Power transaction support device, power transaction support method, and computer program

Abstract

This power transaction support device comprises: a price acquisition unit that acquires a price transition of power generated on the basis of a short-term predicted price and a long-term predicted price; a deterioration simulator that estimates deterioration of a power storage element that corresponds to an input temporary operation pattern; and a determination unit that determines a charge / discharge pattern of the power storage element for power transactions using the acquired price transition of the power and the estimation result by the deterioration simulator.

Description

Energy trading support device, energy trading support method, and computer program

[0001] The present invention relates to an energy trading support device, an energy trading support method, and a computer program that can be applied to energy trading using a power adjustment function provided by an energy storage element.

[0002] Patent Literature 1 discloses an energy trading support device. A plurality of charge / discharge patterns are generated, each having a first time period for charging a storage battery, a second time period for discharging the storage battery, and a third time period for not charging or discharging. For each charge / discharge pattern, the profits from purchasing electricity in accordance with the charge / discharge pattern are calculated based on the difference between the procurement price or predicted price at the first time period and the procurement price or predicted price at the second time period.

[0003] Japanese Patent Application Laid-Open No. 2019-152999

[0004] In Patent Document 1, deterioration of an energy storage element such as a storage battery is not taken into consideration. One aspect of the present invention provides an energy trading support device, an energy trading support method, and a computer program that take deterioration of an energy storage element into consideration.

[0005] According to one aspect of the present invention, an energy trading support device includes a price acquisition unit that acquires a price trend of electricity generated based on a short-term predicted price and a long-term predicted price, a degradation simulator that estimates the degradation of a storage element according to an input hypothetical operation pattern, and a determination unit that determines a charge / discharge pattern of the storage element for energy trading using the acquired price trend of electricity and the estimation result by the degradation simulator.

[0006] According to the above aspect, an energy trading support device is provided that takes into account the deterioration of storage elements, making it possible to consider profit maximization through energy trading in which storage elements are operated for a long period of time (for example, until the storage elements reach their EoL (End of Life)).

[0007] 1 is a diagram showing the relationship between the operation year and the investment profit from energy trading for a plurality of hypothetical charge / discharge patterns. (A) is a diagram showing an example of a charge / discharge pattern in which a storage battery is charged / discharged with a large SOC swing range, and (B) is a diagram showing an example of a charge / discharge pattern in which a storage battery is charged / discharged with a small SOC swing range. (B) is a diagram showing an example of a predicted value of an energy price. (C) is a graph showing the relationship between the number of cycles and the capacity deterioration rate (deterioration value). (D) is a graph showing the transition of the capacity deterioration rate according to the magnitude of SOC fluctuation of an energy storage element in the early stage of operation. (E) is a schematic diagram showing an energy storage system and an energy trading support apparatus. (F) is a diagram showing an example of a hierarchical structure of a group of energy storage modules and a connection form of communication devices. (G) is a schematic diagram showing the operation of a deterioration simulator. (H) is a diagram explaining a method for preventing a deterioration value from substantially increasing while the characteristics of an energy storage element are maintained or improved. (I) is a diagram explaining an example of automatic setting of a charge / discharge time period. (I) is a diagram explaining another example of an operation pattern search. (I) is a diagram showing an example of an energy price difference-lower limit SOC map.

[0008] An outline of an embodiment will be described below: (1) An energy trading supporting device includes a price acquisition unit that acquires a price transition of electricity generated based on a short-term predicted price and a long-term predicted price, a degradation simulator that estimates degradation of an energy storage element according to an input hypothetical operation pattern, and a determination unit that determines a charge / discharge pattern of the energy storage element for energy trading using the acquired price transition of electricity and the estimation result by the degradation simulator.

[0009] In this specification, a "storage element" may include a plurality of storage cells, a plurality of storage modules (storage packs), or a plurality of banks (strings). Alternatively, a "storage element" may be a single storage cell.

[0010] When large-scale facilities such as energy storage systems (ESS) are used in energy trading, it is desirable to aim to maximize overall profits based on information such as the initial cost of constructing the facilities, the facility's operating period (expected lifespan), and the predicted price of electricity.It is known that the operating period during which a battery storage facility can operate as expected varies depending on operating patterns such as charge / discharge patterns and temperature patterns.

[0011] Energy trading often uses battery storage equipment that uses lithium-ion batteries, which have excellent cycle performance. Alternatively, battery storage equipment that uses lead-acid batteries may also be used.

