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WO2016030967A1 - Système de planification de fonctionnement de stockage d'énergie et procédé de détermination de conditions de fonctionnement - Google Patents

Système de planification de fonctionnement de stockage d'énergie et procédé de détermination de conditions de fonctionnement Download PDF

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Publication number
WO2016030967A1
WO2016030967A1 PCT/JP2014/072294 JP2014072294W WO2016030967A1 WO 2016030967 A1 WO2016030967 A1 WO 2016030967A1 JP 2014072294 W JP2014072294 W JP 2014072294W WO 2016030967 A1 WO2016030967 A1 WO 2016030967A1
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Prior art keywords
power storage
deterioration
storage devices
battery
power
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English (en)
Japanese (ja)
Inventor
靖子 小林
井上　亮
篤彦 大沼
高橋　宏文
修子 山内
浩仁 矢野
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Hitachi Ltd
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Hitachi Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries

Definitions

  • the present invention relates to a technique for controlling a system for transferring power from a storage battery in response to a command.
  • the company operating the power system pays the ancillary service company as compensation for the power supplied from the system of the service provider (ancillary service provider) to the power system.
  • power is traded, for example, in units of time zones.
  • Japanese Patent Laid-Open No. 2013-168010 discloses a technique for improving the balance of an ancillary service provider using a storage battery.
  • a business operator's system includes a storage battery that can be charged from the power system and discharged to the power system, and a control device that controls charging / discharging of the storage battery.
  • the control device determines the time zone to be bid by referring to the history of consideration for providing ancillary service in the market, and controls charging / discharging of the storage battery. More specifically, the distribution ratio between the power supply from the electrical equipment equipped with the generator and the power supply from the storage battery is selected.
  • An object of the present invention is to provide a technique for appropriately controlling and improving an index value in consideration of deterioration of a plurality of storage batteries.
  • An energy storage operation planning system is an energy storage operation planning system for determining operation conditions for controlling a plurality of chargeable / dischargeable power storage devices, and is measured for each of the power storage devices.
  • An internal information collecting unit for acquiring collected data; an internal state estimating unit for estimating a degree of deterioration of each of the power storage devices based on the collected data; a deterioration behavior model indicating the deterioration behavior of the power storage device; and the degree of deterioration
  • a life prediction unit that calculates a predicted life that predicts how long each of the power storage devices can supply power, and a combined output of the plurality of power storage devices follows an expected input waveform, and the power storage
  • An output distribution unit that generates a list including one or more combinations of the operating conditions, and an operating condition that maximizes the cumulative value of the index value for evaluating the utility of power supply up to the
  • the combined output of the power storage device follows the predicted input waveform, a plurality of power storage devices are extended in consideration of deterioration, and the combination of operation conditions that optimizes the index value is selected.
  • FIG. 1 is a block diagram illustrating a configuration of an entire system according to a first embodiment.
  • 2 is a block diagram showing a configuration of an energy storage operation planning system 103.
  • FIG. 6 is a diagram for explaining a concept of deterioration suppression control that can be implemented as control for extending the life of a battery 99 in Example 1.
  • FIG. It is a figure which shows the input waveform prediction data. It is a figure which shows the following degree constraint data 201.
  • FIG. It is a figure which shows the deterioration behavior model 202.
  • FIG. It is a figure which shows the battery (cell) master data 203.
  • FIG. It is a figure which shows the KPI unit data 204. It is a figure which shows the operation condition data.
  • FIG. 5 is a flowchart showing processing of an output distribution unit 213. It is a flowchart which shows the detailed process of step 2136 of the output distribution part 213.
  • FIG. It is a figure which shows the example of the candidate of output distribution.
  • FIG. It is a conceptual diagram of the lifetime extension of a battery by capacity
  • FIG. It is a figure which shows the operating condition change data 206 in Example 2.
  • FIG. It is a figure which shows the operation condition data 205 in Example 2.
  • FIG. 12 is a flowchart illustrating a process of a KPI optimization unit 214 in the second embodiment.
  • FIG. 10 is a block diagram illustrating a configuration of an entire system mounted on a vehicle in a third embodiment.
  • Example 1 illustrates a case in which storage battery deterioration suppression control is applied in a system of an operator (ancillary service operator) that receives and receives power from an electric power system through an ancillary service.
  • operator ancillary service operator
  • the ancillary service has various purposes such as frequency adjustment, operation reserve procurement, and instantaneous reserve procurement.
  • the ancillary service in this embodiment refers to frequency adjustment.
  • ancillary services including those for the purpose of frequency adjustment, provide power output by the ancillary service provider in response to charge / discharge instructions from the operator (system operating organization) of the power system.
  • the higher the followability the higher the amount of compensation to the ancillary service provider.
  • rapid charging / discharging is performed in order to increase the follow-up degree, the battery is likely to deteriorate, and the period until the battery life is shortened.
  • the energy storage of the ancillary service provider includes a plurality of batteries, and each battery includes a plurality of cells. Assume that the internal state of each battery or cell can be measured, and the power to be output to each of the plurality of batteries or cells can be assigned (output distribution).
  • the degree of deterioration of each battery or cell is compared, and output distribution is performed so as to reduce the amount of deterioration of the battery or cell while observing the restriction condition of the following degree in the ancillary service. Determine the operating conditions for deterioration suppression control.
  • FIG. 1 is a block diagram illustrating a configuration of the entire system according to the first embodiment.
  • the overall system includes a plurality of ancillary service provider systems 10, a power system 50, a communication network 60, and a system operating organization system 80.
  • the ancillary service provider system 10 includes a plurality of batteries 99 including a plurality of cells 100, a PCS 101, a storage battery control device 102, and an energy storage operation planning system 103.
  • an ancillary service provider exemplifies a configuration provided with only a storage battery as a power source for frequency adjustment, but the present invention is not limited to this.
  • the ancillary service provider may further include a power generation device.
