Power grid supply and demand planning system, power grid supply and demand planning method, and program

Abstract

We create highly accurate supply and demand plans tailored to the characteristics of electric vehicles. [Solution] The power grid supply and demand planning system of the embodiment includes: a prediction data creation unit that predicts power demand, the amount of power generated by renewable energy generators, and EV behavior including driving and charging for each predetermined time period in the future within the target area of ​​the power grid supply and demand planning, and creates prediction data; an operation constraint data creation unit that creates operation constraint data for power plants and energy storage facilities from characteristic data of power plants and energy storage facilities within the target area; and a supply and demand planning calculation unit that creates the supply and demand plan based on the prediction data and the operation constraint data.

Description

Technical Field

[0001] Embodiments of the present invention relate to a power system supply-demand planning system, a power system supply-demand planning method, and a program.

Background Art

[0002] In recent years, in various organizations, the electrification of automobiles has been promoted to reduce greenhouse gas emissions in the transportation sector, and the spread of electric vehicles with low CO2 emissions during driving is expected. In this specification, the electric vehicle is not limited to an EV (Electric Vehicle) that uses only electricity as a power source, but also includes automobiles that use electricity and other (for example, gasoline) as power sources, such as plug-in hybrid vehicles (PHV: Plug-in Hybrid Vehicle). Further, hereinafter, for the sake of simplicity of description, all electric vehicles are represented as "EV".

[0003] Charging from a charging facility is required for the EV to run. This charging demand is considered to be a different power demand from the conventional one, and an increase in power source and fuel procurement accompanying the charging demand is a concern. On the other hand, since the storage battery mounted on the EV can be charged and discharged, it is also expected to be used as a regulating force for adjusting fluctuations in the power generation amount by a renewable energy generator.

[0004] Thus, when viewed from the power system, the EV has two characteristics: a charging load and a power regulation means. Therefore, by controlling the EV by taking advantage of each characteristic, it is considered possible to improve the load factor (improve the power supply efficiency), adjust the fluctuations in the renewable energy generation amount, suppress the increase in power source and fuel procurement, and even reduce it.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] As a conventional technique, for example, there is a technique for maintaining supply-demand adjustment by inducing the charging behavior of an EV according to the time zone. However, in that case, a method for controlling the discharge of the EV is not considered. For example, in the case of a load curve where the power demand increases only in a certain time zone, it may be possible to suppress power source and fuel procurement by utilizing the discharge of a storage battery or the like rather than starting a generator for a short time. Therefore, for reducing power source and fuel procurement, it is more preferable to formulate a supply-demand plan assuming not only the charging but also the discharge of the EV is controlled.

[0007] Also, for example, there is a technique for proposing a supply-demand plan for general power transmission and distribution operators assuming the utilization of an EV as a storage battery system. However, in that case, behaviors such as the driving and charging patterns of the EV are not considered. Different from a stationary storage battery that can always charge and discharge, the driving time of an EV cannot be utilized as an adjustment power, and it is also necessary to charge in accordance with the next driving timing.

[0008] As described above, the constraints on the available time and capacity of the EV are severe. If these constraints are not satisfied, for example, the capacity of the EV during parking may be insufficient as an adjustment power. In that case, there is a risk that the supply-demand balance cannot be maintained, or the power source and fuel procurement cannot be reduced, etc., and the accuracy (appropriateness) of the supply-demand plan may decrease.

[0009] Therefore, the present invention has been made in view of the above circumstances, and an object thereof is to provide a power system supply-demand plan creation system, a power system supply-demand plan creation method, and a program that can create a highly accurate supply-demand plan according to the characteristics of an electric vehicle.

Means for Solving the Problems

[0010] The power grid supply and demand planning system of the embodiment includes: a prediction data creation unit that predicts power demand, the amount of power generated by renewable energy generators, and EV behavior including driving and charging for each predetermined time period in the future within the target area of ​​the power grid supply and demand planning, and creates prediction data; an operation constraint data creation unit that creates operation constraint data for power plants and energy storage facilities from characteristic data of power plants and energy storage facilities within the target area; and a supply and demand planning calculation unit that creates the supply and demand plan based on the prediction data and the operation constraint data. The prediction data creation unit includes a power demand prediction unit that predicts the power demand, a renewable energy generation prediction unit that predicts the amount of power generated by renewable energy generators, and an EV charging behavior prediction unit that predicts EV behavior and creates operation constraint data for the EV for the charge and discharge plan. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is an overall diagram of the power system according to the first embodiment. [Figure 2A] Figure 2A is a functional configuration diagram of the power grid supply and demand planning system according to the first embodiment. [Figure 2B] Figure 2B is a flowchart showing the processing performed by the power grid supply and demand planning system of the first embodiment. [Figure 3] Figure 3 is a functional configuration diagram of the power grid supply and demand planning system according to the second embodiment. [Figure 4] Figure 4 is a functional configuration diagram of the power grid supply and demand planning system according to the third embodiment. [Figure 5] Figure 5 is a functional configuration diagram of the power grid supply and demand planning system according to the fourth embodiment. [Figure 6] Figure 6 is a functional configuration diagram of the power grid supply and demand planning system according to the fifth embodiment. [Figure 7] Figure 7 is a functional configuration diagram of the power grid supply and demand planning system according to the sixth embodiment. [Modes for carrying out the invention]

[0012] Hereinafter, embodiments (Embodiment 1 to Embodiment 6) of the power system supply-demand planning creation system, the power system supply-demand planning creation method, and the program of the present invention will be described with reference to the drawings. In the following embodiments after the second embodiment, descriptions of the same matters as those described in the previous embodiments will be omitted as appropriate.

