Energy operation management device and energy operation management method

Abstract

The purpose of the present disclosure is to form an energy operation plan that takes into account anomalous events. A first event prediction unit (434) predicts whether an anomalous event will occur in a first period on the basis of stored data. A first prediction data creation unit (431) predicts at least one of an energy price, energy demand, and generated power in the first period on the basis of the stored data and first event prediction data. A first problem creation unit (432) creates a first optimization problem on the basis of the stored data and first prediction data, which is the result of the prediction by the first prediction data creation unit. A second prediction data creation unit (441) predicts on the basis of the stored data at least one of an energy price, energy demand, and generated power in a second period shorter than the first period. A second problem creation unit (442) creates a second optimization problem on the basis of the stored data, second prediction data, which is the result of the prediction by the second prediction data creation unit, and a first operation plan.

Description

[Technical Field]

[0001] The present disclosure relates to creating an energy operations plan. [Background technology]

[0002] In recent years, power generation using renewable energy sources such as photovoltaic (PV) and wind power has been attracting attention in an effort to realize a decarbonized society. However, the output of renewable energy sources fluctuates greatly depending on the weather, making it difficult to maintain a supply-demand balance. While it is possible to maintain a supply-demand balance by suppressing the output of renewable energy sources, this is not rational from the perspective of effective energy utilization.

[0003] To address this issue, conventionally, surplus renewable energy has generally been stored in storage batteries. However, while storage batteries can respond to frequent fluctuations in renewable energy output and provide instantaneous output, they have the drawback of being unable to store large amounts of energy. Therefore, there is a need to develop technology that can effectively utilize surplus renewable energy.

[0004] Patent Document 1 focuses on a technology in which surplus electricity from renewable energy sources is used to electrolyze water to generate hydrogen, which is then stored in a tank, and electricity is generated at different times using fuel cells or the like that use the hydrogen as fuel. Hydrogen storage has the disadvantages of not being able to output power instantaneously compared to storage batteries and of having large energy conversion losses due to fuel cells, but it is possible to store large amounts of energy for long periods of time. Therefore, Patent Document 1 proposes a power supply system that uses both storage batteries and hydrogen storage, allowing them to complement each other's drawbacks and creating a charging and discharging plan that takes into account both short-term (24-hour) and long-term (7-day) fluctuations in the supply and demand balance. This power supply system suppresses reverse power flow and enables local production and consumption of electricity.

[0005] Patent Document 2 proposes a plant control device that can automatically control the production of hydrogen, ammonia, or the like using renewable energy and sell the produced substances when the price is higher. This plant control device makes it possible to increase the economic value of the produced substances. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-111871 [Patent Document 2] Patent No. 7027623 Summary of the Invention [Problem to be solved by the invention]

[0007] The technology disclosed in Patent Document 1 had the problem that it was not possible to create an energy operation plan that took economic efficiency into account, because it did not take into account the market prices of energy such as electricity or gas when creating a plan to generate hydrogen from surplus renewable energy.

[0008] The technology disclosed in Patent Document 2 takes into account energy prices in the market and effectively utilizes surplus renewable energy power. However, market energy prices, demand, and renewable energy output several months into the future (long term) are significantly affected by abnormal weather such as typhoons, El Niño, or La Niña. The technology disclosed in Patent Document 2 does not take into account fluctuations in market energy prices, demand, or renewable energy output caused by unusual events such as abnormal weather, and therefore has the problem of being unable to create energy operation plans that take long-term economic efficiency into account.

[0009] The present disclosure has been made to solve the above problems, and aims to create an energy operation plan that takes into account unusual events. [Means for solving the problem]

[0010] The energy operations management device disclosed herein creates an operation plan for multiple energy supply devices that supply energy to a group of consumers using power generated by a renewable energy facility and energy purchased from an energy market. The energy operations management device disclosed herein includes a data acquisition unit, a first event prediction unit, a first prediction data creation unit, a first problem creation unit, a first solution-finding unit, a second prediction data creation unit, a second problem creation unit, a second solution-finding unit, and an output unit. The data acquisition unit acquires stored data including actual values ​​of energy prices in the energy market, energy demand of a group of consumers, power generated by the renewable energy facility, and weather data, and stores the data in the memory unit. The first event prediction unit predicts, based on the stored data, whether an unusual event, which is weather that affects at least one of the energy price, energy demand, and generated power, will occur during a first time period. The first prediction data creation unit predicts at least one of the energy price, energy demand, and generated power during the first time period based on the stored data and first event prediction data, which is a prediction result of the first event prediction unit. The first problem creation unit creates a first optimization problem for determining a first operation plan, which is an operation plan for multiple energy supply devices for a first period, based on the stored data and first forecast data that is the prediction result of the first forecast data creation unit. The first solution finding unit determines the first operation plan by solving the first optimization problem. The second forecast data creation unit predicts at least one of energy price, energy demand, and power generation for a second period that is shorter than the first period, based on the stored data. The second problem creation unit creates a second optimization problem for determining a second operation plan, which is an operation plan for multiple energy supply devices for the second period, based on the stored data, second forecast data that is the prediction result of the second forecast data creation unit, and the first operation plan. The second solution finding unit determines the second operation plan by solving the second optimization problem. The output unit makes an energy purchase request to the energy market and instructs the multiple energy supply devices to operate based on the second operation plan. The first operating plan includes the amount of energy purchased and the amount of electricity sold in the energy market at each time point during a first period. The second operating plan includes the amount of energy purchased and the amount of electricity sold in the energy market at each time point during a second period. The first problem creation unit includes a term obtained by multiplying the amount of energy purchased in the first operating plan by the energy price and a term obtained by multiplying the amount of electricity sold in the first operating plan by the energy price in an objective function of the first optimization problem. The second problem creation unit includes a term obtained by multiplying the amount of energy purchased in the second operating plan by the energy price and a term obtained by multiplying the amount of electricity sold in the second operating plan by the energy price in an objective function of the second optimization problem. [Effects of the Invention]

[0011] The energy operations management device of the present disclosure determines a first operation plan taking into account an unusual event that may occur during a first period, and determines a second operation plan, which is an energy operation plan for a second period, based on the first operation plan. Therefore, the energy operations management device of the present disclosure makes it possible to determine an energy operation plan that takes into account an unusual event. Objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description and the accompanying drawings. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram showing a configuration of an energy management system according to a first embodiment. [Figure 2] 1 is a diagram illustrating a configuration of an energy operation management device according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing forecast data of power demand according to a single scenario. [Figure 4] FIG. 10 is a diagram showing forecast data of power demand based on multiple scenarios. [Figure 5] 4 is a flowchart showing the operation of the energy operation management device according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating types of input and output of each device in the energy supply system. [Figure 7] FIG. 2 is a diagram showing the connection relationship of each device in the energy supply system. [Figure 8] FIG. 10 is a diagram illustrating a configuration of an energy operations management device according to a second embodiment. [Figure 9] 10 is a flowchart showing the operation of the energy operation management device according to the second embodiment. [Figure 10] FIG. 10 is a diagram illustrating a configuration of an energy management system according to a third embodiment. [Figure 11] FIG. 10 is a diagram showing the configuration of an energy operations management device according to a third embodiment that is applied when a carbon tax is introduced. [Figure 12] FIG. 10 is a diagram showing the configuration of an energy operations management device according to a third embodiment that is applied when a CO2 emissions trading system is introduced. [Figure 13] It is a diagram showing the carbon emission trading between regions A and B through a carbon emission management system. [Figure 14] It is a diagram showing the configuration of the energy operation management system according to Embodiment 4. [Figure 15] It is a diagram showing the configuration of the energy operation management device according to Embodiment 4 applied when a carbon tax is introduced. [Figure 16] It is a diagram showing the configuration of the energy operation management device according to Embodiment 4 applied when a CO2 emission trading system is introduced. [Figure 17] It is a diagram showing the hardware configuration of the energy operation management device. [Figure 18] It is a diagram showing the hardware configuration of the energy operation management device.

Embodiments for Carrying Out the Invention

[0013] <A. Embodiment 1> (Overall Configuration) FIG. 1 shows the configuration of an energy operation management system 1001 according to Embodiment 1.

[0014] The energy operation management system 1001 includes a weather management system 2, an energy market 3, an energy operation management device 4A, an energy supply system 5, a renewable energy facility (hereinafter, "renewable energy facility") 6, and a group of consumers 7. The energy operation management device 4A is connected to the weather management system 2, the energy market 3, a plurality of devices 50 included in the energy supply system 5, the renewable energy facility 6, and consumers 70 constituting the group of consumers 7 via communication networks 100, 101, 102, 103, 104, respectively.

[0015] The energy supply system 5 includes a plurality of devices 50 that are energy supply devices. Although three devices 50 are shown in FIG. 1, the number of devices 50 included in the energy supply system 5 is not limited to this, and may be one or more.