[0012] The degradation simulator (1) above estimates the degradation of the energy storage element according to an input tentative charge / discharge pattern and / or a tentative temperature pattern. The degradation simulator may estimate the degradation of the energy storage element for each of a plurality of tentative operation patterns. For example, the degradation simulator may estimate the degradation and operation period of the energy storage element for each of a charge / discharge pattern with a large SOC swing range that performs deep charge / discharge and a charge / discharge pattern with a small SOC swing range that performs shallow charge / discharge.

[0013] The determination unit determines a charge / discharge pattern of the energy storage element for energy trading, using the trend of the predicted value of the electricity price determined according to the supply-demand balance (time-series change in price) and the estimation result by the degradation simulator. With the support of this energy trading support device, it is possible to pursue maximization of profits from energy trading by operating the energy storage element over a long period of time, while taking into account the degradation of the energy storage element.

[0014] Figure 1 shows a simulation example of the transition of operating profit when conducting electricity trading with a storage battery facility using lithium-ion batteries operated with a large SOC swing range, and the transition of operating profit when conducting electricity trading with a small SOC swing range.

[0015] In this specification, the swing range of the SOC (state of charge) refers to the difference between the SOC upper limit value (alternatively, the voltage upper limit value) when charging the storage element and the SOC lower limit value (alternatively, the voltage lower limit value) when discharging the storage element. The upper limit value may be set to a value lower than the upper limit value of the usage range of the storage element, and the lower limit value may be set to a value higher than the lower limit value of the usage range of the storage element. The upper limit value and the lower limit value may be changed as the operation period of the storage element passes.

[0016] Figure 1 shows that in the early stages of operation, operation in which the storage element is charged and discharged with a large SOC swing range (solid line) results in greater operational profits. Figure 1 also shows that after 11 years of operation, operation in which the storage element is charged and discharged with a small SOC swing range (dashed line) results in greater operational profits. A more detailed explanation follows.

[0017] 2A shows a simplified example of a charge / discharge pattern for charging and discharging a storage element with a large SOC swing range. In one cycle per day (one charge, one discharge), charging of 400 kilowatts (kW) or more (positive direction of the Y axis in the graph) and discharging of 400 kW or more (negative direction of the Y axis) are performed in the early stages of operation. FIG. 2B shows an example of a charge / discharge pattern for charging and discharging a storage element with a small SOC swing range. In one cycle per day (one charge, one discharge), charging and discharging of less than 400 kW are performed from the early stages of operation.

[0018] Many lithium-ion batteries tend to deteriorate more when the SOC swing range is large and deteriorate less when the SOC swing range is small. On the other hand, the larger the SOC swing range, the greater the amount of charge and discharge power, and the greater the amount of power trading.

[0019] When the SOC swing width is large, as shown in Figure 2(A), the battery's chargeable and dischargeable power decreases quickly due to significant deterioration with cycling, and the battery reaches EoL in approximately 10 years. As a result, the battery's charge and discharge power after 10 years is 0 kW. When the SOC swing width is small, as shown in Figure 2(B), the battery's deterioration with cycling is small, making it possible to operate the battery for 15 years.

[0020] The solid line in Figure 1 (corresponding to Figure 2(A)) indicates that after the 10th year from the start of operation, the battery's charge / discharge power amount becomes zero and the investment profit becomes zero. The dashed line in Figure 1 (corresponding to Figure 2(B)) indicates that it is possible to continue earning investment profits by continuing energy trading using batteries for 15 years from the start of operation, and from the 11th year onwards, the investment profits exceed the solid line.

[0021] The deterioration simulator and determination unit described in (1) above make it possible to determine a charge / discharge pattern that maximizes profits from a long-term perspective, taking into account the deterioration of the storage elements, rather than determining a charge / discharge pattern in a short-term perspective. In this specification, "determining a charge / discharge pattern" may mean at least one of selecting or generating a waveform as shown in Fig. 2, determining an upper limit and / or a lower limit of the SOC (voltage), determining a time period for charge / discharge, and changing the waveform or the upper and lower limits of the SOC (voltage) at a predetermined timing.