[0013] (Embodiment 1) First, referring to FIG. 1, the overall configuration of the power system S of the first embodiment will be described. FIG. 1 is an overall configuration diagram of the power system S of the first embodiment.

[0014] The power system S includes a power system supply-demand planning creation system 100, an aggregator 510, a power system 500, and each device within the target area 5. The target area 5 is an area for which the power system supply-demand planning creation system 100 formulates a supply-demand plan. Note that the supply-demand plan may be a supply-demand plan of a general power transmission and distribution business operator, a supply-demand plan of an aggregator, or a supply-demand plan of another person.

[0015] Within the target area 5, there are a renewable energy system 501, a power storage facility 502, a power plant 503, business / industrial facilities 504, household facilities 505, and a charging station 506, and they are connected (linked) to the power system 500.

[0016] Note that the configuration within the target area 5 is not limited to the example of FIG. 1, and other systems and facilities may exist and be connected to the power system 500.

[0017] The renewable energy system 501 is a system composed of devices that convert renewable energy such as sunlight, wind power, hydraulic power, and biomass into electric power and output the converted electric power.

[0018] The power storage facility 502 is a facility that stores electric power (energy) and can output it when electric power (energy) is required. For example, it may be a storage battery or other power storage facilities.

[0019] The power plant 503 is power generation equipment owned by a power company or the like, and for example, it is thermal power generation equipment, hydroelectric power generation equipment, nuclear power generation equipment, and the like.

[0020] The business / industrial equipment 504 is equipment that consumes power within a factory or business establishment, and the power demand thereof includes the charging power of EVs charged within the business establishment.

[0021] The household equipment 505 is equipment that consumes power in ordinary households, and the power demand thereof includes the charging power of EVs charged within the household.

[0022] The charging station 506 is a facility for charging EVs, and the charging power of EVs charged within the station becomes the power demand.

[0023] Hereinafter, a consumer refers to at least any one of the renewable energy system 501, the power storage equipment 502, the business / industrial equipment 504, the household equipment 505, and the charging station 506. The aggregator 510 is a business operator who endeavors to maximize the utilization of the energy resources of consumers. The aggregator 510 receives a request from the power company and sends an adjustment request (instruction) regarding power generation of the renewable energy system 501, charge / discharge of the power storage equipment 502, and power demand including EV charging of each consumer.

[0024] The power grid supply and demand plan creation system 100 creates a supply and demand plan including a power generation plan for the power plant 503 and a charge / discharge plan for the power storage equipment 502, etc., while maintaining the balance between the generated power (discharge power) of the renewable energy system 501, the power storage equipment 502, and the power plant 503, and the power demand of the business / industrial equipment 504, the household equipment 505, and the charging station 506, for purposes such as minimizing the power generation cost (fuel cost (fuel cost of thermal power generation equipment and nuclear power generation equipment), start / stop cost), maximizing the utilization amount of renewable energy power generation, and minimizing the CO2 emission amount. Regarding CO2 emission, for example, when CO2 is emitted to generate the power used for EV driving, it may be treated as if CO2 is emitted by EV driving.

[0025] The created supply and demand plan assumes that the power grid operation constraints in the power grid 500, the power generation equipment of the power plant 503, and the operation constraints of the energy storage equipment 502 are met. In this case, if the supply and demand plan is created assuming that the electricity demand cannot be controlled (discharge control) for EVs, it is expected that fuel costs and start-up / shutdown costs will increase because the increased EV charging demand will require additional power generation.

[0026] By controlling the timing of EV charging and discharging, such as charging EVs when there is a surplus in the renewable energy system 501's power generation, and utilizing the discharge from EVs instead of starting up the power generation equipment of the power plant 503 during temporary increases in electricity demand, it is thought that power generation costs can be further reduced and renewable energy generation can be utilized more effectively. Therefore, with the expected spread of EVs in the future, the power grid supply and demand planning system 100 will need to be able to create EV charging and discharging plans.

[0027] The power grid supply and demand planning system 100 is a system that can create EV charging and discharging plans, and these plans are transmitted to each consumer as charging and discharging commands via the aggregator 510. The method of creating supply and demand plans using the power grid supply and demand planning system 100 will be described in detail below.