  • the ancillary service provider places a bid on the transaction market in advance for the amount of power that can be responded. Then, the ancillary service provider system 10 sends and receives power to the power system 50 in response to a command from the power system 50.
  • a system operating organization system 80 and an ancillary service provider system 10 are connected to the communication network 60.
  • the grid operation engine system 80 transmits a power generation amount or load amount instruction for frequency adjustment to the ancillary service provider system 10 via the communication network 60. At that time, the grid operation engine system 80 predicts frequency fluctuations of the power system 50, and ancillary that bids the power generation amount or load amount for maintaining the frequency of the entire power system 50 within a predetermined range in the trading market. A command is given to at least one of the service provider systems 10.
  • the PCS (Power Conditioning Subsystem) 101 is also called a power conditioner, and controls the supply of power discharged from the battery 99 or the cell 100 to the power system 50, and the power discharged from the battery 99 or the cell 100 is used as power. It has a function of converting so that it can be supplied to the system 50.
  • the storage battery control device 102 controls the battery 99 or the cell 100 via the PCS 101 according to the operation condition of each battery 99 or the cell 100 determined by the energy storage operation planning system 103.
  • FIG. 2 is a block diagram showing the configuration of the energy storage operation planning system 103.
  • FIG. 2 shows a functional configuration of the energy storage operation planning system 103 and data held by a storage device (not shown).
  • the energy storage operation planning system 103 holds input waveform prediction data 200, tracking degree constraint data 201, deterioration behavior model 202, battery (cell) master data 203, KPI unit data 204, and operation condition data 205.
  • the input waveform prediction data 200 includes information predicting the output power requested for frequency adjustment from the grid operating engine system 80.
  • the input waveform prediction data 200 is shown in FIG.
  • the tracking degree constraint data 201 includes information on the upper limit value and the lower limit value of the tracking degree determined by the grid operating organization and notified from the grid operating organization system 80. If you deviate from the upper limit or lower limit of the degree of tracking, you will not be able to get ancillary service rewards.
  • the following degree constraint data 201 is shown in FIG.
  • the deterioration behavior model 202 is data obtained by modeling the deterioration amount with respect to the behavior of the battery 99 or the cell 100. Even if the battery 99 or the cell 100 behaves in the same manner, the amount of deterioration (deterioration amount) varies depending on the degree of deterioration (deterioration degree) at that time.
  • the deterioration behavior model 202 is shown in FIG.
  • Battery (cell) master data 203 includes information on the performance of battery 99 or cell 100 and operational constraints.
  • the battery (cell) master data 203 is shown in FIG. 5A described later.
  • the KPI (Key Performance Indicators) unit data 204 includes information relating to the unit price of an ancillary reward (ancillary reward unit price) determined by the grid operating organization and notified from the grid operating organization system 80, for example. .
  • the KPI unit data 204 is shown in FIG. KPI is an index for evaluating the utility of power supply by an ancillary service provider.
  • the operation condition data 205 includes information on operation conditions for each battery 99 or 100 that conditions the control of the battery 99 or the cell 100, as determined by the energy storage operation planning system 103. By setting a combination of operating conditions of a plurality of batteries 99 or cells 100, the output distribution of each battery 99 or cell 100 can be set.
  • the operation condition data 205 is shown in FIG.
  • the energy storage operation planning system 103 includes an internal information collection unit 210, an internal state estimation unit 211, a life prediction unit 212, an output distribution unit 213, and a KPI optimization unit 214.
  • the internal information collection unit 210 has a function of collecting current (current), voltage, and temperature data (collected data) of the battery 99 in operation. Further, when monitoring and control is performed in units of cells 100, the positive electrode potential and the negative electrode potential are acquired in addition to the current, voltage, and temperature of the cell 100.
  • the internal state estimation unit 211 has a function of estimating an internal state representing the degree of deterioration of the battery 99 or the cell 100 from collected data such as current, voltage, and temperature acquired by the internal information collection unit 210.
  • the internal state estimation unit 211 calculates the positive electrode utilization rate, the negative electrode utilization rate, and the Li loss amount from the positive electrode potential and the negative electrode potential, and estimates the degree of deterioration of the cell 100 from these values. .
  • the life prediction unit 212 has a function of outputting the predicted life of the battery 99 or the cell 100 based on the deterioration behavior model 202.
  • the predicted life is a prediction of how long the battery 99 or the cell 100 can supply power.
  • the predicted life of the battery 99 or the cell 100 is calculated from the current degree of deterioration based on the deterioration behavior model 202.
  • the deterioration behavior model includes future operation conditions (C rate, central SOC, ⁇ SOC, etc.) as parameters, and the life of the battery 99 or the cell 100 changes when the future operation conditions are changed. Note that the accuracy of the predicted lifetime is higher when the lifetime is predicted in units of 100 cells than in the unit of battery 99, that is, when the lifetime of the battery 99 or the cell 100 is predicted using measurement data in units of positive and negative electrodes. .
  • the output distribution unit 213 calculates the deterioration level of each battery 99 or cell 100 calculated by the internal state estimation unit 211 and received via the life prediction unit 212, the input waveform prediction data 200, the tracking degree constraint data 201, and the deterioration behavior. Based on the model 202, the output distribution of each battery 99 or cell 100 is determined so that the following condition constraint is satisfied in an arbitrary period and the total deterioration amount of the battery 99 or cell 100 is reduced. In addition, a combination of operation condition patterns of each battery 99 or cell 100 is generated and listed. The processing flow of the output distributor 213 is shown in FIG.
  • the KPI optimization unit 214 Based on the list of combinations of operation condition patterns of each battery 99 or cell 100 received from the output distribution unit 213 and the KPI unit data 204, the KPI optimization unit 214 performs the KPI value in a long span until the predicted lifetime. It has a function of determining a combination of operation condition patterns for each period in which the cumulative value of the maximum is. The KPI optimization unit 214 calculates the cumulative value of the KPI value up to the predicted life for each combination of the operational condition patterns for each period, and determines the combination of the operational condition patterns for each period that maximizes the cumulative value. The processing flow of the KPI optimization unit 214 is shown in FIG.