[0022] Furthermore, the energy trading support device described in (1) above improves the accuracy of profit calculations by acquiring electricity price trends generated based on short-term and long-term predicted prices. This energy trading support device aims to maximize profits from the operation of energy storage elements over the long term (e.g., until EoL), and therefore requires a corresponding long-term electricity price forecast value. The accuracy of electricity price forecast values ​​generally varies depending on the period (short-term, medium-term, long-term). A highly accurate short-term forecast value can be combined with a long-term forecast value, or multiple short-term forecast values ​​can be combined, to generate a forecast value of long-term electricity price trends.

[0023] Specific examples of generated price forecast values ​​will be described. FIG. 3(A) shows a forecast value (short-term forecast value) of the price for each minimum time unit (e.g., 30 minutes) in the electricity trading market, FIG. 3(B) shows a forecast value (medium-term forecast value) of the price for each time slot of a day, and FIG. 3(C) shows a forecast value (long-term forecast value) of the price for each month. The short-term, medium-term, and long-term forecast values ​​are not limited to these. FIG. 3(D) shows a forecast value of the electricity price trend that combines the short-term, medium-term, and long-term forecast values. In the example of FIG. 3(D), in order to improve the accuracy of profit calculation, a short-term forecast value is applied for a period when it is available, and a medium-term forecast value is applied for a period when it is available. Other examples of forecast values ​​of the electricity price trend will be described later.

[0024] (2) In the energy trading support device described in (1) above, the degradation simulator may estimate a first degradation of the storage element at the first time point based on the operation history of the storage element from the start of operation to the first time point, and may estimate a second degradation of the storage element according to a hypothetical operation pattern of the storage element from the first time point to a later second time point based on the first degradation.

[0025] In this specification, "operation history" may include the charge / discharge history (current history, voltage history, SOC history, etc.) and / or temperature history that the energy storage element has experienced up to a predetermined point in time. The degradation simulator preferably uses a mathematical model that represents the state of the energy storage element and parameters that are applied to the mathematical model. The "state of the energy storage element" here may include not only the state of the energy storage element itself, but also the state of the surrounding environment, such as the placement of the energy storage element.

[0026] The mathematical model mathematically describes the characteristics of the energy storage element and its surroundings using, for example, algebraic equations, differential equations, and characteristic parameters. The mathematical model may be executable code executed by a programming language or numerical analysis software, or may be definition information or a library file referenced by the programming language or numerical analysis software.

[0027] The deterioration simulator described in (2) above can accurately estimate how the energy storage element has actually been operated and how much it has deteriorated up to a first point in time (e.g., the present time), and then estimate the deterioration at a second point in time (e.g., a future point in time). Based on the estimation result, the determination unit can select or generate a waveform of a charge / discharge pattern, determine upper and / or lower limit values, and determine a time period for charge / discharge.

[0028] The degradation state of an energy storage element varies depending on the environment and how it is charged and discharged. For example, the degradation that occurs during operation of a lithium-ion battery includes various degradation modes, such as isolation of active material particles, a decrease in charge carriers (charge carriers present in the solid phase or liquid phase; lithium ions Li+) involved in charging and discharging, an increase in electrical resistance, and a decrease in the conductivity of the electrolyte.

[0029] An estimation method that uses only physical quantities measurable from the energy storage element at a first time point (e.g., the present time) without considering the operation history of the energy storage element cannot identify detailed conditions, such as which degradation mode is progressing in the energy storage element. Therefore, it is difficult to improve the accuracy of estimating the second degradation at the second time point. In contrast, the degradation simulator described in (2) above can accurately estimate the first degradation by referring to the operation history of the energy storage element up to the first time point, thereby improving the accuracy of estimating the second degradation. It is also possible to perform simulations for multiple hypothetical future operation patterns and change the operation pattern at a predetermined timing to improve future operational profits.

[0030] (3) The degradation simulator described in (1) or (2) above may be configured not to substantially increase the degradation value indicating the degradation of the storage element during the initial period of operation of the storage element while the characteristics are maintained or improved.

[0031] Here, the "characteristics" may be, but are not limited to, the full charge capacity or high-rate characteristics of the energy storage element. The "degradation value" is preferably a capacity degradation rate that indicates the degree of decrease in the full charge capacity as a percentage. Alternatively, the "degradation value" may be a value that reflects other degradation factors, such as an increase in internal resistance (ohmic resistance, non-ohmic resistance). "Not substantially increasing the degradation value" may mean (a) maintaining the degradation value at a value near zero while the characteristics of the energy storage element are maintained or improved, or (b) gradually increasing the degradation value from a negative initial value to a positive value, thereby not increasing the degradation value.

