Planning device, planning method, and program

Abstract

According to the present invention, a planning device that plans charging / discharging of a storage battery for a DR target period has a first acquisition unit that acquires power demand for a predetermined number of samples for each of a plurality of spots that represent respective time slots within the target period, an upper / lower limit first setting unit that sets an upper / lower limit for the power demand of the spots, a second acquisition unit that acquires a contract capacity for the target period and a baseline for each spot, a third acquisition unit that acquires a parameter related to the storage battery, a second setting unit that sets an adjustment parameter that includes a weight related to requirement non-achievement, a weight related to opportunity loss due to over-discharge, and a parameter related to the uncertainty of the power demand, a solution-finding unit that uses the power demand, the upper / lower limit, the contract capacity, the baseline, the parameter related to the storage battery, and the adjustment parameter to find a charge / discharge amount for each spot by means of a distributed robust optimization method, and a third setting unit that sets the charge / discharge amounts as a charging / discharging plan.

Description

Planning device, planning method and program

[0001] The present disclosure relates to a planning device, a planning method, and a program.

[0002] In order for electricity consumers to participate in the capacity market and respond to demand response (DR) that occurs when supply and demand in the power grid becomes tight, it is important to secure the amount of electricity demand suppression equivalent to the contracted capacity when the DR is issued, and to reduce economic losses. Many studies on consumer-side power control have been reported in the past, and Non-Patent Document 1 describes many studies that utilize mathematical optimization.

[0003] In order to ensure the amount of electricity demand suppression when DR is issued, it is possible to consider self-consumption of electricity stored in storage batteries in advance. In this case, it is necessary to appropriately determine a plan for charging and discharging storage batteries in accordance with information on predicted electricity demand when DR is issued.

[0004] Panda, Subhasis, et al. "Residential Demand Side Management model, optimization and future perspective: A review." Energy Reports, 8, 3727-3766, 2022.

[0005] However, power demand forecasts generally contain prediction errors, and if charging and discharging plans are planned without taking prediction errors into account, for example, economic penalties may be incurred due to insufficient discharge or inefficient consumption of stored energy due to excessive discharge.

[0006] The present disclosure has been made in consideration of the above points, and provides a technology that can plan the charging and discharging of a storage battery while taking into account prediction errors in power demand.

[0007] a first upper and lower limit value setting unit that sets upper and lower limit values ​​for the power demand at each spot; a second acquisition unit that acquires a contracted capacity within the target period and a baseline for each spot; a third acquisition unit that acquires parameters related to the storage battery; a second setting unit that sets adjustment parameters including a weight related to a penalty representing non-achievement of a requirement, a weight related to a penalty representing opportunity loss due to over-discharge, and a parameter related to uncertainty of the power demand; a solution-finding unit that calculates a charge / discharge amount for each spot using a distributional robust optimization method using the power demand, the upper and lower limit values, the contracted capacity, the baseline, the parameters related to the storage battery, and the adjustment parameters; and a third setting unit that sets the charge / discharge amount for each spot as a charge / discharge plan for the storage battery.

[0008] A technology is provided that can plan the charging and discharging of storage batteries while taking into account prediction errors in power demand.

[0009] FIG. 1 is a diagram showing an example of an operation period and a demand response target period. FIG. 2 is a diagram showing an example of a charge and discharge plan for a storage battery. FIG. 3 is a diagram showing an example of a problem when a prediction error is included in power demand. FIG. 4 is a diagram showing an example of the hardware configuration of a charge and discharge planning device according to the present embodiment. FIG. 5 is a diagram showing an example of the functional configuration of a charge and discharge planning device according to the present embodiment. FIG. 6 is a flowchart showing an example of the flow of charge and discharge planning processing according to the present embodiment.

[0010] An embodiment of the present invention will be described below.

[0011] <Conventional methods for consumer-side power control and their issues> Non-Patent Document 1 is a survey paper on methods for consumer-side power control using mathematical optimization. Among these methods, stochastic optimization and robust optimization are cited as mathematical optimization techniques that are particularly used for DR-compatible power control that takes into account prediction errors in power demand forecasts.

[0012] Stochastic optimization is a method of constructing a sample distribution based on previously accumulated data representing power demand, and optimizing by regarding this sample distribution as the true probability distribution itself.On the other hand, robust optimization is a method of determining in advance a set of possible power demands including prediction errors, and optimizing for the worst-case power demand that belongs to that set.

