Energy storage system control device and energy storage system control method

Abstract

The energy storage system control device disclosed herein includes a communication unit that receives external power production and internal power consumption; a user interface unit that receives input of a cost function selection command from a user; and a control unit that determines an objective function based on the cost function selected by the user and performs optimization of battery control based on the external power production, the internal power consumption, and the objective function. The control unit performs the optimization of battery control based on a first time point, determines a predicted value of battery charge / discharge power based on the optimization of battery control performed at the first time point, determines a prediction interval that includes the predicted value of battery charge / discharge power, and re-executes the optimization of battery control based on the fact that the actual value of battery charge / discharge power is outside the range of the prediction interval.

Description

[Technical Field]

[0001] This application claims priority under Korean Patent Application No. 10-2023-0110622 dated August 23, 2023, and all content disclosed in the said patent application is incorporated herein by reference. The embodiments disclosed herein relate to an energy storage system control device and an energy storage system control method. [Background technology]

[0002] An Energy Storage System (ESS) is a device that stores generated electricity in a storage device such as a battery and supplies it when needed, thereby improving the efficiency of electricity use. Such an ESS can store electricity generated from renewable energy sources such as solar and wind power, or electricity transmitted from power plants, in batteries. During nighttime hours when electricity consumption is low, electricity is stored, and during daytime hours when electricity consumption is high, the stored electricity can be used.

[0003] Efficient scheduling of charging and discharging is necessary to effectively allocate power usage in energy storage systems, but conventional methods have been limited by the inability to account for uncertainties in predicting renewable energy generation and consumption, resulting in errors. [Overview of the project] [Problems that the invention aims to solve]

[0004] According to one embodiment disclosed herein, an energy storage system control device and an energy storage system control method are provided that take into account the uncertainty of predictions for renewable energy generation and consumption in order to improve the efficiency of the energy storage system.

[0005] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other technical problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0006] An energy storage system control device according to one embodiment includes a communication unit that receives external power production and internal power consumption, a user interface unit that receives input of a cost function selection command from a user, and a control unit that determines an objective function based on the cost function selected by the user and performs optimization of battery control based on the external power production, the internal power consumption, and the objective function. The control unit performs the optimization of battery control based on a first time point, determines a predicted value of battery charge / discharge power based on the optimization of battery control performed at the first time point, determines a prediction interval that includes the predicted value of battery charge / discharge power, and re-executes the optimization of battery control based on the fact that the actual value of battery charge / discharge power is outside the range of the prediction interval.

[0007] The control unit can adjust the variables for optimizing the battery control based on the fact that the actual value of the battery charging and discharging power is outside the range of the prediction interval. The control unit can re-execute the optimization of the battery control based on the variables adjusted for the optimization of the battery control.

[0008] The control unit can receive weights for determining the priority of the cost function from the user via the user interface unit, and can determine the objective function by a weighted sum of the weights and the cost function.

[0009] The user interface unit further includes an input unit, the input unit may consist of a UI (User Interface) in which the weight ratio is adjusted based on the user's input.

[0010] The control unit can recommend the weight to the user based on the fact that the weight has not been entered by the user within a predetermined time period. The control unit can generate combinations based on the number of cost functions and the interval information of the weights, and recommend to the user the combination with the lowest power purchase cost among the combinations.

[0011] The aforementioned cost function may include a function that minimizes the difference between the backup State of Charge (SOC) used in the home energy storage system and the user's required SOC. The aforementioned cost function may include a function that minimizes the difference between the current indoor temperature used by the home energy storage system and the user's required temperature.

[0012] The control unit can modify the charge / discharge schedule based on the re-executed battery control optimization and perform charging and discharging based on the modified charge / discharge schedule.

[0013] An energy storage system control method according to one embodiment includes the steps of receiving an external power production amount and an internal power consumption amount, receiving a cost function selection command input from a user, determining an objective function based on the cost function selected by the user, and performing battery control optimization based on the external power production amount, the internal power consumption amount, and the objective function, wherein the step of performing battery control optimization includes the step of performing the battery control optimization with reference to a first time point, determining a predicted value of battery charge / discharge power based on the battery control optimization performed at the first time point and determining a prediction interval including the predicted value of battery charge / discharge power, and re-performing the battery control optimization based on the fact that the actual value of battery charge / discharge power is outside the range of the prediction interval.