[0032] The inventors of the present invention have noticed that in storage cells such as lithium-ion batteries, characteristics are maintained or improved, such as an increase in full charge capacity, at the beginning of operation (the beginning of a charge / discharge cycle). The increase in full charge capacity is presumably due to the electrolyte permeating into the positive electrode plate, the negative electrode plate, and the separator, or between the positive electrode plate or the negative electrode plate and the separator, or due to cracks occurring in the secondary particles of the positive electrode active material caused by charge / discharge of the storage cell (expansion / contraction of the positive electrode active material), thereby increasing the surface area of ​​the positive electrode active material that contributes to the charge / discharge reaction, thereby improving high-rate characteristics.

[0033] The conventional estimation line shown by the solid line in Figure 4, which simulates the degradation value by starting to increase from the early stage of the cycle, may deviate from the actual degradation behavior of the energy storage cell shown by the dashed line (an approximate line fitting actual measured values ​​shown by circles). This is because the conventional estimation line does not take into account the maintenance / improvement of the characteristics of the energy storage element observed in the early stage of operation. By preventing the degradation value from substantially increasing in the early stage of operation (e.g., up to approximately 200 to 350 cycles), as shown by the approximate line shown by the dashed line in Figure 4, the accuracy of the degradation estimation can be improved, and ultimately the accuracy of the calculation of profits from energy trading can be improved. The horizontal axis of Figure 4 represents the number of times (the number of cycles) charging and discharging have been repeated between 0% and 100% SOC. The term "cycle" as used herein is not limited to this example and may refer to repeated charging and discharging within a partial range between 0% and 100% SOC.

[0034] (4) The degradation simulator described in (3) above may determine an increase in the degradation value based on the magnitude of fluctuations in SOC at an early stage of operation of the storage element.

[0035] The inventors discovered that different degradation behaviors occur depending on the magnitude of fluctuations in the SOC of the energy storage elements during the initial operation stage, and came up with the configuration described in (4) above. This configuration can improve the accuracy of estimating the degradation of the energy storage elements and the accuracy of calculating profits from energy trading.

[0036] (5) The degradation simulator described in (4) above may increase the full charge capacity of the storage element at the beginning of operation to a greater extent the greater the fluctuation in SOC, and may increase the degradation value of the storage element whose full charge capacity has been increased.

[0037] As shown in FIG. 5 , the capacity degradation rate of a storage element in the early stages of operation may take on a negative value (i.e., the full charge capacity increases) as the number of cycles increases. In FIG. 5 , the solid line shows the change in the capacity degradation rate when the storage element is charged and discharged between 75% and 100% SOC, and the dashed line shows the change in the capacity degradation rate when the storage element is charged and discharged between 0% and 100% SOC. The larger the SOC fluctuation, the more likely the capacity degradation rate of a storage element in the early stages of operation tends to take on a large negative value (i.e., the full charge capacity increases). Therefore, by taking this tendency into account (reflecting it in the estimation), the accuracy of the degradation estimation of the storage element can be improved, and the accuracy of the calculation of profits from energy trading can be improved.

[0038] (6) In any of the above-described (1) to (5) energy trading support devices, the degradation simulator may estimate the degradation of the energy storage element according to a tentative charge / discharge pattern generated using a power purchase threshold and a power sale threshold set for the price trend of the energy, and the determination unit may determine the charge / discharge pattern of the energy storage element for energy trading using the generated tentative charge / discharge pattern and the estimation result by the degradation simulator.

[0039] With the above configuration, it is possible to automatically determine, from predicted electricity price trends, the time periods for buying and selling electricity (time periods for charging and discharging the energy storage elements) that contribute to maximizing profits from electricity trading while taking into account the deterioration of the energy storage elements. Details will be described later ( FIG. 10 ).

[0040] (7) In any of the energy trading support devices (1) to (5) above, the determination unit may determine a charging / discharging pattern of the storage elements for energy trading in accordance with an energy price difference within a predetermined time period.

[0041] The above configuration makes it possible to automatically determine a charge / discharge pattern that contributes to maximizing profits from energy trading while taking into consideration the deterioration of the energy storage elements. Details will be described later (FIGS. 11 and 12).