[0013] However, in reality, stochastic optimization has the problem that the error between the sample distribution and the true probability distribution has a significant impact on performance, and optimization performance deteriorates significantly, especially when the amount of data is small.On the other hand, robust optimization only captures the worst case within the set of possible power demands, and therefore cannot take into account the frequency distribution within the set of possible power demands, resulting in a conservative optimization approach that does not effectively utilize data representing power demand accumulated in advance.

[0014] Therefore, we propose a method that incorporates the distributional robust optimization method (Reference 1). The distributional robust optimization method is a robust optimization method that takes into account the uncertainty of the assumed probability distribution. In this proposed method, we define a set that represents uncertainty based on the Wasserstein distance, which measures the degree of discrepancy between the true probability distribution and the actual sample distribution. We then use this set to formulate a battery charging / discharging plan to meet DR requests in the capacity market as distributional robust optimization. We then reduce this distributional robust optimization to a solvable class of optimization problems, thereby achieving a charging / discharging plan that is robust to the uncertainty of power demand forecasts.

[0015] <Proposed Method> The proposed method will be explained below. In this proposed method, the capacity market is the target, and as shown in Figure 1, a battery charge / discharge schedule is considered that appropriately responds to DR requests within a three-hour period specified by a DR activation command. In the example shown in Figure 1, a DR activation command is issued at a certain time during the operation period, and the three hours between two and five hours later are the DR target period. Note that the baseline in the example shown in Figure 1 refers to the expected power demand if no control related to power demand suppression is implemented (Reference 2).

[0016] In this proposed method, consumers specify the amount of charge and discharge of their storage batteries every 30 minutes during the DR period. These 30-minute intervals are called "spots." For example, as shown in Figure 2, consumers determine the charge and discharge plan for their storage batteries by specifying the amount of charge and discharge for each spot. The example shown in Figure 2 illustrates a scenario in which a certain amount of energy is charged for the first 30 minutes of the DR period (the first spot), discharged for the next 30 minutes (the second spot), discharged for the next 30 minutes (the third spot), and so on, until a certain amount of energy is discharged for the final 30 minutes (the sixth spot). Note that in the example shown in Figure 3, the baseline minus contract capacity is the value obtained by subtracting the contract capacity (i.e., the contracted power demand) from the baseline. If the baseline minus contract capacity exceeds the power demand, the shortage is specified to be met by discharging the storage batteries. If the baseline minus contract capacity is less than the power demand, the difference is specified to be used for charging the storage batteries.

[0017] However, power demand generally includes prediction errors. Therefore, as shown in FIG. 3, for example, there is a possibility that the requirement will not be met due to insufficient discharge, or that the battery will be inefficiently consumed due to excessive discharge. In the example shown in FIG. 3, the actual power demand at the second power spot exceeded the power demand forecast, resulting in insufficient discharge, and as a result, the requirement was not met. This may result in an economic penalty. Furthermore, the actual power demand at the fourth power spot exceeded the power demand forecast, resulting in excessive discharge (i.e., excessive charging) of the battery, and as a result, the battery also discharged, resulting in inefficient battery consumption.

[0018] Therefore, in this proposed method, we will explain how to determine a battery charge / discharge plan that meets DR requirements robustly against prediction errors in power demand (in other words, robustly against uncertainty regarding the probability distribution of power demand).

[0019] The set of spots within the DR period is K:={1, 2, ..., 6}. In this case, the contract capacity is p, and the power demand at each spot k∈K is dｋ , the baseline value is b ｋ , the charge and discharge amount of the storage battery is x ｋ The baseline value b ｋ is determined based on the past power demand record when a DR command is issued, and is assumed to be obtained in advance. ｋ When it takes a positive value, it means charging, and when it takes a negative value, it means discharging.

[0020] Next, we define the parameters for the storage battery that will be charged and discharged. The maximum capacity of the storage battery that will be DR compatible is C, the charge / discharge rate (charge / discharge performance per unit time) is a, and the charge amount (initial remaining amount) at the start of the DR target period is x. ０ Let's say.