[0016] The renewable energy facility 6 is a facility that generates electricity using renewable energy.

[0017] The consumer group 7 is made up of a plurality of consumers 70. Although two consumers 70 are shown in Fig. 1, the number of consumers 70 constituting the consumer group 7 is not limited to this, and may be one or more.

[0018] The energy supply system 5 is physically connected to the energy market 3, renewable energy facilities 6, and consumer groups 7 by infrastructure facilities 200 such as an electric power system or gas pipes. Each element constituting the energy operation management system 1001 will be described below.

[0019] (Weather Management System 2) The weather management system 2 predicts future weather data at a certain location and provides the predicted weather data values ​​to the energy operations management device 4 A. The weather data predicted by the weather management system 2 includes data such as temperature, wind speed, wind direction, precipitation amount, snowfall amount, atmospheric pressure, solar radiation amount, and cloud cover.

[0020] The weather management system 2 also acquires actual weather data values ​​at observation points every few hours or every few minutes and provides the acquired values ​​to the energy operations management device 4 A. The weather management system 2 acquires actual weather data values ​​from, for example, a measuring instrument installed at the observation point.

[0021] The weather management system 2 predicts weather data for the next few weeks, hours, or minutes. Alternatively, the weather management system 2 may predict weather data for more than a few weeks in the future. For example, the weather management system 2 may predict the occurrence of a typhoon several weeks in the future. The weather management system 2 may also predict weather data for periods spanning seasons, such as predicting summer weather data in the spring or winter weather data in the fall.

[0022] The weather management system 2 may also forecast the renewable energy output, particularly PV power generation, several months into the future after forecasting weather for several months into the future. When an El Niño phenomenon occurs, temperatures tend to be lower and sunshine hours shorter in the summer and higher in the winter near Japan. On the other hand, when a La Niña phenomenon occurs, temperatures tend to be higher in the summer and lower in the winter near Japan. Therefore, the weather management system 2 may forecast the occurrence of an El Niño or La Niña phenomenon. Such events have a significant impact on the energy consumption of consumers 70, the output of renewable energy facilities 6, and energy prices in the energy market 3, and are referred to herein as anomalous events. The weather management system 2 provides the energy operations management device 4A with anomalous event information, including the date and time when the anomalous event actually occurred and weather data at the time of the occurrence. In this specification, information regarding the occurrence of an anomalous event is referred to as anomalous event information to distinguish it from weather data.

[0023] (Energy Market 3) The energy market 3 presents the energy price per unit amount at a certain date and time to the energy operations management device 4A. In addition, the energy market 3 supplies energy to each device 50 in the energy supply system 5 via the infrastructure facility 200 in response to an energy purchase request from the energy operations management device 4A.

[0024] (Energy operation management device 4A) Fig. 2 is a block diagram showing a schematic configuration of the energy operations management device 4A. As shown in Fig. 2, the energy operations management device 4A includes a data acquisition unit 41, a storage unit 42A, a first plan creation device 43, a second plan creation device 44A, and an output unit 45.

[0025] The first plan creation device 43 includes a first prediction data creation unit 431 , a first problem creation unit 432 , a first solution-finding unit 433 , and a first event prediction unit 434 .

[0026] Second plan creation device 44A includes second prediction data creation unit 441, second problem creation unit 442, and second solution finding unit 443.

[0027] (Data Acquisition Unit 41) The data acquisition unit 41 acquires data T1 from the weather management system 2. The data T1 includes weather data (actual values, predicted values) and peculiar event information. The data acquisition unit 41 also acquires energy price data T2 from the energy market 3, and acquires data T4 of each device 50 from the energy supply system 5. The data acquisition unit 41 also acquires data T6 of actual values ​​of power generation at each time from the renewable energy facility 6, and acquires data T7 of actual values ​​of demand (electricity, heat, etc.) at each time from each consumer 70. The data acquisition frequency is, for example, every day, every few hours, or every few minutes.

[0028] The data T4 of each device 50 may include data indicating characteristics such as the capacity of each device 50, upper and lower input / output limits, coefficients related to input / output characteristics, or types of input / output energy. The data T4 may also include values ​​actually output by each device 50 in response to a command T5 from the energy operation management device 4A. The data T4 may also include data indicating how each device 50 is connected. The data T4 is held by each device.

[0029] (Storage unit 42A) The storage unit 42A includes an energy price database (DB) 421, a weather information DB 422, an unusual event information DB 423, a load power generation information DB 424, and an equipment information DB 425, and these databases store the data acquired by the data acquisition unit 41. The data acquired by the data acquisition unit 41 and stored in the storage unit 42A is also referred to as stored data.

[0030] The energy price DB421 stores energy price data T2 acquired by the data acquisition unit 41 from the energy market 3. Of the data T1 acquired by the data acquisition unit 41 from the weather management system 2, the weather data is stored in the weather information DB422, and the peculiar event information is stored in the peculiar event information DB423. The load power generation information DB424 stores data T6 of the actual value of the power generation amount acquired by the data acquisition unit 41 from the renewable energy facility 6, and data T7 of the actual value of demand acquired by the data acquisition unit 41 from the consumer group 7. The device information DB425 stores data T4 acquired by the data acquisition unit 41 from each device 50 in the energy supply system 5.

[0031] (First plan creation device 43) The first plan creation device 43 creates an energy operation plan for a first period. The energy operation plan for the first period is also referred to as a first operation plan. The energy operation plan determines what type of energy the energy supply system 5 will purchase from the energy market 3, when, and how much, and what type of energy each device 50 will produce, when, and how much.

[0032] The first period, which is the target period for which the first plan creation device 43 creates an energy management plan, is longer than the second period, which is the target period for which the second plan creation device 44A, described later, creates an energy management plan. For example, the first period is several weeks or several months. The first plan creation device 43 includes a first prediction data creation unit 431, a first problem creation unit 432, a first solution-finding unit 433, and a first event prediction unit 434.

[0033] (First event prediction unit 434) The first event prediction unit 434 predicts whether a singular event will occur in the first time period. This prediction is made, for example, using singular event information stored in the singular event information DB 423. Alternatively, this prediction is made using data stored in the singular event information DB 423, the weather information DB 422, and the load power generation information DB 424. The first event prediction unit 434 can predict the occurrence of a singular event through machine learning, using, for example, elements such as the month, time, or temperature that are closely related to the singular event to be predicted as feature quantities. However, the method of extracting feature quantities is not limited to this. The first event prediction unit 434 may also predict the occurrence of a singular event using statistics. The prediction method in the first event prediction unit 434 is not limited to these.

[0034] The first event prediction unit 434 may calculate the probability or risk of an unusual event occurring in the first time period. The occurrence probability of an unusual event may be data included in the unusual event information acquired from the weather management system 2, or may be calculated by the first event prediction unit 434.

[0035] The first event prediction unit 434 outputs the prediction result regarding the occurrence of the unusual event in the first period to the first prediction data creation unit 431 as first event prediction data.

[0036] (First prediction data creation unit 431) The first prediction data creation unit 431 predicts the output of the renewable energy facility 6, the energy consumption of the consumer group 7, and the energy price in the energy market 3 for each time point in the first period, based on the data stored in the memory unit 42A and the first event prediction data output from the first event prediction unit 434. Hereinafter, the prediction data of the output of the renewable energy facility 6, the energy consumption of each consumer, and the energy price in the energy market 3 for each time point in the first period will also be referred to as the "first prediction data."

[0037] The first prediction data creation unit 431 predicts the output of the renewable energy facility 6 and the demand of the consumers 70 using weather data stored in the weather information DB 422, and data T6 of actual output values ​​of the renewable energy facility 6 and data T7 of actual demand values ​​of the consumer group 7, both stored in the load power generation information DB 424. For example, the first prediction data creation unit 431 can predict the output of the renewable energy facility 6 and the energy consumption of the consumers 70 through machine learning, using elements closely related to the prediction target (such as month, type of day of the week, time, or temperature) as features. Furthermore, when the first prediction data creation unit 431 obtains the probability of occurrence of an unusual event in the first period as first event prediction data from the first event prediction unit 434, the first prediction data creation unit 431 also takes the probability of occurrence of the unusual event into consideration when creating the first prediction data. However, the method of extracting features is not limited to this.

[0038] The energy price in the energy market 3 is likely to be related to the output of the renewable energy facility 6 and the demand of each consumer 70. The energy price in the energy market 3 is also likely to be affected by the occurrence of a unique event. Therefore, the first prediction data creation unit 431 may predict the energy price in the energy market 3 for the first period by statistics, machine learning, or the like, based on the data stored in the energy price DB 421, the weather information DB 422, and the load power generation information DB 424, and the first event prediction data output from the first event prediction unit 434.