  • the internal information collection unit 210 collects data on the current, voltage, and temperature of the battery 99 or the cell 100 from the battery 99 or the cell 100 at an arbitrary cycle, and also the positive electrode potential and the negative electrode potential of the cell 100.
  • the internal state estimation unit 211 estimates the degree of degradation as soon as data collected in a predetermined period is available.
  • the life prediction unit 212 predicts the life of the battery 99 or the cell 100 from the degree of deterioration received from the internal state estimation unit 211.
  • the output distribution unit 213 compares the degree of deterioration of the plurality of batteries 99 or cells 100, observes the restriction on the degree of tracking, and outputs to each battery 99 or cell 100 so as to reduce the amount of deterioration thereafter.
  • a combination of operating conditions for distribution and output distribution is created, and a list of the combinations is created.
  • the KPI optimization unit 214 selects a combination of operation conditions 205 for each period such that the accumulated KPI value until the lifetime becomes the maximum from the list created by the output distribution unit 213.
  • the selected combination of operation conditions 205 is passed to the storage battery control device 102.
  • FIG. 3 is a diagram for explaining the concept of deterioration suppression control that can be implemented as control for extending the life of the battery 99 in this embodiment.
  • FIG. 3 shows the transition of the capacity of the battery 99 or the cell 100 from the start of operation of the battery 99 to the end of its life for each of the case where normal control without deterioration suppression is applied and the case where deterioration suppression control is applied. ing.
  • a solid curve 321 represents a change in capacity when normal control is performed without considering deterioration suppression.
  • a broken curve 322 is a transition of the capacity when the deterioration suppression control is performed. As shown in the figure, the deterioration of the battery 99 or the cell 100 is moderated by performing the deterioration suppression control.
  • the charge / discharge capacity and the accumulated charge / discharge capacity which are integral values of the current, are one of the major deterioration factors.
  • the description will be given by limiting to the cumulative charge / discharge capacity.
  • Deterioration suppression control refers to control that suppresses the deterioration of the capacity of the battery 99 or the cell 100 compared to the normal control by setting the C rate as a control parameter to an arbitrary value.
  • the C rate is a ratio between the value of the current flowing through the battery 99 or the cell 100 and the capacity of the battery 99 or the cell 100. As the integrated value of the C rate is decreased, the effect of suppressing deterioration is enhanced.
  • FIG. 4A is a diagram showing the input waveform prediction data 200.
  • the input waveform prediction data 200 includes a time 200a and a prediction input command 200b.
  • a plurality of times 200a and the value of the predicted input command 200b at that time form an input waveform.
  • the time 200a indicates a certain point in the predicted input waveform.
  • the predicted input command 200b indicates a command value at a certain point in time in the predicted input waveform.
  • FIG. 4B is a diagram showing the tracking degree constraint data 201.
  • the tracking degree constraint data 201 includes a service ID 201a, a lower limit value 201b, an upper limit value 201c, a best value 201d, an index value calculation formula 201e, a cumulative index 201f, and a coefficient 201g.
  • the service ID 201a is an ID for uniquely identifying a plurality of applied ancillary services.
  • the lower limit value 201b is a lower limit value of the following degree allowed by the ancillary service.
  • the upper limit value 201c is an upper limit value of the following degree allowed by the ancillary service.
  • Each ancillary service has different rewards for the ancillary service provider depending on the lower limit of the tracking level.
  • FIG. 4C is a diagram showing the deterioration behavior model 202.
  • the deterioration behavior model 202 includes a model ID 202a and a deterioration behavior model formula 202b.
  • the model ID 202a is an ID for uniquely identifying a plurality of deterioration behavior model expressions depending on the deterioration degree of the battery 99 or the cell 100.
  • the deterioration behavior model expression 202b is an expression obtained by modeling the deterioration amount with respect to the control parameter of the battery 99 or the cell 100.
  • An example of the deterioration behavior model formula is shown in Formula 0.
  • the cumulative discharge capacity is a control parameter that affects the deterioration as described above
  • the cumulative discharge capacity is ⁇ X discharge_s when the discharge capacity in the period s is X discharge_s
  • the deterioration amount R is determined based on the deterioration behavior model equation.
  • the deterioration amount is determined only by the accumulated discharge capacity, but a deterioration behavior model equation including other parameters may be used.
  • FIG. 5A is a diagram showing battery (cell) master data 203.
  • Battery (cell) master data 203 includes battery ID 203a, initial capacity 203b, SOC lower limit 203c, SOC upper limit 203d, and cost 203e.
  • the battery ID 203a is an ID for identifying the battery 99 or the cell 100.
  • the initial capacity 203b is the nominal capacity (Ah) of the battery 99 or the cell 100.
  • the SOC lower limit value 203c is a lower limit value of the SOC that the battery 99 or the cell 100 can take.
  • the SOC upper limit value 203d is an upper limit value of the SOC that the battery 99 or the cell 100 can take.
  • the cost 203e is the price of the battery 99 or the cell 100, and is an initial cost. By distributing this initial cost to each period until the lifetime, the battery cost of each period can be obtained.
  • FIG. 5B is a diagram showing KPI unit data 204.
  • the KPI unit data 204 includes a service ID 204a, a KPI ID 204b, and a KPI calculation formula 204c.
  • the KPI unit data 204 includes information on the ancillary reward unit price determined by the grid operating organization and notified by the grid operating organization system 80.
  • the ancillary reward unit price is a reward amount for the unit power amount, and may be different for each time zone.
  • the service ID 204a is an ID for uniquely identifying a plurality of applied ancillary services.
  • the KPI IV ID 204b is an ID for uniquely identifying the KPI in the applied ancillary service. In the case of the ancillary service, there is a profit as an example of the KPI.
  • the KPI IV ID 204b is an identifier assigned to this KPI.
