Simulation device and simulation method

Abstract

The present invention provides a simulation parameter setting method capable of preferentially securing simulation accuracy at the upper and lower limits of a usable range. Provided is a device that performs a storage battery operation simulation and comprises: a parameter setting unit that sets simulation parameters from among a plurality of temporary parameter sets; and a simulation execution unit that uses the simulation parameters to perform a simulation for a prescribed simulation target value when a module battery is operated on the basis of a preset operation plan. The parameter setting unit sets the simulation parameters so as to minimize an error evaluation value E represented by the equation E = cu ∙ eu + cl ∙ el in which are used the upper limit side error eu and the lower limit side error el of the usable range pertaining to the simulation target value as well as the upper limit side weighting coefficient cu and the lower limit side weighting coefficient cl.

Description

Simulation Device and Simulation Method

[0001] The present invention relates to a device and a method for simulating the operation of a storage battery.

[0002] The storage battery is used not only for adjusting power supply and demand but also for generating profits through power trading in the power market (for example, the wholesale power market, the capacity market, and the supply-demand adjustment market).

[0003] There is already a known device that obtains an operation plan (charge-discharge plan) for a sodium-sulfur battery (NaS battery), which is a type of storage battery, and determines whether the state of the NaS battery is within an allowable range when the operation plan is executed, based on simulations of battery temperature and remaining capacity (SOC), and outputs guidance regarding the operation of the NaS battery (see, for example, Patent Document 1). The operation plan is created, for example, by the user of a power storage system consisting of a NaS battery and its associated devices (control devices and auxiliary equipment such as heaters and fans).

[0004] As disclosed in Patent Document 1, a NaS battery is usually composed of a plurality of module batteries connected in series and in parallel. Each module battery is composed of incorporating a plurality of single batteries into one housing container.

[0005] In a NaS battery, when over-discharge progresses, sodium polysulfide is generated on the anode side and sodium in the cathode is depleted, making subsequent charge and discharge impossible. Also, when over-charging progresses, the solid electrolyte is damaged, making subsequent charge and discharge impossible. Furthermore, when the temperature of a single battery becomes too high, phenomena such as the internal pressure of each single battery becoming abnormally high or the negative electrode active material directly contacting the positive electrode active material due to damage to the solid electrolyte and the temperature of each single battery rising abnormally may occur, and there is a possibility that the housing container will be damaged.

[0006] In order to be able to avoid such over-discharge, over-charging, and other problems, as described above, the guidance device disclosed in Patent Document 1 determines the feasibility of an operation plan created by the user of the power storage system prior to its execution.

[0007] In Patent Document 1, the internal resistance of the battery, the amount of heat dissipated by the module battery, and the thermal capacity of the module battery are used as parameters when simulating battery temperature and remaining capacity.

[0008] When performing a simulation on a module battery according to the method disclosed in Patent Document 1, it is necessary to set the simulation parameters by the following five-step process prior to performing the simulation.

[0009] (1) Create multiple hypothetical parameter sets with different combinations of settings for each simulation parameter.

[0010] (2) Operate the module battery and obtain measured values ​​of the values ​​to be simulated.

[0011] (3) For the same operating pattern (charge / discharge schedule) as in (2) above, simulations will be performed at predetermined time intervals using each set of provisional parameters.

[0012] (4) For each set of provisional parameters, calculate the difference between the simulated values ​​(calculated values ​​obtained by simulation) and the measured values ​​at all simulation execution times during the test period.

[0013] (5) The setting values ​​in the provisional parameter set that have the smallest sum of the "mean squared difference between the simulated value and the measured value" at all times during the test period will be adopted as the simulation parameters.

[0014] However, setting simulation parameters using the procedure described above has the problem of being computationally intensive and requiring many resources to run the simulation, resulting in high costs. This problem becomes more pronounced as the number of module batteries constituting a single battery increases.

[0015] Furthermore, in order to accurately determine the feasibility of executing an operational plan, it is necessary to perform accurate simulations. However, when simulations are performed using conventional methods, simulation errors occur, which are the differences between simulated values ​​and actual measured values. This can lead to misjudging an operational plan that is actually feasible as unfeasible. When using battery storage for the purpose of profitability, such a judgment can result in abandoning a highly profitable operational plan that could have been implemented and instead selecting an operational plan with low bid volume, leading to a decrease in market revenue.

[0016] Conversely, simulation errors can occur, making it impossible to actually implement an operational pattern that was deemed feasible. When using battery storage for profitability, such situations can lead to penalties.

[0017] Furthermore, even if this situation does not occur, simulation errors may cause the fan to operate earlier than the appropriate time to cool the battery, requiring heating with a heater to counteract the effect. In such cases, extra costs (electricity charges) will be incurred to operate the heater and fan.

[0018] However, concerns about losses (penalties or costs) arising from simulation errors during battery operation arise when the simulation errors occur near the upper and lower limits of the battery's operational range (the range of possible battery temperature and SOC). Therefore, when simulating battery operation plans, it is desirable to prioritize ensuring the accuracy of the simulation near the upper and lower limits of the operational range.

[0019] Japanese Patent Publication No. 2008-210586

[0020] This invention has been made in view of the above problems, and aims to provide a method for setting simulation parameters that can prioritize the accuracy of simulations at the upper and lower limits of the operational range when simulating the operation plan of a storage battery.

[0021] To solve the above problems, a first aspect of the present invention is a device for simulating the operation of a battery consisting of one or more module batteries based on an operation plan, comprising: a parameter setting unit for setting simulation parameters to be used for the simulation from a plurality of provisional parameter sets; and a simulation execution unit for performing a simulation on predetermined simulation target values ​​when the one or more module batteries are operated based on a pre-set operation plan using the simulation parameters, wherein the parameter setting unit sets the simulation parameters such that the error evaluation value E, expressed by the formula E = cu・eu + cl・el, is minimized, using the upper limit error eu and lower limit error el of the operational range for the predetermined simulation target value, and the upper limit weighting coefficient cu and lower limit weighting coefficient cl, at least one of which is not zero.