[0028] When utilizing EV charging and discharging in supply and demand planning, all EVs within the target area 5 can be assumed to be a single energy storage facility, and supply and demand planning can be created by considering them as a facility that can control power in both charging and discharging directions, similar to energy storage facility 502. On the other hand, while an EV is running, it cannot be used as a balancing force, and it also needs to be charged in line with the next driving timing. Therefore, unlike energy storage facility 502, setting operational constraints that take into account EV behavior is necessary to create a highly accurate supply and demand plan. Accordingly, the power grid supply and demand planning system 100 has a function to predict EV behavior and create EV operational constraints based on the prediction results.

[0029] Figure 2A is a functional configuration diagram of the power grid supply and demand planning system 100 according to the first embodiment. The power grid supply and demand planning system 100 is composed of a computer device and includes a processing unit 10, a storage unit 104, and a display unit 105.

[0030] The processing unit 10 is composed of, for example, a CPU (Central Processing Unit) and performs various calculations. The processing unit 10 includes a forecast data creation unit 101, an operation constraint data creation unit 102, and a supply and demand planning calculation unit 103. In the following description, when describing processes other than those in units 101 to 103 of the processing unit 10, the operating entity may be referred to as the processing unit 10.

[0031] The forecast data creation unit 101 predicts electricity demand, the amount of electricity generated by the renewable energy system 501 (renewable energy generator), and EV behavior, including driving and charging, for predetermined time periods (for example, every 30 minutes) within the target area 5 of the power grid 500's supply and demand plan, and creates forecast data.

[0032] The forecast data creation unit 101 comprises, as a functional configuration (software module), a power demand forecasting unit 211, a renewable energy generation forecasting unit 212, and an EV charging behavior forecasting unit 213.

[0033] The power demand forecasting unit 211 forecasts power demand, creates power demand forecast data, and outputs the power demand forecast data to the supply and demand planning calculation unit 103. For example, the power demand forecasting unit 211 forecasts power demand using weather information and actual power demand data. However, the underlying data is not limited to these. For example, power demand can be forecasted using only weather information or using actual power demand data, and it is also possible to forecast it without using weather information or actual power demand data.

[0034] The renewable energy generation forecasting unit 212 predicts the amount of electricity generated by the renewable energy system 501, creates renewable energy generation forecasting data, and outputs the renewable energy generation forecasting data to the supply and demand planning calculation unit 103. For example, the renewable energy generation forecasting unit 212 predicts the amount of electricity generated by the renewable energy system 501 using weather information and renewable energy performance data (power generation performance data from the renewable energy system 501). However, the underlying data is not limited to these. For example, renewable energy generation can also be predicted using only weather information or using renewable energy performance data, and it is also possible to predict without using weather information or renewable energy performance data. Furthermore, when predicting renewable energy generation, geographic information (latitude, longitude, topographic information such as mountains and plains, etc.) may be used alone or in combination with the other information mentioned above.

[0035] The EV charging behavior prediction unit 213 predicts EV behavior and creates EV behavior prediction data (such as EV mileage and charging amount data for each time period). Furthermore, it creates EV operational constraint data for the charge / discharge plan based on the EV behavior prediction data and outputs the EV operational constraint data to the supply and demand planning calculation unit 103. For example, the EV charging behavior prediction unit 213 predicts EV behavior using weather information and actual EV traffic volume data. However, the underlying data is not limited to these. For example, EV behavior can be predicted using only weather information or actual EV traffic volume data, and it is also possible to predict it without using weather information or actual EV traffic volume data. In addition, when predicting EV behavior, the results of surveys such as street surveys may be used alone or in combination with the other information mentioned above.

[0036] Furthermore, EV traffic volume data can be calculated proportionally based on, for example, actual traffic volume data for all vehicles (total of EVs and non-EVs) for each time period, and data on the total number of vehicles sold and the number of EVs sold during a specified period. In addition to weather information and EV traffic volume data, survey results (for example, the results of surveys on users' preferences regarding EV charging (e.g., preferred charging times)) may also be used.

[0037] Furthermore, the method for creating EV behavior prediction data can be one that utilizes prediction techniques based on input data, such as machine learning, or it can be any other method.

[0038] The operational constraint data creation unit 102 creates operational constraint data for the power plant 503 and the energy storage facility 502 from the characteristic data of the power plant 503 and the energy storage facility 502 within the target area 5. The operational constraint data creation unit 102 comprises a generator operational constraint data creation unit 221 and an energy storage facility operational constraint data creation unit 222.

[0039] The generator operation constraint data creation unit 221 takes equipment information, operational information, etc. as input and outputs operation constraint data to the supply and demand planning calculation unit 103 based on the characteristic data of the generators of the power plant 503 (startup time, shutdown time, maximum efficiency point, maximum output, etc.).

[0040] The energy storage equipment operation constraint data creation unit 222 takes equipment information, operational information, etc. as input and outputs operation constraint data based on the characteristic data of the energy storage equipment 502 (charging efficiency, discharge efficiency, maximum output, etc.) to the supply and demand planning calculation unit 103.