  • the KPI calculation formula 204c is a formula for calculating the KPI index value.
  • the amount of reward per MWhr is set.
  • a mathematical expression for calculating KPI may be set in the KPI calculation expression 204c.
  • FIG. 6 is a diagram showing the operation condition data 205.
  • the operation condition data 205 includes a battery ID 205a, an operation condition ID 205b, a C rate 205d, a central SOC 205e, a ⁇ SOC 205f, a start time 205g, and an end time 205h.
  • the battery ID 205a is an ID for identifying the battery 99 or cell 100 whose operation condition is to be changed. This battery ID 205a is the same value as the battery ID of the operation change master data.
  • the operation condition ID 205b is an ID for identifying a plurality of operation conditions.
  • the maximum C rate 205d sets the value of the maximum C rate after the operation condition is changed by applying the deterioration suppression control when the deterioration suppression control is applied. Details of the deterioration suppression control will be described later.
  • the central SOC 205e sets the value of the central SOC (State of Charge) after changing the operation condition by applying the degradation suppression control when the degradation suppression control is applied.
  • ⁇ SOC205f sets the value of ⁇ SOC after operation condition change by applying deterioration suppression control when applying deterioration suppression control.
  • ⁇ SOC is the amount of change in SOC.
  • the start time 205g is set to the time at which application of operating conditions starts.
  • the end time 205h is set to a time at which application of the operating conditions is ended.
  • FIG. 7 is a flowchart showing the processing of the output distribution unit 213.
  • step 2131 the output distribution unit 213 acquires the latest deterioration degree of each battery 99 or cell 100 from the life prediction unit 212.
  • the output distribution unit 213 performs the following steps 2132 to 2138 for each of the following degrees of selectable services.
  • step 2132 the output distribution unit 213 acquires the upper limit value and the lower limit value of the tracking degree in the service from the tracking degree constraint data 201. At this time, if there are a plurality of services having different follow-up degrees, the upper limit value and the lower limit value of the follow-up degrees for all services are acquired.
  • step 2133 the output distribution unit 213 acquires an input waveform prediction for an arbitrary period s from the input waveform prediction data 200.
  • step 2134 the output distribution unit 213 acquires the charge / discharge capacity waveform prediction by time-integrating the input waveform prediction in the arbitrary period acquired in step 2133.
  • step 2135 the output distribution unit 213 classifies the capacity waveform prediction acquired in step 2134 according to the magnitude of the inclination and the direction of the capacity change.
  • step 2136 the output distribution unit 213 selects and acquires an appropriate deterioration behavior model 202 based on the deterioration degree of each battery 99 or cell 100, and further, the total deterioration amount is minimized for the waveforms classified in step 2135.
  • the output distribution of the battery 99 or the cell 100 is determined so that When determining output distribution to the battery 99 or the cell 100, the output distribution unit 213 refers to the battery (cell) master data 203. Details will be described with reference to FIG.
  • step 2137 the output distribution unit 213 sets the maximum C rate, the center SOC, and ⁇ SOC of each battery 99 or cell 100 as operating conditions from the output distribution to the battery 99 or cell 100 determined in step 2136.
  • step 2138 the output distribution unit 213 calls the life prediction unit 212, and obtains a predicted deterioration level and a predicted life when the application of the set operation conditions is finished. Upon completion of step 2138, the output distribution unit 213 applies the above steps 2133 to 2138 for the next period s + 1, and repeats the period until the predicted life is reached.
  • steps 2132 to 2138 are performed for each of the following degrees of the plurality of services, and if the operating condition for reducing the deterioration amount is set for the following degrees of each service, the step is Proceed to 22139.
  • step 2139 the output distribution unit 213 transmits, to the KPI optimization unit 214, the operation condition pattern of each battery 99 or cell 100 in each period until the lifetime is reached for each follow-up degree.
  • FIG. 8 is a flowchart showing the detailed processing of step 2136 of the output distribution unit 213.
  • step 21361 the output distribution unit 213 sets the current deterioration degree from the plurality of deterioration behavior models included in the deterioration behavior model 202 based on the transition of the deterioration degree so far for each battery 99 or cell 100. A deterioration behavior model suitable for the closest deterioration degree is acquired.
  • step 21362 the output distribution unit 213 sets the lower limit value of the follow-up degree acquired in step 2132 as the target follow-up degree (target follow-up degree) in the control.
  • the output distribution unit 213 is optimal for the plurality of batteries 99 or cells 100 so that the deterioration amount in the period s is minimized with respect to the charge / discharge capacity waveform classification classified in step 2135 in the period s. Distribute output to.
  • the objective function of the optimal distribution problem solved in this process is a function that minimizes the total amount of deterioration of all the batteries 99 or cells 100 for all classified waveforms, and is expressed by Equation 1.
  • R ij is a deterioration amount due to the distribution of the battery (cell) j for the waveform i.
  • R ij is expressed by Equation 2 where f j is a deterioration behavior model of battery (cell) j and x ij is an output distribution amount of battery (cell) j with respect to waveform i.
  • Expression 2 may be changed according to the deterioration behavior model expression 202b.
  • Expression 3 is a constraint condition representing the relationship between the output distribution amount and the C rate.
  • the output distribution amount is obtained by the product of the battery capacity Q s in the period s and the C rate Crate_ij.
  • Equation 4 is a constraint condition of the output distribution amount of the battery (cell) j of the waveform i, and is a constraint that the distribution is performed in the range of 0 to 1.
  • Equation 5 is a constraint on the C rate of each battery 99 or cell 100.
  • the C rate taken by the battery (cell) j for the waveform i is not less than the C rate lower limit value C rate_min and not more than the C rate upper limit value C rate_max .
  • Equation 6 is an equation showing the remaining capacity and output constraints of each battery 99 or cell 100.
  • the remaining capacity b i, j of battery (cell) j for waveform i is the sum of the remaining capacity of battery (cell) j for waveform i-1 and the output x ij of battery (cell) j for waveform i. It is the value.