[0022] A second aspect of the present invention is a simulation apparatus according to the first aspect, characterized in that the parameter setting unit calculates the upper limit error eu and the lower limit error el for each of the plurality of provisional parameter sets based on the simulation result value of the predetermined simulation target value obtained by applying the provisional parameter set to a pre-set operation pattern for parameter setting, and the actual measured value of the predetermined simulation target value obtained by operating the storage battery with the operation pattern for parameter setting.

[0023] A third aspect of the present invention is a simulation apparatus according to the second aspect, characterized in that the parameter setting unit sets the mean square of the difference between the measured value and the simulation result value as the upper limit error eu while the measured value of the predetermined simulation target value is within a preset upper limit range, and sets the mean square of the difference between the measured value and the simulation result value as the lower limit error el while the measured value is within a preset lower limit range.

[0024] A fourth aspect of the present invention is a simulation apparatus according to the second aspect, characterized in that the parameter setting unit sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum value above a preset upper threshold temperature as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature as the lower limit error el.

[0025] A fifth aspect of the present invention is a simulation apparatus according to the second aspect, characterized in that the parameter setting unit sets the difference between the maximum value of the measured value and the maximum value of the simulation result value while the measured value of the predetermined simulation target value is within a preset upper limit range as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value each reach a preset lower limit threshold temperature as the lower limit error el.

[0026] A sixth aspect of the present invention is a simulation apparatus according to the second aspect, characterized in that the parameter setting unit sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum above a preset upper threshold temperature as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature as the lower limit error el.

[0027] A seventh aspect of the present invention is a simulation device according to any of the first to sixth aspects, characterized in that the predetermined simulation target value is the temperature of one or more module batteries.

[0028] An eighth aspect of the present invention is a method for simulating the operation of a battery consisting of one or more module batteries based on an operation plan, comprising: a parameter setting step of setting simulation parameters to be used for the simulation from a plurality of provisional parameter sets; and a simulation execution step of performing a simulation for predetermined simulation target values ​​when the one or more module batteries are operated based on a pre-set operation plan using the simulation parameters, wherein in the parameter setting step, the simulation parameters are set such that the error evaluation value E, expressed by the formula E = cu・eu + cl・el, is minimized, using the upper limit error eu and lower limit error el of the operational range for the predetermined simulation target value, and the upper limit weighting coefficient cu and lower limit weighting coefficient cl, at least one of which is not zero.

[0029] A ninth aspect of the present invention is a simulation method according to the eighth aspect, characterized in that, in the parameter setting step, the upper limit error eu and the lower limit error el for each of the plurality of provisional parameter sets are calculated based on the simulation result value of the predetermined simulation target value obtained by applying the provisional parameter set to a pre-set operation pattern for parameter setting, and the actual measured value of the predetermined simulation target value obtained by operating the storage battery with the operation pattern for parameter setting.

[0030] A tenth aspect of the present invention is a simulation method according to the ninth aspect, characterized in that, in the parameter setting step, the mean square of the difference between the measured value and the simulation result value is defined as the upper limit error eu while the measured value of the predetermined simulation target value is within a preset upper limit range, and the mean square of the difference between the measured value and the simulation result value is defined as the lower limit error el while the measured value is within a preset lower limit range.

[0031] An eleventh aspect of the present invention is a simulation method according to the ninth aspect, characterized in that, in the parameter setting step, the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum above a preset upper threshold temperature is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature is defined as the lower limit error el.

[0032] A twelfth aspect of the present invention is a simulation method according to the ninth aspect, characterized in that, in the parameter setting step, the difference between the maximum value of the measured value and the maximum value of the simulation result while the measured value of the predetermined simulation target value is within a preset upper limit range is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value each reach a preset lower limit threshold temperature is defined as the lower limit error el.

[0033] A thirteenth aspect of the present invention is a simulation method according to the ninth aspect, characterized in that, in the parameter setting step, the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum above a preset upper threshold temperature is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature is defined as the lower limit error el.

[0034] A fourteenth aspect of the present invention is a simulation method according to any eighth to thirteenth aspect, characterized in that the predetermined simulation target value is the temperature of one or more module batteries.

[0035] According to the first to fourteenth aspects of the present invention, compared to conventional methods, the occurrence of simulation errors near the upper and lower limits of the operational range of the simulated value is suppressed. In other words, the accuracy of the simulation near the upper and lower limits of the operational range is prioritized. This expands the feasible range of the operational plan. Furthermore, the risk of situations where an operational plan deemed feasible cannot actually be implemented is reduced. Moreover, it is possible to reduce the energy and costs required to maintain the battery within the operational range.

[0036] Figure 1 is a diagram illustrating the schematic configuration of the battery storage system 100. Figure 2 is a block diagram showing the functional components of the battery storage control device 10 and the operation guidance device 11. Figure 3 is a diagram illustrating the simulation flow. Figure 4 is a diagram illustrating an example of an operation pattern P0 that can be used as a standard operation pattern. Figure 5 is a diagram illustrating how to interpret simulation errors. Figure 6 is a diagram illustrating how to interpret simulation errors. Figure 7 is a diagram illustrating how to calculate the upper limit error eu and lower limit error el based on the first interpretation. Figure 8 is a diagram illustrating how to calculate the upper limit error eu and lower limit error el based on the second interpretation.

[0037] <Battery System> Figure 1 is a diagram showing a schematic configuration of a battery system 100 according to an embodiment of the present invention. The battery system 100 mainly comprises a battery 100B and a battery control system 100C.

[0038] The storage battery 100B is configured as a module string in which a number of module batteries 12 are connected in series. However, in Figure 1, only one module battery 12 is shown for the sake of simplicity in the illustration. Alternatively, the storage battery 100B may be configured with only one module battery 12.