[0041] The supply and demand planning calculation unit 103 creates a supply and demand plan based on the forecast data created by the forecast data creation unit 101 and the operational constraint data created by the operational constraint data creation unit 102.

[0042] Specifically, the supply and demand planning calculation unit 103 takes as input the power demand forecast data from the power demand forecasting unit 211, the renewable energy generation forecast data from the renewable energy generation forecasting unit 212, and the operational constraint data of each facility (such as the energy storage facility 502 and the power generation facilities of the power plant 503) to create a supply and demand plan that satisfies the operational constraints, maintains the supply and demand balance, and aims to minimize power generation costs. The supply and demand plan includes the power generation plans for each power generation facility in the power plant 503, as well as the charge and discharge plans for the energy storage facility 502 and EVs, and is created so that power can be supplied to the shared load which is the difference between the power demand forecast data and the renewable energy generation forecast data.

[0043] Furthermore, cost information such as fuel costs and startup / shutdown costs can also be calculated from the supply and demand plan. Methods for creating the supply and demand plan include mathematical methods such as mathematical optimization, methods utilizing prediction techniques based on vast amounts of data such as machine learning, or other methods.

[0044] The memory unit 104 is composed of a storage medium such as semiconductor memory or a hard disk, and stores data such as the supply and demand planning results (power generation plan, charge / discharge plan) and cost information (fuel costs, start-up / shutdown costs) derived by the supply and demand planning calculation unit 103.

[0045] Specifically, the memory unit 104 stores generator output data 401 (power generation plan), energy storage equipment / EV charge / discharge amount / SOC (State of Charge) data 402 (charge / discharge plan), fuel cost data 403, start-up / shutdown cost data 404, and the like.

[0046] The display unit 105, controlled by the processing unit 10, is composed of, for example, a liquid crystal display, a plasma display, etc., and displays data such as supply and demand planning results and cost information derived by the supply and demand planning calculation unit 103.

[0047] Furthermore, although not shown in the diagram, the power grid supply and demand planning system 100 also includes means for user information input (e.g., keyboard, mouse, touch panel, etc.) and communication interfaces with external devices.

[0048] Figure 2B is a flowchart showing the processing performed by the power grid supply and demand planning system 100 of the first embodiment.

[0049] In step S1, the forecast data creation unit 101 creates forecast data by predicting electricity demand, the amount of electricity generated by the renewable energy system 501, and EV behavior including driving and charging for each predetermined time period in the future within the target area 5 of the power grid 500 supply and demand plan. Specifically, the electricity demand forecasting unit 211, the renewable energy generation forecasting unit 212, and the EV charging behavior forecasting unit 213 perform the above-mentioned processing.

[0050] Next, in step S2, the operational constraint data creation unit 102 creates operational constraint data for the power plant 503 and the energy storage facility 502 from the characteristic data of the power plant 503 and the energy storage facility 502 within the target area 5. Specifically, the generator operational constraint data creation unit 221 and the energy storage facility operational constraint data creation unit 222 perform the above-mentioned processes.

[0051] Next, in step S3, the supply and demand planning calculation unit 103 creates a supply and demand plan based on the forecast data created in step S1 and the operational constraint data created in step S2, and stores the data of the created supply and demand plan in the storage unit 104.

[0052] Next, in step S4, the processing unit 10 displays the supply and demand plan created in step S3 on the display unit 105.

[0053] As described above, the power grid supply and demand planning system 100 of this embodiment creates various types of prediction data, including EV behavior, using the prediction data creation unit 101, and creates operational constraint data for the power plant 503 and energy storage equipment 502 using the operational constraint data creation unit 102. Then, using this prediction data and operational constraint data, the supply and demand planning calculation unit 103 can create a highly accurate supply and demand plan according to the characteristics of the EV. Specifically, it is as follows.

[0054] The EV charging behavior prediction data and operational constraint data obtained by the EV charging behavior prediction unit 213 make the constraints on EV supply and demand adjustment capabilities more accurate, thereby improving the accuracy of supply and demand planning.

[0055] Furthermore, by inputting weather information, actual electricity demand, and actual renewable energy data into the forecast data creation unit 101, as well as actual EV traffic volume data and survey results, it is possible to predict EV behavior and derive operational constraints for utilizing EVs as a supply and demand adjustment force with high accuracy. In addition, by inputting electricity demand forecast data, renewable energy generation forecast data, operational constraints of the generators and storage equipment 502 of the power plant 503, as well as operational constraints of EVs into the supply and demand planning calculation unit 103, the accuracy of supply and demand planning when utilizing EVs as a supply and demand adjustment force can be improved.

[0056] Furthermore, from the perspective of power transmission and distribution operators, the following benefits can be obtained: By utilizing the charging and discharging of EVs, the load factor can be reduced, which can decrease fuel consumption at power plant 503's generators, reduce the number of starts and stops, or curb the need to add more generators due to insufficient adjustment capacity. In addition, by taking EV behavior into account, the accuracy of supply and demand planning can be improved, preventing sudden shortages of adjustment capacity.