  • Equation 7 is an equation showing the capacity limitation of each battery 99 or cell 100.
  • the remaining capacity b i, j of the battery j in the case of the waveform i is in the range from the SOC lower limit SOC min_j to the SOC upper limit SOC max_j .
  • Qs is a capacity in the period s.
  • Expression 8 is a constraint on the target tracking degree. Assuming that the input waveform prediction for an arbitrary period is Y 1 , the output scheduled waveform for an arbitrary period is Y 2 , and the target follow-up degree is k, the correlation function C between Y 1 and Y 2 , which is the similarity between the two functions, is the target follow-up. This is a constraint that the degree is k or more. In this embodiment, the value of the correlation function is used as the tracking degree, but the present invention is not limited to this. The tracking degree may be derived by an arbitrary method according to the applied service.
  • the output distribution unit 213 determines the optimal output distribution of the battery 99 or the cell 100 based on an objective function that satisfies the above-described constraint conditions and minimizes the total deterioration amount in an arbitrary period.
  • step 21364 the output distribution unit 213 updates the target tracking degree.
  • the target follow-up degree is updated by arbitrarily adding N% (N is a positive number).
  • the above steps 21373 to 21374 are repeatedly executed until the target follow-up degree reaches the follow-up degree upper limit value. If no solution satisfying the target tracking degree is found before reaching the upper limit value, the process is terminated.
  • step 21365 the output distribution unit 213 passes the generated output distribution candidate to the processing in the next step 2137.
  • An example of the generated output distribution candidate is shown in FIG.
  • FIG. 9 is a diagram showing an example of output distribution candidates.
  • the output distribution unit 213 outputs a candidate for output distribution of each battery 99 that minimizes the total amount of deterioration while achieving a predetermined degree of tracking in an arbitrary period.
  • FIG. 8 shows output distribution candidates when there are three batteries with different deterioration levels and six classified waveforms in the range of tracking degrees 80% to 100% as an example. The number of batteries or cells and the number of classified waveforms are not limited.
  • FIG. 9 is a diagram showing output distribution of each battery 99 or cell 100 for each classified waveform.
  • output distribution candidates 1, 2, and 3 are shown.
  • As each output distribution candidate a table in which the classified waveforms are arranged in the horizontal direction and a plurality of batteries 99 are arranged in the vertical direction is shown.
  • Output distribution candidate 1 is an output distribution candidate for battery 99 that satisfies 100% of the following degree
  • output distribution candidate 2 is an output distribution candidate for battery 99 that satisfies 90% of the following degree
  • output distribution candidate 3 has a following degree of 8 This is a candidate for output distribution of the battery 99 satisfying the percentage.
  • the battery 99 or cell 100 having a low degree of deterioration is 0.7, and the battery 99 or cell 100 having a medium degree of deterioration is used. It is shown that distributing the output at a rate of 0.1 to the battery 99 having a large deterioration level of 0.1 is the smallest deterioration amount in the plurality of batteries 99 or the cell 100 as a whole.
  • the output distribution unit 213 refers to the output distribution candidate, determines output distribution according to the slope and direction of the waveform and the degree of deterioration of each battery 99 or cell 100, and sends the determined output distribution amount to the KPI optimization unit 214. Output. By performing the output distribution process described above, the output distribution is performed so that the amount of deterioration is minimized as a whole of the plurality of batteries 99 while satisfying the target follow-up degree without concentrating the power to be output to the battery 99 having a small deterioration degree. It can be determined.
  • FIG. 10 is a flowchart showing the processing of the KPI optimization unit 214.
  • step 2141 the KPI optimization unit 214 acquires the KPI calculation formula 204 c from the KPI unit data 204.
  • step 2142 the KPI optimization unit 214 acquires a list of operation condition patterns of each battery 99 or cell 100 from the output distribution unit 213 until the predicted lifetime for the degree of tracking of each service.
  • the KPI optimization unit 214 calculates the follow-up degree and the KPI for the follow-up degree of each service and the KPI value of the period s when the operation condition is applied to the input waveform prediction for each period s. Calculate from the formula.
  • the KPI value of the period s can be obtained by an operation based on the KPI calculation formula using the possible bid amount, the degree of tracking, the parameter of the KPI calculation formula 204c, and other coefficients.
  • the KPI value of the ancillary service is the balance of the ancillary service provider, the balance of the arbitrary period s is calculated.
  • the balance is the sum of income and expenditure.
  • the income is a reward by the ancillary service
  • the expenditure is the sum of the cost (battery cost) expecting the deterioration of the battery 99 and the cost (operation condition change cost) by changing the operation condition.
  • the ancillary service reward for an arbitrary period s can be obtained by, for example, the product of the bid amount, the degree of tracking, and the ancillary reward unit price.
  • the calculation method of the ancillary service reward shall be in accordance with a predetermined calculation method determined by the grid operating organization that organizes the ancillary service.
  • the expenditure may include various costs such as the cost for installing the battery 99 and the cost for replacing the battery 99 in addition to the battery cost and the operating condition change cost.
  • the KPI optimizing unit 214 selects a service that maximizes the cumulative KPI value until the predicted lifetime, and determines an optimum combination of operation condition patterns of the battery 99 or the cell 100 for each period.
  • the objective function of the optimization problem solved in this process is a function that maximizes the cumulative KPI value.
  • the cumulative KPI value is the cumulative balance.
  • Equation 9 defines an optimization problem that maximizes the cumulative balance.
  • Expression 10 is a constraint expression regarding the cumulative balance ⁇ s Ps. Assuming that the ancillary reward unit price for period s is R s , the bid amount for period s is b s , the tracking degree for period s is k s , and the battery cost is C init , the balance Ps for any period s is the ancillary reward amount (R s , B s , k s ) and battery cost C init . ks is given for each operation condition candidate list generated by the output distribution unit 213. Note that the definition of the formula for calculating the cumulative balance in this embodiment is an example, and may be changed for each service to be applied in accordance with the KPI unit data 204.