[0039] Each module battery 12 generally has a configuration in which a battery array 13, which is composed of multiple individual cells (not shown), is built into the housing.

[0040] Each module battery 12 is further equipped with a fan 14 and a heater 15 as temperature maintenance means to maintain the temperature of the module battery 12 within a certain operating range during operation. Furthermore, a temperature sensor 16 for measuring the temperature of the module battery 12 is also provided. Note that multiple temperature sensors 16 may be arranged on a single module battery 12.

[0041] A single cell is, for example, a sodium-sulfur cell (NaS cell) in which sulfur is the positive electrode active material and metallic sodium is the negative electrode active material. When a single cell is an NaS cell, an exothermic reaction occurs in each cell in which metallic sodium and sulfur react to produce sodium polysulfide, and the temperature of the module cell 12 rises accordingly. However, there are limits to the heat resistance of the components of the NaS cell, particularly the solid electrolyte tube, the aluminum container, the α-alumina insulating ring interposed between them when they are joined, and the glass joints, TCB joints, and aluminum welds that seal these components. Furthermore, if these components are in contact with highly chemically active sodium, sulfur, sodium polysulfide, etc., at high temperatures for a long period of time, corrosion and degradation are likely to occur. Therefore, it is undesirable for the temperature of the single cell to exceed a certain value due to the continuation of discharge, i.e., the continuation of the above-mentioned exothermic reaction.

[0042] On the one hand, the conductivity of sodium ions with respect to β-alumina, which is a solid electrolyte, and the conductivity of sulfur as the anode active material and the graphite felt impregnated with sulfur increase as the temperature rises. That is, the higher the temperature becomes due to the heat generated by discharge, the smaller the internal resistance of the module battery 12. Therefore, when the single cell is a NaS battery, from the perspective of charge-discharge efficiency, it is preferable to operate the module battery 12 at a high temperature. Moreover, considering the diffusibility of the active material at the anode and the equilibrium of the heat generation reaction during discharge, operation at a low temperature is disadvantageous in terms of charge recovery performance.

[0043] Based on the utilization of the heat generated by the heat generation reaction during discharge as described above and the constraints on the characteristics of the materials and components constituting the NaS battery, when the single cell is a NaS battery, the module battery 12 is generally operated within a predetermined operating temperature range selected from the temperature range of 280°C to 350°C.

[0044] Alternatively, prioritizing reducing the internal resistance of the battery and facilitating charge-discharge recovery performance, the operating temperature range of the module battery 12 with a single cell being a NaS battery may be set to 305°C to 360°C.

[0045] The module string in which each module battery 12 is configured as described above is connected to the DC side of a known PCS (AC-DC converter: Power Conversion System) via a charge-discharge current detector (none of which are shown). The charge-discharge current detector is for measuring the charge-discharge current flowing through the storage battery 100B. The AC side of the PCS is connected to a load, an external system, etc. via a transformer.

[0046] The storage battery control system 100C mainly includes a storage battery control device 10 that controls the operation of the storage battery 100B and an operation guidance device 11 that is responsible for processes related to the operation of the storage battery 100B, such as creating an operation plan for the storage battery 100B and simulating it. That is, in the present embodiment, the operation guidance device 11 functions as an operation plan creation support device that supports the creation of an operation plan for the storage battery 100B.

[0047] In this embodiment, generally, the charge / discharge operation and temperature (battery temperature) of each module battery 12 are controlled by the battery control device 10. More specifically, by controlling the PCS by the battery control device 10, the charge / discharge operation of the module string composed of a large number of module batteries 12 is controlled, and by controlling the fan 14 and heater 15 of each module battery 12 by the battery control device 10, the battery temperature is controlled. FIG. 2 is a block diagram showing the functional components of the battery control device 10 and the operation guidance device 11.

[0048] The battery control device 10 can be realized by a general-purpose or dedicated computer (control computer) including a CPU, a memory, a storage, etc. When a predetermined program stored in the storage is read and executed by the CPU, the battery control device 10 mainly includes, as functional components, a charge / discharge schedule acquisition unit 1, a charge / discharge control unit 2, a heater control unit 3, and a heat dissipation control unit 4.

[0049] The charge / discharge schedule acquisition unit 1 acquires the charge and discharge schedules (charge / discharge schedules) in the battery 100B, and provides the charge / discharge schedules to the charge / discharge control unit 2, the heater control unit 3, and the heat dissipation control unit 4.

[0050] In this embodiment, the charge / discharge schedule refers to a operation plan (operation plan) related to the charge and discharge schedules in the battery 100B created in the battery control system 100C or externally, and among them, the operation plan determined to be adoptable as a result of the simulation previously executed in the operation guidance device 11.

[0051] The charge / discharge schedule describes, for example, the start time and end time of charge and discharge, the output during discharge, the charge amount during charge, the operating temperature range, etc.

[0052] The charge / discharge control unit 2 controls the charging and discharging operations of the battery 100B according to the contents of the charge / discharge schedule. Generally, when the discharge start time described in the charge / discharge schedule arrives, the charge / discharge control unit 2 connects the battery 100B to the outside and starts discharging from each module battery 12 to the outside. Then, when the discharge end time arrives, it disconnects the battery 100B from the outside and ends the discharge. Similarly, when the charge start time described in the charge / discharge schedule arrives, it connects the battery 100B to the outside and starts charging the battery 100B from the outside. Then, when the charge end time arrives, it disconnects the battery 100B from the outside and ends the charging of each module battery 12 from the outside.

[0053] The charge / discharge control unit 2 controls the charging and discharging operations in the storage battery 100B by managing the depth of discharge of the entire storage battery 100B. The depth of discharge is an indicator of the degree of discharge in the individual cells that make up the collective battery 13 of the individual module batteries 12 in the storage battery 100B. When the depth of discharge is 0%, the module battery 12 is at the end of charging, and when the depth of discharge is 100%, the module battery 12 is at the end of discharging.