[0014] An energy storage system control method according to one embodiment may further include the step of adjusting a variable for optimizing the battery control based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

[0015] An energy storage system control method according to one embodiment may further include the step of re-executing the optimization of the battery control based on variables adjusted for the optimization of the battery control.

[0016] The step of determining the objective function may include receiving weights from the user via the user interface unit to determine the priority of the cost function, and determining the objective function by a weighted sum of the weights and the cost function.

[0017] An energy storage system control method according to one embodiment may further include the step of adjusting the weight ratio based on user input input to an input unit included in the user interface unit.

[0018] An energy storage system control method according to one embodiment may further include the step of recommending the weights to the user based on the fact that the weights have not been input by the user within a predetermined time period.

[0019] The step of recommending the weights to the user may include the steps of generating combinations that can be generated based on the number of cost functions and interval information of the weights, and recommending to the user the combination with the lowest power purchase cost among the combinations.

[0020] The aforementioned cost function may include a function that minimizes the difference between the backup State of Charge (SOC) used in the home energy storage system and the user's required SOC. Said cost function may include a function that minimizes the difference between the current indoor temperature utilized in a household energy storage system and a user's requested temperature.

[0021] According to one embodiment, the energy storage system control method may further comprise the steps of: correcting a charge-discharge schedule based on the re-executed optimization of battery control; and performing charge and discharge based on the corrected charge-discharge schedule.

Effect of the Invention

[0022] According to the energy storage system control apparatus of one embodiment, to optimize the energy storage system, uncertainty in prediction of renewable energy power generation amount and power consumption amount can be taken into consideration, thereby reducing errors in optimization of the energy storage system.

[0023] According to the energy storage system control apparatus of one embodiment, a user can select an objective function and weights in the process of optimizing the energy storage system, thereby realizing optimization that conforms to the user's intention.

Brief Description of Drawings

[0024] [Figure 1] A schematic configuration of an energy storage system control apparatus and a power operation system according to one embodiment is shown. [Figure 2] A block diagram showing the configuration of an energy storage system control apparatus according to one embodiment is shown. [Figure 3] A flow of determining whether to re-execute optimization by a control unit of an energy storage system control apparatus according to one embodiment is schematically shown. [Figure 4] Conditions for optimizing an energy storage system by the energy storage system control apparatus according to one embodiment are shown as a graph. [Figure 5] A user interface unit of an energy storage system control apparatus according to one embodiment is shown. [Figure 6]A table is shown for recommending weights in an energy storage system control device according to one embodiment. [Figure 7] A flowchart of an energy storage system control method according to one embodiment is shown. [Figure 8] Following Figure 7, a flowchart of an energy storage system control method according to one embodiment is shown. [Modes for carrying out the invention]

[0025] The various embodiments disclosed in this document will be described in detail below with reference to the attached drawings. In this document, the same reference numerals are used for identical components in the drawings, and redundant descriptions of identical components will be omitted.

[0026] With respect to the various embodiments disclosed herein, any specific structural or functional descriptions are provided merely as examples for the purpose of illustrating the embodiments, and the various embodiments disclosed herein may be implemented in various forms and should not be construed as being limited to the embodiments described herein.

[0027] Expressions such as “first,” “second,” “first,” or “second,” as used in various embodiments, may modify various components regardless of their order and / or importance, and do not limit such components. For example, without departing from the scope of rights of the embodiments disclosed herein, the first component may be named the second component, and similarly, the second component may be renamed the first component.

[0028] The terminology used in this document is used solely to describe specific embodiments and is not intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise.

[0029] All terms used herein, including technical or scientific terms, may have the same meaning as those generally understood by a person of ordinary skill in the art of the embodiments disclosed herein. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning as in the context of the relevant art, and not as ideal or overly formal unless explicitly defined herein. In some cases, terms defined herein should not be interpreted in a way that excludes the embodiments disclosed herein.

[0030] Figure 1 schematically shows the configuration of an energy storage system control device and a power operation system according to one embodiment. Referring to Figure 1, the Energy Storage System 20 (ESS) may also refer to a system that stores surplus electricity separately in a battery and supplies it when needed, or it may mean a configuration that can solve the electricity supply and demand problem by storing electricity that would otherwise be wasted at night and supplying it during peak hours.