  • Equation 11 is a constraint equation related to the bid amount.
  • the bid amount of the arbitrary period s is the sum of the maximum discharge output and the maximum charge output of the arbitrary period s.
  • the maximum discharge output is obtained from the product of the maximum discharge C rate and capacity, and the maximum charge output is obtained from the product of the maximum charge C rate and capacity.
  • the maximum discharge C rate is a maximum C rate at the time of discharge in an arbitrary period s, and is given for each output distribution candidate list generated by the output distribution unit 213.
  • the maximum charge C rate is also the maximum C rate at the time of charging in an arbitrary period s, and is similarly given for each output distribution candidate list.
  • Equation 12 is a constraint equation regarding battery cost.
  • the battery cost C init is the cost of the entire battery, and is the sum of the costs C init_j of all the batteries (cells) j.
  • step 2416 the KPI optimization unit 214 sets operation conditions for the output distribution list for each period output in step 2415. Specifically, by synthesizing the output waveform of each battery 99 or cell 100 from the output distribution, the maximum C rate, center SOC, ⁇ SOC, period start time, and period end time of each battery 99 or cell 100 are obtained. .
  • the first embodiment of the present invention compares the degree of deterioration of each battery 99 or cell 100 with respect to a plurality of batteries 99 or cells 100, and observes the following conditions of the degree of tracking, in units of batteries 99 or cells 100. It is possible to determine an output distribution that minimizes the amount of degradation and an operation plan that maximizes the KPI value in the case of ancillary service.
  • recovery capacity is also output. These data are output as operation condition data 205.
  • the life prediction unit 212 calculates the predicted life based on the deterioration behavior model 202 and the deterioration degree of the plurality of batteries 99 or the cells 100 (power storage devices).
  • the output distribution unit 213 follows the input waveform in which the combined output of the plurality of power storage devices is expected, and distributes the output to each power storage device every predetermined period so that the period until the predicted life of the power storage devices becomes longer.
  • a list including one or more combinations of operation conditions of each power storage device is generated.
  • the KPI optimization unit 214 selects, from the list, a combination of operation conditions that maximizes the cumulative value of the KPI value until the predicted lifetime.
  • the combined output of the power storage device follows the predicted input waveform, a plurality of power storage devices can be extended in consideration of deterioration, and a combination of operating conditions that optimizes the index value can be selected.
  • the power storage device can be appropriately controlled to extend the life and improve the index value.
  • FIG. 11 is a diagram for explaining the extension of life and the improvement of the index value according to the present embodiment.
  • the horizontal axis of FIG. 11 is a period (year) in which energy storage is used, and the vertical axis is an index value accumulated value (gain or loss of balance).
  • the solid curve indicates that when the deterioration suppression control is not performed, the battery 99 or the cell 100 reaches the end of life at the EOL1, and the accumulated profit of KPI1 is obtained until then.
  • the broken line curve indicates that when the deterioration suppression control is performed and an appropriate operation condition is used so that the cumulative value of the KPI value becomes the maximum, the battery 99 or the cell 100 reaches the end of life in the EOL2, and the cumulative profit of KPI2 until then. Is obtained.
  • the lifetime is extended, and the cumulative value of KPI values obtained during the lifetime is increasing.
  • the dashed-dotted curve shows a comparative example in which the battery 99 or the cell 100 reaches the end of life in the EOL3 when the deterioration suppression control is performed but the inappropriate operating condition is used, and the accumulated profit of KPI3 is obtained until then. It is. Although the life has been extended, the cumulative value of the KPI value obtained during the period up to the life has decreased.
  • the output distribution unit 213 sets the operating condition of each power storage device so that the combined output of the plurality of power storage devices follows the input waveform and the total amount of deterioration of the power storage devices is reduced. Generate a list of combinations. Since the combination of operation conditions is selected and listed so that the total deterioration amount of the plurality of power storage devices is reduced, it is possible to appropriately control the plurality of power storage devices as a whole to extend the life.
  • the output distribution unit 213 operates each power storage device such that the combined output follows the input waveform with each tracking level and the total deterioration amount of the power storage device is reduced. Generate a combination of conditions and include them in the list.
  • the KPI optimization unit 214 selects, from the list, a combination of operating conditions for the tracking degree that maximizes the cumulative value of the index value until the predicted lifetime.
  • an appropriate tracking degree can be selected from various tracking degrees.
  • the deterioration behavior model 202 is information that enables calculation of the deterioration amount of the power storage device. Then, the output distribution unit 213 calculates the ratio of each output of the plurality of power storage devices in the combined output so that the total amount of deterioration of the plurality of power storage devices calculated based on the deterioration behavior model 202 is minimized. decide. Since the output distribution is determined so that the total amount of deterioration is minimized, it is possible to appropriately suppress the deterioration of the entire plurality of power storage devices with easy processing.
  • a plurality of deterioration behavior models 202 corresponding to a plurality of deterioration degrees are held.
  • the life prediction unit 212 selects and uses one of the plurality of deterioration behavior models 202 based on the degree of deterioration estimated by the internal state estimation unit 211.
  • a plurality of deterioration behavior models are prepared in advance for power storage devices with different deterioration behaviors depending on the deterioration degree, and a suitable one according to the deterioration degree is selected and used. A long life can be predicted.
  • the energy storage is used to transfer power to the power system as an ancillary service
  • the index value is a value based on a reward for power supply in the ancillary service. Therefore, in the ancillary service, the total amount of reward until the power storage device reaches the end of its life can be maximized.
  • Example 2 illustrates a case where storage battery capacity recovery control is applied in an ancillary service provider's system.
  • the system of the ancillary service provider in the second embodiment can perform the capacity recovery control in addition to the deterioration suppression control applied in the first embodiment as the longevity control.
  • the difference between the second embodiment and the first embodiment will be mainly described.