[0039] FIG. 3 shows the power demand [kW] of a group of consumers 7 in a first period as an example of first forecast data. The horizontal axis of FIG. 3 represents time in the first period. In FIG. 3, one piece of forecast data for power demand is created for each of times t1, t2, and t3 in the first period. In other words, here, forecast data for power demand is created for a single scenario. Forecast data for the output of renewable energy facilities 6 or forecast data for energy prices in the energy market 3 may also be created for a single scenario.

[0040] FIG. 4 shows an example of first forecast data, in which the power demand [kW] of a group of consumers 7 in a first period is created assuming multiple scenarios. In FIG. 4, three sets of power demand forecast data are created for each of times t1, t2, and t3 in the first period. That is, here, power demand forecast data is created for three scenarios, scenarios 1, 2, and 3. Similarly, forecast data for the output of renewable energy facilities 6 or forecast data for energy prices in the energy market 3 may also be created for multiple scenarios. In this way, by creating multiple sets of first forecast data assuming multiple scenarios, it becomes possible to create an energy operation plan that takes into account uncertainties such as demand or energy prices.

[0041] When the first prediction data creation unit 431 creates multiple first prediction data assuming multiple scenarios, each scenario has an occurrence probability. Here, if there are N scenarios and the occurrence probability of the jth scenario is pj, the first prediction data creation unit 431 calculates the probability distribution of the uncertainties so that p1 + p2 + ··· + pN = 1. For example, in FIG. 4, N = 3, and the occurrence probability p1 of scenario 1 is 0.6, the occurrence probability p2 of scenario 2 is 0.25, and the occurrence probability p3 of scenario 3 is 0.15. The probability distribution of the uncertainties at each time t in the first period is calculated based on statistical prediction, but may also be calculated using techniques other than statistics. In this way, the first prediction data creation unit 431 may create multiple first prediction data with different occurrence probabilities for each time in the first period.

[0042] 3 and 4 show the forecast data of the electricity demand of the consumer group 7. Similarly, when the forecast data of the output of the renewable energy facility 6 is shown in a diagram, the unit of the vertical axis is also [kW]. Similarly, when the forecast data of the electricity price per unit amount is shown in a diagram, the unit of the vertical axis is [yen / kWh]. Similarly, when the forecast data of the heat demand of the consumer 70 is shown in a diagram, the unit of the vertical axis is [m 3 Similarly, when plotting the predicted data for the heat price per unit amount, that is, the heat unit price, the unit of the vertical axis is [yen / m 33 and 4, i.e., the time granularity of the first predicted data created by first predicted data creating unit 431 may be, but is not limited to, several hours or several minutes.

[0043] (First problem creation unit 432 and first solution finding unit 433) The first problem creation unit 432 creates an optimization problem (hereinafter also referred to as the “first optimization problem”) for creating an energy operation plan for the first period based on the first prediction data created by the first prediction data creation unit 431 and the data T4 of each device 50 stored in the device information DB 425.

[0044] First solution finding unit 433 creates an energy operation plan for the first period by solving the first optimization problem created by first problem creating unit 432. First solution finding unit 433 may solve the first optimization problem by mathematical programming or a heuristic method, or may solve the first optimization problem by another method.

[0045] Here, the objective function of the first optimization problem will be explained. The objective of the first problem creation unit 432 is to minimize the total sum of the electricity buying and selling costs and the gas purchasing costs of the energy supply system 5 during the first period while satisfying multiple constraints. An example of the objective function created for the first forecast data of a single scenario is shown below in equation (1).

[0046]

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[0047] In equation (1), t represents time, eBuy(t) represents the electricity purchase price, EBuy(t) represents the amount of electricity purchased, eSell(t) represents the electricity sales price, ESell(t) represents the amount of electricity sold, gBuy(t) represents the gas purchase price, and GBuy(t) represents the amount of gas purchased. Note that eBuy(t), EBuy(t), eSell(t), ESell(t), gBuy(t), and GBuy(t) are all positive values.

[0048] For simplicity's sake, we've assumed that there is only one type of electricity and gas purchase and sale target, but there may be multiple types. For example, if there are two types of electricity purchase targets, eBuy(t) × EBuy(t) in equation (1) becomes e1Buy(t) × E1Buy(t) + e2Buy(t) × E2Buy(t).

[0049] The first solution finding unit 433 can create an energy purchasing plan and sales plan that maximizes profits by minimizing equation (1). Note that equation (1) considers only electricity and gas as energy to be bought and sold, but it may also consider purchasing other fuels such as coal or oil.

[0050] First problem creation unit 432 sets the following equations (2) to (12) as constraints for the first optimization problem for the first forecast data of a single scenario. In the explanation of these equations, the i-th device 50 will also be referred to as device i.

[0051]

number

[0052] Equation (2) represents the upper and lower limit constraints on the input of the device 50. Vi_in(t) represents the input of the device i at time t, Vi_in_min represents the minimum input of the device i, and Vi_in_max represents the maximum input of the device i.

[0053]

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[0054] Equation (3) represents the upper and lower output limit constraints of device 50. Vi_out(t) represents the output of device i at time t, Vi_out_min represents the minimum output of device i, and Vi_out_max represents the maximum output of device i. In equations (2) and (3), variable ui(t) represents the activation / deactivation state of device i at time t. When device i is in the deactivation state, variable ui(t) is 0, and when device i is in the activation state, variable ui(t) is 1.

[0055]

number

[0056] Equation (4) shows the constraints on the output fluctuation range of each device 50 from time t-1 to time t. Vi_rate_min shows the lower limit of the output fluctuation range of device i from time t-1 to t, and Vi_rate_max shows the upper limit of the output fluctuation range of device i from time t-1 to t.

[0057] Equations (2), (3), and (4) are general-purpose models that can also accommodate equipment i, such as energy storage equipment, that does not start or stop, by treating the variable ui(t) as a constant 1. Furthermore, while equations (2), (3), and (4) show V as the input and output value of general-purpose energy, by replacing V with P (electricity), G (gas), or H (heat), it is possible to describe constraints tailored to each equipment 50. Heat may be further classified into hot water, cold water, steam, and the like. Furthermore, for equipment 50 with multiple inputs, multiple upper and lower limit constraints are set for the inputs. Furthermore, for equipment 50 with multiple outputs, multiple upper and lower limit constraints are set for the outputs.

[0058]

number

[0059] Equation (5) shows upper and lower limit constraints on the amount of energy stored related to the storable capacity of the energy storage device. In the explanation of the equation, device 50, which is the i-th energy storage device, will also be referred to as energy storage device i. SOCi_min shows the minimum value of the amount of energy stored in energy storage device i, and SOCi_max shows the maximum value of the amount of energy stored in energy storage device i. SOCi(t) shows the amount of energy stored in energy storage device i at time t.

[0060]

number

[0061] Equation (6) shows an initial value constraint on the energy storage amount related to the storable capacity of the energy storage device. The energy storage amount SOC(t_first) of the energy storage device i at the start time t=t_first of the first period is defined as SOC_first.

[0062]

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[0063] Equation (7) shows the final value constraint of the energy storage amount related to the storable capacity of the energy storage device. The energy storage amount SOC(t_last) of the energy storage device i at the final time t=t_last of the first period is equal to or greater than SOC_last. In other words, SOC_last shows the minimum energy storage amount of the energy storage device i at the final time t=t_last of the first period.

[0064]

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[0065] Equation (8) shows the calculation formula for the storage capacity of an energy storage device. a is the charging efficiency, b is the natural discharge amount or natural heat release amount, and Δt is the time interval from time t to time t+1.

[0066]

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[0067] Equation (9) shows the constraints on the amount of electricity purchased and sold at each time in the first period. Pi_in(t) represents the power input to device i at time t.

[0068]

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[0069] Equation (10) shows the constraint on the gas purchase amount. Gi_in(t) shows the amount of gas input to device i at time t.

[0070]

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[0071] Equation (11) shows the supply and demand balance constraint for electricity at each time in the first period. Pdem(t) shows the predicted value of electricity demand for the consumer group 7.

[0072]

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[0073] Equation (12) shows the heat supply and demand balance constraint at each time in the first period. Hdem(t) shows the predicted value of heat demand for the consumer group 7.

[0074] The first solution-finding unit 433 can create an energy operation plan for the first period that maximizes profits by solving the optimization problem of minimizing equation (1) under the constraints of equations (2) to (12) based on the first prediction data created by the first prediction data creation unit 431.

[0075] Here, the startup cost of each device i may be added as a positive term to equation (1). Furthermore, for devices that require startup and shutdown, constraints of minimum operation time or minimum shutdown time may be added to equations (2) to (12). The minimum operation time refers to the amount of time that a device must continue operating after it is started. The minimum shutdown time refers to the amount of time that a device must remain stopped after it is stopped. Furthermore, an upper limit may be set on the number of startups and shutdowns of each device during the calculation period. Furthermore, for energy storage devices, a constraint may be set that only input or output can be performed at time t. Furthermore, regardless of whether the device is in a startup or shutdown state, a time delay between input and output for each device may be set as a constraint.