[0057] (Second Embodiment) Next, a second embodiment will be described. The second embodiment is a modification of the first embodiment and aims to improve the accuracy of EV operation constraints by deriving the EV's energy storage capacity and maximum output.

[0058] Figure 3 is a functional configuration diagram of the power grid supply and demand planning system 100 according to the second embodiment. An EV characteristics creation unit 214 has been added to the prediction data creation unit 101 of the first embodiment shown in Figure 2A. The EV characteristics creation unit 214 is a function that is configured by a software module.

[0059] The EV characteristics creation unit 214 calculates the EV storage capacity and EV maximum output (and other data, such as the number of EVs owned) within the target area 5 from, for example, weather information and EV traffic volume data (and other data, such as survey results). However, the underlying data is not limited to these.

[0060] For example, the number of EVs owned within area 5 can be determined from survey results and statistical data, and the total storage capacity and maximum output for all EVs in area 5 can be derived from the storage capacity and rated output per EV. In this process, the accuracy of the derivation results can be improved by determining the number of EVs owned separately for regular cars and large cars, which have different storage capacities and rated outputs per vehicle.

[0061] The EV charging behavior prediction unit 213 further uses the calculation results from the EV characteristics creation unit 214 to predict EV behavior and create EV operational constraint data for the charge / discharge plan. Specifically, the EV charging behavior prediction unit 213 checks whether the EV operational constraints obtained in the same manner as in the first embodiment satisfy the conditions for storage capacity and maximum output obtained by the EV characteristics creation unit 214. For example, if the upper limit of output obtained as an EV operational constraint is greater than or equal to the maximum output, there is a risk of insufficient output even if all EVs in the target area 5 are controlled, so the upper limit of output is updated to the maximum output value obtained by the EV characteristics creation unit 214.

[0062] Thus, according to the second embodiment, by using the number of EVs, storage capacity, and maximum output within the target area 5 determined by the EV characteristic creation unit 214, the EV charging behavior prediction unit 213 derives operational constraints on EVs, thereby preventing the creation of a charge / discharge plan that includes excessive charging and discharging of EVs. This improves the accuracy of supply and demand planning when EVs are used as a supply and demand adjustment force.

[0063] (Third embodiment) Next, a third embodiment will be described. The third embodiment is a modification of the first embodiment and aims to improve the accuracy of EV operation constraints by deriving the charge / discharge capacity and maximum output for each time period using the driving behavior of the EV.

[0064] Figure 4 is a functional configuration diagram of the power grid supply and demand planning system 100 according to the third embodiment. An EV adjustment force constraint creation unit 215 has been added to the prediction data creation unit 101 of the first embodiment shown in Figure 2A. The EV adjustment force constraint creation unit 215 is a function configured by a software module.

[0065] The EV adjustment force constraint creation unit 215 calculates the EV charging / discharging capacity and EV maximum output for each time period within the target area 5 from, for example, weather information, EV traffic volume data (and other data such as survey results). However, the underlying data is not limited to these.

[0066] The EV charging behavior prediction unit 213 further uses the calculation results of the EV adjustment force constraint creation unit 215 to predict EV behavior and create EV operational constraint data for the charge / discharge plan. Specifically, for example, the EV charging behavior prediction unit 213 creates EV behavior prediction data based on input information from the EV adjustment force constraint creation unit 215, in addition to inputs such as weather information, EV traffic volume data, and survey results, and outputs EV operational constraint data based on that data.

[0067] For example, the number of EVs connected to the power grid 500 within the target area 5 for each time period is determined from traffic volume information, survey results on the proportion of EVs used, and statistical results. From the storage capacity and rated output per EV, the charging / discharging capacity and maximum output within the target area 5 for each time period are then derived.

[0068] The EV charging behavior prediction unit 213 checks whether the EV operation constraints obtained by the same method as in the first embodiment satisfy the charge / discharge capacity and maximum output obtained by the EV adjustment force constraint creation unit 215. If there is no charge / discharge capacity per hour as an EV operation constraint, or if the maximum output value in the EV operation constraint is greater than the result obtained by the EV adjustment force constraint creation unit 215, the number of EVs connected to the power grid 500 may be less than the charge / discharge plan derived in the supply and demand plan, potentially resulting in insufficient output. For this reason, for example, the charge / discharge capacity and maximum output value in the EV operation constraint obtained by the same method as in the first embodiment are updated to the charge / discharge capacity and maximum output value obtained by the EV adjustment force constraint creation unit 215.

[0069] Thus, according to the third embodiment, the EV charging behavior prediction unit 213 derives operational constraints for the EV using the EV charging / discharging capacity and EV maximum output within the target area 5 for each time period, which are determined by the EV adjustment force constraint creation unit 215. This prevents the creation of charging / discharging plans that include excessive charging and discharging of the EV, and improves the accuracy of supply and demand planning when utilizing EVs as a supply and demand adjustment force.

