Power procurement plan formulation support device, power procurement plan formulation support method, and power procurement plan formulation support system

Abstract

To support formulation of an electric power procurement plan considering uncertainty of generated electric power.SOLUTION: An electric power procurement plan formulation support device 1 includes: a storage device that stores generator cost information that is information on costs related to a generator 42 that generates electric power usable by a user 3; and an arithmetic device that executes demand amount prediction processing for calculating a predicted value of an electric power demand amount of the user 3, electric power generation amount prediction processing for calculating a predicted value of electric power generation of the generator 42, and cost calculation processing for stochastically calculating costs of the user 3 required to procure electric power corresponding to the predicted vale of the demand amount from at least the generator 42 based on the predicted value of the demand amount, the predicted value of the electric power generation amount, and the generator cost information, and outputting information related to the calculated costs.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a power procurement plan formulation support device, a power procurement plan formulation support method, and a power procurement plan formulation support system.

Background Art

[0002] In recent years, in response to the increasing global awareness of environmental protection, companies are increasingly being required to cover their power consumption with renewable energy. Against this background, electric power retailers have started to provide various environmentally-friendly power plans. In addition, on the side of corporate consumers, for the purpose of procuring additional renewable energy-derived power over a long period of time, there are an increasing number of cases where they enter into a corporate power purchase agreement (corporate PPA), which is an agreement to purchase all of the power generated at a fixed price over a long period of time from a power plant that generates power using renewable energy.

[0003] As power procurement methods diversify in this way, it has become increasingly difficult for consumers to formulate an optimal power procurement plan for themselves. If a power procurement plan is incorrect, there is a risk of suffering financial damage, such as purchasing unnecessary excess power or being forced to purchase insufficient power at a high price. Furthermore, if a sufficient amount of renewable energy-derived power cannot be procured, it may even damage the corporate brand.

[0004] Under such circumstances, Patent Document 1 and Patent Document 2 disclose technologies for supporting consumers in formulating power procurement plans.

[0005] Patent Document 1 states that "even with limited consumption data, it is possible to select the optimal electricity rate plan by utilizing an electricity usage model that approximates the consumption characteristics of that electricity consumer. As a result, economically rational electricity use can be achieved." It discloses that electricity rates are estimated by modeling the electricity consumption of consumers, and that the plan that results in the lowest electricity rate is selected from a variety of electricity rate plans, taking into account the introduction of solar power generation and energy storage equipment.

[0006] Furthermore, Patent Document 2 states, "Considering stability, economic efficiency, environmental friendliness, and safety, it provides a mathematical model for finding the optimal combination of power generation and transmission to optimize the gain balance between power companies and consumers, and a decision system, decision method, and algorithm for optimizing the supply and demand balance of the electricity market considering VPP Cities, addressing the long-term supply and demand probability planning problem of the electricity market considering the 'environmental value' of renewable energy." It discloses that it seeks the optimal combination of power generation and transmission amounts for each power generation method, as well as procurement and storage amounts from suppliers, that minimizes the total cost for power companies and consumers, while satisfying predetermined constraints on power generation and transmission, additional power generation capacity, power purchase and storage from suppliers, and the required percentage of renewable energy. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2016-45560 [Patent Document 2] Japanese Patent Publication No. 2022-15383 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] Here, the most common renewable energy sources, such as solar and wind power, are difficult to control in terms of output, and inherently carry the risk of output fluctuations. Furthermore, as the proportion of these sources in total domestic power generation increases, the range of price fluctuations in the wholesale electricity market is steadily expanding. Therefore, when procuring electricity from renewable energy sources, the challenge of how to manage these fluctuation risks always exists.

[0009] However, in both the methods disclosed in Patent Document 1 and Patent Document 2, the evaluation function used as an indicator in formulating the power procurement plan is the expected value of the cost of power procurement, and a unique value is provided. In other words, the inherent risk of power fluctuations from power plants such as solar or wind power is not adequately considered, which may result in a plan that incurs enormous costs in some cases. Furthermore, consumers may not be aware of this possibility.

[0010] This invention has been made in view of the above background, and its purpose is to provide a power procurement plan formulation support device, a power procurement plan formulation support method, and a power procurement plan formulation support system that can support the formulation of a power procurement plan that takes into account the uncertainty of generated power. [Means for solving the problem]

[0011] One of the present inventions for solving the above problems is a power procurement plan formulation support device comprising: a storage device for storing generator cost information, which is cost information relating to a generator that generates electricity available to a consumer; and a computing device that performs a demand forecasting process for calculating a predicted value of the amount of electricity the consumer demands; a power generation forecasting process for calculating a predicted value of the amount of power generated by the generator; and a cost calculation process that probabilistically calculates the cost to the consumer necessary to procure electricity corresponding to the predicted value of the demand from at least the generator, based on the predicted value of the demand, the predicted value of the amount of power generated, and the generator cost information, and outputs information relating to the calculated cost. [Effects of the Invention]

[0012] According to the present invention, it is possible to support the formulation of a power procurement plan that takes into account uncertainty in generated power. Configurations, effects, and the like other than those described above will be clarified in the following description of embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] [Figure 1] FIG. 1 is a diagram showing an example configuration of a power procurement plan formulation support system according to the present embodiment. [Figure 2] FIG. 2 is a diagram showing an example of details of a program that constitutes a power procurement planning unit. [Figure 3] FIG. 3 is a diagram showing an example of details of a program that constitutes a distributed energy source control unit. [Figure 4] FIG. 4 is a flow diagram illustrating an outline of power plan support processing. [Figure 5] FIG. 5 is a flow diagram illustrating details of power procurement plan formulation processing. [Figure 6] FIG. 6 is a flow diagram illustrating details of demand curve modeling processing. [Figure 7] FIG. 7 is a diagram showing an example of pattern data. [Figure 8] FIG. 8 is a diagram showing an example of demand curve data. [Figure 9] FIG. 9 is a diagram showing an example of weather data. [Figure 10] FIG. 10 is a diagram showing an example of a demand curve model. [Figure 11] FIG. 11 is a flow diagram illustrating details of power generation curve modeling processing. [Figure 12] FIG. 12 is a diagram showing an example of generator data. [Figure 13] FIG. 13 is a diagram showing an example of power generation curve data. [Figure 14] FIG. 14 is a diagram showing an example of a power generation curve model. [Figure 15] FIG. 15 is a flow diagram illustrating details of cost probability distribution calculation processing. [Figure 16] FIG. 16 is a diagram showing an example of PPA price data. [Figure 17] It is a diagram showing an example of power plan data. [Figure 18] It is a diagram showing an example of renewable energy certificate price data. [Figure 19] It is a diagram showing an example of storage battery data. [Figure 20] It is a diagram showing an example of energy storage station data. [Figure 21] It is a diagram showing an example of capital investment data. [Figure 22] It is a diagram showing an example of provisional power procurement plan data. [Figure 23] It is a flow diagram illustrating details of matching result determination processing. [Figure 24] It is a diagram showing an example of risk tolerance data. [Figure 25] It is a diagram showing an example of a power procurement planning screen. [Figure 26] It is a flow diagram illustrating an overview of distributed energy source control processing. [Figure 27] It is a flow diagram illustrating details of demand forecasting processing. [Figure 28] It is a diagram showing an example of real-time demand data. [Figure 29] It is a diagram showing an example of weather forecast data. [Figure 30] It is a flow diagram illustrating details of power generation prediction processing. [Figure 31] It is a diagram showing an example of real-time power generation data. [Figure 32] It is a flow diagram illustrating details of storage battery operation planning processing. [Figure 33] It is a diagram showing an example of real-time power storage data. [Figure 34] It is a diagram showing an example of power storage facility data. [Figure 35] It is a diagram showing an example of a spot market contract result table. Mode for Carrying Out the Invention

[0014] Embodiments of the present invention will be described below with reference to the drawings. The power procurement planning support system according to this embodiment predicts the amount of electricity used by consumers and the amount of electricity generated by various power plants (hereinafter simply referred to as "power plants") that provide environmentally friendly electricity using renewable energy. Based on this, it estimates the total cost incurred by consumers (the cost of maintaining or securing the electricity demanded by consumers with electricity supplied by power plants, etc.) in the form of a probability distribution, assuming that a power purchase contract is concluded with each power plant (and, in some cases, also using storage batteries as described later). The power procurement planning support system identifies the optimal power plant supplier (hereinafter referred to as "billionaire power purchase supplier") by calculating the probability that this estimated cost will be greater than or equal to the cost acceptable to the consumer.

[0015] Unless otherwise specified, the above agreement shall be considered a Corporate Power Purchase Agreement (PPA).

[0016] Subsequently, the power procurement planning support system predicts the amount of power generated by power plants and the amount of electricity used by consumers based on the power generation status of the power plants with which the power is purchased, the electricity usage status of consumers, the charging and discharging status of various storage batteries, and predetermined weather forecast data. Based on these predicted values, it estimates the costs incurred when operating the storage batteries and controls the storage batteries to minimize those costs (make it the most economical option).