[0031] The energy storage system (ESS) 20 can receive and store electricity from a power grid 21, a renewable energy generation module 22, or an electric vehicle 23, and the battery module included in the energy storage system (ESS) 20 may consist of a configuration that combines series and parallel connections of multiple battery cells.

[0032] Here, the power grid 21 can mean a network that is interconnected with power suppliers that generate and supply electricity. In other words, the power grid 21 can mean a network that is interconnected to supply electricity from power generators to electricity consumers.

[0033] Furthermore, the renewable energy generation module 22 may refer to a module that generates electrical energy using solar, wind, geothermal, ocean, and bioenergy sources.

[0034] The energy storage system (ESS) 20 may further include a power converter (not shown) that can convert alternating current power supplied from the power grid 21 into direct current power and store the direct current power in a battery module. The power converter (not shown) can also convert the output of the battery module or the output of the renewable energy generation module 22 into alternating current power and supply it to the load 24 or transmit it to the power grid 21.

[0035] The energy storage system control device 1 can control the charging and discharging of the battery module, monitor the voltage, current, and temperature of the battery module, and control and manage it to prevent overcharging and over-discharging, etc. For example, it may include a BMS (Battery Management System).

[0036] The energy storage system control device 1 may be configured to be connected to the power grid 21, renewable energy generation modules 22, electric vehicles 23, loads 24, and the inputs and outputs of the energy storage system (ESS) 20, and to control the distribution and connection of mutual inputs and outputs according to the power status of the energy storage system (ESS) 20.

[0037] The detailed configuration of the energy storage system control device 1 according to one embodiment will be described below with reference to Figure 2. Figure 2 shows a block diagram illustrating the configuration of an energy storage system control device according to one embodiment.

[0038] Referring to Figure 2, an energy storage system control device 1 according to one embodiment includes a control unit 100 containing at least one processor 110 and memory 120, a communication unit 200, and a user interface unit 300, and can control the energy storage system 20 by communicating with an external device 2 via the communication unit 200. In one embodiment, the energy storage system control device 1 may refer to all electronic devices, including the processor 110 and the memory 120.

[0039] The communication unit 200 may include a wireless communication unit 210 and a wired communication unit 220 for communicating with an external device 2. The communication unit 200 can send and receive various data related to the cost function and weights from a separately provided user interface unit 300.

[0040] The wireless communication unit 210 may include at least one of a short-range communication module and a long-range communication module. The near-field communication module can communicate with an external device 2 adjacent to the energy storage system control device 1 using a near-field communication method. Here, the near-field communication module can utilize one of the following communication methods: Bluetooth®, BLE (Bluetooth Low Energy), infrared communication (IrDA, Infrared Data Association), Zigbee, Wi-Fi, Wi-Fi Direct, UWB (Ultra Wideband), or near-field communication (NFC).

[0041] The long-distance communication module includes communication modules that perform various types of long-distance communication and may include a mobile communication unit 200. The mobile communication unit 200 can send and receive radio signals with at least one of a base station, an external terminal, or an external device 2 on a mobile communication network. The long-distance communication module can also communicate with external device 2 or other electronic devices 2 via a nearby access point (AP). The access point (AP) can connect the local network (LAN) to which the energy storage system control device 1 is connected to a wide area network (WAN) to which the communication server is connected. As a result, the energy storage system control device 1 can connect to the communication server via the wide area network (WAN) and communicate with the external device 2.

[0042] The wired communication unit 220 can connect to a wired communication network and communicate with an external device 2 via the wired communication network. For example, the wired communication unit 220 can connect to a wired communication network via Ethernet (IEEE 802.3 technical standard) or via CAN communication, and can send and receive data with the external device 2 via the wired communication network.

[0043] An energy storage system control device 1 according to one embodiment may include a user interface unit 300 having an input unit 310 and an output unit 320.

[0044] The input unit 310 can be implemented by at least one input means from among a touch screen, a push button, a membrane button, a dial, and a slider switch, but is not limited thereto.

[0045] The output unit 320 can be implemented using, but is not limited to, display means such as a plasma display panel (PDP), liquid crystal display (LCD) panel, light-emitting diode (LED) panel, organic light-emitting diode (OLED) panel, active-matrix organic light-emitting diode (AMOLED) panel, and curved display panel.