[0021] Let M be a |K|-dimensional measurable space, and let the power demand vector d:=[d １ , d ２ , ..., d ｜Ｋ｜ ] (Actually, Q is represented by blackboard letters (or outline letters), but in the text of this specification, it will be written in regular letters as "Q". Other blackboard letters or outline letters will also be written in regular letters in the text of this specification.) In order to define the uncertainty set for a probability distribution Q of １ , Q ２ For ∈M, the Wasserstein distance W: M×M→R (where R is the set of all real numbers) is defined as follows:

[0022] where Ξ is ξ １ , ξ ２ represents the possible region, and Π represents the marginal distribution Q １ , Q ２ The upper limit of the power demand at each spot is d ｕ，ｋ , the lower limit is d ｌ，ｋ The region Ξ that the power demand vector d can take is described below.

[0023] In this case, the uncertainty set regarding the probability distribution Q of the power demand vector d is defined by the following equation (1).

[0024] Here, ^P Ｎ (To be precise, the hat symbol "^" is P Ｎ Although it is written just above P Ｎ ) is the sample distribution when N power demand vectors are given. Also, ε is the true probability distribution Q and the sample distribution ^P Ｎ is a parameter that defines the Wasserstein distance between

[0025] The uncertainty set given by equation (1) above means that the true probability distribution Q lies within a Wasserstein sphere defined by radius ε.

[0026] Based on the above preparations, we formulate a battery charge / discharge plan that appropriately responds to DR requests even under uncertainty regarding the probability distribution of power demand defined by the above equation (1) as distributional robust optimization shown in (2a) to (2c) below.

[0027] Here, the function J ｋ (x ｋ , d ｋ ) is described by the following equation (3).

[0028] The function J shown in the above formula (3) ｋ (x ｋ , d ｋ ) in which p-b ｋ +d ｋ +x ｋ ≧0 means that the requirement is not met, which is a penalty for not meeting the DR requirement. ｋ +d ｋ +x ｋ < 0 means that a penalty occurs in relation to opportunity loss due to over-discharge (power that is not used to the benefit of consumers as a result of discharging more than the minimum amount of power required to prevent non-achievement of requirements). ｋ １ , a ｋ ２ ≧0 represents the penalty weight at each spot.

[0029] In the optimization problem described by the above equations (2a) to (2c), the probability distribution Q of the power demand vector d fluctuates within the range constrained by the uncertainty set D. In this situation, the function J given by the above equation (3) is used to calculate the worst-case probability distribution Q. ｋ (x ｋ , d ｋ ) and charge / discharge amount x ｋ L of a vector with elements １ The expected value of the norm is minimized (as shown in (2a)). １ This is a weight related to the norm. In addition to the penalty for not meeting the requirement and the penalty for lost opportunity, this also takes into account a regularization term related to the charge / discharge amount, and serves to penalize repeated unnecessary charging and discharging. Furthermore, the inequality shown in (2b) implies the constraint that each spot cannot charge more than its storage capacity, and cannot discharge even if the capacity is insufficient. Finally, the inequality shown in (2c) represents the constraint on the charge / discharge capacity of each spot, with T = 1800 [s].

[0030] In order to provide an algorithm for solving the optimization problems shown in the above equations (2a) to (2c), we reduce them to a class of optimization problems that are equivalent to and solvable by equations (2a) to (2c). The power demand sampled at the i-th time is expressed as d ｋ （ｉ） Then, the above equations (2a) to (2c) can be reduced to the following optimization problem:

[0031] Here, N is the number of samples of power demand.

[0032] The optimization problems shown in the above equations (4a) to (4i) are linear programming problems, and therefore can be easily solved using existing solvers.

[0033] Below, the charge / discharge amount x at each spot during the DR target period using this proposed method is ｋ The following describes a charge / discharge planning device 10 that can plan the charge / discharge of a storage battery by calculating the following equation.

[0034] <Example of Hardware Configuration of Charge / Discharge Planning Device 10> An example of a hardware configuration of the charge / discharge planning device 10 according to this embodiment is shown in Fig. 4. As shown in Fig. 4, the charge / discharge planning device 10 according to this embodiment includes an input device 101, a display device 102, an external I / F 103, a communication I / F 104, a RAM (Random Access Memory) 105, a ROM (Read Only Memory) 106, an auxiliary storage device 107, and a processor 108. Each of these pieces of hardware is connected to each other via a bus 109 so as to be able to communicate with each other.