[0017] Figure 1 shows an example of the configuration of the power procurement plan formulation support system 100 according to this embodiment. The power procurement plan formulation support system 100 consists of a power procurement plan formulation support device 1, an intermediary terminal 61 managed by an intermediary business 6, a customer terminal 31 and customer battery 32 managed by each customer 3, a power plant operator terminal 41 and generator 42 managed by each power plant operator 4, a power storage operator terminal 51 and operator battery 52 managed by a power storage operator 5, and power equipment 71 managed by a power retail business 7. These devices and information processing systems are connected to each other via a wired or wireless communication network 2 such as the Internet, LAN (Local Area Network), WAN, or dedicated line.

[0018] The customer terminal 31 displays information received from the power procurement planning support device 1 and also transmits information about customer 3 (for example, information on customer 3's past electricity usage) to the power procurement planning support device 1. The customer battery 32 is an energy storage device that stores or discharges energy in response to commands from the power procurement planning support device 1. The customer battery 32 is a device that can supply power to customer 3 by discharging.

[0019] The power plant operator terminal 41 displays information received from the power procurement plan formulation support device 1 and also transmits information from the power plant operator 4 (for example, information on the past power generation amount of the generator 42) to the power procurement plan formulation support device 1. The generator 42 is, for example, a power generation facility that generates power remotely in response to commands from the power procurement plan formulation support device 1.

[0020] The generator 42 is an environmentally friendly generator, and is a power generation facility that generates electricity using renewable energy sources such as wind turbines, solar power generators, geothermal power generators, and biomass power generators. The generator 42 generates electricity that can be used by consumers. The generator 42 may be a facility that is already capable of generating electricity, or it may be a facility that is planned to become capable of generating electricity (for example, under construction or before construction). The generator 42 is located in each area managed by the power plant operator 4.

[0021] The power storage facility operator terminal 51 displays information received from the power procurement planning support device 1 and transmits information from the power storage facility operator 5 (for example, information on the amount of energy stored in the operator's battery 52) to the power procurement planning support device 1. The operator's battery 52 is an energy storage facility that can exchange power with (charge and discharge) the consumer 3. The operator's battery 52 has the function of being remotely operated in response to commands from the power procurement planning support device 1, and performs energy storage and discharge. The operator's battery 52 is a facility that can supply power to the consumer 3 by discharging.

[0022] The intermediary terminal 61 displays the information received from the power procurement plan formulation support device 1 and also transmits the information entered by the intermediary 6 to the power procurement plan formulation support device 1.

[0023] The electricity retailer 7 is a business that contracts with each customer 3 and supplies electricity to them. The electricity retailer 7 has various power facilities 71 for supplying electricity to each customer 3. The power facilities 71 are facilities capable of supplying electricity to customers 3. The power facilities 71 may include power generation facilities that generate electricity using renewable energy, or may include power generation facilities that generate electricity without using renewable energy.

[0024] Next, the power procurement plan formulation support device 1 stores the programs of the power procurement plan formulation unit 101 and the distributed energy source control unit 102.

[0025] The power procurement planning unit 101 predicts the amount of electricity demand from customer 3 based on information obtained from customer terminal 31 or customer battery storage 32. The power procurement planning unit 101 also predicts the amount of power generated by each generator 42 based on information obtained from power plant operator terminal 41 or generator 42.

[0026] Then, the Power Procurement Planning Department 101 calculates the total cost (hereinafter referred to as the total power cost) necessary to procure as much power as possible from the selected generators 42 that corresponds to the power required by customer 3 (i.e., to procure power in an environmentally conscious manner), based on the predicted amount of electricity demand from customer 3 and information from customer battery 32, the predicted amount of power generated by each generator 42, and information from the battery 52 of the power storage facility operator 5.

[0027] The total electricity cost is the sum of the cost of procuring electricity from the generator 42 (hereinafter referred to as the power generation procurement cost), the cost of procuring the electricity that is insufficient to cover the total electricity cost (hereinafter referred to as the deficit electricity procurement cost), and the maintenance costs of the customer battery 32 and the operator battery 52 (hereinafter referred to as the energy storage equipment cost). The power procurement planning department 101 calculates the power generation procurement cost, the deficit electricity procurement cost, and the total electricity cost as data for a probability distribution.

[0028] The power procurement planning unit 101 identifies the generator 42 (i.e., the power plant operator 4 with whom the customer 3 should enter into a corporate PPA; hereinafter referred to as the power purchase agreement partner), the customer battery 32, and the operator battery 52 (i.e., the customer battery 32 of customer 3 or the operator battery 52 of the power storage facility operator 5 that should be used; hereinafter referred to as the energy storage equipment used) that will result in a lower total power cost, and generates information on the identified items as power planning information.

[0029] The intermediary terminal 61 displays the power plan information generated by the power procurement plan formulation support device 1, as well as information on the corporate PPA to be concluded.

[0030] Next, the distributed energy source control unit 102 predicts the amount of power generated by the generator 42 and the amount of electricity used by the customer 3, respectively, and controls the customer battery 32 and the operator battery 52 so that the amount of electricity demanded by the customer 3 and the amount of electricity procured (from the generator 42, customer battery 32, and operator battery 52) are as equal as possible, that is, in the most economical way.

[0031] Here, the power procurement planning support device 1 includes, as hardware, a CPU (Central Processing Unit) 10, an input / output device 11, a communication device 12, and a storage device 13. The CPU 10 is a processor that controls the operation of the entire power procurement planning support device 1. The input / output device 11 consists of an input device and an output device. The input device is hardware for the user to perform various operations, such as a keyboard, mouse, or touch panel. The output device is hardware that outputs images and sound, such as a liquid crystal display and a speaker. The communication device 12 has the function of communicating with an external terminal using a communication method compliant with a predetermined communication standard. The storage device 13 consists of semiconductor memory and is mainly used to store and retain various programs.

[0032] The CPU 10 executes the program stored in the storage device 13, thereby performing various processes as a whole for the power procurement planning support device 1, as described later. The power procurement planning support device 1 may be a local server installed in a specific location, or it may be a cloud server.

[0033] Furthermore, the customer terminal 31, the power plant operator terminal 41, the energy storage facility operator terminal 51, and the intermediary operator terminal 61 are equipped with the same hardware as the power procurement plan formulation support device 1.

[0034] (Power Procurement Planning Department) Next, Figure 2 shows an example of the details of the program that constitutes the power procurement planning unit 101. As shown in the figure, the power procurement planning unit 101 includes a demand curve modeling unit 201, a power generation curve modeling unit 202, a power plant / consumer matching unit 203, a cost probability distribution calculation unit 204, and a matching result determination unit 205.

[0035] Furthermore, the power procurement planning unit 101 stores the following as databases for managing necessary information: pattern data DB1, demand curve data DB2, weather data DB3, power generation curve data DB4, generator data DB5, PPA price data DB6, wholesale electricity market price data DB7, power plan data DB8, renewable energy certificate price data DB9, battery data DB10, power storage station data DB11, risk tolerance data DB12, demand curve model DB13, power generation curve model DB14, provisional power procurement plan data DB15, capital investment data DB21, and power procurement plan DB22.

[0036] The demand curve modeling unit 201 is a module that has the function of calculating a demand curve model that predicts the future electricity consumption of customer 3. The demand curve model predicted by the demand curve modeling unit 201 is input into the demand curve model DB 13.

[0037] The power generation curve modeling unit 202 is a module that has the function of calculating a power generation curve model that predicts the future power generation of the generator 42. The power generation curve model predicted by the power generation curve modeling unit 202 is input into the power generation curve model DB 14.

[0038] The power plant / consumer matching unit 203 is a module that has the function of generating combinations of power plant operators 4 and consumers 3 (matching power plant operators 4 and consumers 3). The information of the combinations of power plant operators 4 and consumers 3 matched by the power plant / consumer matching unit 203 is input to the cost probability distribution calculation unit 204.

[0039] The cost probability distribution calculation unit 204 calculates each cost (total electricity cost, power generation procurement cost, and power shortage procurement cost) in the form of a probability distribution for the matched combination in which customer 3 enters into a corporate PPA with power plant operator 4. Each cost calculated by the cost probability distribution calculation unit 204 is input to the matching result determination unit 205.

[0040] The matching result determination unit 205 is a module that has the function of determining whether it is appropriate for customer 3 to conclude a corporate PPA with power plant operator 4 (whether power plant operator 4 is an appropriate counterparty for bilateral electricity purchase contracts) based on each cost, in the combination generated by the power plant / consumer matching unit 203.

[0041] (Distributed energy source control unit) Next, Figure 3 shows an example of the details of the program that constitutes the distributed energy source control unit 102. As shown in the figure, the distributed energy source control unit 102 includes a demand forecasting unit 301, a power generation forecasting unit 302, a battery operation planning unit 303, and a battery control unit 304.

[0042] Furthermore, the distributed energy source control unit 102 stores the following as databases for managing necessary information: pattern data DB1, demand curve model DB13, power generation curve model DB14, real-time demand data DB16, weather forecast data DB17, real-time power generation data DB18, real-time energy storage data DB19, and energy storage equipment data DB20.

