communication unit 103 enables communication with various devices in the power system G1.

[0018] The processor 104 may include, for example, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), etc.

[0019] The memory 105 stores various programs and data of the supply and demand plan formulation support device 10. One example of the memory 105 is a random access memory (RAM). A bus 106 connects the various elements of the supply and demand plan formulation support device 100 .

[0020] The supply and demand plan development support device 100 may be configured by a computer (computer system). The computer includes a processing unit (for example, a processor 104), a storage unit, an input unit 101, a display unit 102, a communication unit 103, and the like.

[0021] 2 is a diagram showing an example of the device configuration of the supply and demand plan development support device 100 according to the first embodiment. The supply and demand plan development support device 100 has a supply and demand plan development unit 20 executed by a processor 104. The supply and demand plan development unit 20 includes a scenario creation unit 21 (see FIG. 15A) that creates a forecast error scenario including time-series data of the forecast error and its occurrence probability using forecast error data that is the difference between the forecast value and the actual value of the demand and the renewable energy (renewable energy power source) for a predetermined period in the past, a reserve margin evaluation unit 22 (see FIG. 15B) that evaluates the supply reserve margin using the forecast error scenario created by the scenario creation unit 21 and power generation plan data, and an additional supply plan development unit 23 (see FIG. 15C) that develops an additional supply plan using the evaluation result of the supply reserve margin by the reserve margin evaluation unit 22 and additional supply measure candidate data that compensates for a supply capacity shortage.

[0022] The supply and demand plan targeted by the supply and demand plan development support device 100 is at least one week long. The supply and demand plan development support device 100 preferably operates the supply and demand plan development unit 20 at regular intervals.

[0023] The additional supply plan formulation unit 23 formulates additional supply measures using lead time, priority (priority order), and cost information in the additional supply measure candidate data. The additional supply plan formulation unit 23 may formulate at least two additional supply plans. The additional supply plan formulation unit 23 can calculate a measure index that has a positive correlation with the occurrence probability of the forecast error scenario. Additional measure candidates targeted by the additional supply plan formulation unit 23 include pumped storage power generation, generators under balance shutdown, generators under work suspension, and activation command power sources. Details will be described later.

[0024] 3 is a diagram showing an example of the demand forecast database DB1 of the supply and demand plan development support device 100. In the demand forecast database DB1, demand forecast values ​​in a target power system are stored as time-series data for each forecast date and time and each target forecast date and time. Note that the demand forecast data to be stored may not only be the total demand forecast value of the target power system, but also be demand forecast values ​​divided for each area or bus.

[0025] Fig. 4 is a diagram showing an example of the renewable energy forecast database DB2 of the supply and demand plan development support device 100. In the renewable energy forecast database DB2, the predicted output values ​​of wind power generation and solar power in the target power system are stored as time-series data for each predicted date and time and the target prediction date and time. Note that the stored renewable energy forecast data may not only be the predicted value for the entire target power system, but also may store predicted values ​​divided for each area or bus, or predicted values ​​for each power plant. Note that in Fig. 4, the solar power forecast value is 0 MW because it is nighttime.

[0026] 5 is a diagram showing an example of the demand result database DB3 of the supply and demand plan development support device 100. In the demand result database DB3, the demand result values ​​in the target power system are stored as time-series data for each date and time. Note that the demand forecast values ​​to be stored may not only be the demand result values ​​for the entire target power system, but also the demand result values ​​divided for each area or bus.

[0027] Fig. 6 is a diagram showing an example of the renewable energy performance database DB4 of the supply and demand plan development support device 100. In the renewable energy performance database DB4, output performance values ​​of wind power generation and solar power in a target power system are stored as time-series data for each target date and time. Note that the stored renewable energy performance data may not only be performance values ​​of the entire target power system, but also prediction values ​​divided by area or bus, and performance values ​​for each power plant. Note that in Fig. 6, the solar power performance value is 0 MW because it is nighttime.