[0046] The user interface unit 300 can provide an interface for sending and receiving data by connecting the input unit 310 and the output unit 320 to the processor 110.

[0047] The memory 120 can store various information necessary for operating the energy storage system control device 1. Specifically, the memory 120 can store the operating system and programs necessary for operating the energy storage system control device 1, or it can store the data necessary for operating the energy storage system control device 1.

[0048] Specifically, the memory 120 can store various programs relating to the amount of power generated by the renewable energy generation module 22 and the amount of power consumed by the load 24 or electric vehicle 23. The memory 120 may also store various battery data, such as voltage, current, temperature, and characteristic value data for each battery cell. Furthermore, memory 120 can store the cost function selected by the user and the combination of cost function and weights for recommending weights.

[0049] Memory 120 may include volatile memory 120 such as S-RAM (Static Random Access Memory) and D-RAM (Dynamic Random Access Memory) for temporarily storing data. Memory 120 may also include non-volatile memory 120 such as ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read Only Memory) for long-term data storage.

[0050] The processor 110 outputs control signals and controls the energy storage system control device 1 overall. The processor 110 may include one or more CPUs (central processing units) and GPUs (Graphics Processing Units). In this case, the processor 110 may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor 110 and a memory 120 storing a program executable by the microprocessor 110.

[0051] The aforementioned memory 120 and processor 110 can be included in the control unit 100, and the control unit 100 can control the aforementioned components to determine abnormal battery data among the battery data.

[0052] Specifically, the control unit 100 can determine an objective function based on a cost function selected by the user, and can perform battery control optimization based on the external power output, internal power consumption, and objective function.

[0053] Furthermore, the control unit 100 can perform optimization of the battery control based on a first time point, determine a predicted value of the battery charge / discharge power based on the battery control optimization performed at the first time point, determine a prediction interval that includes the predicted value of the battery charge / discharge power, and re-execute the battery control optimization based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

[0054] Furthermore, the control unit 100 can adjust the variables for optimizing battery control based on whether the actual value of the battery charge / discharge power is outside the predicted range, and the control unit 100 can re-execute the battery control optimization based on the variables adjusted for battery control optimization.

[0055] Furthermore, the control unit 100 can modify the charge / discharge schedule based on the re-executed battery control optimization, and perform charging and discharging based on the modified charge / discharge schedule.

[0056] Furthermore, the control unit 100 can receive weights from the user via the user interface unit 300 to determine the priority of the cost function, and can determine the objective function by the weighted sum of the weights and the cost function.

[0057] Furthermore, the control unit 100 can recommend weights to the user based on the fact that no weights have been input by the user within a predetermined time period, and the control unit 100 can generate combinations that can be generated based on the number of cost functions and interval information of the weights, and can recommend to the user the combination with the lowest power purchase cost among the generated combinations.

[0058] As a result, the energy storage system control device 1 according to one embodiment can reduce uncertainties that may occur during the battery optimization process and optimize the charge and discharge schedule of the energy storage system (ESS) 20 while taking into account the user's intentions, thereby improving the reliability of the optimization and significantly increasing user satisfaction.

[0059] Figure 3 schematically shows the process by which the control unit of an energy storage system control device according to one embodiment decides whether or not to re-execute the optimization. Referring to Figure 3, the control unit 100 of the energy storage system control device 1 according to one embodiment can decide whether or not to re-execute optimal control of the battery based on the external power output, internal power consumption, and cost function.

[0060] Specifically, external power production refers to the amount of electricity produced from the power grid 21, renewable energy generation modules 22, and electric vehicles 23; internal power consumption refers to the amount of electricity consumed by the power grid 21, electric vehicles 23, and loads 24; and the cost function may refer to a function received from the user via the input unit 310 that minimizes errors.

[0061] Subsequently, the weight determination unit 101 of the control unit 100 can determine the weights based on whether or not a command to select weights has been input from the user. Specifically, when a user inputs weights via the input unit 310, the control unit 100 can determine the input weights to be used to generate the objective function.

[0062] In contrast, if the user does not input weights via the input unit 310, the control unit 100 can recommend weights to the user, generate combinations that can be created based on the number of cost functions and the interval information of the weights, and recommend to the user the weights in the combination with the lowest power purchase cost among the generated combinations. This will be explained later with reference to Figure 6.