[0043] The demand forecasting unit 301 is a module that has the function of predicting the amount of electricity used by customer 3 at a predetermined point in the future (for example, 30 minutes later). The amount of electricity used predicted by the demand forecasting unit 301 is input to the battery operation planning unit 303.

[0044] The power generation prediction unit 302 is a module that has the function of predicting the amount of power generated by the generator 42 at a predetermined point in the future (for example, 30 minutes later). The amount of power generated predicted by the power generation prediction unit 302 is input to the battery operation planning unit 303.

[0045] The battery operation planning unit 303 is a module that has the function of formulating an operation plan (hereinafter referred to as the battery operation plan) for the customer battery 32 and the operator battery 52 based on the predicted values ​​of power consumption and power generation received from the demand forecasting unit 301 and the power generation forecasting unit 302, respectively. The battery operation plan formulated by the battery operation planning unit 303 is input to the battery control unit 304.

[0046] The battery control unit 304 is a module that has the function of controlling the customer battery 32 and the operator battery 52 based on the battery operation plan.

[0047] The functions of each information processing unit in the power procurement plan formulation support system 100 described above are realized by the computing unit reading programs from memory or external storage devices. Furthermore, each program can be recorded and distributed, for example, on a portable or fixed recording medium. These programs, in whole or in part, may be realized using virtual information processing resources provided using virtualization technology, process space isolation technology, etc., such as virtual servers provided by a cloud system. Also, all or part of these programs may be realized by services provided by a cloud system via an API (Application Programming Interface), for example. Next, we will explain the processes performed by the power procurement plan formulation support system 100.

[0048] <Power Planning Support Processing> Figure 4 is a flowchart illustrating the outline of the process (hereinafter referred to as the power planning support process) that generates power planning information for customer 3 and controls power equipment based on that power planning information. The power planning support process is initiated, for example, when a predetermined execution instruction is input from customer terminal 31 to power procurement plan formulation support device 1.

[0049] First, the power procurement planning unit 101 of the power procurement planning support device 1 executes a power procurement planning process S101 to determine the power purchasing contract partners and the energy storage facilities to be used, and to generate power planning information. The power procurement planning process S101 is executed repeatedly, for example, until the customer terminal 31 inputs an acceptance regarding the determined power purchasing contract partners and energy storage facilities to be used.

[0050] The distributed energy source control unit 102 then executes a distributed energy source control process S102 that controls the utilization and storage equipment in a manner that is most economical for customer 3, with respect to the generator 42 and utilization and storage equipment related to the power purchase contract partner determined by the power procurement planning unit 101 (and accepted by the customer terminal 31). The distributed energy source control process S102 is executed repeatedly at predetermined timings (for example, at predetermined times or at predetermined time intervals (for example, every 30 minutes)) while customer 3 is using electricity. Next, we will explain the details of the power procurement planning process S101 and the distributed energy source control process S102.

[0051] <Power Procurement Planning Process> Figure 5 is a flowchart illustrating the details of the power procurement planning process S101.

[0052] First, the demand curve modeling unit 201 executes a demand curve modeling process S201 to calculate the demand curve model for customer 3.

[0053] Furthermore, the power generation curve modeling unit 202 executes a power generation curve modeling process S202 to calculate the power generation curve model of the power plant operator 4.

[0054] The power plant / consumer matching unit 203 then generates combinations of power plant operators 4 and consumers 3 (S203). For example, the power plant / consumer matching unit 203 randomly selects a power plant operator 4 from among multiple power plant operators 4 and generates combinations of the selected power plant operator (hereinafter referred to as the selected power plant operator) and the consumer 3 related to the consumer terminal 31 into which the execution instruction has been entered.

[0055] The cost probability distribution calculation unit 204 executes a cost probability distribution calculation process S204 that predicts and calculates each cost in the form of a probability distribution when the above-mentioned consumer 3 enters into a corporate PPA with the selected power plant operator and receives power supply from the selected power plant operator.

[0056] Next, the matching result determination unit 205 executes a matching result determination process S205 that determines whether the selected power plant operator is suitable as a bilateral electricity purchase contract partner, based on each cost calculated by the cost probability distribution calculation unit 204.

[0057] If the selected power plant operator is not suitable as a bilateral electricity purchase contract partner (S205: NO), the power plant / consumer matching unit 203 repeats the process of S203 to generate a new combination of power plant operator 4 and consumer 3.

[0058] If the selected power plant operator is deemed suitable as a bilateral electricity purchase contract partner (S205: YES), the matching result determination unit 205 generates information on each cost, such as the power generation procurement cost calculated by the cost probability distribution calculation unit 204, as power planning information, and outputs a screen related to the generated power planning information (referred to as the power procurement planning screen) (S206). The power procurement planning screen is displayed, for example, on the customer terminal 31, the power plant operator terminal 41, the storage station operator terminal 51, or the intermediary operator terminal 61. Details of the power procurement planning screen will be described later.

[0059] <Demand curve modeling process> Figure 6 is a flowchart illustrating the details of the demand curve modeling process S201.

[0060] First, the demand curve modeling unit 201 acquires pattern data DB1, demand curve data DB2, and weather data DB3 (S211).

[0061] (Pattern data) Here, Figure 7 shows an example of pattern data DB1. Pattern data DB1 is data that defines data (features) that characterize a certain period (one day in this embodiment) and the probability that a period having those features will occur. Specifically, pattern data DB1 has data items including pattern ID 701, which is set to the ID (pattern ID) of a pattern (weather pattern) consisting of a combination of multiple weather data; occurrence probability 702, which is set to the probability of occurrence of that weather pattern; month 703, which is set to the month's data as weather data that characterizes that weather pattern; day of the week 704, which is set to the day of the week's data; and weather 705, which is set to the weather data. In this embodiment, weather data was used as features in this way, but this is not intended to limit it to these, and any features that can affect the amount of power generated by the generator 42 may be used.

[0062] (Demand curve data) Figure 8 shows an example of the demand curve data DB2. The demand curve data DB2 records the time changes in each customer's past electricity usage (i.e., actual values ​​of electricity demand). Specifically, the demand curve data DB2 has data items that include a timestamp 801 in which time data is set, and measured electricity usage values ​​802 in which the value of the amount of electricity used by each customer 3 is set.

[0063] (Weather data) Figure 9 shows an example of the weather data DB3. The weather data DB3 is a database that records past weather patterns for each area. Specifically, the weather data DB3 has data items including an area ID 901 where the area ID is set, a timestamp 902 where the date and time is set, a temperature 903 where the temperature for each date and time is set as weather pattern data, a precipitation 904 where the amount of precipitation for each date and time is set, a solar radiation 905 where the amount of solar radiation for each date and time is set, a relative humidity 906 where the relative humidity for each date and time is set, and a weather 907 where the weather for each date and time is set. In addition, other weather data such as wind speed may also be set.

[0064] Next, as shown in S212 of Figure 6, the demand curve modeling unit 201, based on the pattern data DB1 and weather data DB3, classifies the changes in electricity consumption for each past period of a consumer, as shown in the demand curve data DB2, into one of several patterns consisting of combinations of the change pattern and the weather patterns shown in the weather data DB3, and calculates the probability of occurrence for each pattern.

[0065] For example, the demand curve modeling unit 201 classifies the changes in electricity consumption for each day in the past into multiple patterns using a predetermined clustering process, and calculates the probability that each classified pattern will occur. The demand curve modeling unit 201 also refers to the pattern data DB1 and the weather data DB3 to identify the weather pattern to which each day in the past belonged, and calculates the probability that the day on which the electricity consumption changes for each classified pattern were observed belonged to that weather pattern. Based on this, the demand curve modeling unit 201 further calculates the probability of occurrence of the electricity consumption changes for each pattern for each weather pattern.

[0066] Then, the demand curve modeling unit 201 calculates a demand model curve representing the change in electricity consumption for each pattern classified in S212 (S213). For example, the demand curve modeling unit 201 calculates a function DemandModel_(c,i) (t) representing the electricity consumption of consumer c at time t on a day of pattern i (a day with a certain weather pattern having a certain change in electricity consumption) by taking the arithmetic mean using the following equation 1 (that is, it adopts the average value of each change in each group).

number

[0067] Here, the electricity demand DemandData_(c,day) (t) is the electricity demand of consumer c at time t on a day (day∈Pt_i) belonging to pattern i (corresponding to the demand curve data DB2).

[0068] Then, the demand curve modeling unit 201 stores each demand model curve calculated in S213 in the demand curve model DB13, associating it with each pattern (S214).

[0069] (Demand curve model database) Figure 10 shows an example of the demand curve model DB13. The demand curve model DB13 is data that records the time changes in electricity consumption for each customer for each pattern. Specifically, the demand curve model DB13 has data items including a customer ID 1001 on which the customer's ID is set, a pattern ID 1002 on which the ID of the pattern of the time change in electricity consumption is set, a timestamp 1003 on which the date and time data is set, and an electricity consumption 1004 on which the value of electricity consumption at that date and time is set.