[0042] (8) In an electricity trading support method, a computer acquires electricity price trends generated based on short-term predicted prices and long-term predicted prices, estimates deterioration of storage elements according to a hypothetical operating pattern, and determines a charging / discharging pattern of the storage elements for electricity trading using the acquired electricity price trends and the estimated results.

[0043] (9) The computer program causes the computer to execute a process of acquiring electricity price trends generated based on short-term predicted prices and long-term predicted prices, estimating deterioration of storage elements according to a hypothetical operating pattern, and determining a charging / discharging pattern of the storage elements for electricity trading using the acquired electricity price trends and the estimation results.

[0044] The above-mentioned energy trading support method and computer program may be executed by a computer located close to the energy storage elements, or may be executed by a computer located remotely from the energy storage elements. The above-mentioned energy trading support method and computer program may be implemented by a method equivalent to the configurations described in (2) to (6) above.

[0045] Hereinafter, embodiments will be described in detail with reference to the drawings. Fig. 6 shows an example of remote monitoring of multiple energy storage systems for energy trading installed in a certain region or on a certain site. The multiple energy storage systems may be distributed across multiple sites or may be centrally installed in one location. A container C that houses a group of energy storage modules L may be a battery panel or rack installed indoors, or may be a cubicle installed outdoors. The container C may also be the housing of a storage battery-equipped device (for example, a power conditioner with a storage battery or a backup power supply).

[0046] Each of the plurality of power storage systems may be connected to a local network CN via the communication device 1 and transmit status data of each power storage element to the local management device 2A. The status data may include cell voltage values, cell internal resistance values, bank current values, temperatures, etc.

[0047] The communication device 1 may be a measurement monitor that communicates with a battery management unit (BMU) provided in the energy storage element to receive information about the energy storage element, or may be an ECHONET / ECHONET Lite (registered trademark) compatible controller. The communication device 1 may be a network card type device. One communication device 1 is provided for each group consisting of multiple energy storage modules in order to acquire information about the energy storage module group L in the energy storage system.

[0048] The status data transmitted from the plurality of power storage systems is received by a server device 2 for remote monitoring via a dedicated line DN or a network N. The status data is stored in the server device 2 as an operation history in association with identification data that identifies the power storage systems.

[0049] The energy trading supporting apparatus 300 is communicatively connected to a server apparatus 2 for remote monitoring and a price data management apparatus 400 that generates or stores predicted values ​​of electricity prices. In this embodiment, the energy trading supporting apparatus 300, the server apparatus 2, and the price data management apparatus 400 are managed by a manufacturer of the energy storage element or energy storage system, and are communicatively connected via a local network MN for the manufacturer or a dedicated line. The network MN may include a VPN (Virtual Private Network).

[0050] Alternatively, the server device 2 may incorporate the functions of the energy trading support device 300, or the energy trading support functions / services may be provided as a subset of the remote monitoring functions / services by the server device 2. The server device 2 may include a web server function, and may present information obtained from the communication devices 1 installed in / connected to each power storage system to be monitored in response to access from the client device 3.

[0051] The energy trading supporting apparatus 300 may use a server computer 301 and may include a storage unit 311. In this embodiment, the server computer 301 is described as a single server computer, but processing may be distributed among multiple server computers. In this embodiment, the server computer 301 functions as a degradation simulator 3a, a price acquisition unit, and a determination unit, which will be described later.

[0052] The server computer 301 includes a control unit and a communication unit (not shown). The control unit is a processor using a CPU or a GPU (Graphics Processing Unit), and controls each component to execute processing using built-in memories such as ROM and RAM. The control unit executes communication and information processing based on a server program stored in the storage unit 311. The program may include a web server program, and the control unit may function as a web server that provides web pages to the client device 3.

[0053] The storage unit 311 uses a nonvolatile memory such as a hard disk or a flash memory. The above-mentioned server program and data processing program are stored in the storage unit 311. The server program P and data processing program stored in the storage unit 311 may be server program P and data processing program stored in a recording medium (not shown) that have been read and copied to the storage unit 311.

[0054] The network N includes a public communication network, which is the Internet, and a carrier network that realizes wireless communication according to a predetermined mobile communication standard. The public communication network includes a general optical fiber line, and the network N includes a dedicated line to which the server device 2 is connected. The carrier network includes a base station BS, and the client device 3 can communicate with the server device 2 from the base station BS via the network N.

[0055] An access point AP is connected to the network MN, and the client device 3 can send and receive information to and from the server computer 301 via the access point AP.