[0076] The above has described how to create an energy operation plan for the first period based on the first forecast data of a single scenario. Next, an example of an objective function created by first problem creation unit 432 for the first forecast data of multiple scenarios is shown in the following equation (13).

[0077]

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[0078] Here, the first prediction data creation unit 431 creates N scenarios. The occurrence probability of the jth scenario is represented as pj. In equation (13), for scenario j at time t, the electricity purchase price is represented as ejBuy(t), the electricity purchase amount is represented as EjBuy(t), the electricity sales price is represented as ejSell(t), the electricity sales amount is represented as EjSell(t), the gas purchase price is represented as gjBuy(t), and the gas purchase amount is represented as GjBuy(t). By solving the optimization problem that minimizes equation (13), the first solution-finding unit 433 can create an energy operation plan for the first period that maximizes expected profits, that is, minimizes expected costs, while taking multiple scenarios into consideration.

[0079] As a method for creating an objective function taking each scenario into consideration, other than minimizing the expected cost, minimizing the worst cost value, etc. may be considered, but the creation method is not limited to this.

[0080] Below is an example of the constraint equations for the optimization problem that takes each scenario into consideration. For example, it is possible to consider the start / stop state of device i and the amount of energy stored in energy storage device i at time t as common to each scenario, and to find the input / output of the device, the amount of energy purchased, and the amount of energy sold for each scenario. In this case, equation (2) can be expressed as equation (14), equation (3) as equation (15), equation (4) as equation (16), equation (9) as equation (17), equation (10) as equation (18), equation (11) as equation (19), and equation (12) as equation (20).

[0081]

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[0082]

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[0083]

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[0084]

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[0085]

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[0086]

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[0087]

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[0088] Here, Vij_in(t) indicates the input of device i at time t and scenario j, and Vij_out(t) indicates the output of device i at time t and scenario j. Furthermore, Pjdem(t) indicates the predicted value of electricity demand at time t and scenario j, and Hjdem(t) indicates the predicted value of heat demand at time t and scenario j.

[0089] (Second plan creation device 44A) The second plan creation device 44A creates an energy management plan for a second period. The energy management plan for the second period is also referred to as a second operation plan. The second period, which is the target period for which the second plan creation device 44A creates the energy management plan, is shorter than the first period, which is the target period for which the first plan creation device 43 creates the energy management plan. For example, the second period is several days or several weeks. The second plan creation device 44A includes a second prediction data creation unit 441, a second problem creation unit 442, and a second solution-finding unit 443.

[0090] (Second prediction data creation unit 441) The second prediction data creation unit 441 predicts the output of the renewable energy facility 6, the energy consumption of the consumer group 7, and the energy price in the energy market 3 for each time in the second period based on the data stored in the memory unit 42A. Hereinafter, the prediction data of the output of the renewable energy facility 6, the energy consumption of each consumer, and the energy price in the energy market 3 for each time in the second period will also be referred to as "second prediction data."

[0091] The second forecast data creation unit 441 predicts the output of the renewable energy facility 6 and the demand of the consumer 70 using weather data stored in the weather information DB 422, and data T6 of the actual output value of the renewable energy facility 6 and data T7 of the actual demand value of the consumer group 7, which are stored in the load power generation information DB 424. For example, the second forecast data creation unit 441 can predict the output of the renewable energy facility 6 and the energy consumption of the consumer 70 through machine learning, using elements closely related to the prediction target (such as month, type of day of the week, time, or temperature) as feature quantities. However, the method of extracting feature quantities is not limited to this. Furthermore, the second forecast data creation unit 441 may create the second forecast data using statistics. The method of creating the second forecast data is not limited to the above-mentioned method.

[0092] The energy price in the energy market 3 is likely to be related to the output of the renewable energy facility 6 and the demand of each consumer 70. Therefore, the second prediction data creation unit 441 may predict the energy price in the energy market 3 by statistics, machine learning, or the like, based on the data stored in the energy price DB 421, the weather information DB 422, and the load power generation information DB 424.

[0093] When creating second forecast data for each of the output of the renewable energy facility 6, the energy purchase price or sales price in the energy market 3, or the amount of energy consumed by the consumer 70, the second forecast data creation unit 441 may create a single piece of second forecast data assuming a single scenario, or may create multiple pieces of second forecast data assuming multiple scenarios. In other words, the second forecast data creation unit 441 may create multiple pieces of second forecast data having different occurrence probabilities for each time of the second period.

[0094] (Second problem creation unit 442 and second solution finding unit 443) The second problem creation unit 442 creates an optimization problem (hereinafter also referred to as the "second optimization problem") for creating an energy operation plan for the second period based on the second prediction data created by the second prediction data creation unit 441, the data T4 of each device 50 stored in the device information DB 425, and the energy operation plan for the first period created by the first solution-finding unit 433.

[0095] Second solution finding unit 443 creates an energy operation plan for the second period by solving the second optimization problem created by second problem creating unit 442. Second solution finding unit 443 may solve the second optimization problem by mathematical programming or a heuristic method, or may solve the second optimization problem by another method.

[0096] Here, the objective function of the second optimization problem will be explained. The objective of the second problem creation unit 442 is to minimize the total sum of the electricity buying and selling costs and the gas purchasing costs of the energy supply system 5 during the second period while satisfying multiple constraints. An example of the objective function of the second optimization problem created for the second forecast data of a single scenario is expressed by equation (1), similar to the objective function of the first optimization problem. Furthermore, the constraints of the second optimization problem are expressed by equations (2) to (21).

[0097] Note that the startup cost of each device i may be added as a positive term to equation (1). Furthermore, for devices that require startup and shutdown, a minimum operating time or minimum shutdown time constraint may be added in addition to equations (2) to (12). Furthermore, an upper limit may be set on the number of startup and shutdown times for each device during the calculation period. Furthermore, for energy storage devices, a constraint may be set that only input or output can be performed at time t. Furthermore, regardless of whether the device is in a startup and shutdown state, a time delay between input and output for each device may be set as a constraint.

[0098] Furthermore, a constraint condition related to the energy operation plan for the first period created by the first solution finding unit 433 is assigned to the second optimization problem. For example, the amount of energy stored in the energy storage device i at time t in the first period may be added as a constraint condition. This constraint condition is expressed by equation (21).

[0099]

number

[0100] In equation (21), SOCi1(t) represents the amount of energy stored in the energy storage device i at time t in the first period determined by the first solution finding unit 433. SOCi2(t) represents the amount of energy stored in the energy storage device i at time t in the second period. c is a constant between 0 and 1. The second solution finding unit 443 minimizes equation (1) using equation (21) as a constraint, thereby making it possible to create an operation plan for the energy storage amount of the energy storage device i in the second period that follows the operation plan for the energy storage amount of the energy storage device i in the first period as closely as possible. In this way, the energy operation plan for the second period is created based on the energy operation plan for the first period determined in consideration of maximizing expected profit.

[0101] However, the energy operation plan for the first period that is considered as a constraint for the energy operation plan for the second period is not limited to the above. Furthermore, if second prediction data creating unit 441 creates second prediction data assuming multiple scenarios, second problem creating unit 442 may also create an optimization problem that takes each scenario into consideration.

[0102] (Output section 45) The output unit 45 purchases energy from the energy market 3 based on the energy operation plan for the second period determined by the second solution finding unit 443, and sends commands to each device 50 in the energy supply system 5. Each device 50 supplies energy to the group of consumers 7 in accordance with the commands received from the output unit 45.

[0103] 5 is a flowchart showing an example of the steps of an energy operation plan creation method implemented by the energy operation management device 4 A. The operation of the energy operation management device 4 A will be described below with reference to the flow of FIG.

[0104] First, the data acquisition unit 41 acquires various data and updates the data stored in the storage unit 42A (step S101). Specifically, the data acquisition unit 41 acquires data T1 including weather data and peculiar event information from the weather management system 2, acquires energy price data T2 from the energy market 3, acquires data T4 on each device 50 from the energy supply system 5, acquires data T6 on actual values ​​of power generation amount at each time from the renewable energy facility 6, and acquires data T7 on actual values ​​of demand at each time from each consumer 70.

[0105] Next, the first event prediction unit 434 predicts whether a peculiar event will occur in the first period using the weather information stored in the weather information DB 422 and the peculiar event information stored in the peculiar event information DB 423 (step S102).

[0106] Then, the first prediction data creation unit 431 creates prediction data for the output of the renewable energy facility 6, the energy consumption of the group of consumers 7, and the energy price in the energy market 3 during the first period as first prediction data based on the data stored in the memory unit 42 and the prediction results of the first event prediction unit 434 (step S103).