[0070] <Power generation curve modeling process> Next, Figure 11 is a flowchart illustrating the details of the power generation curve modeling process S202.

[0071] First, the power generation curve modeling unit 202 determines whether or not the generator 42 related to the selected power plant operator has started operation (S221). For example, the power generation curve modeling unit 202 may determine the operating status of the generator 42 by communicating with the generator 42, or it may determine the operating status of the generator 42 by obtaining information on the operating status of the generator 42 from the power plant operator terminal 41.

[0072] If the generator 42 related to the selected power plant operator has started operation (S221:YES), the power generation curve modeling unit 202 executes the process in S222. If the generator 42 related to the selected power plant operator has not started operation (S221:NO), the power generation curve modeling unit 202 executes the process in S224.

[0073] In S222, the power generation curve modeling unit 202 acquires pattern data DB1, generator data DB5, and weather data DB3.

[0074] (Generator data) Here, Figure 12 shows an example of the generator data DB5. The generator data DB5 is data that records the output characteristics (factors that affect the amount of power generated) of each generator 42. Specifically, the generator data DB5 has data items including a generator name 1201, which sets the name or identification of each generator 42; a wind speed 1202, which sets the wind speed as an output characteristic of that generator 42; and an output 1203, which sets the output value at that wind speed.

[0075] Next, as shown in S223 of Figure 11, the power generation curve modeling unit 202 calculates a power generation curve model representing the time change in the amount of power generated by the generator 42 (target generator) which was determined to have started operation in S221, from the generator data DB5 and the weather data DB3. After that, the process in S227 is performed.

[0076] For example, the power generation curve modeling unit 202, based on the weather patterns for each day identified in S212 and the above and the generator data DB5, classifies the changes in the amount of power generated by the generator 42 for each past day into either patterns of changes in the amount of power generated or patterns of combinations of weather patterns, respectively, by a predetermined clustering process.

[0077] For example, the power generation curve modeling unit 202 calculates a function GenerationModel_(g,i) (t) representing the amount of power generated at time t on a day of pattern i for the solar power plant g, based on the weather data value WeatherData_day (t) (for example, the wind speed or solar radiation value for the area to which the solar power plant g belongs in the weather data DB3) for the day (day∈Pt_i) belonging to pattern i, and the generator data DB5 corresponding to that weather data value (for example, the output value corresponding to the wind speed or solar radiation value).

[0078] Meanwhile, in S224, the power generation curve modeling unit 202 acquires pattern data DB1, power generation curve data DB4, and weather data DB3.

[0079] (Power generation curve data) Figure 13 shows an example of the power generation curve data DB4. The power generation curve data DB4 records the time change (actual value) of the past power generation amount of each generator 42. Specifically, the power generation curve data DB4 has data items that include a timestamp 1301 which sets the date and time, and an actual power generation value 1302 which sets the power generation amount of the generator 42 at that date and time.

[0080] Then, the power generation curve modeling unit 202 classifies the time change in power generation shown in the power generation curve data DB4 acquired in S224 into multiple patterns based on the pattern data DB1 and the weather data DB3 (S225).

[0081] For example, the power generation curve modeling unit 202 calculates the probability of occurrence of changes in power generation for each change pattern and weather pattern by processing similarly to S212.

[0082] The demand curve modeling unit 201 then calculates a power generation curve model representing the change in power generation for each classified pattern (S226). For example, the demand curve modeling unit 201 calculates the power generation model curve GenerationModel_(g,i) (t) representing the amount of power generated at time t on a day for pattern i of power plant g by taking the arithmetic mean, as shown in equation 2 below (that is, it adopts the average value of each change in each group).

number

[0083] Here, GenerationData_(g,day) (t) is the amount of power generated by power plant g on a day belonging to pattern i (day∈Pt_i) (corresponding to the power generation curve data DB4).

[0084] The power generation curve modeling unit 202 stores each power generation curve model calculated in S226 in the power generation curve model DB14, associating it with each pattern (S227).

[0085] (Power generation curve model DB) Figure 14 shows an example of the power generation curve model DB14. The power generation curve model DB14 is data that records the predicted power generation amount of each generator 42 for each pattern. Specifically, the power generation curve model DB14 has data items including a power plant ID 1401 on which the ID of the generator 42 is set, a pattern ID 1402 on which the ID of the pattern of change in power generation over time is set, a timestamp 1403 on which the date and time is set, and a power generation amount 1404 on which the power generation amount of the generator 42 at that date and time is set.

[0086] <Cost probability distribution calculation process> Figure 15 is a flowchart illustrating the details of the cost probability distribution calculation process S204.

[0087] The cost probability distribution calculation unit 204 obtains the demand curve model calculated in S201 and the power generation curve model calculated in S202 (S241).

[0088] Furthermore, the cost probability distribution calculation unit 204 acquires PPA price data DB6 (S242).

[0089] (PPA pricing data) Here, Figure 16 shows an example of PPA price data DB6. PPA price data DB6 records data on the electricity price of each power generation facility operator's corporate PPA (generator cost information related to generator 42). Specifically, PPA price data DB6 has data items for power plant ID 1601, which is set to the name or identifier of power plant operator 4, and electricity sales price 1602, which is set to the unit price at which the electricity provided by power plant operator 4 under the corporate PPA is sold.

[0090] Then, as shown in S243 of Figure 15, the cost probability distribution calculation unit 204 calculates the cost of procuring generated electricity based on the power generation curve model acquired in S241 and the PPA price data DB6 acquired in S242.

[0091] For example, the cost probability distribution calculation unit 204 calculates a function PPACost_(g,i) that represents the power procurement cost for a generator 42 (power plant g) related to the selected power plant operator on a day of pattern i, using the following equation 3.

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[0092] Here, GenerationModel_(g,i) (t) is a power generation model curve that predicts the amount of power generated at time t on a day with pattern i for power plant g (calculated in power generation curve modeling process S202), and PPAPrice_g is the electricity selling price for power plant g shown in PPA price data DB6.

[0093] Next, the cost probability distribution calculation unit 204 acquires the power plan data DB8, the renewable energy certificate price data DB9, the battery data DB10, the power storage station data DB11, and the capital investment data DB21 (S244).

[0094] (Electricity plan data) Figure 17 shows an example of the electricity plan data DB8. The electricity plan data DB8 is data (equipment cost information related to the electricity retailer 7) that records information about the electricity plans that each electricity retailer 7 provides to customers 3. Specifically, the electricity plan data DB8 has the following data items: plan ID 1701 where the ID of the electricity plan is set, plan name 1702 where the name of the electricity plan is set, contract capacity 1703 where the contract capacity of the electricity covered by the electricity plan (the amount of electricity that customers 3 can use) is set, contract capacity unit 1704 where the calculation unit of the electricity related to that contract capacity is set, basic charge 1705 where the basic charge of the electricity plan is set, and energy charge 1706 where the additional charge for the electricity plan (in this case, the additional amount per unit of electricity) is set.

[0095] (Renewable energy certificate price data) Figure 18 shows an example of the renewable energy certificate price data DB9. The renewable energy certificate price data DB9 is data that records the market price (acquisition price) of certificates that prove that electricity has been procured using renewable energy (hereinafter referred to as renewable energy certificates) at various points in the past (generator cost information related to generator 42). Specifically, the renewable energy certificate price data DB9 has data items that include a timestamp 1801 in which the date and time are set, and a contract price 1802 in which the price of the renewable energy certificate at that date and time is set.

[0096] (Battery data) Figure 19 shows an example of the battery data DB10. The battery data DB10 is data that records data regarding the characteristics of each customer's battery 32 (energy storage equipment cost information related to the customer's battery 32). Specifically, the battery data DB10 has the following data items: battery name 1901, which sets the name or identifier of the customer's battery 32; maximum charge output 1902, which sets the maximum charge output of that customer's battery 32; maximum discharge output 1903, which sets the maximum discharge output of that customer's battery 32; maximum charge amount 1904, which sets the maximum charge amount of that customer's battery 32; and installation cost 1905, which sets the installation cost of that type of customer's battery 32.

[0097] (Power storage data) Figure 20 shows an example of the power station data DB11. The power station data DB11 is data that records data on the characteristics of each power station operator's battery 52 (power storage equipment cost information related to the operator's battery 52). Specifically, the power station data DB11 has the following data items: power station ID2001, which sets the name or identifier of the operator's battery 52; contractable output 2002, which sets the contractable output as a characteristic of the operator's battery 52; contractable capacity 2003, which sets the contractable capacity as a characteristic; timestamp 2004, which sets the date and time; and usage fee 2005, which sets the usage fee at that date and time.

[0098] (Capital investment data) Figure 21 shows an example of the capital investment data DB 21. The capital investment data DB 21 records data indicating each customer 3's willingness to invest in each piece of equipment, such as generators 42, customer batteries 32, and operator batteries 52. Specifically, the capital investment data DB 21 has data items for customer ID 2101, which is set as the name or identifier of customer 3, and capital investment amount 2102, which is set as the maximum amount that customer 3 can invest in each piece of equipment.