[0054] However, when multiple module batteries 12 are connected in series to form a module string, the depth of discharge of the individual cells constituting the battery collection 13 in each module battery 12 is generally uniform. Therefore, in this embodiment, the depth of discharge of the entire storage battery 100B is represented by a single common value, and this value is referred to as the depth of discharge management value.

[0055] The charge / discharge control unit 2, in principle, sets the discharge depth to 0% when the battery 100B reaches the end of its charge. Each time a charge / discharge operation is performed, it calculates a discharge depth management value by successively adding or subtracting the charge / discharge amount, calculated from the charge / discharge current value detected by the charge / discharge current detector, from this initial value, and maintains the latest value. The charge / discharge operation of the battery 100B is then controlled so that this discharge depth management value remains between 0% (corresponding to the end of charge) and 100% (corresponding to the end of discharge). Known control methods can be appropriately applied to the control of the charge and discharge operations in the charge / discharge control unit 2, including the management of the discharge depth.

[0056] Furthermore, the charge / discharge control unit 2 monitors the measured values ​​of the temperature sensors 16 provided in each module battery 12. If the measured value obtained from any of the temperature sensors 16 is within the operating temperature range but approaches the upper or lower limit of that range, or if it does not meet the operating temperature range, the charge / discharge control unit 2 stops or postpones charging or discharging according to the contents of the charge / discharge schedule.

[0057] The measured values ​​(actual values) from the temperature sensor 16 and the discharge depth management values ​​are passed from the charge / discharge control unit 2 to the parameter setting unit 7a (described later) of the operation guidance device 11.

[0058] The heater control unit 3 controls the operation of the heater 15 (switching ON / OFF) based on the temperature measurements of each module battery 12 taken by the temperature sensor 16 and the set heat retention temperature. In general terms, the lower limit of the operating temperature range described above is set as the heat retention temperature, and the heater control unit 3 controls the operation of the heater 15 so that the temperature of each module battery 12 does not fall below the set heat retention temperature. As a result, the temperature of the module batteries 12 is maintained within the operating temperature range even when the module batteries 12 are in standby or charging mode.

[0059] The heat dissipation control unit 4 controls the operation of the fan 14 (switching between ON and OFF) during the heat dissipation control period described in the heat dissipation schedule, which is set based on the charge / discharge schedule.

[0060] The operation guidance device 11, like the battery control device 10, can be implemented using a general-purpose or dedicated computer (control computer) equipped with a CPU, memory, storage, etc. A predetermined program stored in the storage is loaded into the CPU and executed, and the operation guidance device 11 mainly comprises an operation plan creation unit 6, a simulation processing unit 7, and an adoption / rejection determination unit 8 as its functional components.

[0061] The operation plan creation unit 6 is responsible for creating an operation plan (charge / discharge plan) for the storage battery 100B. The operation plan includes settings for the charge / discharge period and charge / discharge output for a predetermined planning period. This operation plan is created by an operator of the operation guidance device 11 by operating input means such as a mouse, keyboard, or touch panel (not shown) provided on the control computer.

[0062] In this embodiment, a day (from 0:00 to 24:00) is divided into 30-minute time units, also called "frames," and the operation plan is created by setting the charge and discharge output for each of the 48 frames for each day for, for example, two weeks.

[0063] The simulation processing unit 7 is responsible for processing related to the simulation of the charge and discharge operation of the battery 100B in accordance with the operation plan. In this embodiment, the simulation of the charge and discharge operation of the battery 100B means the simulation of state indicator values ​​that indicate the state of the module batteries 12 constituting the battery 100B, such as battery temperature and remaining capacity (SOC), when the battery 100B is operating in accordance with the operation plan. The state indicator values ​​that are the subject of the simulation are also called the simulation target values.

[0064] The simulation processing unit 7 comprises a parameter setting unit 7a and a simulation execution unit 7b.

[0065] The parameter setting unit 7a is responsible for setting the parameters (simulation parameters) required during the simulation in the simulation execution unit 7b.

[0066] The simulation execution unit 7b uses the simulation parameters set by the parameter setting unit 7a to calculate the simulation target values ​​at predetermined time increments within each unit time segment.

[0067] When setting the initial value of the remaining capacity used in the simulation, the discharge depth management value held in the charge / discharge control unit 2 is referenced. When setting the initial value of the battery temperature, the temperature of the module battery 12 obtained by the battery control device 10 from the temperature sensor 16 is referenced. In addition, if an abnormality occurs in the state of the battery 100B, the simulation execution unit 7b acquires that information and uses it to correct the simulation parameters.

[0068] The Adoption / Failure Determination Unit 8 determines whether to adopt the operational plan that was the subject of the simulation, based on the results of the simulation performed by the Simulation Execution Unit 7b. In general terms, the Adoption / Failure Determination Unit 8 determines whether to operate the operational plan based on whether the SOC and battery temperature of each module battery 12 obtained by the simulation are within a predetermined allowable range for each unit time segment included in the operational plan. Based on the results of this determination, it then determines whether to adopt the operational plan that was the subject of the simulation. The upper and lower limits of the allowable range (operable range) of the simulation target values ​​are predetermined and stored in the operational guidance device 11.

[0069] Only operation plans that are deemed "acceptable" by the acceptance / rejection determination unit 8 are included in the charge / discharge schedule acquisition unit 1 of the battery control device 10. Operation plans that are deemed "unacceptable" by the acceptance / rejection determination unit 8 are subject to modification or re-creation by the operation plan creation unit 6.