[0028] FIG. 7 is a diagram showing an example of the power generation plan database DB5 of the supply and demand plan development support device 100. The power generation plan database DB5 stores power generation plans of generators connected to a target power system. The power generation plan stores facility data such as power source type and facility capacity, and time-series data such as start / stop status at each time and power generation plan values. The power generation plan may store data received from a power generation company, or may store plan values ​​formulated by a system operator. In FIG. 7, balanced shutdown refers to stopping a generator for efficient supply and demand operation when supply capacity is sufficiently large relative to power demand.

[0029] 8 is a diagram showing an example of the additional supply measure candidate database DB6 of the supply and demand plan development support device 100. The additional supply measure candidate database DB6 stores information such as additional supply amount, priority, cost, and lead time of various additional supply measures. In addition, constraint conditions such as an upper limit on the number of startups may be stored. Note that values ​​set by the grid operator or values ​​provided by the power generation company may be used for these parameters.

[0030] 9 is a diagram showing an example of the forecast error scenario database DB7 of the supply and demand plan formulation support device 100. The forecast error scenario database DB7 stores scenario information created by the scenario creation unit 21. The scenario information stores the occurrence probability of a target scenario and the expected demand / renewable energy values ​​at each time.

[0031] 10 is a diagram showing an example of the reserve margin evaluation result database DB8 of the supply and demand plan development support device 100. The reserve margin evaluation result database DB8 stores, as time-series data, the demand, supply capacity, and reserve margin for each scenario calculated by the reserve margin evaluation unit 22. Note that in addition to the time-series data, information such as the minimum reserve margin for each date in each scenario may also be stored.

[0032] 11 is a diagram showing an example of the additional supply plan database DB9 of the supply and demand plan formulation support device 100. The additional supply plan database DB9 stores, as time-series data, the countermeasure index, countermeasure limit, and additional supply amount and the reserve margin after the additional supply measure as the countermeasure content for each scenario calculated by the additional supply plan formulation unit 23. Also, the specific additional supply amount of each additional supply measure is entered in the additional supply amount.

[0033] Next, the processing of each section of the supply and demand plan formulation section 20 (see FIG. 2) will be described. (Scenario Creation Department 21) FIG. 12 is a flowchart showing the scenario creation process S21 of the scenario creation unit 21. In step S100, the demand forecast value is read from the demand forecast database DB1, the renewable energy forecast value is read from the renewable energy forecast database DB2, the demand actual value is read from the demand actual database DB3, and the renewable energy actual value is read from the renewable energy actual database DB4. At this time, in order to improve the calculation accuracy of the prediction error, the data to be read as the target of the prediction data and actual data may be limited. For example, if the plan target date is set to 2023 / 4 / 1 to 2023 / 4 / 7, the prediction data and actual data of 2021 / 3 / 1 to 2021 / 5 / 31 and 2022 / 3 / 1 to 2022 / 5 / 31 are used, and by limiting according to the target date of the plan to be formulated, data with similar demand characteristics can be extracted, and the calculation accuracy of the prediction error can be improved. In addition, by limiting the prediction data and actual data to be used according to meteorological information such as the temperature and weather of the plan target date other than the date, data with similar renewable energy output characteristics can be extracted, and the calculation accuracy of the prediction error can be improved.

[0034] In step S101, a demand forecast error value is calculated from the demand forecast value and the actual demand value read in step S100. Demand forecast error value = actual demand value - forecast demand value (1) Similarly, for renewable energy, the renewable energy prediction error value is calculated from the renewable energy forecast value and the renewable energy actual value. Renewable energy forecast error value = Renewable energy actual value - Renewable energy forecast value (2)

[0035] In step S102, a demand forecast error scenario is calculated from the demand forecast error value calculated in step S101. As a method of calculating the demand forecast error scenario, for example, if the forecast error value is normally distributed, the scenario and its occurrence probability may be calculated using the variance σ. For example, in the case of a +1σ scenario, the occurrence probability is 34%, and the demand forecast error value corresponding to +1σ for each frame is adopted. In addition to this, multiple scenarios such as a +2σ scenario and a +3σ scenario may be formulated according to settings made by the operator.