[0035] The input device 101 is, for example, a keyboard, a mouse, a touch panel, a physical button, etc. The display device 102 is, for example, a display, a display panel, etc. Note that the charge and discharge planning device 10 does not necessarily have to include at least one of the input device 101 and the display device 102, for example.

[0036] The external I / F 103 is an interface with an external device such as a recording medium 103a. Examples of the recording medium 103a include a CD (Compact Disc), a DVD (Digital Versatile Disk), an SD memory card (Secure Digital memory card), and a USB (Universal Serial Bus) memory card.

[0037] The communication I / F 104 is an interface for connecting the charge / discharge planning device 10 to a communication network. The RAM 105 is a volatile semiconductor memory (storage device) that temporarily stores programs and data. The ROM 106 is a non-volatile semiconductor memory (storage device) that can store programs and data even when the power is turned off. The auxiliary storage device 107 is a storage device (storage device) such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory. The processor 108 is one of various arithmetic devices such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).

[0038] 4 is an example, and the hardware configuration of the charge / discharge planning device 10 is not limited to this. For example, the charge / discharge planning device 10 may have a plurality of auxiliary storage devices 107 or a plurality of processors 108, may not have some of the hardware shown in the figure, or may have various hardware other than the hardware shown in the figure.

[0039] <Example of functional configuration of charge / discharge planning device 10> An example of functional configuration of the charge / discharge planning device 10 according to this embodiment is shown in Fig. 5. As shown in Fig. 5, the charge / discharge planning device 10 according to this embodiment includes an electricity demand accumulation data acquisition unit 201, a capacity market parameter acquisition unit 202, a storage battery parameter acquisition unit 203, a solution-finding unit 204, and a charge / discharge planning unit 205. Each of these units is realized, for example, by a process in which one or more programs installed in the charge / discharge planning device 10 are executed by the processor 108 or the like. Note that when a DR activation command is received, the DR target period is specified, for example, by the activation command or the like.

[0040] The power demand accumulation data acquisition unit 201 acquires the power demand d ｋ （ｉ） Here, for example, the power demand d ｋ （ｉ） is stored in advance in a database or the like, and the power demand storage data acquisition unit 201 acquires N samples of power demand d ｋ （ｉ） In addition, each power demand d ｋ （ｉ） The data may be any data that can be used to construct a sample distribution of the power demand at each spot (i.e., each 30-minute time period, for example). For example, it is possible to assume that the power demand at each time period follows the same probability distribution on all days, and obtain the power demand values ​​for the past N days. Alternatively, the data may be a predicted value of the power demand at each time period obtained by some prediction method.

[0041] In addition, the power demand accumulation data acquisition unit 201 acquires the power demand d ｋ （ｉ） Using the above, the upper and lower limits of the power demand at each spot dｕ，ｋ , d ｌ，ｋ The upper limit value d ｕ，ｋ is, for example, the power demand d ｋ （１） , ..., d ｋ （Ｎ） Similarly, the lower limit value d ｌ，ｋ is, for example, the power demand d ｋ （１） , ..., d ｋ （Ｎ） or the minimum value of the buffer value subtracted therefrom.

[0042] The capacity market parameter acquisition unit 202 acquires the contract capacity p and the baseline value b ｋ The capacity market parameter acquisition unit 202 acquires the contract capacity p and the baseline value b at each spot from, for example, a predetermined database or the like. ｋ Just obtain the following.

[0043] The storage battery parameter acquisition unit 203 acquires the storage battery capacity C, the charge / discharge rate a, and the charge amount (initial remaining amount) x at the start of the DR target period. ０ The storage battery parameter acquisition unit 203 acquires the storage battery capacity C, the charge / discharge rate a, and the initial remaining capacity x from, for example, a predetermined database. ０ Just obtain the following.

[0044] The solution-finding unit 204 calculates the penalty weight at each spot using a parameter a ｋ １ , a ｋ ２ ≧0 and L １ After setting the parameter β≧0 representing the weight related to the norm and the parameter ε≧0 related to the Wasserstein sphere for defining the uncertainty of the power demand, the optimization problem shown in the above equations (4a) to (4i) is solved to obtain the charge / discharge amount x ｋ These parameters (adjustment parameters) a ｋ １ , a ｋ ２ ≧0, β≧0, and ε≧0 can be set arbitrarily.