[0107] Next, first problem creation unit 432 creates an optimization problem for the energy management plan for the first period using the data stored in device information DB 425 and the first prediction data. Then, first solution finding unit 433 solves the optimization problem and creates the energy management plan for the first period (step S104).

[0108] Note that energy operations management device 4A may perform steps S101 to S104 above at intervals shorter than the first period, for example, every few months or weeks, and update the energy operation plan for the first period as needed. That is, first event prediction unit 434 updates the first event prediction data at intervals shorter than the first period, first prediction data creation unit 431 updates the first prediction data based on the updated first event prediction data and the stored data, first problem creation unit 432 updates the first optimization problem based on the stored data and the updated first prediction data, and first solution finding unit 433 updates the first operation plan by solving the updated first optimization problem.

[0109] Next, the second forecast data creation unit 441 uses the data stored in the memory unit 42A to create forecast data for the output of the renewable energy facility 6, the energy consumption of the consumer group 7, and the energy price in the energy market 3 during the second period as second forecast data (step S105).

[0110] Thereafter, second problem creation unit 442 creates an optimization problem for the energy management plan for the second period based on the data stored in device information DB 425, the second prediction data, and the energy management plan for the first period created by first solution creation unit 433. Second solution creation unit 443 solves the optimization problem for the energy management plan for the second period to create the energy management plan for the second period (step S106).

[0111] Note that energy operations management device 4A may perform steps S105 and S106 at intervals shorter than the second period, for example, every few weeks or days, and revise the energy operation plan for the second period as needed. That is, second prediction data creation unit 441 updates the second prediction data at intervals shorter than the second period, second problem creation unit 442 updates the second optimization problem based on the stored data and the updated second prediction data, and second solution creation unit 443 updates the second operation plan by solving the updated second optimization problem.

[0112] Finally, the output unit 45 purchases energy from the energy market 3 in accordance with the energy operation plan for the second period created by the second solution finding unit 443, and issues an output command to each device 50 in the energy supply system 5 (step S107). This completes the processing of the energy operation management device 4A.

[0113] (Energy Supply System 5) There are a wide variety of devices 50 that make up the energy supply system 5. Depending on how the devices 50 are connected, various patterns are conceivable for the configuration of the energy supply system 5. Here, an example of the energy supply system 5 is shown.

[0114] FIG. 6 shows energy type information of input and output of devices 50, which are devices A to H, among data T4 related to devices 50. FIG. 7 is a diagram showing the connection relationships of devices A to H. In FIG. 7, an energy market 3 is configured including a Japan Electric Power Exchange (JEPX) 31, a retail electricity supplier 32, and a gas company 33. An energy supply system 5 receives energy a, i.e., electricity, from JEPX 31, the retail electricity supplier 32, and a renewable energy facility 6, and sells the electricity to JEPX 31. In addition, the energy supply system 5 purchases energy b, i.e., gas, from the gas company 33.

[0115] Based on instructions from the energy operation management device 4A, devices A to H supply electricity and heat to each consumer 70 and sell the electricity to JEPX 31. Specifically, device A uses electricity to produce energy d, i.e., heat. Device B uses energy c, i.e., hydrogen, to produce electricity. Device C uses electricity and water to produce hydrogen. Device D uses hydrogen and carbon dioxide to produce heat. In Figure 7, carbon dioxide is captured from the air by device J, stored, and input into device D. One possible method of capturing carbon dioxide is to capture it from emissions from a biomass power plant or factory.

[0116] Device E generates heat using gas. Device F generates electricity using gas. Device G is a tank capable of storing gas. Device H is a storage battery, and device I is a tank capable of storing hydrogen. Note that energy d is heat, which may be further classified as hot water, cold water, or steam. Furthermore, the heat demand of consumer 70 may also be further classified as hot water, cold water, or steam. Consumer 70 is, for example, an ordinary household, a factory, a building, or a public facility, but is not limited to these. Furthermore, each of devices A to J may be composed of multiple devices. In this way, in the energy supply system 5 shown in FIG. 7, devices A to J are connected to each other via energy.

[0117] An energy operations management device 4A according to the first embodiment creates an operation plan for a plurality of devices 50 that supply energy to a consumer group 7 using power generated by a renewable energy facility 6 and energy purchased from the energy market 3. The energy operations management device 4A includes a data acquisition unit 41, a first event prediction unit 434, a first prediction data creation unit 431, a first problem creation unit 432, a first solution-finding unit 433, a second prediction data creation unit 441, a second problem creation unit 442, and an output unit 45.

[0118] The data acquisition unit 41 acquires stored data including actual values ​​of the energy price in the energy market 3, the energy demand of the consumer group 7, the power generated by the renewable energy facility 6, and weather data, and stores the data in the memory unit 42A. The first event prediction unit 434 predicts, based on the stored data, whether an unusual event, which is weather that will affect at least one of the energy price, energy demand, and power generation, will occur during a first period. The first prediction data creation unit 431 predicts at least one of the energy price, energy demand, and power generation during a first period, based on the stored data and first event prediction data, which is the prediction result of the first event prediction unit 434.

[0119] The first problem creation unit 432 creates a first optimization problem for determining a first operation plan, which is an operation plan for a plurality of devices 50 in a first period, based on the stored data and the first prediction data that is the prediction result of the first prediction data creation unit 431. The first solution unit 433 determines the first operation plan by solving the first optimization problem.

[0120] The second prediction data creation unit 441 predicts at least one of the energy price, energy demand, and generated power in a second period that is shorter than the first period based on the stored data. The second problem creation unit 442 creates a second optimization problem for determining a second operation plan, which is an operation plan for a plurality of devices 50 in the second period, based on the stored data, the second prediction data that is the prediction result of the second prediction data creation unit 441, and the first operation plan. The second solution unit 443 determines the second operation plan by solving the second optimization problem.

[0121] The output unit 45 makes an energy purchase request to the energy market 3 and instructs the plurality of devices 50 to operate based on the second operation plan.

[0122] With the above configuration, the energy operation management device 4A can create an energy operation plan for each device 50 in consideration of a specific event that has a significant impact on the energy demand, renewable energy output, or energy price.

[0123] <B. Embodiment 2> The configuration of the energy operation management system 1002 of Embodiment 2 is as shown in FIG. 1. The energy operation management system 1002 includes an energy operation management device 4B instead of the energy operation management device 4A compared to the configuration of the energy operation management system 1001 of Embodiment 1. Since the configuration of the energy operation management system 1002 other than the energy operation management device 4B is the same as that of the energy operation management system 1001, the description thereof is omitted.

[0124] The energy operations management device 4A of the first embodiment creates an energy operations plan that takes into account unusual events only for the first period. However, unusual events such as typhoons or localized heavy rain may also occur in the second period. The unusual events that occur in the second period may cause fluctuations in the amount of energy consumed by consumers 70, the output of renewable energy facilities 6, and the energy price in the energy market 3. Therefore, the energy operations management device 4B of the second embodiment creates an energy operations plan that also takes into account unusual events that may occur in the second period.

[0125] 8 is a block diagram showing the configuration of an energy operations management device 4B according to embodiment 2. Compared to the configuration of energy operations management device 4A according to embodiment 1, energy operations management device 4B includes a second plan creation device 44B instead of second plan creation device 44A.

[0126] The second program creation device 44B includes a second event prediction unit 444 in addition to the configuration of the second program creation device 44A according to the first embodiment.

[0127] (Second event prediction unit 444) The second event prediction unit 444 predicts whether a singular event will occur in the second time period based on the singular event information acquired from the weather management system 2, past singular event information stored in the singular event information DB 423, and data stored in the weather information DB 422 and the load power generation information DB 424. The second event prediction unit 444 can predict the occurrence of a singular event through machine learning, using, for example, elements such as month, time, and temperature that are closely related to the singular event to be predicted as feature quantities. However, the method of extracting feature quantities is not limited to this. The second event prediction unit 444 may also predict the occurrence of a singular event using statistics. The prediction method used by the second event prediction unit 444 is not limited to these.

[0128] The second event prediction unit 444 may calculate the probability or risk of an unusual event occurring in the second time period. The occurrence probability of an unusual event may be data included in the unusual event information acquired from the weather management system 2, or may be calculated by the second event prediction unit 444.

[0129] (Second prediction data creation unit 441) The second prediction data creation unit 441 according to the second embodiment predicts the output of the renewable energy facility 6, the energy consumption of the group of consumers 7, and the energy price in the energy market 3 for each time in the second period based on the data stored in the memory unit 42B and the prediction results of the second event prediction unit 444.