[0099] As shown in Figure 15, the cost probability distribution calculation unit 204 calculates the cost of procuring the shortfall, which is the cost of procuring the amount of electricity that cannot be met by the electricity demand of customer 3 using only the generators 42 of the selected power plant operator, based on the power plan data DB8 and the renewable energy certificate price data DB9 (S245).

[0100] For example, the cost probability distribution calculation unit 204 calculates the function SupplementalCost_(c,g,i), which represents the cost of procuring the deficit on a day of pattern i when customer c has selected power plant g as the power plant operator, using the following equation 4.

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[0101] Here, DemandModel_(c,i) (t) is a function representing the amount of electricity consumed by consumer c at time t on a day of pattern i (calculated in demand curve modeling process S201), GenerationModel_(g,i) (t) is a function representing the amount of electricity generated by power plant g (calculated in generation curve modeling process S202), Base_e is the basic charge for electricity plan e of electricity retailer 7, Rate_e is the usage charge for the said electricity plan e, and RECPrice(t) is the issuance price of renewable energy certificates for the said electricity plan e. Note that RECPrice(t) is 0 if electricity plan e also includes the issuance price of renewable energy certificates.

[0102] Furthermore, if customer c further procures electricity by introducing customer battery storage 32 or by procuring electricity from business battery storage 52, the cost probability distribution calculation unit 204 modifies the power generation model curve GenerationModel_(g,i) (t) in Equation 4 to the effective power generation model curve EffectiveGenerationModel_(g,i) (t) with predetermined constraints set.

[0103] Specifically, the cost probability distribution calculation unit 204 calculates the effective power generation model curve EffectiveGenerationModel_(g,i) (t) for the newly introduced customer battery 32 (battery b) using the following equation 5, under predetermined constraint conditions (equations 6, 7, and 8). The cost probability distribution calculation unit 204 calculates various EffectiveGenerationModel_(g,i) (t) that satisfy the constraint conditions (for example, by calculating them randomly).

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number

number

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[0104] Here, MaxChargePower_b is the maximum charging output of the customer's battery 32, MaxDischargePower_b is the maximum discharging output of the customer's battery 32, MaxCapacity_b is the maximum charge capacity of the customer's battery 32, BatteryCost_b is the installation cost of the customer's battery 32, Budget_c is the amount of capital investment available to customer c (obtained from capital investment data DB21), TotalCost is the total cost, PPACost is the cost of procuring generated electricity, and SupplementalCost is the cost of procuring the deficit.

[0105] On the other hand, when introducing the operator battery 52 of the energy storage facility operator 5, the cost probability distribution calculation unit 204 calculates the effective power generation model curve EffectiveGenerationModel_(g,i) (t) based on equation 5 and the data from the energy storage facility data DB11 acquired above. That is, the cost probability distribution calculation unit 204 calculates the effective power generation model curve EffectiveGenerationModel_(g,i) (t) based on the power generation model curve GenerationModel_(g,i) (t), with the contractable output, contractable capacity, and usage fee of the operator battery 52 as constraints, respectively.

[0106] Then, the cost probability distribution calculation unit 204 calculates the total cost TotalCost_(c,g,b,i) for each deficiency power procurement cost SupplementalCost_(c,g,i) at customer c, based on each calculated effective power generation model curve EffectiveGenerationModel_(g,i) (t), using the following equation 9.

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[0107] Here, BatteryCost_b is the cost of installing the business battery storage system 52.

[0108] Then, the cost probability distribution calculation unit 204 calculates the expected value of the total cost, ExpTotalCost_(c,g,b), based on the total cost TotalCost_(c,g,b,i) for each pattern i day and the probability of occurrence p_i for pattern i day calculated in S212, using the following equation 10.

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[0109] The cost probability distribution calculation unit 204 uses the expected value of the total cost, ExpTotalCost_(c,g,b), as an evaluation function and temporarily stores the effective power generation model curve EffectiveGenerationModel_(g,i) (t), the total cost TotalCost_(c,g,b,i), and the expected value of the total cost ExpTotalCost_(c,g,b) in the storage device 13 when the value of several tens is minimized.

[0110] The cost probability distribution calculation unit 204 performs the above processing for all combinations of customer batteries 32 and operator batteries 52 identified by the battery data DB 10 and the power plant data DB 11. The cost probability distribution calculation unit 204 then calculates the sum of the installation cost of the customer battery 32 and the usage fee of the operator battery 52 for the combination of customer batteries 32 and operator batteries 52 in which the expected value of the total cost ExpTotalCost_(c,g,b) is smallest, as the adjustment capacity securing cost (S246).

[0111] The cost probability distribution calculation unit 204 then stores information regarding the selected power plant operator, the combination of the customer battery 32 and the operator battery 52, and the capacities of the customer battery 32 and the operator battery 52 in the power procurement plan provisional data DB 15 (S247).

[0112] Furthermore, the cost probability distribution calculation unit 204 calculates and stores data (probability distribution data of power procurement costs) that associates the power procurement cost ElectricityCost_(c,g,b,i), which is the sum of the PPA cost PPACost_(g,i) and the deficiency power procurement cost SupplementalCost_(c,g,i) on a day of pattern i, with the occurrence probability p_i of a day of pattern i (S248). This completes the cost probability distribution calculation process S204.

[0113] (Provisional data for power procurement plan) Figure 22 shows an example of the provisional power procurement plan data DB15. The provisional power procurement plan data DB15 is data that records information on the generators 42, customer batteries 32, and business batteries 52 from which each customer 3 should procure electricity. Specifically, the provisional power procurement plan data DB15 has the following data items: customer ID 2201, which sets the ID or name of each customer 3; PPA partner 2202, which sets the selected power plant operator for that customer 3; power plan 2203, which sets the power plan provided by the selected power plant operator; battery name 2204, which sets the customer battery 32 to be used; battery output 2205, which sets the output of the customer battery 32 of the customer 3 to be used; battery capacity 2206, which sets the contractable capacity of the customer battery 32 of the customer 3 to be used; power plant ID 2207, which sets the ID of the operator battery 52 of the power plant operator 5 to be used; power plant output 2208, which sets the output of the operator battery 52 of the power plant operator 5 to be used; and power plant capacity 2209, which sets the capacity of the operator battery 52 to be used.

[0114] <Matching result determination process> Figure 23 is a flowchart illustrating the details of the matching result determination process S205. First, the matching result determination unit 205 obtains the probability distribution data of power procurement costs calculated in the cost probability distribution calculation process S204 and the risk tolerance data DB12 (S251).

[0115] (Risk tolerance data) Here, Figure 24 shows an example of the risk tolerance data DB12. The risk tolerance data DB12 is data that records information on the cost risks that each customer 3 can tolerate. Specifically, the risk tolerance data DB12 has the following data items: customer ID 2401, which is set to the name or identifier of each customer 3; power procurement cost threshold 2402, which is set to the upper limit of the power procurement cost; and tolerance probability 2403, which is set to the probability that the upper limit will be tolerated.

[0116] Next, as shown in S252 of Figure 23, the matching result determination unit 205 determines whether the probability distribution of power procurement costs obtained in S251 is within the acceptable range for customer 3.

[0117] Specifically, the matching result determination unit 205 determines that the probability distribution is not within the acceptable range if the following equation 11 does not hold true for the electricity procurement cost threshold ElectricityCostThreshold_c and tolerance probability Tolerance_c of customer c indicated by the risk tolerance data DB12, and that the probability distribution is within the acceptable range if it does hold true.

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[0118] If the probability distribution is outside the acceptable range (S252: NO), the matching result determination unit 205 executes the process in S203 (S253). If the probability distribution is outside the acceptable range (S252: YES), the matching result determination unit 205 executes the process in S254.

[0119] In S254, the matching result determination unit 205 obtains information on power procurement plans corresponding to the probability distribution data of power procurement costs obtained in S251 from the power procurement plan provisional data DB15, sets the obtained power procurement plan information in the power procurement plan DB22, and executes the process in S206 (S255). Subsequently, the matching result determination unit 205 displays the power procurement plan formulation screen.

[0120] (Power procurement plan development screen) Figure 25 shows an example of the power procurement planning screen 2500. The power procurement planning screen 2500 displays the graph 2501 of the demand curve model DB13 calculated in S201, the graph 2502 of the power generation curve model DB14 calculated in S202, the contents of the power procurement plan 2503 calculated in S204, and the graph 2504 of the probability distribution of the total cost. The user, customer 3, can create a power procurement plan by referring to the power procurement planning screen 2500.

[0121] The graph 2504 of the probability distribution of total costs uses the cost value (price range) of customer 3 on one axis (horizontal axis) and the probability of that cost occurring on the other axis (vertical axis). In other words, the matching result determination unit 205 calculates the probability that customer 3's cost will be in each price range, based on the cost of customer 3 for each pattern and the probability of occurrence of each pattern calculated by the cost probability distribution calculation unit 204.