[0045] The charge / discharge planning unit 205 calculates the charge / discharge amount x calculated by the solution calculation unit 204. ｋ is set as the charge / discharge plan for the storage battery. As a result, when the time period indicated by spot k arrives, the charge / discharge amount x ｋ The battery is charged and discharged by this.

[0046] <Charge / Discharge Planning Process Flow> Hereinafter, the flow of the charge / discharge planning process according to this embodiment will be described with reference to FIG. 6 .

[0047] First, the power demand accumulation data acquisition unit 201 acquires the power demand d ｋ （ｉ） In other words, the power demand accumulation data acquisition unit 201 acquires N samples of d for each k∈K. ｋ （１） , ..., d ｋ （Ｎ） Get.

[0048] Next, the power demand accumulation data acquisition unit 201 acquires the power demand d ｋ （ｉ） Using the above, the upper and lower limits of the power demand at each spot d ｕ，ｋ , d ｌ，ｋ is set (step S102).

[0049] Next, the capacity market parameter acquisition unit 202 acquires the contract capacity p and the baseline value b ｋ and are acquired (step S103).

[0050] Next, the storage battery parameter acquisition unit 203 acquires the storage battery capacity C, the charge / discharge rate a, and the charge amount (initial remaining amount) x at the start of the DR target period. ０ and are acquired (step S104).

[0051] Next, the solution-finding unit 204 calculates the parameter a representing the weight of the penalty at each spot. ｋ １ , a ｋ ２ ≧0 and L １ A parameter β≧0 representing a weight related to the norm and a parameter ε≧0 related to the Wasserstein sphere for defining the uncertainty of the power demand are set (step S105).

[0052] Next, the solution-finding unit 204 calculates the power demand d ｋ （ｉ） and upper and lower limit values ​​d ｕ，ｋ , d ｌ，ｋ , contract capacity p, and baseline value b at each spot ｋ , the battery capacity C, the charge / discharge rate a, and the initial remaining capacity x at the start of the DR target period. ０ and parameter a ｋ １ , a ｋ ２ , β, ε are used to solve the optimization problem shown in the above equations (4a) to (4i), and the charge / discharge amount x ｋ (Step S106). The solution-finding unit 204 calculates the charge / discharge amount x ｋ (k∈K) is found.

[0053] Then, the charge / discharge planning unit 205 calculates the charge / discharge amount x calculated in step S106. ｋ (kεK) is set as the charge / discharge plan for the storage battery (step S107).

[0054] <Summary> As described above, the charge / discharge planning device 10 according to this embodiment enables a charge / discharge plan that appropriately minimizes the penalty representing the non-attainment of the requirement and the penalty representing the opportunity loss due to over-discharge in a robust manner against the uncertainty regarding the probability distribution of the power demand. In particular, the weighting of the penalty representing the non-attainment of the requirement at each spot and the penalty representing the opportunity loss due to over-discharge is performed by the parameter a ｋ １ , a ｋ ２ Furthermore, the penalty for repetitive charging and discharging can also be adjusted by the parameter β. This ensures robustness in terms of appropriately minimizing the penalty for not achieving the requirement and the penalty for the opportunity loss due to over-discharging, even if the probability distribution of the power demand estimated based on the data representing the power demand accumulated in advance at the time of planning the charging and discharging differs from the probability distribution of the actual power demand.

[0055] Furthermore, as a characteristic of the distributional robust charging / discharging scheduling algorithm, when the number of samples of data representing pre-accumulated power demand is small, it is expected to produce conservative results that clearly guarantee robustness, taking into full consideration the existence of errors between the sample distribution and the true probability distribution due to the small sample size. On the other hand, when the number of samples of data representing power demand is sufficiently large, it is expected to reduce the conservatism required to guarantee robustness and obtain results closer to those of stochastic programming that fully utilize the characteristics of the data. From the above, it can be said that the distributional robust charging / discharging scheduling algorithm is an algorithm that can determine a charging / discharging schedule that appropriately suppresses penalties representing non-achievement of requirements and penalties representing opportunity losses due to over-discharge by flexibly adjusting the conservatism associated with guaranteeing robustness in accordance with the size of the sample size of data representing power demand.