  • Capacity recovery control refers to control that increases the capacity that can be stored by replenishing a battery or cell with an arbitrary amount of an electrically conductive substance at an arbitrary timing.
  • a lithium ion battery cell in addition to a positive electrode and a negative electrode, a lithium ion battery cell is provided with a lithium ion replenishing electrode that releases lithium ions in advance, so that the capacity of the lithium ion battery can be restored. It becomes.
  • the cost of the lithium ion replenishment electrode as compared with the normal cell, the cost of the entire cell becomes high. The cost also changes depending on the capacity to be recovered.
  • FIG. 12 is a conceptual diagram of extending battery life through capacity recovery control.
  • FIG. 12 shows the transition of the capacity of the battery or cell from the start of battery operation to the end of its life for each case where normal control without capacity recovery control is applied and when capacity recovery control is applied. Yes.
  • a solid curve 1111 in the figure is a transition of capacity in the case of normal control.
  • a broken curve 1112 in the figure represents a transition of the capacity when the capacity recovery control is performed.
  • FIG. 13 is a block diagram illustrating the configuration of the energy storage operation planning system according to the second embodiment.
  • FIG. 13 shows a functional configuration of the energy storage operation planning system.
  • the energy storage operation planning system of the second embodiment is different from that of the first embodiment in that the operation condition change data 206 is held and the KPI optimization unit 214 refers to it.
  • the data structure of the operation condition data 205 of the second embodiment is partially different from that of the first embodiment. Details will be described with reference to FIGS.
  • a process for calling the output distribution unit 213 is added during the process of the KPI optimization unit 214. Details will be described with reference to FIG.
  • FIG. 14A is a diagram illustrating the operation condition change data 206 according to the second embodiment.
  • the operating condition change data 206 includes information on battery ID 206a, capacity recovery upper limit 206b, capacity recovery lower limit 206c, required time 206d, and cost 206e.
  • the battery ID 206a is an ID for identifying the battery 99 or the cell 100.
  • the capacity recovery upper limit value 206b is an upper limit value of the capacity that can be recovered by the capacity recovery control.
  • the capacity recovery lower limit value 206c is a lower limit value of the capacity that can be recovered by the capacity recovery control.
  • the required time 206d is a required time for the capacity recovery control. During this time, cell operation is stopped.
  • a fixed numerical value may be set for the required time, or a calculation formula for the required time may be set when the required time varies depending on the recovery amount.
  • the cost 206e is a supplementary pole cost necessary for capacity recovery control.
  • a fixed numerical value may be set for the cost 206e, and when the cost varies depending on the recovery amount, a cost calculation formula may be set.
  • FIG. 14B is a diagram illustrating the operation condition data 205 in the second embodiment.
  • information on the recovery capacity 205c is added to that of the first embodiment.
  • the recovery capacity 205c is set with the capacity (recovery amount) to be recovered in the capacity recovery control determined by the KPI optimization unit 214.
  • the period for performing the capacity recovery control is from the start time 205g to the end time 205h.
  • FIG. 15 is a flowchart illustrating the process of the KPI optimization unit 214 in the second embodiment. Steps 214B1, 214B2, and 214B3 in FIG. 15 are processes that were not in the first embodiment.
  • step 2141 the KPI optimization unit 214 acquires the KPI calculation formula 204 c from the KPI unit data 204.
  • step 214B1 the KPI optimizing unit 214 obtains the capacity recovery upper limit value 206b, the capacity recovery lower limit value 206c, the required time 206d, and the cost 206e as the constraint conditions for capacity recovery control from the operation condition change data 206. get.
  • step 2142 the KPI optimization unit 214 obtains a list of operation condition pattern combinations that are candidates for the operation conditions of the battery (cell) until the predicted life from the output distribution unit 213.
  • steps 214B2, 214B3, and 2144 are performed for all combinations of cells, periods, and recovery amounts that can be subjected to capacity recovery control.
  • the KPI optimization unit 214 sets the cell 100 and the period for which capacity recovery control is performed, and the initial value of the recovery amount. If the capacity recovery control period of the cell j is st, the recovery amount (%) is m, and the capacity of the cell j before the capacity recovery control is Q st ⁇ 1, j , the capacity recovery control The capacity Q st, j is expressed as in Expression 13. Q ini, j is the initial capacity of cell j.
  • step 21B3 the KPI optimization unit 214 calls the output distribution unit 213, and recalculates the output distribution based on the capacity Q st, j after the capacity recovery for the cell group in the period from the capacity recovery to the predicted lifetime. To obtain a list of combinations of operation condition patterns.
  • step 2144 the KPI optimizing unit 214 determines an optimum operation condition pattern for each period in which the accumulated KPI value from the period s to the predicted lifetime is maximum from the list.
  • Example 2 Other constraints in Example 2 are the same as those in Example 1.
  • Step 2245 the KPI optimizing unit 214 determines that the accumulated KPI value until the predicted lifetime is reached. Outputs the maximum combination of operating conditions for each period.
  • step 214B2, 214B3, 2144 about the cell which performs capacity
  • recovery amount was shown, it is promising, such as when the number of combinations becomes enormous. You may decide to perform the process of step 214B2, 214B3, 2144 focusing on a combination.
  • the cumulative KPI value is maximized by performing the capacity recovery control processing described here in addition to the deterioration suppression control processing performed in the first embodiment. It is possible to determine operation conditions including control parameters for deterioration suppression control, cells for which capacity recovery control is performed, timing, and recovery amount.
  • the KPI optimizing unit 214 causes the output distribution unit 213 to recalculate the operation condition when at least one capacity recovery of the battery 99 or the cell 100 (power storage device) is performed, and the capacity recovery is performed. Create a new list of combinations of operating conditions. Then, the KPI optimization unit 214 selects a combination of operation conditions that maximizes the cumulative index value from the new list. When performing capacity recovery processing, the combination of operating conditions that maximizes the cumulative value of the index value can be selected, so the index value can be improved through appropriate control of multiple power storage devices, including capacity recovery of power storage devices Can be made.