[0122] <Distributed energy source control processing> Figure 26 is a flowchart illustrating the overview of the distributed energy source control process S102.

[0123] First, the demand forecasting unit 301 executes a demand forecasting process S301 to predict the amount of electricity used by customer 3 at a predetermined future point in time (for example, 30 minutes later). The power generation forecasting unit 302 then executes a power generation forecasting process S302 to predict the amount of electricity generated by power plant operator 4 at a predetermined future point in time (for example, 30 minutes later).

[0124] The battery operation planning unit 303 executes a battery operation planning process S303 to generate control plans (hereinafter referred to as battery operation plans) for the customer battery 32 of customer 3 and the operator battery 52 of the power storage facility operator 5, based on the amount of electricity usage predicted in the demand forecasting process S301 and the amount of electricity generated predicted in the power generation forecasting process S302.

[0125] Then, the battery control unit 304 executes a battery control process S304 to control (charge and discharge) the customer battery 32 of customer 3 and the operator battery 52 of power plant operator 5, based on the battery operation plan generated in the battery operation plan formulation process S303. Note that the battery control unit 304 may execute the battery operation plan formulation process S303 via the communication network 2, or it may implement a program that realizes the distributed energy source control unit 102 by incorporating this program into the customer battery 32 and the operator battery 52 and executing this program.

[0126] <Demand forecasting process> Figure 27 is a flowchart illustrating the details of the demand forecasting process S301. The demand forecasting unit 301 acquires pattern data DB1, real-time demand data DB16, and weather forecast data DB17 (S311).

[0127] (Real-time demand data) Figure 28 shows an example of the real-time demand data DB16. The real-time demand data DB16 contains real-time historical data of electricity usage for each customer 3 at each time point. Specifically, the real-time demand data DB16 has data items that include a timestamp 2801 in which the data for each time point is set, and an actual electricity usage value 2802 in which the value of electricity usage for each customer 3 at that time point is set.

[0128] (Weather forecast data) Figure 29 shows an example of the weather forecast data DB17. The weather forecast data DB17 contains data on predicted weather data for each future date and time in each area. Specifically, the weather forecast data DB17 has data items including an area ID 2901 where the ID of each area is set, a timestamp 2902 where date and time information is set, a temperature 2903 where temperature data for each time in each area is set, a precipitation 2904 where precipitation data for each date and time in each area is set, a solar radiation 2905 where solar radiation data for each date and time in each area is set, a relative humidity 2906 where relative humidity data for each date and time in each area is set, and a weather 2907 where the weather for each date and time in each area is set.

[0129] Based on the data acquired in S311, the demand forecasting unit 301 selects the pattern among the various demand model curves that is closest to the pattern relating to the current weather and power generation changes (S312).

[0130] The demand forecasting unit 301 obtains the demand curve model pattern selected in S312 from each demand curve model registered in the demand curve model DB13 (S313). The demand forecasting unit 301 then stores the obtained demand model curve as the predicted value of electricity consumption (S314).

[0131] <Power generation forecasting process> Figure 30 is a flowchart illustrating the details of the power generation forecasting process S302.

[0132] The power generation forecasting unit 302 acquires pattern data DB1, real-time power generation data DB18, and weather forecast data DB17 (S321).

[0133] (Real-time power generation data) Figure 31 shows an example of the real-time power generation data DB 18. The real-time power generation data DB 18 contains historical data of the real-time power generation amount of each generator 42 at each time point. Specifically, the real-time power generation data DB 18 has data items: a timestamp 3101 in which each date and time is set, and a measured power generation value 3102 in which the value of the power generation amount of each generator 42 at that date and time is set.

[0134] Based on the data acquired in S321, the power generation forecasting unit 302 selects the pattern among the various demand model curves that is closest to the pattern relating to the current weather and power generation changes (S322).

[0135] The power generation prediction unit 302 obtains a power generation curve model of the pattern selected in S322 from each power generation curve model registered in the power generation curve model DB14 (S323). Then, the power generation prediction unit 302 stores the obtained power generation model curve as a predicted value of the amount of power generated (S324).

[0136] <Battery operation plan development process> Figure 32 is a flowchart illustrating the details of the battery operation plan development process S303.

[0137] The battery operation planning unit 303 obtains the predicted values ​​for electricity consumption of customer 3 and the predicted values ​​for power generation of power plant operator 4, which are calculated by the demand forecasting unit 301 and the power generation forecasting unit 302, respectively (S331).

[0138] Furthermore, the battery operation planning unit 303 acquires real-time energy storage data DB19 and energy storage equipment data DB20 (S332).

[0139] (Real-time energy storage data) Here, Figure 33 shows an example of the real-time energy storage data DB19. The real-time energy storage data DB19 contains historical data of the amount of energy stored (charged) at each time point in time for each customer battery 32 and business battery 52. ​​Specifically, the real-time energy storage data DB19 has data items that include a timestamp 3301 in which the data for each date and time is set, and a battery charge amount 3302 in which the value of the charge amount of each customer battery 32 and business battery 52 at that date and time is set.

[0140] (Energy storage equipment data) Figure 34 shows an example of the energy storage equipment data DB20. The energy storage equipment data DB20 contains data such as the amount of energy stored and performance of each customer battery 32 and business battery 52. ​​Specifically, it has data items including a battery ID 3401, which is set to the ID or identifier of each customer battery 32 or business battery 52; an actual output 3402, which is set to the actual output of that customer battery 32 or business battery 52; and an actual capacity 3403, which is set to the actual capacity of that customer battery 32 or business battery 52.

[0141] The battery operation planning unit 303 uses the data acquired in S332 as constraints and determines the charging and discharging operations (power transfer) of the battery based on the predicted values ​​acquired in S331 (predicted power consumption of customer 3 and predicted power generation of power plant operator 4) (S333).

[0142] For example, the battery operation planning unit 303 calculates the charge / discharge output of battery b at time t, BatteryControl_b(t) (positive values ​​represent charging, negative values ​​represent discharging), using the following equation 12. Subsequently, the battery control unit 304 performs charge / discharge control on battery b according to equation 12. That is, the battery control unit 304 transmits control information to battery b for performing charge / discharge control. Battery b then performs the control (charge / discharge) indicated by the received control information.

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[0143] Here, MaxChargePower_b is the maximum charging output, MaxDischargePower_b is the maximum discharging output, MaxCapacity_b is the maximum charge amount, ExpGeneration_g (t) is the predicted value of the power generation amount of power plant g, ExpDemand_c (t) is the predicted value of the power consumption amount of consumer c, and BatteryLeft_b (t) is the remaining charge amount of battery b. However, if the remaining charge amount of battery b, BatteryLeft_b (t), satisfies BatteryLeft_b (t) = MaxCapacity_b with respect to the maximum charge amount MaxCapacity_b, then BatteryControl_b (t) ≤ 0, and if BatteryLeft_b (t) = 0, then BatteryControl_b (t) ≥ 0, in which case the positive and negative results are truncated to 0.

[0144] The calculation formula for charge / discharge control shown here is just one example; any formula can be used that brings the predicted value of customer c's electricity consumption closer to the predicted value of power generation g's power generation.

[0145] In the above explanation, the contract between customer 3 and power plant operator 4 is assumed to be a "physical corporate PPA," which is a type of contract where the sale of electricity is conducted on the premise that electricity is physically transmitted from generator 42 to customer 3 via the grid line. However, other types of contracts are also acceptable.

[0146] For example, the present invention can also be applied to a contract called a "virtual corporate PPA," in which the electricity used by consumer 3 is purchased by consumer 3 from an electricity retailer 7 with which consumer 3 has a contract, and the power plant operator 4 sells the entire amount of electricity it generates on the electricity market, and the difference between the market price and the fixed contract price is later settled through a cash settlement between consumer 3 and power plant operator 4. In this case, the following processes are executed in S242, S243, and S245 of the cost probability distribution calculation process S204.

[0147] In other words, the power procurement plan formulation support device 1 manages the results of spot market transactions using the spot market transaction result table DB21 in order to calculate the revenue that the power plant operator 4 will obtain from selling electricity.

[0148] (Spot Market Transaction Results Table) Figure 35 shows an example of the spot market execution results table DB21. The spot market execution results table DB21 has data items including a timestamp 3501 which sets the date and time, and an execution price 3502 which sets the execution price for each spot market at that date and time.

[0149] In S242, the cost probability distribution calculation unit 204 obtains the spot market execution result table DB21 along with the PPA price data DB6. In S243, the cost probability distribution calculation unit 204 calculates the difference between the PPA settlement price shown in the PPA price data DB6 and the market price shown in the spot market execution result table DB21 as the PPA cost.

[0150] Next, in S245, the cost probability distribution calculation unit 204 calculates the cost of procuring the deficit under the condition that the entire amount of electricity in demand is purchased from the electricity retailer 7 (rather than the cost of procuring the difference between the amount of electricity generated and the amount of electricity demanded).

[0151] In this way, the power procurement plan formulation support device 1 can perform processing that corresponds to a virtual corporate PPA.