[0036] In step S103, a renewable energy forecast error scenario is calculated in the same manner as in step S101. That is, the renewable energy forecast error scenario is calculated from the renewable energy forecast error value calculated in step S101.

[0037] (Reserve Rate Evaluation Department 22) FIG. 13 is a flowchart showing the reserve rate evaluation process S22 of the reserve rate evaluation unit 22. In step S200, a target forecast error scenario is set. In step S201, the demand of the target power system is set based on the target forecast error scenario. Demand = Demand forecast value + Demand forecast error of forecast error scenario (3)

[0038] In step S202, the supply capacity is set. Supply capacity = ΣInstalled capacity of regulated power sources +ΣOutput plan value of non-regulated power sources excluding renewable energy +Σ(renewable energy forecast value + renewable energy assumed value) (4)

[0039] In step S203, the reserve margin for the forecast error scenario is calculated using the forecast error scenario set in step S200 and the power generation plan value in the power generation plan database DB5. Reserve margin = (supply capacity – demand) / demand × 100 (5)

[0040] The calculated demand, supply capacity, and reserve rate are stored in a reserve rate evaluation result database DB8. The target reserve rate may be the reserve rate for a single area, or the reserve rate for multiple areas taking into account the interconnection line power flow.

[0041] In step S204, it is confirmed whether the demand, supply capacity, and reserve margin have been calculated for all forecast error scenarios. If the reserve margin has not been calculated for all forecast error scenarios (step S204, No), the process returns to step S200. If the reserve margin has been calculated for all forecast error scenarios (step S204, Yes), the process ends.

[0042] (Additional Supply Planning Division 23) FIG. 14 is a flowchart showing the additional supply plan formulation process S23 of the additional supply plan formulation unit 23. In step S300, a target forecast error scenario is set. In step S301, it is determined whether there are any frames below 8% in the reserve margin evaluation results of the set forecast error scenario. If there are any frames below 8% (step S301, Yes), the process proceeds to step S302, and if there are no frames below 8% (step S301, No), the process proceeds to step S304. Note that 8% as the reserve margin judgment value is just an example, and the operator can set any value.

[0043] In step S302, an additional supply measure plan is formulated for units with a reserve margin below 8% using information on additional supply measure candidates in the additional supply measure candidate database DB6. As a method for formulating the additional supply plan, for example, there is a method in which the required additional supply amount is calculated from the reserve margin, and then, taking into account the lead time, countermeasure candidates are piled up starting from the lowest priority countermeasure candidate until the additional supply amount is satisfied. Additional supply requirement = (1+8 / 100) × demand - supply capacity ···(6)

[0044] Also, a method of determining a countermeasure plan in a merit order using costs in addition to the priority order may be used. In addition, an additional supply plan may be determined by an optimization calculation with the objective function being the sum of the priority order and costs and the constraints being the additional supply amount, lead time, etc., or an additional supply plan may be determined using a generator start / stop plan. Note that multiple additional supply plans may be formulated.

[0045] In step S303, a countermeasure index for the additional supply plan determined in step S302 is calculated. As the countermeasure index, for example, an expected value obtained by multiplying the occurrence probability of the forecast error scenario by the required amount of additional supply is used. Countermeasure index = Probability of occurrence of forecast error scenario × Additional supply required (7)

[0046] The countermeasure index mentioned above increases as the probability of occurrence of forecast errors increases. When the probability of occurrence of forecast errors is high, the manager can determine whether or not to implement additional supply measures depending on the magnitude of this countermeasure index. In addition, since the countermeasure index increases as the additional supply requirement increases, the manager can determine whether or not to implement additional supply measures depending on the impact of the tight supply-demand balance.