[0130] When the second forecast data creation unit 441 creates second forecast data for each of the output of the renewable energy facility 6, the energy purchase price or sales price in the energy market 3, or the amount of energy consumed by the consumer 70, it may create a single second forecast data assuming a single scenario, or it may create multiple second forecast data assuming multiple scenarios.

[0131] Fig. 9 is a flowchart showing an example of the steps of an energy operation plan creation method implemented by the energy operation management device 4 B. The flow in Fig. 9 is obtained by adding step S104A between step S104 and step S105 in the flow in Fig. 4 described in the first embodiment.

[0132] In step S104A, the second event prediction unit 444 uses the information stored in the weather information DB 422 and the unusual event information DB 423 to predict whether an unusual event will occur in the second time period.

[0133] Then, the second prediction data creation unit 441 creates prediction data for the output of the renewable energy facility 6, the energy consumption of the group of consumers 7, and the energy price in the energy market 3 during the second period as second prediction data based on the data stored in the memory unit 42 and the prediction results of the second event prediction unit 444 (step S105).

[0134] The energy operation management device 4B may perform steps S104A, S105, and S106 in FIG. 9 for a period shorter than the second period, for example, every few weeks or days, and modify the energy operation plan for the second period as needed.

[0135] The energy operation management device 4B according to Embodiment 2 includes a second event prediction unit 444 that predicts whether a specific event will occur in the second period based on the stored data. The second prediction data creation unit 441 creates second prediction data based on the stored data and the second event prediction data that is the prediction result of the second event prediction unit. Therefore, according to the energy operation management device 4B, an energy operation plan can be created in consideration of specific events that may occur in the second period.

[0136] <C. Embodiment 3> FIG. 10 is a configuration diagram of an energy operation management system 1003 according to Embodiment 3. In the energy operation management systems 1001 and 1002 according to Embodiments 1 and 2, the amount of greenhouse gas emissions such as carbon dioxide (CO2) was not considered when creating an energy operation plan. The energy operation management system 1003 creates an energy operation plan in consideration of the CO2 emissions in addition to specific events that may occur in the first and second periods.

[0137] The energy operation management system 1003 includes a weather management system 2, an energy market 3, an energy operation management device 4C, an energy supply system 5, a renewable energy facility 6, a group of consumers 7, and a carbon emissions management system 8.

[0138] There are two configurations of the energy operation management device 4C, one shown as the energy operation management device 4C1 in FIG. 11 and the other shown as the energy operation management device 4C2 in FIG. 12.

[0139] The energy operation management device 4C1 is applicable when a carbon tax is introduced. Compared with the energy operation management device 4A of Embodiment 1, the energy operation management device 4C1 includes a storage unit 42C1 instead of the storage unit 42A. The storage unit 42C1 includes a carbon tax DB426 in addition to each database of the storage unit 42A.

[0140] The energy operations management device 4C2 is applied when a CO2 emissions trading system is introduced. Compared to the energy operations management device 4A of the first embodiment, the energy operations management device 4C2 includes a memory unit 42C2 instead of the memory unit 42A. The memory unit 42C2 includes a CO2 emissions trading price DB 427 in addition to the databases of the memory unit 42A.

[0141] The following description will focus on the differences between the configuration of the third embodiment and the first and second embodiments.

[0142] In recent years, in order to move towards a decarbonized society, "carbon tax" or "CO2 emissions trading system" has been considered as a mechanism for putting a price on carbon emissions. A carbon tax is a tax imposed on companies or regions according to the amount of CO2 emissions. The following explanation assumes that the carbon tax is imposed on a region-by-region basis. The amount of carbon tax per unit of CO2 emissions is set as a fixed value by the carbon emissions management system 8, and whenever there is a change, the carbon emissions management system 8 notifies the energy operations management device 4C.

[0143] On the other hand, in the CO2 emissions trading scheme, as shown in Figure 13, an upper limit on the amount of CO2 that can be emitted in a certain period is set for each region. Since Region A's CO2 emissions exceed the allocated emission allowance (1), it purchases additional CO2 emission allowance (3) via the carbon emissions management system 8. On the other hand, Region B's CO2 emissions are less than the allocated emission allowance (2), so it can make a profit by selling the surplus (3) to the carbon emissions management system 8. In the CO2 emissions trading scheme, the CO2 emissions trading price is determined based on the balance between supply and demand of CO2 emissions.

[0144] (Carbon Emissions Management System 8) 10, the carbon emission management system 8 is connected to the energy operation management device 4C via a communication network 105. The following describes the case where a carbon tax is introduced.

[0145] When a carbon tax is introduced, energy operations management device 4C1 shown in Fig. 11 is applied to energy operations management device 4C. Carbon emission management system 8 provides data T8 of the carbon tax levied per unit of CO2 emission to energy operations management device 4C1. Data acquisition unit 41 acquires carbon tax data T8 from carbon emission management system 8 and stores it in carbon tax DB 426 of memory unit 42C1.

[0146] Carbon tax data T8 stored in carbon tax DB426 is input to first problem creation unit 432 and second problem creation unit 442. Then, it becomes possible to create energy operation plans that take CO2 emissions into account for each of the first and second periods. For example, first problem creation unit 432 converts the purchase amounts of electricity, gas, and fuel in the first period into CO2 emissions, and adds a term obtained by multiplying the total CO2 emissions by the carbon tax per unit CO2 emission as a positive term to the objective function of equation (1), thereby making equation (1) into an optimization problem that maximizes profits in the first period, taking into account the carbon tax associated with CO2 emissions.

[0147] Similarly, the second problem creation unit 442 converts the purchase amounts of electricity, gas, and fuel in the second period into CO2 emissions, and adds a term obtained by multiplying the total CO2 emissions by the carbon tax per unit CO2 emission as a positive term to the objective function of equation (1), thereby making equation (1) an optimization problem that maximizes profits in the second period, taking into account the carbon tax associated with CO2 emissions.

[0148] The steps of the energy management plan creation method by energy management device 4C1 are generally as shown in the flowchart in Fig. 9. However, in step S104, first problem creation unit 432 and first solution finding unit 433 create an energy management plan for a first period, taking into account CO2 emissions in addition to unusual events, using information stored in carbon tax DB 426. Similarly, in step S106, second problem creation unit 442 and second solution finding unit 443 create an energy management plan for a second period, taking into account CO2 emissions in addition to unusual events, using information stored in carbon tax DB 426.

[0149] When a CO2 emissions trading system is introduced, an energy operations management device 4C2 shown in Fig. 12 is applied to the energy operations management device 4C. The carbon emissions management system 8 sends the CO2 emissions trading price and the possible CO2 emissions limit for a certain period of time to the energy operations management device 4C2 as data T8. The possible CO2 emissions limit may be updated at a fixed frequency, such as once a year.

[0150] The data acquisition unit 41 of the energy operation management device 4C2 acquires data T8 on the CO2 emission trading price and the CO2 emission allowance from the carbon emission management system 8, and stores the data on the CO2 emission trading price in the CO2 emission trading price DB427 and the data on the CO2 emission allowance in the CO2 emission allowance information DB428.

[0151] The CO2 emission trading price is determined by the supply and demand balance of CO2 emissions. Therefore, the first forecast data creation unit 431 and the second forecast data creation unit 441 create forecast data of the CO2 emission trading price for the target period. The first forecast data creation unit 431 and the second forecast data creation unit 441 may use data stored in the storage unit 42C2 to select feature quantities that have a significant impact on the CO2 emission trading price, such as temperature, demand, and power generation, and create forecast data using machine learning. Alternatively, the first forecast data creation unit 431 and the second forecast data creation unit 441 may create forecast data using statistics, but methods are not limited to these.

[0152] The predicted data of the CO2 emission trading price for the first period created by first prediction data creation unit 431 is input to first problem creation unit 432. This allows first problem creation unit 432 and first solution finding unit 433 to create an energy management plan that takes CO2 emissions into account for the first period. Furthermore, the predicted data of the CO2 emission trading price for the second period created by second prediction data creation unit 441 is input to second problem creation unit 442. This allows second problem creation unit 442 and second solution finding unit 443 to create an energy management plan that takes CO2 emissions into account for the second period.

[0153] For example, first question creating unit 432 and second question creating unit 442 convert the amount of electricity, gas, or fuel purchased into CO2 emissions, and set a constraint that the CO2 emissions be within the allowable CO2 emissions limit.

[0154] The objective function of the second optimization problem includes a term obtained by multiplying the difference between the CO2 emissions calculated based on the energy purchase amount for the second period and the CO2 emission allowance by the CO2 emission trading price for the second period. The energy supply system 5 can obtain profits by multiplying the difference between the CO2 emissions and the CO2 emission allowance by the CO2 emission trading price per unit amount. Therefore, the first solution-finding unit 433 and the second solution-finding unit 443 can be expected to obtain greater profits by creating energy operation plans that minimize CO2 emissions during periods when the first prediction data creation unit 431 or the second prediction data creation unit 441 predicts that the CO2 emission trading price will be high.