[0152] As described above, the power procurement plan formulation support device 1 of this embodiment calculates the predicted amount of electricity demand at customer 3 and the predicted amount of electricity generated by generator 42, respectively. Based on the predicted amount of demand, the predicted amount of electricity generated, and generator cost information, it probabilistically calculates the cost required for customer 3 to procure electricity corresponding to the predicted amount of electricity demand from at least generator 42, and outputs information regarding the calculated cost (power procurement plan formulation screen 2500).

[0153] This allows customer 3 to obtain probabilistic cost information (for example, not based on a uniquely determined value such as an expected value, but from the perspective of value variance or fluctuation risk) for meeting customer 3's electricity demand using power from generator 42. For example, even if generator 42 is a renewable energy generator and there is a risk of output fluctuation, a power procurement plan (especially a plan to procure environmentally friendly power) can be flexibly and appropriately formulated to effectively utilize the power from generator 42 to meet customer 3's electricity demand. As described above, the power procurement plan formulation support device 1 of this embodiment can support the formulation of a power procurement plan that takes into account the uncertainty of generated power.

[0154] Furthermore, the power procurement planning support device 1 of this embodiment classifies the changes in electricity usage for each day in the past into multiple patterns and calculates the probability of occurrence of each pattern of electricity usage change, thereby predicting the future changes in electricity usage for each pattern and the probability of occurrence of each pattern. The predicted changes in electricity usage and the probability of occurrence of each pattern are used as the predicted value of electricity demand at consumer 3. In addition, the power procurement planning support device 1 classifies the changes in power generation for each day in the past into multiple patterns and calculates the probability of occurrence of each pattern of power generation change, thereby predicting the future changes in power generation for each pattern and the probability of occurrence of each pattern. The predicted changes in power generation and the probability of occurrence of each pattern are used as the predicted value of power generation for generator 42.

[0155] In this way, the power procurement plan formulation support device 1 can appropriately assess the risks that exist when procuring and using environmentally friendly electricity, which vary in magnitude from day to day, by classifying changes in power consumption and changes in power generation into daily patterns, calculating the probability of occurrence for each pattern, and using these as predicted values.

[0156] Furthermore, the power procurement planning support device 1 of this embodiment further classifies the changes in each pattern of electricity usage for each day into multiple patterns based on past weather data, predicting the future changes in electricity usage for each pattern and the probability of each pattern occurring, and uses the predicted changes in electricity usage and the probability of each pattern occurring as the predicted value of electricity demand at consumer 3. In addition, the power procurement planning support device 1 further classifies the changes in each pattern of electricity generation for each day into multiple patterns based on past weather data, predicting the future changes in electricity generation for each pattern and the probability of each pattern occurring, and uses the predicted changes in electricity generation and the probability of each pattern occurring as the predicted value of electricity generation at generator 42.

[0157] In this way, the power procurement plan formulation support device 1 can appropriately assess the risks that exist when procuring and using environmentally friendly electricity, which vary in magnitude depending on the weather conditions of each day, by classifying changes in power consumption and changes in power generation into weather patterns, calculating the probability of occurrence for each pattern, and using these as predicted values.

[0158] Furthermore, the power procurement planning support device 1 of this embodiment obtains a predicted value for power consumption for a certain pattern by averaging the changes in power consumption for each day belonging to that pattern. In addition, the power procurement planning support device 1 obtains a predicted value for power generation for a certain pattern by averaging the changes in power generation for each day belonging to that pattern.

[0159] This allows for the use of distinctive pattern data for each pattern of changes in power generation and changes in power consumption.

[0160] Furthermore, the power procurement plan formulation support device 1 of this embodiment probabilistically calculates the cost for customer 3 to procure power corresponding to the predicted demand from at least the generator 42, based on equipment cost information, which is information on the costs of equipment (power retailer's power generation equipment, storage batteries (customer storage battery 32, business storage battery 52), etc.) that can supply electricity available to customer 3.

[0161] In this way, even when customer 3 uses electricity from equipment other than the generator 42 to meet its electricity demand, the costs incurred by customer 3 can be calculated probabilistically, and a flexible electricity procurement plan can be formulated.

[0162] In particular, the power procurement plan formulation support device 1 of this embodiment probabilistically calculates the cost for customer 3 to procure power corresponding to the predicted demand from at least the generator 42 and the storage battery, based on equipment cost information, which is information on the cost of a storage battery that can exchange power with the customer 3.

[0163] In this way, even if customer 3 uses batteries in addition to generators 42 to meet its electricity demand, the costs incurred by customer 3 can be calculated probabilistically, and a flexible electricity procurement plan can be formulated.

[0164] Furthermore, the power procurement plan formulation support device 1 of this embodiment calculates the cost of the customer 3 for each pattern based on the changes in power consumption and power generation for each pattern, and calculates the probability that the cost of the customer 3 will reach a predetermined cost value based on the calculated cost of the customer for each pattern and the probability of each pattern occurring.

[0165] This allows us to present customer 3 with, for example, a probabilistically higher cost, enabling customer 3 to flexibly plan their power procurement.

[0166] Furthermore, the power procurement planning support device 1 of this embodiment calculates the probability that the cost value of the consumer 3 will be each value, and outputs a probability distribution graph with each cost value on the horizontal axis and each probability value related to each cost value on the vertical axis (power procurement planning screen 2500).

[0167] This allows consumer 3 to appropriately assess the risks of generator 42 (for example, the instability risk associated with using electricity from generator 42 that utilizes renewable energy) based on the probability of each cost being incurred, and to formulate a power procurement plan.

[0168] Furthermore, the power procurement plan formulation support device 1 of this embodiment predicts the amount of electricity demanded by customer 3 and the amount of electricity generated by generator 42 at a predetermined point in the future (for example, 30 minutes later). Based on the predicted demand and generation amounts, it predicts the amount of electricity exchanged with the battery necessary to secure the amount of electricity corresponding to that demand for customer 3, using at least the electricity supplied from generator 42 and the electricity exchanged with the battery. Based on the predicted amount of electricity, it controls the battery.

[0169] In this way, by identifying the necessary power control settings for the battery based on the predicted demand of customer 3 and the power generation of generator 42, and then implementing those settings, it is possible to ensure that customer 3 has enough power to meet their demand.

[0170] In this case, the power procurement plan formulation support device 1 of this embodiment calculates the amount of electricity demanded by customer 3 and the amount of electricity generated by generator 42 at a predetermined point in the future (for example, 30 minutes later), based on the past electricity usage of customer 3 and the past electricity generation of generator 42, respectively.

[0171] This makes it possible to accurately predict the amount of electricity demanded by customer 3 and the amount of electricity generated by generator 42.

[0172] Furthermore, the power procurement plan formulation support device 1 of this embodiment predicts the amount of electricity demanded by customer 3 and the amount of electricity generated by generator 42 at a predetermined point in the future (for example, 30 minutes later) by selecting one of the patterns of changes in electricity usage and power generation for each pattern.

[0173] This makes it possible to accurately predict the amount of electricity demanded by customer 3 and the amount of electricity generated by generator 42 at a predetermined point in the future, according to the weather patterns of each day.

[0174] Furthermore, the power procurement planning support device 1 of this embodiment predicts the amount of power exchanged with the battery based on the predicted demand and power generation amounts, as well as information on the battery's storage capacity and performance, and controls the battery based on the predicted power.

[0175] This allows for appropriate control based on the battery's attributes, ensuring that sufficient power is available to customer 3.

[0176] Furthermore, in the power procurement planning support system 100 of this embodiment, the customer terminal 31 transmits the past amount of electricity used by customer 3 to the planning support device 1, and the power plant operator terminal 41 transmits the past amount of power generated by the generator 42 that generates electricity available to customer 3 to the planning support device 1. Based on this information, the planning support device 1 transmits charge / discharge control information necessary to secure the electricity corresponding to customer 3's electricity demand from the generator 42 and the battery to the battery, and the battery operates based on the received information.

[0177] This allows customer 3 to use electricity appropriately in conjunction with the generator 42 and the storage battery.

[0178] Specifically, in the power procurement plan formulation support system 100 of this embodiment, the planning support device 1 charges the battery from the consumer 3 or discharges it to the consumer 3.

[0179] This allows consumer 3 to use the battery to perform appropriate power control.

[0180] The present invention is not limited to the embodiments described above, and can be implemented using any components without departing from its spirit. The embodiments and modifications described above are merely examples, and the present invention is not limited to these as long as the features of the invention are not impaired. Furthermore, although various embodiments and modifications have been described above, the present invention is not limited to these. Other embodiments conceivable within the scope of the technical idea of ​​the present invention are also included within the scope of the present invention.

[0181] For example, some of the hardware components of each device in this embodiment may be provided in other devices.

[0182] Furthermore, each program of each device may be provided in other devices, a program may consist of multiple programs, or multiple programs may be integrated into a single program.