[0056] The client device 3 may be a desktop or laptop personal computer, or may be a so-called smartphone or tablet communication terminal. The client device 3 includes a control unit, a storage unit, a communication unit, a display unit 33, and an operation unit 34, all of which are not shown.

[0057] The display unit 33 uses a display such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display unit 33 displays an image of a web page provided by the server device 2. The display unit 33 is preferably a display with a built-in touch panel, but may also be a display without a built-in touch panel.

[0058] The operation unit 34 is a user interface such as a keyboard and pointing device, or a voice input unit, which can input and output data to and from the control unit 30. The operation unit 34 may be a touch panel of the display unit 33 or physical buttons provided on the housing. The operation unit 34 notifies the control unit of operation information by the user.

[0059] The energy storage module group L of the energy storage system has a hierarchical structure. FIG. 7 shows an example of the hierarchical structure of the energy storage module group L and a connection configuration of the communication device 1. The energy storage module group L is configured in a hierarchical structure of, for example, energy storage modules, each of which has a plurality of energy storage cells connected in series, and banks, each of which has a plurality of energy storage modules connected in series. In the example of FIG. 7 , one management device M serving as a BMU is provided for each of the banks numbered 1 to N and for each bank group (also referred to as a domain) in which the banks are connected in parallel. The management device M provided for each bank communicates via serial communication with a control board (CMU: Cell Management Unit) with communication capabilities built into each energy storage module, and acquires measurement data (current, voltage, temperature) of the energy storage cells in the energy storage module. The management device M for each bank transmits measurement data obtained from the energy storage modules of each bank to the management device M provided for the domain. The management device M for a domain aggregates information such as measurement data and detected abnormalities obtained from the management devices M of the banks belonging to that domain.

[0060] Although not shown in detail, the storage cell may include a hollow rectangular parallelepiped case and a pair of cell terminals with opposite polarities provided on one side (terminal surface, top surface) of the case. The case contains an electrode assembly formed by stacking a positive electrode, a separator, and a negative electrode, and an electrolyte (electrolytic solution). The storage cell is not limited to a prismatic cell, and may be a cylindrical lithium-ion battery or a laminated (pouch-type) lithium-ion battery. The storage cell may also be an all-solid-state lithium-ion battery using a solid electrolyte.

[0061] The communication device 1 receives an operation pattern transmitted from the management device 2A or the energy trading support device 300 shown in FIG. 6, and charges and discharges each bank according to the operation pattern via the management device M of the domain and the management device M for each bank.

[0062] 8 is a schematic diagram showing the operation of the degradation simulator 3a. The degradation simulator 3a receives time-series data of SOC (charge / discharge pattern) and time-series data of temperature (temperature pattern) as input data, and estimates (calculates) the degradation value of the storage element. As shown in FIG. 8, the time-series data of SOC indicates fluctuations in SOC from time t1 to time tn (e.g., n SOC values ​​at each time point), and the time-series data of temperature indicates fluctuations in temperature from time t1 to time tn (e.g., n temperature values ​​at each time point).

[0063] The degradation simulator 3a can estimate the decrease in SOH (degradation value) of the storage element from time t1 to time tn based on fluctuations in SOC and temperature from time t1 to time tn. If the SOH at time t1 is SOHt1 and the SOH at time tn is SOHtn, the degradation value is (SOHt1 - SOHtn). That is, if the SOH at time t1 is known, the SOH at time tn can be calculated based on the degradation value. Here, time t1 can be a time in the past, present, or future, and time tn can be a time a predetermined time has elapsed from time t1. The time difference between time t1 and time tn is the degradation prediction target period of the degradation simulator 3a and can be set appropriately depending on how far into the future the degradation value is predicted. The time difference between time t1 and time tn can be, for example, one month, six months, one year, or two years.

[0064] In the example of FIG. 8, time-series data of temperature is input to the deterioration simulator 3a, but alternatively, a representative temperature (for example, the average temperature from time t1 to time tn) may be input to the deterioration simulator 2a.

[0065] The deterioration simulator 3a in this embodiment is configured so as not to substantially increase the deterioration value indicating deterioration (in this embodiment, the deterioration rate of the full charge capacity) during the initial period of operation of the storage element, while the characteristics are maintained or improved.