[0070] In the explanation so far, it has been assumed that the operation plan creation unit 6, the simulation processing unit 7, and the adoption / rejection determination unit 8 are realized as functional components of a computer that functions as an operation guidance device 11. However, this configuration can also be understood as a configuration in which a simulation device realized by a computer having the function of the simulation processing unit 7 combines the functions of the operation plan creation unit 6 and the adoption / rejection determination unit 8. From another perspective, the operation guidance device 11 according to this embodiment can also be said to be a simulation device that combines the functions of the simulation processing unit 7 and the adoption / rejection determination unit 8.

[0071] Alternatively, a simulation device implemented by a computer having the functions of a simulation processing unit 7, an operation plan creation device implemented by a computer having the functions of an operation plan creation unit 6, and an adoption / rejection determination device implemented by a computer having the functions of an adoption / rejection determination unit 8 may be configured separately.

[0072] <Overview of Simulation and Adoption Decision> Next, we will explain the overview of the simulation of the charging and discharging operation of the battery 100B in accordance with the operation plan, which is performed in the simulation execution unit 7b, and the decision on whether or not to adopt the operation plan, which is performed in the adoption decision unit 8 based on the results of the simulation.

[0073] Figure 3 shows the flow of the simulation. The simulation is performed on all "time intervals," or unit time segments, of the created operational plan, but Figure 3 illustrates a simulation that targets a single time interval.

[0074] First, the following initial values ​​are set (step S1).

[0075] Operating output time: T; Time step: Δt; Operating output: Pn; Remaining capacity: SOC; Battery temperature: Temp;

[0076] More specifically, the operational output time T is 30 minutes, which is the length of the time slot. The time step Δt is set to approximately 10 seconds to 10 minutes. The operational output Pn is set to the charge / discharge power value of the time slot as described in the operational plan.

[0077] The initial value of the remaining capacity (SOC) is set based on the discharge depth control value at the start of the operational plan that is the subject of the simulation. In this embodiment, the remaining capacity (SOC) = 100% - discharge depth (control value).

[0078] The initial value of the battery temperature Temp is set to a temperature within the operating temperature range, based on the battery temperature at the start of the operation plan being simulated.

[0079] In principle, the SOC and battery temperature of each individual module battery 12 constituting the storage battery 100B may differ. However, since these module batteries 12 are connected in series to form a collective battery 13, in the simulation, under the assumption that the module batteries 12 operate similarly, it may be assumed that the remaining capacity SOC and battery temperature Temp of each module battery 12 are the same, without distinguishing between them, and each value may be represented by a single simulation value. Alternatively, if differences in battery temperature and SOC during charging and discharging may occur among the module batteries 12 constituting the collective battery 13 due to manufacturing variations in the module batteries 12 or the failure status of cells (single cells) within the module batteries 12, it may be possible to represent them with simulation values ​​obtained from the module battery 12 that tends to have the highest battery temperature, the module battery 12 that tends to have the lowest battery temperature, the module battery 12 that tends to have the highest SOC, or the module battery 12 that tends to have the lowest SOC.

[0080] Next, the number of loops N executed in the latter half of the simulation is calculated using the formula N = T / Δt (step S2).

[0081] Next, with n set to 1 (step S3), the battery current In is determined from the operational output Pn (step S4).

[0082] Furthermore, using the battery current In value calculated in step S4, the time step Δt set as the initial value in step S1, and the most recent remaining capacity SOC value, the remaining capacity SOC at the point when time has advanced by Δt is calculated using the formula SOC = SOC - In × Δt (step S5).

[0083] Furthermore, using the value of the battery current In calculated in step S4, the time step Δt set as the initial value in step S1, the value of the most recent battery temperature Temp, the internal resistance r of the module battery 12, the heat dissipation loss, and the heat capacity C, the battery temperature Temp at the point when time has advanced by Δt is calculated using the formula Temp = Temp + (In × In × r) × Δt / C (step S6).

[0084] Here, the internal resistance r, heat dissipation loss, and heat capacity C of the module battery 12 are simulation parameters (constants) that are predetermined in the parameter setting unit 7a and stored in a memory unit (not shown) of the operation guidance device 11. Details of setting the simulation parameters by the parameter setting unit 7a will be described later.

[0085] The order of steps S5 and S6 may be reversed, and they may be performed in parallel.

[0086] Next, n = n + 1 (step S7), and if n > N for the new n is not true (NO in step S8), then steps S5 to S7 are repeated at time intervals Δt.

[0087] On the other hand, if n > N (YES in step S8), the latest calculated value of the remaining capacity SOC and the calculated value of the battery temperature Temp are output as the remaining capacity and battery temperature of the storage battery 100B at the end of the frame (step S9).

[0088] If, for all module batteries 12, the remaining capacity and battery temperature output in step S9 are both within the predetermined allowable range, the adoption determination unit 8 determines that the operation plan subject to simulation is feasible ("operable"). Conversely, if, for any module battery 12, at least one of the remaining capacity and battery temperature output in step S9 deviates from the predetermined allowable range, the adoption determination unit 8 determines that the operation plan subject to simulation is not feasible ("not operable").

[0089] <Setting Simulation Parameters> Next, the method for setting the simulation parameters in this embodiment will be described in detail. As mentioned above, the simulation parameters set in this embodiment are the internal resistance r of the module battery 12, the heat dissipation amount loss, and the heat capacity C.

[0090] In this embodiment, the parameter setting unit 7a sets these simulation parameters by performing a process consisting of the following six steps (a) to (f).

[0091] [Step (a)]: Obtain multiple hypothetical parameter sets with different combinations of set values ​​for internal resistance r, heat dissipation loss, and heat capacity C.

[0092] The provisional parameter set may be created by the parameter setting unit 7a itself, or it may be input by the operator of the operation guidance device 11 as appropriate.

[0093] [Step (b)]: The simulation execution unit 7b is instructed to perform a simulation by applying each set of provisional parameters to a pre-set standard operating pattern (charge / discharge schedule).