[0063] Subsequently, the objective function determination unit 102 of the control unit 100 can determine the objective function based on the weights and cost function selected by the user, and the cost function (J n ) can be configured in various ways based on the user's intent, as follows:

[0064]

number

[0065]

number

[0066]

number

[0067]

number

[0068]

number

[0069]

number

[0070]

number

[0071] Specifically, Equation 1 may represent the cost function that minimizes the most important power consumption, Equation 2 may represent the cost function related to peak shaving that minimizes the peak load 24, and Equation 3 may represent the cost function that minimizes internal resistance in relation to the degradation of battery performance.

[0072] Furthermore, Equation 4 may represent a cost function that minimizes the rapid changes in SOC (State of Charge) associated with the degradation of battery performance, and Equation 5 may represent a cost function that minimizes the deviation between battery cells.

[0073] Furthermore, equations 6 and 7 may represent cost functions used in the home energy storage system 20, where equation 6 may represent a cost function that minimizes the difference between the backup SOC (State of Charge) and the user's requested SOC in order to maintain the backup SOC (State of Charge) used in the home energy storage system, and equation 7 may represent a cost function that minimizes the difference between the current indoor temperature and the user's requested temperature.

[0074] In this case, f in the cost function may represent a function relating to the degree of battery cell degradation, and g in the cost function may represent a function relating to user inconvenience.

[0075] The control unit 100 can receive a command from the user to select a cost function, and can determine the objective function (Min. Z) as shown in Equation 8 below by the weighted sum of the selected cost function and weights.

[0076]

number

[0077] At this time,

[0078]

number

[0079] As a state variable,

[0080]

number

[0081] Includes,

[0082]

number

[0083] The control variable is,

[0084]

number

[0085] It can include...

[0086] In the cost function and objective function,

[0087]

number

[0088] This refers to the amount of electrical energy [kW] taken from the grid at a specific time k.

[0089]

number

[0090] represents the output power of the energy storage device at a specific time k, and SOC k may represent the SOC of the energy storage device at a specific time k. Further, in the cost function and the objective function, T k represents the temperature [°C] of the energy storage device at a specific time k, and H k represents the indoor humidity [%] inside a home at a specific time k, and C k represents the power usage time cost ($ / kWh) based on the power cost at a specific time k, and R k represents the internal resistance [ohm] at a specific time k, and Q k represents the capacity [Ah] at a specific time k, and w k represents the trade-off weights (0<=w1, ..., w j ).

[0091] Thereafter, the optimization execution unit 103 of the control unit 100 can perform battery optimization based on external power production, internal power consumption, and a predetermined objective function. Here, the battery optimization may mean determining a charge / discharge schedule such that the predetermined objective function has a minimum value.

[0092] After performing the optimization, the control unit 100 can determine whether re-optimization of battery control is required in the optimization re-execution determination unit 104. Specifically, the control unit 100 executes optimization of battery control based on a first time point, determines a predicted value of battery charge / discharge power based on the optimization of battery control executed at the first time point, determines a prediction interval including the predicted value of battery charge / discharge power, and can re-execute optimization of battery control based on that an actual value of the battery charge / discharge power is outside the range of the prediction interval.

[0093] In other words, the control unit 100 can set the first time point to a point one day before the second time point, which is the actual time when the battery is charged and discharged, and can perform optimization in advance at the first time point. Subsequently, the control unit 100 can determine the predicted values ​​of the charge and discharge power at the second time point based on the results of the optimization.

[0094] In this case, when the second time point is reached, the control unit 100 can receive the actual value of the charge / discharge power from the energy storage system 20, and if the actual value of the charge / discharge power is outside a preset reference range (for example, a 95% range of prediction error), the control unit 100 can re-execute the optimization of the battery control.

[0095] One embodiment of the energy storage system control device 1 can reduce the error between predicted values ​​and actual values, thereby minimizing uncertainty and reducing cost losses. Based on the fact that the optimization has been re-executed, the control unit 100 can notify the user by sending the optimization results to the user terminal and server device.