[0070] (Fourth Embodiment) Next, a fourth embodiment will be described. The fourth embodiment is a modification of the first embodiment and aims to improve the accuracy of supply and demand planning by deriving trends in EV charging demand.

[0071] Figure 5 is a functional configuration diagram of the power grid supply and demand planning system 100 according to the fourth embodiment. An EV charging demand creation unit 216 has been added to the forecast data creation unit 101 of the first embodiment shown in Figure 2A. The EV charging demand creation unit 216 is a function that is configured by a software module.

[0072] The EV charging demand generation unit 216 generates EV charging demand data for each time period within the target area 5 (trends in EV charging demand) from, for example, weather information, EV traffic volume data (and other data such as survey results). However, the underlying data is not limited to these.

[0073] The EV charging behavior prediction unit 213 further uses the EV charging demand to predict EV behavior and create EV operational constraint data for the charge / discharge plan.

[0074] Specifically, for example, the EV charging behavior prediction unit 213 creates EV behavior prediction data based on input information from the EV charging demand creation unit 216, in addition to input information such as weather information, EV traffic volume data, and survey results, and outputs EV operational constraint data based on that data.

[0075] The EV charging behavior prediction unit 213 obtains EV charging demand forecast data expected within the target area 5 from, for example, weather information, survey results on charging timing, and statistical results. The EV charging behavior prediction unit 213 outputs the EV charging demand forecast data created by the EV charging demand creation unit 216 to the supply and demand planning calculation unit 103, along with the EV operation constraints obtained in the same manner as in the first embodiment.

[0076] The supply and demand planning calculation unit 103 calculates more accurate power demand forecast data for the entire target area 5 based on the power demand forecast data created by the power demand forecasting unit 211 and the EV charging demand forecast data created by the EV charging demand creation unit 216, and creates a supply and demand plan for that power demand.

[0077] Thus, according to the fourth embodiment, by utilizing the EV charging demand forecast data obtained by the EV charging demand creation unit 216, a supply and demand plan can be created for more accurate power demand within the target area 5. This improves the accuracy of the supply and demand plan when EVs are used as a supply and demand adjustment force.

[0078] (Fifth embodiment) Next, a fifth embodiment will be described. When controlling the charging and discharging of EVs in the use of EVs in the power grid 500, incentives for permitting the use of EVs may be effective. Therefore, in order to minimize procurement costs, it is preferable to formulate a supply and demand plan that also takes into account the payment of incentives.

[0079] The fifth embodiment is a variation of the first embodiment, and aims to more accurately minimize power generation costs by creating a supply and demand plan that takes into account the payment of incentives to EV users who are permitted to use the system as a supply and demand adjustment force.

[0080] Figure 6 is a functional configuration diagram of the power grid supply and demand planning system 100 according to the fifth embodiment. An EV incentive creation unit 217 has been added to the forecast data creation unit 101 of the first embodiment shown in Figure 2A. The EV incentive creation unit 217 is a function that is configured as a software module.

[0081] The EV incentive creation unit 217 calculates, for example, incentive fee information for EV users who have been authorized to use EVs as a supply and demand adjustment force, based on statistical information related to incentives. However, the underlying data is not limited to this.

[0082] Specifically, for example, the EV incentive creation unit 217 takes the results of surveys and statistical results from street surveys and other investigations regarding incentives as input, derives the incentive fee information necessary for EV users to allow the use of incentives as a supply and demand adjustment mechanism, and outputs it to the EV charging behavior prediction unit 213.

[0083] Furthermore, the incentive pricing information may include information on the number of EVs that, even if they receive incentives, are not permitted to be used as a supply-demand adjustment tool.

[0084] The EV charging behavior prediction unit 213 further uses incentive fee information to predict EV behavior and create EV operational constraint data for the charging and discharging plan.

[0085] Specifically, for example, the EV charging behavior prediction unit 213, in addition to inputs such as weather information, EV traffic volume data, and survey results, derives the EV storage capacity and EV maximum output from the number of EVs that can be used as supply and demand adjustment force within the target area 5, based on input information from the EV incentive creation unit 217. The EV charging behavior prediction unit 213 outputs the derived EV storage capacity and EV maximum output, as well as incentive fee information, to the supply and demand planning calculation unit 103.

[0086] For example, the EV incentive creation unit 217 determines the percentage of each incentive rate range based on survey results and statistics, such as street surveys, regarding the incentive rates that EV users need to be permitted to use as a supply and demand adjustment force. The supply and demand planning calculation unit 103 adds the payment of incentive rates to the power generation cost and creates a supply and demand plan aimed at minimizing power generation costs.

[0087] The supply and demand plan involves utilizing EVs with low required incentive fees first, and then, once the effectiveness of the incentive payments in adjusting supply and demand (reductions in fuel costs and start-stop costs) becomes small, the charging and discharging plan will stop utilizing EVs with higher incentive fees.