[0066] The degradation simulator 3a estimates the extent to which the characteristics of the energy storage element will be maintained or improved based on the magnitude of fluctuation in the SOC and temperature during the initial operation of the energy storage element, and determines the number of cycles at which the capacity degradation rate will increase as a positive value. In the estimated line in FIG. 9 , the capacity degradation rate increases as a positive value from around 350 cycles. Up to 350 cycles, the capacity degradation rate is maintained at zero. In other words, the cycle degradation value is maintained at zero while the full charge capacity of the energy storage element increases.

[0067] The conventional estimation line shown by the dashed line in Figure 9 simulates the degradation value by starting to increase from the early stage of the cycle. The conventional estimation line deviates from the actual measured value indicated by the triangular dots because it does not take into account the maintenance / improvement of the characteristics of the energy storage element observed during the early stage of operation. In contrast, the estimation line according to this embodiment shown by the solid line in Figure 9 maintains the capacity degradation rate at zero during the early stage of the cycle and then increases the capacity degradation rate to match the approximate line fitted to the actual measured value. In this way, by taking into account the maintenance / improvement of the characteristics of the energy storage element during the early stage of operation and not substantially increasing the degradation value during that operation period, the accuracy of the degradation estimation can be improved. The improved accuracy of the degradation estimation can reduce the occurrence of events such as energy storage system failures and failure to meet requirements.

[0068] 6 acquires short-term, medium-term, and long-term electricity price forecasts (see, for example, FIGS. 3A to 3C) from the price data management device 400, and combines them to generate a long-term electricity price trend forecast (see, for example, FIG. 3D). The server computer 301 may also acquire a long-term electricity price trend forecast (see, for example, FIG. 3D) that has been generated in advance and stored in the price data management device 400.

[0069] The server computer 301 shown in Fig. 6 inputs a hypothetical operation pattern for the future into the degradation simulator 3a and estimates a degradation value corresponding to the operation pattern. The server computer 301 determines a charge / discharge pattern for the energy storage element for energy trading using a predicted value of the electricity price transition obtained from the price data management device 400 and the estimation result by the degradation simulator 3a. For example, if a simulation result like that shown in Fig. 1 is obtained, a charge / discharge pattern with a small SOC swing width is selected.

[0070] Next, another method for determining the charge / discharge pattern will be described with reference to FIG.

[0071] The times of day when electricity prices are lowest (times suitable for purchasing electricity) and highest (times suitable for selling electricity) vary depending on the season and / or the operating status of existing power plants such as nuclear power plants.

[0072] 10(A) shows a predicted future electricity price trend taking into account the season and the operating status of existing power plants. A power purchase threshold (a criterion for determining whether electricity can be purchased when the electricity price is below that value) and a power sale threshold (a criterion for determining whether electricity can be sold when the electricity price is above that value) are set for the price trend of the generated electricity. In this example, the power purchase threshold is set to 27 yen / kilowatt-hour [kWh], and the power sale threshold is set to 40 yen / kWh.

[0073] The server computer 301 (FIG. 6) generates a tentative charge / discharge pattern shown in FIG. 10(B) from the information shown in FIG. 10(A). This tentative charge / discharge pattern is set to charge when the power price is equal to or lower than the power purchase threshold, and discharge when the power price is equal to or higher than the power sale threshold. The server computer 301 inputs this tentative charge / discharge pattern into the deterioration simulator 3a, estimates a deterioration value according to the operation pattern, and then generates an operable charge / discharge pattern that takes deterioration into consideration, as shown in FIG. 10(C).

[0074] According to the above configuration, the time periods for purchasing and selling electricity (time periods for charging and discharging the storage elements) can be automatically determined based on predicted trends in electricity prices, and at the same time, it is possible to pursue maximization of profits through electricity trading by operating the storage elements for a long period of time.

[0075] Next, another method for determining the charge / discharge pattern will be described with reference to FIG.

[0076] The difference in electricity prices throughout the day varies depending on the season or other factors. When the price difference is large (solid line in FIG. 11(A)), the amount of charge and discharge electricity (electricity trading volume) is increased to increase operational profits. Conversely, when the price difference is small (dashed line in FIG. 11(A)), the amount of charge and discharge electricity is reduced to suppress the progression of deterioration of the storage elements. An example of a charge and discharge pattern determined by such an algorithm is shown in FIG. 11(B).