[0047] In step S304, it is confirmed whether the additional supply plan has been formulated and the countermeasure index has been calculated for all forecast error scenarios. If calculation has not been completed for all forecast error scenarios (step S304, No), the process returns to step S300. If calculation has been completed for all forecast error scenarios (step S304, Yes), the process flow ends.

[0048] Next, an example of the processing result will be described. Fig. 15A is a diagram showing an example of creating a forecast error scenario by the supply and demand plan formulation unit 20. Fig. 15B is a diagram showing an example of a reserve margin evaluation of each forecast error scenario by the supply and demand plan formulation unit 20. Fig. 15C is a diagram showing an example of formulating an additional supply plan by the supply and demand plan formulation unit 20.

[0049] Fig. 15A shows an example of creating a forecast error scenario. In the graph, the vertical axis represents the forecast error, and the horizontal axis represents the forecast period (the time difference between the forecast point and the forecast section). Each point represents a forecast error calculated from past forecast data and actual data, and a forecast error scenario is created based on this forecast error and the scenario creation conditions. Fig. 15A shows Scenario A (dashed line) and Scenario B (dotted line). Scenario B has a higher forecast error than Scenario A over the forecast period.

[0050] As the current focus is on supply shortages due to a decline in the reserve margin, scenarios are created for upside (forecast < actual) for demand and downside (forecast > actual) for renewable energy, but this is not limited to the above.

[0051] Figure 15B shows an example of a reserve margin evaluation for each forecast error scenario. In the graph, the vertical axis represents the reserve margin, and the horizontal axis represents the forecast period (the time difference between the forecast point and the forecast horizon). As a result of the reserve margin evaluation, in scenario B (dashed dotted line), there are periods between 4 and 5 days later and 6 and 7 days later where the reserve margin falls below 8%, so an additional supply plan is formulated.

[0052] FIG. 15C shows an example of formulating an additional supply plan. In the graph above, the vertical axis represents additional supply capacity, and the horizontal axis represents the forecast period (the time difference between the forecast point and the forecast section). An additional supply plan is formulated for a period in which the reserve margin falls below 8%. The required amount of additional supply is selected from the candidate measures, taking into account the lead time. Specifically, in Scenario B (dashed line) in FIG. 15B, a plan is shown to provide additional supply 4 to 5 days from now through pumped storage power generation, power generation by additional startup of generators under balance shutdown, and power generation by activation command sources.

[0053] In the graph at the bottom of Figure 15C, the vertical axis represents the reserve margin, and the horizontal axis represents the forecast period (the time difference between the forecast point and the forecast horizon). In Scenario B, the reserve margin falls below 8%, but in Scenario B-1, which plans to implement additional supply measures, we can see that the reserve margin will improve as a result of the additional supply.

[0054] 16 is a diagram showing an example of a display on the display unit 102 according to the first embodiment. Scenario information 161, additional supply information 162, and scenario reserve margin information 163 are displayed on a display screen 160 of the display unit 102. The scenario information 161 includes information such as the occurrence probability of each scenario, the minimum reserve margin, countermeasure index, countermeasure case, formulation deadline, and cost. For example, in the case of scenario B, the minimum reserve margin may be 5%. The manager needs to consider countermeasures based on this information.

[0055] In the present embodiment, the display method is a monitor display of a computer system, but a mobile terminal or a screen display type accessory may be used. Also, the control for each event case may be displayed.

[0056] In this embodiment, by displaying the reserve margin for each scenario, the contents of the additional supply plan, the countermeasure index, and the limit, the operator can decide to implement additional supply measures according to the probability of occurrence of a forecast error and its impact. In addition, by displaying the cost of the additional supply measures, it becomes possible to decide on their implementation while taking economic efficiency into consideration. Furthermore, when there are multiple additional supply measures, displaying them side by side can support the operator in implementing the most suitable measures.