[0088] Thus, according to the fifth embodiment, by using the incentive fee information obtained by the EV incentive creation unit 217 to create a supply and demand plan in the supply and demand planning calculation unit 103, it is possible to prevent the use of EVs that have little effect on reducing power generation costs and to effectively reduce power generation costs.

[0089] Furthermore, from the perspective of EV users, the following benefits are available: By distributing charge and discharge charges with greater precision based on EV behavior, the reduction in electricity prices associated with EV charging and the estimation of incentive rewards become more accurate. This makes it easier to participate in the utilization of EV resources.

[0090] (Sixth Embodiment) Next, the sixth embodiment will be described. The sixth embodiment is a variation of the fourth embodiment and aims to improve the accuracy of supply and demand planning by deriving the trend of EV charging demand, taking into account that the charging timing is guided by electricity rates for different time periods (equivalent to incentive charges for charging utilization).

[0091] Figure 7 is a functional configuration diagram of the power grid supply and demand planning system 100 according to the sixth embodiment. An EV incentive creation unit 217 has been added to the forecast data creation unit 101 of the fourth embodiment shown in Figure 5. The EV incentive creation unit 217 is a function that is configured as a software module.

[0092] The EV charging demand creation unit 216 creates the EV charging demand (trends in EV charging demand) for each time period within the target area 5 based on weather information, EV traffic volume data (and other data such as survey results).

[0093] The EV incentive creation unit 217 calculates incentive fee information for EV users who have been authorized to use EVs as a supply and demand adjustment tool, based on statistical information related to incentives.

[0094] Specifically, for example, the EV incentive creation unit 217 takes the results of surveys and statistical results from street surveys and other investigations regarding incentives as input, derives the incentive fee information necessary for EV users to allow the use of incentives as a supply and demand adjustment mechanism, and outputs it to the EV charging behavior prediction unit 213.

[0095] The EV charging behavior prediction unit 213 further uses incentive fee information to predict EV behavior and create EV operational constraint data for the charging and discharging plan.

[0096] Specifically, for example, the EV charging behavior prediction unit 213 creates EV charging demand data after charging behavior guidance based on input information from the EV charging demand creation unit 216 and the EV incentive creation unit 217, in addition to input information such as weather information, EV traffic volume data, and survey results, and outputs it to the supply and demand planning calculation unit 103.

[0097] For example, if the EV incentive creation unit 217 derives incentive information indicating that an incentive can be received by charging during the daytime hours of 9:00 to 12:00, the EV charging behavior prediction unit 213 creates charging demand data based on the EV charging demand trends derived by the EV charging demand creation unit 216, assuming that charging demand will shift to 9:00 to 12:00.

[0098] The EV charging behavior prediction unit 213 outputs EV charging demand forecast data, which takes into account the guidance of charging behavior created by the EV charging demand creation unit 216 and the EV incentive creation unit 217, along with the EV operational constraints obtained in the same manner as in the first embodiment, to the supply and demand planning calculation unit 103. The supply and demand planning calculation unit 103 adjusts the power demand forecast data created by the power demand forecast unit 211 and the EV charging demand forecast data that takes into account the guidance of charging behavior to create more accurate power demand forecast data for the entire target area 5, and creates a supply and demand plan for that power demand.

[0099] Thus, according to the sixth embodiment, by utilizing the EV charging demand forecast data that takes into account the induction of charging behavior obtained by the EV charging demand creation unit 216 and the EV incentive creation unit 217, it is possible to create a supply and demand plan for more accurate electricity demand within the target area 5. This improves the accuracy of the supply and demand plan when EVs are used as a supply and demand adjustment force.

[0100] The program executed by the power grid supply and demand planning system 100 of this embodiment is provided as a file in an installable or executable format, recorded on a computer-readable recording medium such as a CD-ROM, flexible disk (FD), CD-R, or DVD (Digital Versatile Disk).

[0101] Furthermore, the program may be provided by storing it on a computer connected to a network such as the Internet and allowing users to download it via the network. Alternatively, the program may be provided or distributed via a network such as the Internet. Furthermore, the program may be provided pre-installed in ROM or similar media.

[0102] The program has a modular structure that includes various parts within the processing unit. In actual hardware, the CPU reads the program from the storage medium and executes it, loading each of these parts onto the main memory and generating the program in the main memory.