[0155] The steps of the energy management plan creation method by the energy management device 4C2 are generally as shown in the flowchart in Fig. 9. However, in step S103, the first prediction data creation unit 431 creates prediction data for the CO2 emission trading price for the first period in addition to the output of the renewable energy facility 6, the energy consumption of the consumer group 7, and the energy price in the energy market 3. In step S104, the first problem creation unit 432 and the first solution finding unit 433 create an energy management plan for the first period that takes into account CO2 emissions in addition to the singular event.

[0156] Furthermore, in step S105, the second prediction data creation unit 441 creates prediction data for the CO2 emission trading price for the second period in addition to the output of the renewable energy facility 6, the energy consumption of the consumer group 7, and the energy price in the energy market 3. In step S106, the second problem creation unit 442 and the second solution finding unit 443 create an energy operation plan for the second period that takes into account CO2 emissions in addition to the singular event.

[0157] When a carbon tax is introduced, in the energy operation management device 4C1 according to Embodiment 3, the first operation plan includes the energy purchase amount in the energy market at each time in the first period, and the second operation plan includes the energy purchase amount in the energy market at each time in the second period. The stored data includes data on the carbon tax price per unit of CO2 emissions. The first problem creation unit includes, in the objective function of the first optimization problem, a term obtained by multiplying the CO2 emissions calculated based on the energy purchase amount in the first operation plan by the carbon tax price per unit of CO2 emissions, and the second problem creation unit includes, in the objective function of the second optimization problem, a term obtained by multiplying the CO2 emissions calculated based on the energy purchase amount in the second operation plan by the carbon tax price per unit of CO2 emissions. Thereby, according to the energy operation management device 4C1, an energy operation plan considering the carbon tax imposed with energy purchase can be created.

[0158] When a CO2 emissions trading system is introduced, in the energy operation management device 4C2 according to Embodiment 3, the first operation plan includes the energy purchase amount in the energy market at each time in the first period, and the second operation plan includes the energy purchase amount in the energy market at each time in the second period. The stored data includes data on the CO2 emission allowances allocated to a plurality of energy supply devices. The constraint condition of the first optimization problem includes the condition that the CO2 emissions calculated based on the energy purchase amount of the plurality of devices in the first period are within the CO2 emission allowances, and the constraint condition of the second optimization problem includes the condition that the CO2 emissions calculated based on the energy purchase amount of the plurality of devices in the second period are within the CO2 emission allowances. Thereby, according to the energy operation management device 4C2, an energy operation plan considering the limitation of the CO2 emission allowances can be created.

[0159] <D. Embodiment 4> FIG. 14 is a configuration diagram of an energy operations management system 1004 according to a fourth embodiment. In the energy operations management systems 1001-1003 according to the first to third embodiments, trends in international affairs were not taken into consideration when creating an energy operations plan. For example, if a country with a large volume of fuel exports, such as oil, is subject to economic sanctions from other countries due to war or the like and reduces its fuel export volume, fuel prices may skyrocket. Therefore, the energy operations management system 1005 creates an energy operations plan taking into consideration trends in international affairs as well as unusual events caused by abnormal weather.

[0160] The energy operation management system 1004 includes a weather management system 2, an energy market 3, an energy operation management device 4D, an energy supply system 5, a renewable energy facility 6, a consumer group 7, and a carbon emission management system 8, as well as a Ministry of Foreign Affairs 9.

[0161] 14, the Ministry of Foreign Affairs 9 is connected to the energy operation management device 4D via a communication network 109. The Ministry of Foreign Affairs 9 follows the trends of international situations such as wars, and outputs information T10 relating to the international situation to the energy operation management device 4D.

[0162] There are two configurations of the energy operations management device 4D: one shown as energy operations management device 4D1 in Fig. 15, and the other shown as energy operations management device 4D2 in Fig. 16. The energy operations management device 4D1 is applied when a carbon tax is introduced, and the energy operations management device 4D2 is applied when a CO2 emissions trading system is introduced.

[0163] Compared to the energy operations management device 4C1 according to embodiment 3, the energy operations management device 4D1 includes a memory unit 42D1 instead of the memory unit 42C1. The memory unit 42D1 includes an international situation DB 429 in addition to the configuration of the memory unit 42C1.

[0164] Compared to the energy operations management device 4C2 according to embodiment 3, the energy operations management device 4D2 includes a memory unit 42D2 instead of the memory unit 42C2. The memory unit 42D2 includes an international situation DB 429 in addition to the configuration of the memory unit 42C2. The energy operations management device 4D1 will be described below.

[0165] The data acquisition unit 41 acquires information T10 on international affairs from the Ministry of Foreign Affairs 9 and stores it in the international affairs DB 429.

[0166] The first prediction data creation unit 431 creates first prediction data in consideration of information T10 on the international situation.

[0167] The second prediction data creation unit 441 creates second prediction data in consideration of information T10 on the international situation.

[0168] For example, the first forecast data creation unit 431 and the second forecast data creation unit 441 may use information stored in the storage unit 42 to create forecast data for fuel prices based on the similarity between international situations that have affected fuel prices in the past and information T10 currently acquired by the data acquisition unit 41. However, the method for creating the first forecast data and the second forecast data is not limited to this.

[0169] As a result, first question preparation unit 432 and second question preparation unit 442 can prepare an energy management plan that takes into account the international situation.

[0170] The procedure of the energy operation planning method by the energy operation management device 4D is generally as shown in the flowchart of FIG. 9. However, in step S103, the first prediction data creation unit 431 creates the first prediction data in consideration of the data on the international situation stored in the international situation DB 429. As a result, in step S104, an energy operation plan for the first period considering the international situation is created. Further, in step S105, the second prediction data creation unit 441 creates the second prediction data in consideration of the data on the international situation stored in the international situation DB 429. As a result, in step 106, an energy operation plan for the second period considering the international situation is created.

[0171] In the energy operation management device 4D according to Embodiment 4, the stored data stored in the storage units 42D1 and 42D2 includes information on the international situation. In the energy operation management device 4D, since the first prediction data and the second prediction data are created based on the information on the international situation, an energy operation plan considering the international situation can be created.

[0172] <E. Hardware Configuration> The data acquisition unit 41, the storage units 42A, 42B, 42C1, 42C2, 42D1, 42D2, the first plan creation device 43, and the second plan creation devices 44A and 44B in the above-described energy operation management devices 4A, 4B, 4C1, 4C2, 4D1, and 4D2 are realized by the processing circuit 81 shown in FIG. 17. That is, the processing circuit 81 includes the data acquisition unit 41, the storage units 42A, 42B, 42C1, 42C2, 42D1, 42D2, the first plan creation device 43, and the second plan creation devices 44A and 44B (hereinafter, the data acquisition unit 41, etc.). A dedicated hardware may be applied to the processing circuit 81, or a processor that executes a program stored in a memory may be applied. The processor is, for example, a central processing unit, a processing device, an arithmetic device, a microprocessor, a microcomputer, a DSP (Digital Signal Processor), or the like.

[0173] When the processing circuit 81 is dedicated hardware, the processing circuit 81 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of each unit such as the data acquisition unit 41 may be realized by multiple processing circuits 81, or the functions of each unit may be realized together by a single processing circuit.

[0174] When the processing circuit 81 is a processor, the functions of the data acquisition unit 41 and the like are realized by a combination of software, etc. (software, firmware, or software and firmware). The software, etc. is written as a program and stored in memory. As shown in FIG. 18 , the processor 82 applied to the processing circuit 81 realizes the functions of each unit by reading and executing a program stored in memory 83. That is, the energy operation management devices 4A, 4B, 4C1, 4C2, 4D1, and 4D2 include memory 83 for storing a program that, when executed by the processing circuit 81, results in the processing of each unit, such as the data acquisition unit 41. In other words, this program can be said to cause a computer to execute the procedure or method of the data acquisition unit 41 and the like. Here, the memory 83 may be, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), HDD (Hard Disk Drive), magnetic disk, flexible disk, optical disk, compact disk, mini disk, DVD (Digital Versatile Disk) and its drive device, or any storage medium that will be used in the future.

[0175] The above describes a configuration in which each function of the data acquisition unit 41, etc. is realized either by hardware or software, etc. However, this is not limited to this, and a configuration in which part of the data acquisition unit 41, etc. is realized by dedicated hardware and another part is realized by software, etc.

[0176] As described above, the processing circuit can realize the above-mentioned functions by hardware, software, or a combination of these. Note that the storage units 42A, 42B, 42C1, 42C2, 42D1, and 42D2 are configured as a memory 83.