[0094] A standard operating pattern is one in which power or current is kept as constant as possible and charging / discharging is infrequent. Figure 4 illustrates an example of an operating pattern P0 that can be used as a standard operating pattern. Setting the parameter set using an operating pattern with frequent power or current fluctuations or frequent charging / discharging is not recommended because the parameter set values ​​cannot be set appropriately due to the effects of transient voltage changes.

[0095] [Step (c)]: The charge / discharge control unit 2 is instructed to operate the battery 100B in a standard operating pattern, and the measured values ​​of the simulation target at that time are obtained via the charge / discharge control unit 2.

[0096] [Step (d)]: For each set of provisional parameters, calculate the upper limit error eu and lower limit error el, which indicate the degree of error (simulation error) of the simulated value at the upper and lower limits of the operational range, respectively.

[0097] This takes into account that if simulation errors occur at both the upper and lower limits of the range of values ​​being simulated, there is a high probability that inconveniences or malfunctions will occur that will have a significant impact on the formulation and implementation of operational plans.

[0098] Furthermore, the lower limit error el can be said to be a value that indicates the degree of simulation error related to the increase or decrease in energy or cost necessary to maintain the battery 100B in an operational state, in other words, the amount of energy or cost necessary to maintain the module battery 12 within the operating temperature range.

[0099] The specific methods for calculating the upper limit error eu and the lower limit error el will be described later.

[0100] [Step (e)]: For each set of provisional parameters, calculate the error evaluation value E using the following equation (1) with the upper limit weighting coefficient cu and the lower limit weighting coefficient cl.

[0101] E = cu・eu + cl・el ... (1) However, the upper limit weighting coefficient cu and the lower limit weighting coefficient cl are set to the values ​​that minimize the loss index value L in equation (2) below, based on the lost revenue and increased costs that are assumed to occur when simulation errors occur.

[0102] L = cu * (L1 * F1 + L2 * F2) * eu + cu * (L3 * F3) * el ... (2) In equation (2), L1 is the estimated loss amount when an operating pattern deemed to be operational cannot actually be implemented due to an upper limit error eu, and F1 is the frequency of such occurrences.

[0103] Furthermore, L2 represents the estimated loss amount when the capacity bid on in the electricity market is reduced based on simulation results in order to avoid penalties incurred when an operational pattern deemed feasible cannot actually be implemented, and F2 represents the frequency of such occurrences.

[0104] Furthermore, L3 is the estimated increase in heater power charges when the heater 15 is operated additionally due to the occurrence of a lower limit error el, and F3 is the frequency of such occurrences.

[0105] [Step (f)]: The internal resistance r, heat dissipation loss, and heat capacity C settings in the hypothetical parameter set that minimizes the error evaluation value E are set as simulation parameters.

[0106] By using the simulation parameters set in this way to simulate the operation plan of the battery 100B, the occurrence of simulation errors near the upper and lower limits of the operational range of the simulated values ​​is suppressed compared to conventional methods.

[0107] The set simulation parameters may be used fixedly from the start of operation until the operation of the battery 100B ends, or they may be reset (updated) during the process.

[0108] <Calculation of Upper and Lower Limit Errors> The following section explains how to calculate the upper limit error eu and the lower limit error el used in equations (1) and (2).

[0109] Figures 5 and 6 are diagrams illustrating how simulation errors are treated in this embodiment, which form the basis for calculating the upper limit error eu and the lower limit error el, respectively. In Figures 5 and 6, two simulation profiles PFsa and PFsb of the battery temperature of the module battery 12, obtained by performing a simulation based on a common standard operating pattern P1 using different hypothetical parameter sets, are shown in comparison with the measured temperature profile (measured profile) PFr of the module battery 12 when the storage battery 100B is actually operated based on the operating pattern P1.

[0110] Furthermore, the simulation profile PFsa and simulation profile PFsb differ around 23:00 to 2:00, when the battery temperature shifts from decreasing to maintaining a constant value (305°C). Consequently, there is also a difference in the simulation error, which is the difference between the two profiles and the measured profile PFr.

[0111] The simulation error can be understood as the difference between the simulated value and the measured value at the time of simulation execution, such as the difference ΔT between the maximum value of the measured profile PFr and the maximum value of the simulated profile PFsa in Figure 5. Hereafter, this will be referred to as the first way of understanding it.

[0112] Alternatively, it can be understood as the difference in time between the simulated value and the measured value reaching their respective upper and lower limits, such as Δt1, the difference in timing between the measured profile PFr and the simulated profile PFsb in Figure 6, where both decrease from their maximum values ​​and begin to stabilize around 305°C, or Δt2, the difference in timing between the measured profile PFr and the simulated profile PFsa, where both reach their maximum values. Hereafter, this will be referred to as the second approach.

[0113] In this embodiment, it is possible to calculate the upper limit error eu and the lower limit error el based on both the first and second approaches. The methods for calculating the upper limit error eu and the lower limit error el based on each approach will be described below.

[0114] Hereafter, the battery temperature will be assumed to be the value to be simulated. The upper and lower limits of the operating temperature range of the module battery 12 will be defined as the upper limit temperature Tu (e.g., 340°C) and the lower limit temperature Tl (e.g., 305°C), respectively. The upper limit threshold temperature Tut (e.g., 338°C) will be defined as a temperature that is lower than the upper limit temperature Tu by a predetermined upper limit range ΔTu (e.g., 2°C), and the lower limit threshold temperature Tlt (e.g., 307°C) will be defined as a temperature that is higher than the lower limit temperature Tl by a predetermined lower limit range ΔTl (e.g., 2°C).

[0115] Furthermore, if the battery temperature is between the upper limit temperature Tu and the upper limit threshold temperature Tut (i.e., within the upper limit range ΔTu), the temperature of the module battery 12 is considered to have substantially reached the upper limit of the operating temperature range. Similarly, if the battery temperature is between the lower limit temperature Tl and the lower limit threshold temperature Tlt (i.e., within the lower limit range ΔTl), the temperature of the module battery 12 is considered to have substantially reached the lower limit of the operating temperature range.