[0096] Figure 4 shows a graph illustrating the conditions under which the energy storage system is optimized by the energy storage system control device according to one embodiment. Referring to Figure 4, the control unit 100 can determine that the difference between the actual value (a) and the predicted value (b) of the charge / discharge power is the error (d). The energy storage system control device 1 according to this disclosure can operate with the aim of minimizing the error (d) between the actual value (a) and the predicted value (b).

[0097] In this case, the control unit 100 can determine a prediction interval (c) that includes a predicted value of the charge / discharge power, and if the control unit 100 determines that the actual value of the charge / discharge power (a) exceeds the range of the prediction interval (c), it can re-execute the battery optimization.

[0098] In other words, the control unit 100 can perform optimization at the first time point and derive a predicted charge-discharge schedule (f) from the already performed charge-discharge schedule (e). At this time, the control unit 100 can derive a predicted value (b) and a predicted interval (c) of charge-discharge power based on the predicted charge-discharge schedule (f). At this time, if it is determined that the actual value (a) of charge-discharge power at the actual second time point exceeds the range of the predicted interval (c), charge-discharge can be performed using a corrected charge-discharge schedule (g).

[0099] The control unit 100 can determine the prediction interval (c), which serves as the basis for determining whether the actual value (a) of the charge / discharge power exceeds the range of the prediction interval (c), as shown in the following equation 15.

[0100]

number

[0101] At this time,

[0102]

number

[0103] The mean of the predictor variable is

[0104]

number

[0105] The predicted value of the reaction variable,

[0106]

number

[0107] This is the value of the predictor variable, S x S is the standard deviation of the predictor variable. E is the standard deviation of the error term, t *This can represent the percentile of a t-distribution with n-2 degrees of freedom. In this way, the control unit 100 can be used to determine the prediction interval and as a basis for re-executing battery optimization.

[0108] Figure 5 shows the user interface section of an energy storage system control device according to one embodiment. Referring to Figure 5, the input unit 310 of the energy storage system control device 1 according to one embodiment can be configured as a UI (User Interface) in which the weight ratio is adjusted based on user input.

[0109] This allows users to operate the UI according to their intended use of the energy storage system 20, and the cost function and weights can be easily adjusted through UI operation.

[0110] In this case, the UI can have a structure that maintains the sum of the weights as 1, and the structure of the UI can be diverse, such as a circle or a bar, and its form is not limited. Furthermore, the method by which the user adjusts the weights via the UI can include dragging and numerical input, and is not limited as long as it allows for weight adjustment.

[0111] As shown in Figure 5, the user can set a weight of 0.3 for minimizing power costs (a), a weight of 0.2 for minimizing peak shaving to minimize peak load 24 (b), and a weight of 0.5 for minimizing battery degradation (c). According to this, the objective function can be determined as shown in Equation 19 below.

[0112]

number

[0113] If the user does not set weights, the control unit 100 can recommend weights as shown in Figure 6, and as a default value, the weight for minimizing power costs can be set to 1, such as the purpose of installing the energy storage system 20.

[0114] Figure 6 shows a table for recommending weights in an energy storage system control device according to one embodiment. Referring to Figure 6, the control unit 100 can recommend weights based on the fact that it has not received a command from the user to input weights. Specifically, the control unit 100 can generate combinations that can be produced based on the number of cost functions and the interval information of the weights, and can recommend to the user the combination with the lowest power purchase cost among the combinations.

[0115] That is, as shown in the table in Figure 6, if the number of cost functions constituting the objective function is n, the minimum unit of weight change is 0.1, and the changeable weight interval is w, then the control unit 100 will control w n By calculating all possible combinations, the system can recommend to the user the weight of the case that minimizes the total cost. In this case, the cost can be calculated based on the electricity used by the energy storage system 20 and the electricity rate, which varies depending on the time.

[0116] For example, if there are three cost functions, the minimum weight change unit is 0.1, and there are 8 changeable weight intervals (8 intervals from 0.1 to 0.8), then the total number of possible combinations is 8. 3 This was determined to be the case, resulting in a total of 512 combinations.

[0117] In this case, assuming that the total cost C1 in Figure 6 is the smallest cost among all cases, the control unit 100 can recommend to the user that w1 corresponds to case 1 with a cost of 0.1, w2 with a cost of 0.1, and w3 with a cost of 0.8 from among the 512 combinations. If there are multiple combinations with the same cost, the combination can be recommended arbitrarily.