[0103] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0104] For example, some or all of the functions of the power grid supply and demand planning system 100 may be implemented by a cloud computing system. [Explanation of Symbols]

[0105] 10... Processing unit, 100... Power grid supply and demand planning system, 101... Forecast data creation unit, 102... Operational constraint data creation unit, 103... Supply and demand planning calculation unit, 104... Storage unit, 105... Display unit, 211... Power demand forecasting unit, 212... Renewable energy generation forecasting unit, 213... EV charging behavior forecasting unit, 214... EV characteristics creation unit, 215... EV adjustment capacity constraint creation unit, 216... EV charging demand creation unit, 217... EV incentive creation unit, 221...Generator operation constraint data creation unit, 222...Energy storage equipment operation constraint data creation unit, 401...Generator output data, 402...Energy storage equipment, EV charge / discharge amount, SOC data, 403...Fuel cost data, 404...Startup / shutdown cost data, 500...Power grid, 501...Renewable energy system, 502...Energy storage equipment, 503...Power plant, 504...Commercial / industrial equipment, 505...Household equipment, 506...Charging station, S...Power system

Claims

1. A predictive data creation unit creates predictive data by predicting electricity demand, the amount of electricity generated by renewable energy generators, and EV (Electric Vehicle) behavior, including driving and charging, for each predetermined time period in the area covered by the power grid supply and demand plan. An operational constraint data creation unit creates operational constraint data for the power plant and the energy storage facility from characteristic data of the power plant and the energy storage facility within the target area. The system includes a supply and demand planning calculation unit that creates the supply and demand plan based on the forecast data and the operational constraint data, The aforementioned prediction data creation unit, The power demand forecasting unit predicts the aforementioned power demand, A renewable energy power generation prediction unit that predicts the amount of power generated by the renewable energy generator, A power grid supply and demand planning system comprising: an EV charging behavior prediction unit that predicts the aforementioned EV behavior and creates operational constraint data for the EV on the charging and discharging plan.

2. The aforementioned prediction data creation unit, The system further includes an EV characteristics creation unit that calculates the EV storage capacity and maximum EV output within the aforementioned target area. The power grid supply and demand planning system according to claim 1, wherein the EV charging behavior prediction unit further uses the calculation results from the EV characteristic creation unit to predict the EV behavior and create operational constraint data for the EV with respect to the charge and discharge plan.

3. The aforementioned prediction data creation unit, The system further includes an EV adjustment force constraint creation unit that calculates the EV charging / discharging capacity and EV maximum output for each time period within the target area. The power grid supply and demand planning system according to claim 1, wherein the EV charging behavior prediction unit further uses the calculation results of the EV adjustment force constraint creation unit to predict the EV behavior and create EV operational constraint data for the charge and discharge plan.

4. The aforementioned prediction data creation unit, The system further comprises an EV charging demand creation unit that creates the EV charging demand for each time period within the target area, The power grid supply and demand planning system according to claim 1, wherein the EV charging behavior prediction unit further uses the EV charging demand to predict the EV behavior and creates operational constraint data for the EV on the charge and discharge plan.

5. The aforementioned prediction data creation unit, The system further includes an EV incentive creation unit that calculates incentive fee information for EV users who have been authorized to use the aforementioned EVs as a supply and demand adjustment force, The power grid supply and demand planning system according to claim 1, wherein the EV charging behavior prediction unit further uses the incentive fee information to predict the EV behavior and creates operational constraint data for the EV on the charge and discharge plan.

6. The aforementioned prediction data creation unit, The system further includes an EV incentive creation unit that calculates incentive fee information for EV users who have been authorized to use the aforementioned EVs as a supply and demand adjustment force, The power grid supply and demand planning system according to claim 4, wherein the EV charging behavior prediction unit further uses the incentive fee information to predict the EV behavior and creates operational constraint data for the EV on the charge and discharge plan.

7. The forecast data creation unit creates forecast data by predicting electricity demand, the amount of electricity generated by renewable energy generators, and EV behavior including driving and charging for each predetermined time period in the future within the target area of ​​the power grid supply and demand plan. Operational constraint data creation step: The operational constraint data creation unit creates operational constraint data for the power plant and the energy storage facility from characteristic data of the power plant and the energy storage facility within the target area. The supply and demand planning calculation unit includes a supply and demand planning calculation step that creates the supply and demand plan based on the forecast data and the operational constraint data, The aforementioned prediction data creation step is: The power demand forecasting unit performs a power demand forecasting step of forecasting the power demand, The renewable energy generation prediction unit performs a renewable energy generation prediction step in which it predicts the amount of electricity generated by the renewable energy generator, A method for creating a power grid supply and demand plan, comprising: an EV charging behavior prediction step in which an EV charging behavior prediction unit predicts the EV behavior and creates operational constraint data for the EV for the charge and discharge plan.

8. Computers, A predictive data creation unit creates predictive data by predicting electricity demand, the amount of electricity generated by renewable energy generators, and EV (Electric Vehicle) behavior, including driving and charging, for each predetermined time period in the area covered by the power grid supply and demand plan. An operational constraint data creation unit creates operational constraint data for the power plant and the energy storage facility from characteristic data of the power plant and the energy storage facility within the target area. A program for functioning as a supply and demand planning calculation unit that creates the supply and demand plan based on the forecast data and the operational constraint data, The aforementioned prediction data creation unit, The power demand forecasting unit predicts the aforementioned power demand, A renewable energy power generation prediction unit that predicts the amount of power generated by the renewable energy generator, A program comprising: an EV charging behavior prediction unit that predicts the aforementioned EV behavior and creates operational constraint data for the EV on the charging and discharging plan.

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