[0116] (Calculation based on the first approach) Figure 7 is a diagram illustrating how to calculate the upper limit error eu and the lower limit error el based on the first approach. In Figure 7, a temperature change profile (simulated temperature Ts) obtained by simulating the battery temperature using a certain set of hypothesis parameters for a predetermined standard operating pattern is shown together with a temperature change profile (measured temperature Tr) obtained by actually operating the storage battery 100B using the same set of hypothesis parameters.

[0117] In the first approach, the time range during which the measured temperature is within the upper limit range ΔTu is defined as the upper limit hold time tu. Furthermore, the sum of predetermined times tla (e.g., 5 minutes) and tlb (e.g., 5 minutes) before and after the time when the decreasing measured temperature reaches the lower limit threshold temperature Tlt (lower limit arrival time t1) is defined as the time before and after the lower limit arrival time tl.

[0118] Then, for each of these upper limit holding time tu and lower limit reaching time tl, the average of the squared difference between the measured temperature Tr and the simulated temperature Ts at multiple simulation execution times for each time step Δt is calculated (hereinafter referred to as the mean square), and these are defined as the upper limit error eu and the lower limit error el, respectively.

[0119] Based on the first approach, the calculation of the upper limit error eu and lower limit error el is performed only on the upper limit holding time tu and the time before and after reaching the lower limit tl. Therefore, the amount of computation required to set the simulation parameters is reduced compared to the conventional method of calculating the mean square over the entire test period.

[0120] (Calculation based on the second approach) Figure 8 is a diagram illustrating how to calculate the upper limit error eu and the lower limit error el based on the second approach. Similar to Figure 7, Figure 8 also shows the profile of the simulated temperature Ts and the profile of the measured temperature Tr together.

[0121] In the second approach, the upper limit error eu is defined as the difference between the measured maximum time t11 and the simulated maximum time t12, when both the measured temperature and the simulated temperature reach a maximum above the upper threshold temperature Tut. Furthermore, the lower limit error el is defined as the difference between the measured lower limit arrival time t13 and the simulated lower limit arrival time t14, when both the decreasing measured temperature and the simulated temperature reach a lower threshold temperature Tlt.

[0122] When using the second approach, unlike the first approach, the mean square is not calculated when determining the upper limit error eu and the lower limit error el. Therefore, the amount of computation required to set the simulation parameters is further reduced compared to the first approach. For example, if there is no substantial difference in the temperature at which the maximum value occurs between the measured temperature and the simulated temperature, the calculation based on the second approach can be considered effective.

[0123] As explained above, in this embodiment, when setting the parameters used for simulating the storage battery, emphasis is placed on the errors at the upper and lower limits of the range of possible values ​​to be simulated. The simulation parameters are set so that the error evaluation value, which is the sum of the upper and lower limit errors with weighting, is minimized.

[0124] By using simulation parameters set in this manner to simulate the operation plan of a battery storage system, the occurrence of simulation errors near the upper and lower limits of the operational range of the simulated values ​​is suppressed compared to conventional methods. In other words, the accuracy of the simulation near the upper and lower limits of the operational range is prioritized.

[0125] This expands the feasible scope of operational plans. For example, it expands the bidding capacity (bid power) and bidding time when using batteries for bidding on the electricity market. It also helps to mitigate the decrease in market revenue caused by mistakenly deeming operational plans that are actually feasible as unfeasible.

[0126] In addition, the risk of situations where operational plans deemed feasible cannot actually be implemented is reduced, thus lowering imbalance charges when batteries are used for supply and demand adjustment by power generators and retailers. Furthermore, it is possible to avoid penalties for non-implementation when batteries are used for bidding purposes in the electricity market.

[0127] Furthermore, it is possible to reduce the energy and costs required to keep the battery operational. For example, unnecessary operation of heaters and fans can be avoided, preventing the extra costs associated with such operation.

[0128] <Modification> In the above embodiment, a set of provisional parameters that gives the upper limit error eu and lower limit error el that minimize the error evaluation value E calculated based on equation (1) is adopted as the simulation parameters. However, it is also possible to adopt a configuration in which the simulation parameters are adopted by considering only the upper limit error eu or the lower limit error el. In other words, this can be said to be a configuration in which one of the upper limit weighting coefficient cu and the lower limit weighting coefficient cl in equation (1) is set to zero. In this case, the non-zero values ​​of the upper limit weighting coefficient cu or the lower limit weighting coefficient cl can be any constant, so it is not necessary to set values ​​based on equation (2).

[0129] Furthermore, when generalized to include the case mentioned above, this also means that in equation (1), at least one of the upper limit weighting coefficient cu and the lower limit weighting coefficient cl is not zero.

[0130] Furthermore, in the above-described embodiment, a common approach is taken for calculating the upper limit error eu and the lower limit error el (the difference between the simulated value and the measured value, or the difference in the time it takes for the simulated value and the measured value to reach the upper and lower limits, respectively). However, the combination of methods for calculating the upper limit error eu and the lower limit error el is not limited to this.

[0131] For example, for the upper limit error eu, the difference ΔT between the maximum value of the measured profile PFr and the maximum value of the simulation profile PFsa shown in Figure 5 may be used, and for the lower limit error el, similar to the second approach, the difference between the time t13 when the measured lower limit is reached and the time t14 when the simulation lower limit is reached, where the decreasing measured temperature and the simulation temperature are both below the lower threshold temperature Tlt, may be used.

[0132] Alternatively, the upper limit error eu may be calculated in the same way as the first approach, by adopting the mean square of the difference between the measured temperature Tr and the simulated temperature Ts at multiple simulation execution times for each time step Δt during the upper limit keeping time tu, when the measured temperature is within the upper limit range ΔTu. The lower limit error el may be calculated in the same way as the second approach, by adopting the difference between the measured lower limit arrival time t13 and the simulated lower limit arrival time t14, when the decreasing measured temperature and the simulated temperature are both below the lower limit threshold temperature Tlt.