[0118] According to one embodiment of the energy storage system control device 1, even if the user has not set weights, weights are recommended based on the minimum cost, increasing convenience.

[0119] Figure 7 shows a flowchart of an energy storage device control method according to one embodiment, and Figure 8, following Figure 7, shows another flowchart of an energy storage device control method according to one embodiment.

[0120] Referring to Figure 7, the control unit 100 can receive external power production and internal power consumption via the communication unit 200 (700). The control unit 100 can also receive a cost function selection command from the user to select a cost function (710). Although Figure 7 shows the receiving of external power production and internal power consumption and the cost function selection command, these may be performed in parallel, and the order is not limited.

[0121] Subsequently, the control unit 100 can determine whether or not it has received weight inputs from the user to determine the priority of the cost function (720). If the control unit 100 determines that it has not received weight inputs from the user to determine the priority of the cost functions (NO in 720), it can generate all possible combinations based on the number of cost functions and the interval information of the weights (730).

[0122] The control unit 100 can recommend to the user the weights included in the combination with the lowest power purchase cost among the generated combinations (740). Subsequently, if the control unit 100 receives weight input from the user to determine the priority of the cost function (YES in 720), or if the user selects the recommended weights, it can determine the objective function based on the selected weights and cost function (750).

[0123] Referring further to Figure 8, the battery control can be optimized at the first time point based on the external power output, internal power consumption, and objective function (800). As mentioned above, the first time point may refer to a point in time one day before the actual charging and discharging takes place.

[0124] The control unit 100 can determine the predicted value and prediction interval of the battery charge / discharge power based on the battery control optimization performed at the first time point (810). The control unit 100 can determine whether the actual value of the battery charge / discharge power exceeds the prediction interval range (820).

[0125] If the control unit 100 determines that the actual value of the battery charge / discharge power exceeds the predicted interval range (YES in 820), it can adjust the variables for optimizing the battery control (830), and can re-execute the battery control optimization based on the adjusted variables (840).

[0126] Thus, the energy storage system control device 1 according to one embodiment has the effect of enabling efficient and user-friendly charge / discharge schedule management because it can eliminate uncertainty in the process of optimizing battery control.

[0127] On the other hand, the disclosed embodiments may be implemented in the form of a recording medium that stores computer-executable instruction words. The instruction words may be stored in the form of program code, and a processor may generate a program module at runtime to perform the operations of the disclosed embodiments. The recording medium may be a computer-readable recording medium.

[0128] Computer-readable recording media include all types of recording media that store computer-readable instruction words. Examples include ROM (read-only memory), RAM (random access memory), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0129] Furthermore, computer-readable recording media may be provided in the form of non-transitory storage media. Here, “non-transitory storage media” simply means a tangible device that does not contain signals (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. As an example, “non-transitory storage media” may include a buffer in which data is temporarily stored.

[0130] According to one embodiment, the methods according to the various embodiments disclosed herein may be provided in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of an instrument-readable recording medium (e.g., compact disc read-only memory (CD-ROM)) or through an application store (e.g., Play Store). TM The computer program product (e.g., download or upload) may be distributed online via a network or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be at least temporarily stored or temporarily generated on an instrument-readable recording medium such as the memory of a manufacturer's server, an application store server, or an intermediary server.

[0131] Although all components constituting the embodiments disclosed in this document have been described as operating either as a single unit or in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all components may operate in combination of one or more units.

[0132] Furthermore, terms such as “includes,” “constitutes,” or “possesses,” as described above, mean that they may contain the component in question, and not exclude other components, unless otherwise specified. All terms, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong, unless otherwise specified. Commonly used terms, such as those defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and not to be interpreted in an ideal or overly formal sense unless explicitly defined herein.

[0133] The above description is merely illustrative of the technical concept disclosed herein, and any person with ordinary skill in the art to which the embodiments disclosed herein belong can make various modifications and variations without departing from the essential characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are for illustrative purposes only, not to limit the technical concept of the embodiments disclosed herein, and the scope of the technical concept disclosed herein is not limited by such embodiments. The scope of protection of the technical concept disclosed herein shall be interpreted according to the claims described below, and all technical concepts within an equivalent scope should be interpreted as being included in the scope of rights of this document.