[0056] The present invention is not limited to the above-described specifically disclosed embodiments, and various modifications, changes, and combinations with known technologies are possible without departing from the scope of the claims.

[0057] [References] Reference 1: Mohajerin Esfahani, Peyman, and Daniel Kuhn. "Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations." Mathematical Programming 171(1-2), 115-166, 2018. Reference 2: Guidelines for Energy Resource Aggregation Business, revised 20200601, Agency for Natural Resources and Energy

[0058] REFERENCE SIGNS LIST 10 Charging and discharging planning device 101 Input device 102 Display device 103 External I / F 103a Recording medium 104 Communication I / F 105 RAM 106 ROM 107 Auxiliary storage device 108 Processor 109 Bus 201 Electricity demand accumulation data acquisition unit 202 Capacity market parameter acquisition unit 203 Storage battery parameter acquisition unit 204 Solution unit 205 Charging and discharging planning unit

Claims

1. A device for planning charging and discharging of power, A device that calculates the amount of charge and discharge for each specified time period using an algorithm that calculates the amount of charge and discharge for each specified time period using the power demand for each specified time period within a specified period, upper and lower limits of the power demand, a contracted capacity for the specified period, a baseline for each specified time period, parameters related to the charging and discharging device, and adjustment parameters.

2. the power demand is power demand for a predetermined number of samples for each of the predetermined time periods within the predetermined period, The apparatus of claim 1 , wherein the algorithm is an optimization technique under uncertainty regarding the power demand.

3. The device according to claim 1 , wherein the charge / discharge amount for each predetermined time period is set as a charge / discharge plan.

4. The device according to claim 1 , wherein the adjustment parameters include a weight related to a penalty representing a requirement not being met, a weight related to a penalty representing an opportunity loss due to over-discharge, and a parameter related to uncertainty of the power demand.

5. 3. The device according to claim 2, wherein the optimization problem formulated by the optimization method is converted into a linear programming problem equivalent to the optimization problem, and the charging / discharging amount for each of the predetermined time periods is calculated by solving the linear programming problem.

6. The device according to claim 1 , wherein the adjustment parameters include a parameter representing a penalty for wastefully repeating charging and discharging within the predetermined period.

7. The apparatus according to claim 4 , wherein the parameter relating to the uncertainty of the power demand is a parameter that defines a Wasserstein distance between the probability distribution of the power demand and a true probability distribution of the power demand.

8. The device according to claim 1 , wherein the parameters relating to the charging / discharging device include a storage capacity of the charging / discharging device, a charging / discharging rate of the charging / discharging device, and an initial remaining capacity of the charging / discharging device at the start of the predetermined period.

9. A computer that plans the charging and discharging of electricity A method for calculating the amount of charge and discharge for each specified time period using an algorithm that calculates the amount of charge and discharge for each specified time period using the power demand for each specified time period within a specified period, upper and lower limits of the power demand, a contracted capacity for the specified period, a baseline for each specified time period, parameters related to the charging and discharging device, and adjustment parameters.

10. The computer that plans the charging and discharging of power A program that calculates the amount of charge and discharge for each specified time period using an algorithm that uses the power demand for each specified time period within a specified period, the upper and lower limits of the power demand, the contract capacity for the specified period, the baseline for each specified time period, parameters related to the charging and discharging device, and adjustment parameters.

11. A planning device that plans charging and discharging of a storage battery within a target period of demand response, a first acquisition unit that acquires a predetermined number of samples of power demand for each spot representing each time period within the target period; a first upper and lower limit value setting unit that sets upper and lower limit values ​​of the power demand at each of the spots; a second acquisition unit that acquires a contracted capacity within the target period and a baseline for each spot; a third acquisition unit that acquires parameters related to the storage battery; a second setting unit that sets adjustment parameters including a weight related to a penalty representing non-attainment of a requirement, a weight related to a penalty representing an opportunity loss due to over-discharge, and a parameter related to the uncertainty of the power demand; a solution-finding unit that calculates the charge / discharge amount for each spot by a distributional robust optimization method using the power demand, the upper and lower limits, the contract capacity, the baseline, parameters related to the storage battery, and the adjustment parameters; a third setting unit that sets the charge / discharge amount for each spot as a charge / discharge plan for the storage battery; A planning device having