[0077] To generate the charge / discharge pattern shown in Fig. 11(B), a rule (map) of the power price difference and the lower limit SOC shown in Fig. 12 may be used. In the example of Fig. 12, when the power price difference is 10 yen or less, the lower limit SOC of the storage element is set to 100%, so that charging / discharging (power trading) of the storage element is not performed. In consideration of the efficiency of the power conditioner (PCS) and power loss depending on the charging / discharging efficiency of the storage element, charging / discharging is not performed when the price difference is small.

[0078] The map in Fig. 12 is optimized to maximize profits from electricity trading that operates energy storage elements over a long period of time. In Fig. 12, when the electricity price difference exceeds 10 yen, the lower limit SOC gradually decreases from 50%. Instead of linearly decreasing the lower limit SOC, the lower limit SOC is decreased nonlinearly (in a downward convex curve, and so that the decrease in the lower limit SOC gradually decreases as the price difference increases) in consideration of the fact that deterioration increases as the SOC swing width increases.

[0079] The present invention is not limited to the above-described embodiment and may be modified as appropriate. The degradation simulator 3a and other computer programs may be executed by the regional management device 2A, the remote monitoring server device 2, or the client device 3, instead of the server computer 301 shown in FIG. 6 .

[0080] 301 Server computer (power trading support device) 311 Storage unit 400 Price data management device

Claims

1. An energy trading support device comprising: a price acquisition unit that acquires electricity price trends generated based on short-term predicted prices and long-term predicted prices; a degradation simulator that estimates degradation of a storage element according to an input hypothetical operation pattern; and a determination unit that determines a charging / discharging pattern of the storage element for energy trading using the acquired electricity price trends and the estimation results by the degradation simulator.

2. The energy trading support device of claim 1, wherein the deterioration simulator estimates a first deterioration of the storage element at the first time point based on an operation history of the storage element from the start of operation to the first time point, and estimates a second deterioration of the storage element according to a hypothetical operation pattern of the storage element from the first time point to a later second time point based on the first deterioration.

3. The energy trading supporting device according to claim 2, wherein the operation history is a charge / discharge history experienced by the storage element up to a predetermined point in time.

4. The energy trading supporting device according to claim 3, wherein the charge / discharge history is at least any one of a current history, a voltage history, and an SOC history.

5. The energy trading support device according to claim 3, wherein the charge / discharge history is a temperature history.

6. The energy trading support device according to claim 1 or 2, wherein the degradation simulator includes a mathematical model that represents a state of the storage element.

7. The energy trading support device according to claim 6, wherein the mathematical model includes at least any one of degradation modes of isolation of active material particles, charge carriers involved in charging and discharging, electrical resistance, and electrical conductivity in the electrolyte.

8. The energy trading support device according to claim 1 or 2, wherein the deterioration simulator is configured not to substantially increase the deterioration value indicating the deterioration of the storage element during the initial period of operation of the storage element while the characteristics are maintained or improved.

9. The energy trading supporting apparatus according to claim 8, wherein the degradation simulator makes a decision regarding an increase in the degradation value based on a magnitude of a fluctuation in a state of charge (SOC) at an early stage of operation of the storage element.

10. The energy trading support device according to claim 9, wherein the degradation simulator increases the full charge capacity of the storage element in the initial stage of operation to a greater extent the greater the magnitude of fluctuation in SOC, and increases the degradation value of the storage element whose full charge capacity has been increased.

11. The energy trading support device of claim 1 or claim 2, wherein the degradation simulator estimates the degradation of the energy storage element according to a tentative charge / discharge pattern generated using a power purchase threshold and a power sale threshold set to the price trend of the electricity, and the determination unit determines the charge / discharge pattern of the energy storage element for energy trading using the generated tentative charge / discharge pattern and the estimation result by the degradation simulator.

12. The energy trading support device according to claim 1 or 2, wherein the determination unit determines a charging / discharging pattern of the storage elements for energy trading in accordance with a difference in electricity prices within a predetermined time period.

13. A method for supporting electricity trading, comprising: a computer acquiring electricity price trends generated based on short-term and long-term predicted prices; estimating deterioration of a storage element according to a tentative operation pattern; and determining a charging / discharging pattern of the storage element for electricity trading using the acquired electricity price trends and the estimation result.

14. A computer program for causing a computer to execute a process of obtaining electricity price trends generated based on short-term and long-term predicted prices, estimating deterioration of a storage element according to a hypothetical operation pattern, and determining a charging and discharging pattern of the storage element for electricity trading using the obtained electricity price trends and the estimation result.

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