Claims

1. A communication unit configured to receive external power production and internal power consumption, A user interface unit configured to receive input from the user for a cost function selection command, A control unit is configured to determine an objective function based on a cost function selected by the user, and to perform battery control optimization based on the external power output, the internal power consumption, and the objective function. Includes, The control unit, The optimization of the battery control is performed based on the first point in time. Based on the optimization of the battery control performed at the first time point, a predicted value for the battery charge / discharge power is determined. An energy storage system control device configured to determine a prediction interval that includes a predicted value of the battery charge / discharge power, and to re-execute the optimization of the battery control based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

2. The control unit, The energy storage system control device according to claim 1, further comprising adjusting a variable for optimizing the battery control based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

3. The control unit, The energy storage system control device according to claim 2, further comprising re-executing the optimization of the battery control based on variables adjusted for the optimization of the battery control.

4. The control unit, The energy storage system control device according to claim 1, which receives weights for determining the priority of the cost function from the user via the user interface unit, and determines the objective function by a weighted sum of the weights and the cost function.

5. The user interface unit further includes an input unit, The aforementioned input unit is The energy storage system control device according to claim 4, configured as a UI (User Interface) in which the ratio of the weights is adjusted based on the user's input.

6. The control unit, The energy storage system control device according to claim 4, which recommends the weight to the user based on the fact that the weight has not been entered by the user within a predetermined time period.

7. The control unit, The energy storage system control device according to claim 6, which generates combinations that can be generated based on the number of cost functions and interval information of the weights, and recommends to the user the combination with the lowest power purchase cost among the combinations.

8. The aforementioned cost function is, The energy storage system control device according to claim 1, comprising a function that minimizes the difference between the backup State of Charge (SOC) used in a home energy storage system and the user's requested SOC.

9. The aforementioned cost function is, The energy storage system control device according to claim 1, comprising a function for minimizing the difference between the current indoor temperature and the user's requested temperature, which is utilized in the household energy storage system.

10. The control unit, The energy storage system control device according to claim 1, which modifies the charge / discharge schedule based on the re-executed optimization of the battery control and performs charging and discharging based on the modified charge / discharge schedule.

11. The steps include receiving external power production and internal power consumption, The steps include receiving input from the user for a cost function selection command, The steps include determining the objective function based on the cost function selected by the user, The steps include optimizing battery control based on the external power output, the internal power consumption, and the objective function, The step of performing the optimization of the battery control is: A step of performing optimization of the battery control based on the first point in time, The steps include determining a predicted value for the battery charge / discharge power based on the optimization of the battery control performed at the first time point, The steps include determining a prediction interval that includes the predicted value of the battery charge / discharge power, An energy storage system control method comprising the step of re-optimizing the battery control based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

12. The energy storage system control method according to claim 11, further comprising the step of adjusting a variable for optimizing the battery control based on the fact that the actual value of the battery charge / discharge power is outside the range of the prediction interval.

13. The energy storage system control method according to claim 12, further comprising the step of re-performing the optimization of the battery control based on variables adjusted for the optimization of the battery control.

14. The step of determining the objective function is, The energy storage system control method according to claim 11, comprising the steps of receiving weights from the user via a user interface unit for determining the priority of the cost function, and determining the objective function by a weighted sum of the weights and the cost function.

15. The energy storage system control method according to claim 14, further comprising the step of adjusting the weight ratio based on the user input input to the input unit included in the user interface unit.

16. The energy storage system control method according to claim 14, further comprising the step of recommending the weight to the user based on the fact that the weight has not been entered by the user within a predetermined time period.

17. The step of recommending the weight to the user is: The energy storage system control method according to claim 16, comprising the steps of generating combinations that can be generated based on the number of cost functions and interval information of the weights, and recommending to the user the combination among the combinations that has the lowest power purchase cost.

18. The aforementioned cost function is, The energy storage system control method according to claim 11, comprising a function that minimizes the difference between a backup State of Charge (SOC) used in a home energy storage system and a user-requested SOC.

19. The aforementioned cost function is, The energy storage system control method according to claim 11, comprising a function for minimizing the difference between the current indoor temperature and the user's requested temperature, which is utilized in the household energy storage system.

20. The energy storage system control method according to claim 11, further comprising the steps of: modifying the charge / discharge schedule based on the re-executed optimization of the battery control; and performing charge / discharge based on the modified charge / discharge schedule.

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