[0133] In either case, the upper limit weighting coefficient cu and lower limit weighting coefficient cl should be predetermined according to the respective upper limit error eu and lower limit error el.

[0134] In the above-described embodiment, the case in which the single cells constituting the module battery 12 are NaS batteries is mainly explained, but a lithium-ion battery may also be used as the single cell.

[0135] In the case of lithium-ion batteries, the operating temperature range during discharge is, for example, -45°C to 60°C, and the operating temperature range during charging is, for example, -20°C to 45°C. Therefore, for both discharge and charging, the upper limit error and lower limit error can be calculated in the same manner as in the embodiment described above, and the simulation parameters can be set so that the error evaluation value E is minimized.

[0136] In this embodiment, in addition to the effect of suppressing the occurrence of simulation errors near the upper and lower limits of the operational range described above, and the effect of reducing the risk of situations where operational patterns deemed operational cannot actually be implemented, it is also possible to obtain the effect of reducing the energy and costs required to heat the cell or its surroundings with heaters, hot water, or warm air, or to cool it with chilled water or chilled air.

Claims

1. A simulation device for performing a simulation of the operation of a battery consisting of one or more module batteries based on an operation plan, comprising: a parameter setting unit for setting simulation parameters to be used for the simulation from a plurality of provisional parameter sets; and a simulation execution unit for performing a simulation of predetermined simulation target values ​​when the one or more module batteries are operated based on a pre-set operation plan, wherein the parameter setting unit sets the simulation parameters such that the error evaluation value E, expressed by the formula E = cu・eu + cl・el, is minimized, using the upper limit error eu and lower limit error el of the operational range for the predetermined simulation target value, and the upper limit weighting coefficient cu and lower limit weighting coefficient cl, at least one of which is not zero.

2. A simulation apparatus according to claim 1, wherein the parameter setting unit calculates the upper limit error eu and the lower limit error el for each of the plurality of provisional parameter sets based on the simulation result value of the predetermined simulation target value obtained by applying the provisional parameter set to a pre-set operation pattern for parameter setting, and the measured value of the predetermined simulation target value obtained by operating the storage battery with the operation pattern for parameter setting.

3. A simulation apparatus according to claim 2, wherein the parameter setting unit sets the mean square of the difference between the measured value and the simulation result value while the measured value of the predetermined simulation target value is within a preset upper limit range as the upper limit error eu, and sets the mean square of the difference between the measured value and the simulation result value while the measured value is within a preset lower limit range as the lower limit error el.

4. A simulation apparatus according to claim 2, wherein the parameter setting unit sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum value above a preset upper threshold temperature, and sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature, and sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature, and sets the difference between the time at which the lower threshold temperature reaches.

5. A simulation apparatus according to claim 2, wherein the parameter setting unit sets the difference between the maximum value of the measured value and the maximum value of the simulation result while the measured value of the predetermined simulation target value is within a preset upper limit range as the upper limit error eu, and sets the difference between the time at which the measured value of the predetermined simulation target value and the simulation result value each reach a preset lower limit threshold temperature as the lower limit error el.

6. A simulation apparatus according to claim 2, wherein the parameter setting unit sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum value above a preset upper threshold temperature, and sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature, and sets the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature, and the lower limit error el.

7. A simulation apparatus according to any one of claims 1 to 6, characterized in that the predetermined simulation target value is the temperature of one or more module batteries.

8. A method for simulating the operation of a battery consisting of one or more module batteries based on an operation plan, comprising: a parameter setting step of setting simulation parameters to be used for the simulation from a plurality of provisional parameter sets; and a simulation execution step of performing a simulation for predetermined simulation target values ​​when the one or more module batteries are operated based on a pre-set operation plan using the simulation parameters, wherein in the parameter setting step, the simulation parameters are set such that the error evaluation value E, expressed by the formula E = cu・eu + cl・el, is minimized, using the upper limit error eu and lower limit error el of the operational range for the predetermined simulation target value, and the upper limit weighting coefficient cu and lower limit weighting coefficient cl, at least one of which is not zero.

9. A simulation method according to claim 8, characterized in that, in the parameter setting step, the upper limit error eu and the lower limit error el for each of the plurality of provisional parameter sets are calculated based on the simulation result value of the predetermined simulation target value obtained by applying the provisional parameter set to a pre-set operation pattern for parameter setting, and the measured value of the predetermined simulation target value obtained by operating the storage battery with the operation pattern for parameter setting.

10. A simulation method according to claim 9, characterized in that, in the parameter setting step, the mean square of the difference between the measured value and the simulation result value is defined as the upper limit error eu while the measured value of the predetermined simulation target value is within a preset upper limit range, and the mean square of the difference between the measured value and the simulation result value is defined as the lower limit error el while the measured value is within a preset lower limit range.

11. A simulation method according to claim 9, characterized in that, in the parameter setting step, the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum above a preset upper threshold temperature is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature is defined as the lower limit error el.

12. A simulation method according to claim 9, characterized in that, in the parameter setting step, the difference between the maximum value of the measured value and the maximum value of the simulation result while the measured value of the predetermined simulation target value is within a preset upper limit range is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value each reach a preset lower limit threshold temperature is defined as the lower limit error el.

13. A simulation method according to claim 9, characterized in that, in the parameter setting step, the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a maximum above a preset upper threshold temperature is defined as the upper limit error eu, and the difference between the time at which the measured value and the simulation result value of the predetermined simulation target value reach a preset lower threshold temperature is defined as the lower limit error el.

14. A simulation method according to any one of claims 8 to 13, characterized in that the predetermined simulation target value is the temperature of one or more module batteries.

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