[0177] It should be noted that the embodiments can be freely combined, and each embodiment can be modified or omitted as appropriate. The above description is an example in all respects. It is understood that countless variations not illustrated can be envisioned. [Explanation of symbols]

[0178] 2 Weather management system, 3 Energy market, 4A, 4B, 4C, 4C1, 4C2, 4D1, 4D2 Energy operation management device, 5 Energy supply system, 6 Renewable energy facility, 7 Consumer group, 8 Carbon emission management system, 9 Ministry of Foreign Affairs, 31 JEPX, 32 Electricity retailer, 33 Gas company, 41 Data acquisition unit, 42, 42A, 42B, 42C1, 42C2, 42D1, 42D2 Memory unit, 43 First plan creation device, 44A, 44B Second plan creation device, 45 Output unit, 50 Equipment, 70 Consumer, 81 Processing circuit, 82 Processor, 83 Memory, 100, 101, 102, 103, 104, 105, 109 Communication network, 200 Infrastructure equipment, 431 First forecast data creation unit, 432 First problem creation unit, 433 First solution-finding unit, 434 first event prediction unit, 441 second prediction data creation unit, 442 second problem creation unit, 443 second solution-finding unit, 444 second event prediction unit, 1001, 1002, 1003, 1004, 1005 energy operation management system.

Claims

1. An energy operations management device that creates an operation plan for a plurality of energy supply devices that supply energy to a group of consumers using power generated by a renewable energy facility and energy purchased from an energy market, a data acquisition unit that acquires stored data including actual values ​​of the energy price in the energy market, the energy demand of the consumer group, the power generated by the renewable energy facility, and weather data, and stores the data in a storage unit; a first event prediction unit that predicts whether a specific weather event that affects at least one of the energy price, the energy demand, and the generated power will occur in a first time period based on the stored data; a first prediction data creation unit that predicts at least one of the energy price, the energy demand, and the generated power during the first period based on the stored data and first event prediction data that is a prediction result of the first event prediction unit; a first problem creation unit that creates a first optimization problem for determining a first operation plan that is an operation plan for the plurality of energy supply devices in the first time period, based on the stored data and first prediction data that is a prediction result of the first prediction data creation unit; a first solution unit that determines the first operation plan by solving the first optimization problem; a second prediction data creation unit that predicts at least one of the energy price, the energy demand, and the generated power for a second period that is shorter than the first period based on the stored data; a second problem creation unit that creates a second optimization problem for determining a second operation plan that is an operation plan for the plurality of energy supply devices in the second time period, based on the stored data, second prediction data that is a prediction result of the second prediction data creation unit, and the first operation plan; a second solution unit that determines the second operation plan by solving the second optimization problem; an output unit that issues an energy purchase request to the energy market based on the second operation plan and instructs the plurality of energy supply devices to operate; the first operation plan includes an energy purchase amount and an electricity sales amount in the energy market at each time point during the first period; the second operation plan includes an energy purchase amount and an electricity sales amount in the energy market at each time point during the second period; the first problem creation unit includes a term obtained by multiplying the energy purchase amount in the first operation plan by the energy price and a term obtained by multiplying the electricity sales amount in the first operation plan by the energy price in an objective function of the first optimization problem, the second problem creation unit includes, in an objective function of the second optimization problem, a term obtained by multiplying the energy purchase amount in the second operation plan by the energy price and a term obtained by multiplying the electricity sales amount in the second operation plan by the energy price. Energy operation management device.

2. The energy operations management device according to claim 1, the first event prediction unit updates the first event prediction data at a cycle shorter than the first period; the first prediction data creation unit updates the first prediction data based on the updated first event prediction data and the stored data; the first problem creation unit updates the first optimization problem based on the stored data and the updated first prediction data; the first solution unit updates the first operation plan by solving the updated first optimization problem; the second prediction data creation unit updates the second prediction data at a cycle shorter than the second period; the second problem creation unit updates the second optimization problem based on the stored data and the updated second prediction data; the second solution unit updates the second operation plan by solving the updated second optimization problem. Energy operation management device.

3. The energy operations management device according to claim 1 or 2, the first prediction data creation unit creates a plurality of the first prediction data having different occurrence probabilities for each time point in the first period; the second prediction data creation unit creates a plurality of the second prediction data having different occurrence probabilities for each time point in the second period. Energy operation management device.

4. The energy operations management device according to claim 1, The energy demand includes electricity demand and heat demand. Energy operation management device.

5. The energy operations management device according to claim 1, a second event prediction unit that predicts whether the specific event will occur in the second period based on the stored data; the second prediction data creation unit creates the second prediction data based on the stored data and second event prediction data that is a prediction result of the second event prediction unit. Energy operation management device.

6. The energy operations management device according to claim 1, the first operating plan includes an amount of energy to be purchased in the energy market at each time point during the first period; the second operating plan includes an amount of energy to be purchased in the energy market at each time point during the second period; The stored data includes data on carbon tax prices per unit of CO2 emissions; the first problem creation unit includes a term obtained by multiplying the amount of CO2 emissions calculated based on the amount of energy purchased in the first operation plan by the carbon tax price per unit of CO2 emissions in an objective function of the first optimization problem, the second problem creation unit includes a term obtained by multiplying the amount of CO2 emissions calculated based on the amount of energy purchased in the second operation plan by the carbon tax price per unit of CO2 emissions in an objective function of the second optimization problem, Energy operation management device.

7. The energy operations management device according to claim 1, the first operating plan includes an amount of energy to be purchased in the energy market at each time point during the first period; the second operating plan includes an amount of energy to be purchased in the energy market at each time point during the second period; the stored data includes data on CO2 emission allowances allocated to the plurality of energy supply devices, a constraint condition for the first optimization problem includes a condition that CO2 emissions calculated based on the energy purchase amounts of the plurality of energy supply devices in the first period are within the CO2 emission allowance; a constraint condition for the second optimization problem includes a condition that CO2 emissions calculated based on the energy purchase amounts of the plurality of energy supply devices in the second period are within the CO2 emission allowance; Energy operation management device.

8. The energy operation management device according to claim 7, The stored data includes data on actual values ​​of CO2 emission trading prices, the first prediction data creation unit predicts the CO2 emission trading price for the first period based on the stored data and the first event prediction data; the second prediction data creation unit predicts the CO2 emission trading price for the second period based on the stored data; an objective function of the first optimization problem includes a term obtained by multiplying a difference between the CO2 emissions calculated based on the energy purchase amount for the first period and the CO2 emission allowance by the CO2 emissions trading price for the first period; an objective function of the second optimization problem includes a term obtained by multiplying a difference between the CO2 emissions calculated based on the energy purchase amount for the second period and the CO2 emission allowance by the CO2 emissions trading price for the second period; Energy operation management device.

9. The energy operation management device according to claim 7 or 8, the stored data includes information about international affairs; Energy operation management device.

10. An energy operations management method for creating an operation plan for a plurality of energy supply devices that supplies energy to a group of consumers using power generated by a renewable energy facility and energy purchased from an energy market, comprising: a data acquisition unit acquires stored data including actual values ​​of the energy price in the energy market, the energy demand of the consumer group, the power generated by the renewable energy facility, and weather data, and stores the data in a storage unit; a first event prediction unit predicts, based on the stored data, whether an unusual weather event that affects at least one of the energy price, the energy demand, and the generated power will occur in a first time period; a first prediction data creation unit predicts, as first prediction data, at least one of the energy price, the energy demand, and the generated power during the first time period based on the stored data and first event prediction data that is a prediction result of the occurrence of the peculiar event during the first time period; a first problem creation unit creates a first optimization problem for determining a first operation plan, which is an operation plan for the plurality of energy supply devices in the first time period, based on the stored data and the first prediction data; a first solution unit that determines the first operation plan by solving the first optimization problem; a second forecast data creation unit that predicts, based on the stored data, at least one of the energy price, the energy demand, and the generated power for a second period that is shorter than the first period, as second forecast data; a second problem creation unit creates a second optimization problem for determining a second operation plan, which is an operation plan for the plurality of energy supply devices in the second time period, based on the stored data and the second prediction data; a second solution unit that determines the second operation plan by solving the second optimization problem; an output unit that issues an energy purchase request to the energy market based on the second operation plan and instructs the plurality of energy supply devices to operate; the first operation plan includes an energy purchase amount and an electricity sales amount in the energy market at each time point during the first period; the second operation plan includes an energy purchase amount and an electricity sales amount in the energy market at each time point during the second period; the first problem formulation unit includes a term obtained by multiplying the energy purchase amount in the first operation plan by the energy price and a term obtained by multiplying the electricity sales amount in the first operation plan by the energy price in an objective function of the first optimization problem, the second problem formulation unit includes a term obtained by multiplying the energy purchase amount in the second operation plan by the energy price and a term obtained by multiplying the electricity sales amount in the second operation plan by the energy price in an objective function of the second optimization problem. Energy operation management methods.

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