[0057] <Aims and Effects of the First Embodiment> In this embodiment, the uncertainty of demand and renewable energy is taken into consideration by evaluating the reserve margin using a forecast error scenario of demand and renewable energy created from past performance. In addition, an additional supply plan according to the priority and cost of measures is formulated after taking into consideration the lead time, and is presented to the operator together with information such as costs and measure indexes, thereby supporting the operator in deciding whether to implement additional supply measures. Note that the decision to implement additional supply measures may not only be made by the operator, but may also be made automatically by the system using the measure indexes.

[0058] <Embodiment 2> The configuration of a supply and demand plan formulation support system according to the second embodiment will be described with reference to FIGS. 17 is a diagram showing an example of a hardware configuration of a supply and demand plan development support device 100A according to the second embodiment. The supply and demand plan development support device 100A differs from the supply and demand plan development support device 100 described in the first embodiment in that the supply and demand plan development support device 100A newly includes a weather forecast database DB11 and a weather record database DB12. The weather forecast database DB11 stores forecast data of meteorological information such as temperature, weather, solar radiation, and wind speed. The weather record database DB12 stores record data of meteorological information such as temperature, weather, solar radiation, and wind speed.

[0059] 18 is a diagram showing an example of a device configuration of a supply and demand plan development support device 100A according to embodiment 2. The supply and demand plan development support device 100A differs from embodiment 1 in that it includes a weather sensitivity calculation unit 24, a scenario creation unit 21A, a weather forecast database DB11, and a weather record database DB12.

[0060] The weather sensitivity calculation unit 24 calculates the sensitivity of demand and renewable energy to weather conditions from the demand history data in the demand history database DB3, the renewable energy history data in the renewable energy history database DB4, and the weather history data in the weather history database DB12, and transmits the calculated weather sensitivity to the scenario creation unit 21A.

[0061] The scenario creation unit 21A creates a forecast error scenario using the weather sensitivity calculated by the weather sensitivity calculation unit 24, the demand forecast data in the demand forecast database DB1, the renewable energy forecast data in the renewable energy forecast database DB2, and the weather forecast data in the weather forecast database DB11.

[0062] FIG. 19 is a flowchart showing the weather sensitivity calculation process S24 of the weather sensitivity calculation unit 24 according to the second embodiment. In step S400, the demand and renewable energy performance data is read. In step S401, weather record data is read and linked to demand and renewable energy record data. In step S402, the performance data is filtered to remove anomalous data. As a method of removal, for example, filtering may be performed by date (for a plan for April, the performance data from March to May of the past two years is used) or by temperature (the performance data within a range of ±5°C of the predicted temperature is used). In step S403, the weather sensitivity is calculated. As a method for calculating the weather sensitivity, for example, an approximation line may be calculated from the performance data by the least squares method, and the sensitivity of the demand and renewable energy to the temperature may be calculated.

[0063] FIG. 20 is a diagram showing an example of processing by the weather sensitivity calculation unit 24 and the scenario creation unit 21A according to the second embodiment. Weather sensitivity 171 in Fig. 20 shows an example of sensitivity calculation by the weather sensitivity calculation process S24 of the weather sensitivity calculation unit 24. In the graph, the vertical axis represents actual demand (renewable energy actual demand in the case of renewable energy) and the horizontal axis represents temperature (solar radiation, wind volume, etc. in the case of renewable energy). Each point represents demand data for past temperatures. The dotted line represents the sensitivity calculated from each point.

[0064] 20 shows an example of a scenario created by the scenario creation unit 21A. In the graph, the vertical axis represents expected demand and the horizontal axis represents time. For example, when assuming a case where the temperature is +1°C as a scenario, the scenario is created by calculating the expected demand using the weather sensitivity calculated by the weather sensitivity calculation unit 24.