[0183] Furthermore, in this embodiment, a storage battery was given as the equipment to be controlled regarding the power used by consumer 3, but other equipment may also be used. For example, in general electrical equipment such as a pump or air conditioning system, the maximum discharge output may be set to 0 so that it is treated as a device with an infinite maximum charge capacity. [Explanation of Symbols]

[0184] 1 Power procurement plan formulation support device, 3 Consumers, 42 Generators, 100 Power procurement plan formulation support system

Claims

1. A storage device for storing generator cost information, which is information about the costs of generators that produce electricity available to consumers, and A demand forecasting process that calculates a predicted value for the amount of electricity demanded by the aforementioned consumer, A power generation prediction process that calculates a predicted value for the amount of power generated by the aforementioned generator, A power procurement planning support device comprising a computing device that performs a cost calculation process which involves probabilistically calculating the cost to the customer necessary to procure at least the power corresponding to the predicted demand from the generator, based on the predicted demand, the predicted power generation, and the generator cost information, and outputting information regarding the calculated cost.

2. The memory device stores the changes in electricity consumption by the consumer over each past period, and the changes in the amount of electricity generated by the generator over each past period, respectively. The aforementioned computing device is In the demand forecasting process described above, the changes in electricity usage over each of the past periods are classified into multiple patterns, and the probability of occurrence of each of the aforementioned patterns is calculated. This allows for the prediction of future changes in electricity usage for each of the aforementioned patterns and the probability of occurrence of each pattern. The predicted changes in electricity usage and the probability of occurrence for each of the aforementioned patterns are then used as the predicted value of the electricity demand. In the power generation forecasting process, the changes in power generation during each period are classified into multiple patterns, and the probability of occurrence of each pattern of power generation change is calculated. This predicts the future changes in power generation for each pattern and the probability of occurrence of each pattern. The predicted changes in power generation and the probability of occurrence for each pattern are then used as the predicted values ​​for the power generation of the generator. The power procurement plan formulation support device according to claim 1.

3. The aforementioned storage device stores weather data for each past period, The aforementioned computing device is In the demand forecasting process described above, each pattern of change in electricity usage during each period is further classified into multiple patterns based on past weather data, thereby predicting the future change in electricity usage for each pattern and the probability of each pattern occurring. The predicted change in electricity usage and the probability of occurrence for each pattern are then used as the predicted value of the electricity demand. In the power generation forecasting process, each pattern of change in power generation during each period is further classified into multiple patterns based on past weather data, thereby predicting the future change in power generation for each pattern and the probability of each pattern occurring. The predicted change in power generation and probability of occurrence for each pattern are then used as the predicted value of the power generation of the generator. The power procurement plan formulation support device according to claim 2.

4. The calculation device, in the demand forecasting process, uses the average change in electricity consumption for each period belonging to the pattern as the predicted value of electricity consumption for the pattern. In the power generation prediction process described above, the average change in power generation for each period belonging to the pattern is used as the predicted value of power generation for the pattern. The power procurement plan formulation support device according to claim 2.

5. The storage device stores equipment cost information, which is information about the costs of equipment capable of supplying the electricity available to the customer to the customer. In the cost calculation process described above, based on the predicted demand, the predicted power generation, the generator cost information, and the equipment cost information, the cost of the customer required to procure the electricity corresponding to the predicted demand from at least the generator and the equipment is calculated probabilistically. The power procurement plan formulation support device according to claim 1.

6. The storage device stores, as the equipment cost information, energy storage equipment cost information, which is information about the cost of a battery capable of exchanging power with the consumer. In the cost calculation process, the computing device probabilistically calculates the cost to the customer required to maintain the power corresponding to the predicted demand using at least the power supplied from the generator and the power exchanged with the battery, based on the predicted demand, the predicted power generation, the generator cost information, and the energy storage equipment cost information. The power procurement plan formulation support device according to claim 5.

7. The aforementioned computing device is In the cost calculation process described above, the cost of the customer is calculated for each pattern based on the change in electricity consumption for each pattern, the change in power generation for each pattern, and the generator cost information. Based on the calculated cost of the customer for each pattern and the probability of occurrence of each pattern, the probability that the cost of the customer will reach a predetermined cost value is calculated. The power procurement plan formulation support device according to claim 2.

8. The aforementioned computing device is In the cost calculation process described above, the probability that the customer's cost will be each predetermined cost value is calculated, and a graph is output with each predetermined cost value as the first axis and the probability values ​​related to each predetermined cost value as the second axis. The power procurement plan formulation support device according to claim 7.

9. The aforementioned computing device is The system predicts the amount of electricity demanded by the customer and the amount of electricity generated by the generator at a predetermined point in the future, and based on the predicted demand and generation amounts, it predicts the amount of electricity exchanged with the battery necessary to maintain the customer with the electricity corresponding to the predicted demand amount, using at least the electricity supplied by the generator and the electricity exchanged with the battery, and based on the predicted amount of electricity, it performs a distributed energy source control process to control the battery. The power procurement plan formulation support device according to claim 6.

10. The aforementioned computing device is In the distributed energy source control process, the amount of electricity demanded by the consumer and the amount of electricity generated by the generator at a predetermined point in the future are calculated based on the consumer's past electricity usage and the generator's past electricity generation, respectively. The power procurement plan formulation support device according to claim 9.

11. The memory device stores the changes in electricity consumption by the consumer over each past period, and the changes in the amount of electricity generated by the generator over each past period, respectively. The aforementioned computing device is In the demand forecasting process described above, the changes in electricity usage over each of the past periods are classified into multiple patterns, and the probability of occurrence of each of the aforementioned patterns is calculated. This allows for the prediction of future changes in electricity usage for each of the aforementioned patterns and the probability of occurrence of each pattern. The predicted changes in electricity usage and the probability of occurrence for each of the aforementioned patterns are then used as the predicted value of the electricity demand. In the power generation forecasting process, the changes in power generation during each period are classified into multiple patterns, and the probability of occurrence of each pattern of power generation change is calculated, thereby predicting the future changes in power generation for each pattern and the probability of occurrence of each pattern. The predicted changes in power generation for each pattern and the probability of occurrence are then used as the predicted values ​​for the power generation of the generator. In the distributed energy source control process, the amount of electricity demand of the consumer at a predetermined future time is predicted by selecting one of the patterns of electricity usage changes for each of the patterns, and the amount of electricity generated by the generator at a predetermined future time is predicted by selecting one of the patterns of electricity generation changes for each of the patterns. The power procurement plan formulation support device according to claim 9.

12. The aforementioned computing device is In the distributed energy source control process, based on the predicted demand and power generation, and the information on the battery's storage capacity and performance, the power exchanged with the battery is predicted, and the battery is controlled based on the predicted power. The power procurement plan formulation support device according to claim 9.

13. The aforementioned computing device is The power procurement plan formulation support device according to claim 9, wherein, in the distributed energy source control processing, the power is charged by the customer or discharged to the customer based on the predicted power.

14. A method for supporting the formulation of a power procurement plan using an information processing device that includes a computing device and a storage device for storing generator cost information, which is information on the costs of generators that generate electricity available to consumers, The aforementioned computing device A demand forecasting process that calculates a predicted value for the amount of electricity demanded by the aforementioned consumer, A power generation prediction process that calculates a predicted value for the amount of power generated by the aforementioned generator, Based on the predicted demand, the predicted power generation, and the generator cost information, the system performs a cost calculation process that probabilistically calculates the cost to the customer necessary to procure at least the electricity corresponding to the predicted demand from the generator, and outputs information regarding the calculated cost. Methods for supporting the formulation of electricity procurement plans.

15. A power procurement plan formulation support system comprising a customer terminal, a power plant operator terminal, a battery capable of exchanging power with the customer, and a planning support device, The customer terminal includes a transmitting device that transmits the customer's past electricity usage to the planning support device. The power plant operator terminal includes a transmitting device that transmits past power generation amounts of generators that generate electricity available to the consumer to the planning support device. The aforementioned planning support device is A storage device that stores generator cost information, which is cost information related to the generator, and energy storage equipment cost information, which is cost information related to the battery, A demand forecasting process that calculates a predicted value of the amount of electricity the customer will need based on the past amount of electricity usage received from the customer terminal, A power generation prediction process that calculates a predicted value for the power generation of the generator based on the past power generation amount of the generator received from the power plant operator's terminal, A cost calculation process that probabilistically calculates the cost to the customer necessary to procure at least the electricity corresponding to the predicted demand from the generator, based on the predicted demand, the predicted power generation, the generator cost information, and the energy storage equipment cost information, and outputs information regarding the calculated cost. The system includes a computing device that performs distributed energy source control processing, which includes predicting the amount of electricity demanded by the customer and the amount of electricity generated by the generator at a predetermined point in the future, predicting the amount of electricity exchanged with the battery necessary to maintain the customer with the electricity corresponding to the predicted amount of electricity demand, using at least the electricity supplied by the generator and the electricity exchanged with the battery, and transmitting information to the battery to control the battery based on the predicted amount of electricity. The storage battery operates based on the information received from the planning support device. A system to support the formulation of power procurement plans.

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