  • operation condition change data including the upper limit value and the lower limit value of the recovery amount, the time required for the capacity recovery, and the cost required for the capacity recovery process are set in advance.
  • the KPI optimizing unit 214 sets a recovery amount between the upper limit value and the lower limit value, sets an execution timing of capacity recovery after the current time, and causes the output distribution unit 213 to recalculate the operation conditions.
  • the KPI optimization unit 214 accumulates an index value including a loss due to failure to supply power in a required time and a cost required for capacity recovery from a new list of recalculated operating conditions. Select the combination of operating conditions that maximizes the value.
  • the index value can be improved by appropriately controlling the plurality of power storage devices.
  • a system capable of capacity recovery control in addition to deterioration suppression control is exemplified as the life extension control.
  • the present invention is not limited to this.
  • a system in which capacity recovery control is applied without applying deterioration suppression control is also possible.
  • the third embodiment the case where the battery life extension control similar to that of the second embodiment is applied to energy storage of an electric vehicle such as an EV (electric vehicle) or HEV (hybrid electric vehicle) that travels with electric power from the mounted storage battery.
  • an electric vehicle such as an EV (electric vehicle) or HEV (hybrid electric vehicle) that travels with electric power from the mounted storage battery.
  • EV electric vehicle
  • HEV hybrid electric vehicle
  • the behavior of the electric vehicle responds to the driving operation by the driver without delay as the followability of the power output with respect to the charge / discharge instruction is higher. Therefore, for example, the responsiveness of the behavior of the electric vehicle to the driving operation or the comfort of the electric vehicle user (driver) due to the behavior can be set as the KPI value.
  • the comfort is a KPI value
  • the comfort is directly proportional to the follow-up degree in a region where the follow-up degree is a predetermined value or less, and the comfort is constant in a region where the follow-up degree is higher than the predetermined value.
  • the KPI value increases as the follow-up degree is increased, but the deterioration of the storage battery proceeds faster. For this reason, the cumulative KPI value is increased while keeping the following degree within a predetermined constraint, and the deterioration of the storage battery is reduced by the deterioration suppression control and / or the capacity recovery control.
  • FIG. 16 is a block diagram showing the configuration of the entire system mounted on the vehicle in the third embodiment.
  • a plurality of batteries 99, a storage battery control device 102, and an energy storage operation planning system 103 are mounted on the vehicle 500.
  • the plurality of batteries 99, the storage battery control device 102, and the energy storage operation planning system 103 are connected to each other via a communication line 501.
  • Each battery 99 includes a plurality of cells 100.
  • the energy storage operation planning system 103 has the functional configuration shown in FIG. 2 as in the first and second embodiments, and holds the data shown in FIGS. 4A to 4C, 5A, 5B, and 6. .
  • a charge / discharge waveform predicted based on the past travel history is set.
  • an upper limit value and a lower limit value are set for the tracking degree for the electric vehicle.
  • a calculation formula for KPI which is a comfort index for electric vehicle users, is set.
  • the calculation formula of the comfort index value is an expression including the following degree as a parameter as exemplified above.
  • the energy storage supplies power to the electric vehicle.
  • the index value is a value based on the response of the behavior of the electric vehicle to the driving operation. Therefore, the cumulative value (cumulative KPI value) of the responsiveness of the electric vehicle or the user comfort index value based on it until the power storage device reaches the end of its life can be maximized.
  • the operation condition including the control parameter for the deterioration suppression control that maximizes the cumulative KPI value in the electric vehicle system, the cell for performing the capacity recovery control, the timing, and the recovery amount. Can be determined.
  • SYMBOLS 10 Ancillary service provider system, 100 ... Cell, 101 ... PCS, 102 ... Storage battery control apparatus, 103 ... Energy storage operation planning system, 200 ... Input waveform prediction data, 200a ... Time, 200b ... Prediction input command, 201 ... Tracking degree constraint data, 201b ... lower limit value, 201c ... upper limit value, 201d ... best value, 201e ... index value calculation formula, 201f ... cumulative index, 201g ... coefficient, 202 ... degradation behavior model, 202a ... model ID, 202b ... degradation Behavior model formula, 203 ... Master data, 203b ... Initial capacity, 203c ...

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

L'invention fait intervenir une unité de collecte d'informations internes qui obtient des données de collecte obtenues en mesurant chaque dispositif de stockage d'électricité. Une unité d'estimation d'état interne estime le degré de dégradation de chaque dispositif de stockage d'électricité d'après les données de collecte. Une unité d'estimation de durée de vie calcule une durée de vie estimée obtenue en estimant, d'après un modèle de comportement de dégradation indiquant le comportement de dégradation des dispositifs de stockage d'électricité et le degré de dégradation, la durée pendant laquelle chaque dispositif de stockage d'électricité peut fournir une alimentation. Une unité de répartition de sortie génère une liste comprenant une ou plusieurs combinaisons des conditions de fonctionnement des dispositifs respectifs de stockage d'électricité, dans laquelle les répartitions de sortie vers les dispositifs respectifs de stockage d'électricité sont spécifiées pour chaque période prédéterminée de telle sorte que la sortie combinée des dispositifs de stockage d'électricité suive une forme d'onde d'entrée estimée et des périodes jusqu'à ce que les durées de vie estimées des dispositifs de stockage d'électricité deviennent longues. Une unité d'optimisation sélectionne une combinaison des conditions de fonctionnement dans la liste, ladite combinaison maximisant la valeur cumulée d'une valeur d'index jusqu'à la durée de vie estimée, ladite valeur d'index étant utilisée pour évaluer les avantages d'une alimentation.
PCT/JP2014/072294 2014-08-26 2014-08-26 Système de planification de fonctionnement de stockage d'énergie et procédé de détermination de conditions de fonctionnement Ceased WO2016030967A1 (fr)

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