[0065] The supply and demand plan development support device 100A of embodiment 2 includes a weather sensitivity calculation unit 24 that calculates weather sensitivity using past actual data on demand, renewable energy power source, and weather information, a scenario creation unit 21A that creates a prediction error scenario using the weather sensitivity calculated by the weather sensitivity calculation unit 24 and future prediction data on demand, renewable energy power source (renewable energy), and weather information, a reserve margin evaluation unit 22 that evaluates a supply reserve margin using the prediction error scenario created by the scenario creation unit 21A and power generation plan data, and an additional supply plan formulation unit 23 that formulates an additional supply plan using the evaluation result of the supply reserve margin by the reserve margin evaluation unit 22 and data on candidate additional supply measures to compensate for a supply capacity shortage.

[0066] The weather sensitivity calculation unit 24 calculates the sensitivity of the power demand to the temperature from the filtered performance data.

[0067] The weather sensitivity calculation unit 24 may calculate the sensitivity of the output of the renewable energy power source to solar radiation and wind speed from the filtered performance data.

[0068] In addition, the scenario creation unit 21A may create a prediction error scenario from the amount of fluctuation in the predicted future temperature.

[0069] <Aim of embodiment 2> In this embodiment, the weather information is used to create a scenario, taking advantage of the fact that the demand and renewable energy output are correlated with the weather, which makes it possible to evaluate the reserve margin and formulate additional supply measures even for unseasonable weather conditions.

[0070] Finally, the method for supporting the formulation of supply and demand plans will be explained. (1) A supply and demand plan formulation support method for supporting the formulation of a supply and demand plan, in which a computer uses forecast error data, which is the difference between forecast values ​​and actual values ​​for a given period in the past for demand and renewable energy sources (renewable energy), to create a forecast error scenario including time-series data of the forecast error and its occurrence probability (see scenario creation process S21), evaluates the supply reserve rate using the forecast error scenario and power generation plan data (see reserve rate evaluation process S22), and formulates an additional supply plan using the evaluation result of the supply reserve rate and candidate data for additional supply measures to compensate for the supply capacity shortage (see additional supply plan formulation process S23). This makes it possible to formulate a supply and demand plan that takes into account the uncertainty of renewable energy and demand and the lead time of measures. (2) A supply and demand plan formulation support method for supporting the formulation of a supply and demand plan, in which a computer calculates weather sensitivity using past performance data on demand, renewable energy power sources, and meteorological information (see weather sensitivity calculation process S24), creates a forecast error scenario using the weather sensitivity and future forecast data on demand, renewable energy power sources, and meteorological information, evaluates a supply reserve rate using the forecast error scenario and power generation plan data, and formulates an additional supply plan using the evaluation result of the supply reserve rate and candidate data for additional supply measures to compensate for a supply capacity shortage. This makes it possible to formulate a supply and demand plan that takes into account the uncertainty of renewable energy and demand and the lead time of measures. [Explanation of symbols]

[0071] 20. Supply and Demand Planning Department 21, 21A Scenario Creation Department 22 Reserve Rate Evaluation Department 23 Additional Supply Planning Department 24 Weather Sensitivity Calculation Section 100,100A Supply and Demand Planning Support Device 101 Input section 102 Display section 103 Communications Department 104 processors 105 Memory 106 Bus 160 display screen 161 Scenario Information 162 Additional Supply Information 163 Scenario Reserve Margin Information 171 Weather Sensitivity 172 Example Scenarios DB1 Demand forecast database DB2 Renewable Energy Forecast Database DB3 Demand performance database DB4 Renewable Energy Performance Database DB5 Power Generation Planning Database DB6 Additional supply measures candidate database DB7 Forecast Error Scenario Database DB8 Reserve margin evaluation results database DB9 Supplemental Supply Planning Database DB10 Program Database DB11 Weather Forecast Database DB12 Weather record database S21 Scenario creation process S22 Reserve margin evaluation process S23 Additional supply planning process S24 Weather sensitivity calculation processing

Claims

1. a scenario creation unit that uses forecast error data, which is a difference between forecast values ​​and actual values ​​of demand and renewable energy power sources for a predetermined period in the past, to create a forecast error scenario including time-series data of the forecast error and its occurrence probability; a reserve margin assessment unit that assesses a supply reserve margin using the forecast error scenario and power generation plan data created by the scenario creation unit; and an additional supply plan formulation unit that formulates an additional supply plan using the evaluation result of the reserve rate by the reserve rate evaluation unit and additional supply measure candidate data for compensating for a supply capacity shortage. A supply and demand planning support device characterized by the above.

2. 2. The supply and demand plan formulation support device according to claim 1, The additional supply plan formulation unit formulates additional supply measures using lead time, priority, and cost information in the additional supply measure candidate data. A supply and demand plan formulation support device characterized by:

3. 2. The supply and demand plan formulation support device according to claim 1, The additional supply plan formulation unit formulates at least two additional supply plans. A supply and demand plan formulation support device characterized by:

4. 2. The supply and demand plan formulation support device according to claim 1, The additional supply planning unit calculates a countermeasure index having a positive correlation with the occurrence probability of the forecast error scenario. A supply and demand plan formulation support device characterized by:

5. 2. The supply and demand plan formulation support device according to claim 1, The supply and demand plan targeted by the supply and demand plan formulation support device is at least one week or longer. A supply and demand plan formulation support device characterized by:

6. 2. The supply and demand plan formulation support device according to claim 1, The supply and demand planning support device operates periodically. A supply and demand plan formulation support device characterized by:

7. 2. The supply and demand plan formulation support device according to claim 1, The additional measures to be considered by the additional supply planning department include pumped storage power generation, generators under balance shutdown, generators under operation shutdown, and power sources to be activated. A supply and demand plan formulation support device characterized by:

8. A weather sensitivity calculation unit that calculates weather sensitivity using past performance data of demand, renewable energy power source, and weather information; a scenario creation unit that creates a prediction error scenario using the weather sensitivity calculated by the weather sensitivity calculation unit and future prediction data of demand, the renewable energy power source, and meteorological information; a reserve margin assessment unit that assesses a supply reserve margin using the forecast error scenario and power generation plan data created by the scenario creation unit; and an additional supply plan formulation unit that formulates an additional supply plan using the evaluation result of the reserve rate by the reserve rate evaluation unit and additional supply measure candidate data for compensating for a supply capacity shortage. A supply and demand planning support device characterized by the above.

9. The supply and demand plan formulation support device according to claim 8, The weather sensitivity calculation unit calculates the sensitivity of the power demand to the temperature from the filtered performance data. A supply and demand plan formulation support device characterized by:

10. The supply and demand plan formulation support device according to claim 8, The weather sensitivity calculation unit calculates the sensitivity of the output of the renewable energy power source to solar radiation and wind speed from the filtered performance data. A supply and demand plan formulation support device characterized by:

11. The supply and demand plan formulation support device according to claim 8, The scenario creation unit creates the prediction error scenario from a fluctuation amount of a future predicted temperature. A supply and demand plan formulation support device characterized by:

12. A supply and demand plan formulation support method for supporting formulation of a supply and demand plan, comprising: The computer Using forecast error data, which is the difference between forecast values ​​and actual values ​​for a specified period in the past for demand and renewable energy power sources, a forecast error scenario is created that includes time-series data on the forecast error and its occurrence probability; Evaluating a reserve margin using the forecast error scenario and power generation plan data; Formulate an additional supply plan using the results of the reserve supply rate evaluation and data on additional supply measures to compensate for supply capacity shortages. A method for supporting the formulation of a supply and demand plan, comprising:

13. A supply and demand plan formulation support method for supporting formulation of a supply and demand plan, comprising: The computer Calculate weather sensitivity using historical data on demand, renewable energy sources and weather information, creating a forecast error scenario using the weather sensitivity and future forecast data of demand, the renewable energy source, and weather information; Evaluating a reserve margin using the forecast error scenario and power generation plan data; Formulate an additional supply plan using the results of the reserve supply rate evaluation and data on additional supply measures to compensate for supply capacity shortages. A method for supporting the formulation of a supply and demand plan, comprising:

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