JP6950776B2 - Information processing equipment, information processing methods, and programs - Google Patents

Description

The present invention relates to a method for creating a power procurement plan.

A method of generating a power generation plan based on power demand by using an optimization method has been proposed. For example, Patent Document 1 formulates an operation plan for calculating an operation plan for a plurality of generators by solving an optimization problem in which the total power generation cost, which is the sum of the power generation costs weighted by the power demand for each time zone, is the objective function. The device is described. Further, Patent Document 2 generates a plurality of forecast scenarios relating to uncertain factors affecting the profit / loss risk of an electric power company, and uses a profit / loss model created by using the relationship between the forecast scenarios and the profit / loss to obtain a profit / loss risk. The profit and loss analysis system that calculates

Japanese Unexamined Patent Publication No. 2016-63609 JP-A-2019-79537

The method of Patent Document 1 calculates an operation plan for electric power demand, but does not determine an optimum operation plan in consideration of constraints having time continuity. Further, since the method of Patent Document 2 does not consider replanning and only considers the safety for randomly selected scenarios, it cannot be said that the safety for all demand scenarios is guaranteed. ..

The main subject of the present invention is to prepare a power procurement plan that can be adapted to any demand scenario on the premise of replanning.

In one aspect of the present invention, the information processing device
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. A model generation means for generating an optimized model having a constraint on the amount of power that can be procured from the power procurement means, and a model generation means.
By solving the optimization model by a mathematical optimization method, a determination means for determining an electric power procurement plan indicating the amount of electric power to be procured from each of the plurality of electric power procurement means, and a determination means.
Equipped with a,
The plurality of power procurement means include imbalance adjustment.
The objective function is given as the sum of the total cost and the regularization term for imbalance adjustment .

In another aspect of the present invention, the information processing method
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
By solving the optimization model by a mathematical optimization method, a power procurement plan indicating the amount of power to be procured from each of the plurality of power procurement means is determined .
The plurality of power procurement means include imbalance adjustment.
The objective function is given as the sum of the total cost and the regularization term for imbalance adjustment .

In yet another aspect of the invention, the program
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
By solving the optimization model by a mathematical optimization method, a computer is made to execute a process of determining a power procurement plan indicating the amount of power to be procured from each of the plurality of power procurement means .
The plurality of power procurement means include imbalance adjustment.
The objective function is given as the sum of the total cost and the regularization term for imbalance adjustment .

According to the present invention, it is possible to create a power procurement plan that can be adapted to any demand scenario by assuming replanning.

The hardware configuration of the power procurement plan creation device according to the embodiment is shown. It is a block diagram which shows the functional structure of the power procurement plan making apparatus. It is a flowchart of the power procurement plan creation process. An example of the configuration of the procurement optimization unit is shown. The state before the discretization of the demand scenario in the calculation of the optimum solution is shown. The state after discretization of the demand scenario in the calculation of the optimum solution is shown. The constraints given to the variables y and z for determining the optimum power generation plan are shown. A display example of the power procurement plan is shown.

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.
[Hardware configuration]
FIG. 1 is a block diagram showing a hardware configuration of a power procurement plan creating device according to an embodiment of the present invention. As shown in the figure, the power procurement plan creation device 10 includes an interface (I / F) 12, a processor 13, a memory 14, a recording medium 15, a database (DB) 16, an input unit 17, and a display unit 18. And.

The interface 12 acquires the information necessary for generating the power procurement plan. The processor 13 is a computer such as a CPU (Central Processing Unit), and controls the power procurement plan creation device 10 by executing a program prepared in advance. Specifically, the processor 13 executes the power procurement plan creation process described later.

The memory 14 is composed of a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The memory 14 stores the optimization model used by the power procurement planning device 10. The memory 14 is also used as a working memory during execution of various processes by the processor 13.

The recording medium 15 is a non-volatile, non-temporary recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the power procurement plan creation device 10. The recording medium 15 records various programs executed by the processor 13. When the power procurement plan creation device 10 executes various processes, the program recorded on the recording medium 15 is loaded into the memory 14 and executed by the processor 13. The database 16 stores data input via the interface 12.

The input unit 17 is composed of a keyboard, a mouse, and the like, and is used when a user gives an instruction or inputs a predetermined data. The display unit 18 is, for example, a liquid crystal display or the like, and displays the power procurement plan generated by the power procurement plan creation device 10 by a graph or the like.

[Functional configuration]
FIG. 2 is a block diagram showing a functional configuration of the power procurement plan creation device 10. The electric power procurement plan creating device 10 generates an electric power procurement plan for each predetermined target period by using information such as demand record, variable factor information, electric power price, and procurement conditions. The “demand record” includes past demand history, past procurement history, and the like. "Fluctuating factor information" indicates information that becomes a fluctuation factor of electric power demand, such as weather conditions. "Electricity price" indicates the past electricity procurement price. The “procurement condition” includes information such as available power procurement means, the range of the amount of power that can be procured by the power procurement means (upper and lower limits), and the rate of change in power output. Some information, such as the upper and lower limits of the amount of power procured based on the relative power supply contract, is input by the user via the input unit 17.

The electric power procurement plan creating device 10 predicts the electric power demand for each target period, and procures an electric power procurement plan for procuring electric power satisfying the predicted electric power demand (hereinafter, referred to as “predicted demand”) by a plurality of electric power procurement means. Generate. The target period is a period that is a unit for generating a power procurement plan, and is usually a period of 30 minutes. The electric power procurement plan is a plan showing the amount of electric power procured by each electric power procurement means during the target period.

Power procurement means include relative power sources, spot markets, constant backup power sources, pre-hour markets, and imbalance adjustments. The "relative power source" is a means for procuring electric power through a bilateral contract with a large-scale power plant, and is a means for securing a base electric power. The "spot market" is an electric power trading market operated by the Japan Electric Power Exchange (JPEX), and electric power can be procured until 10:00 the day before. The "always-on backup power source (JBU)" is a means for procuring electric power from the company's own power source or a small amount of external suppliers, and is a relative power source for adjustment. The "pre-hour market" is the electricity trading market operated by the Japan Electric Power Exchange, which is open 24 hours a day and can procure electricity up to one hour before. "Imbalance adjustment" is a supply and demand adjustment performed by a general power transmission and distribution business operator when the demand forecast or power generation forecast changes due to changes in weather conditions or troubles, and the amount of power increases due to fluctuations in the demand forecast. If there is a shortage, power will be procured from a general power transmission and distribution company. The power procurement plan indicates the amount of power procured by each of the above power procurement means.

As shown in FIG. 2, the electric power procurement plan creating device 10 functionally includes a demand forecasting unit 21, an electric power price forecasting unit 22, and a procurement optimizing unit 23. The demand forecasting unit 21 predicts the power demand in the target period by using a predetermined algorithm, a forecasting model, or the like based on the past demand record and fluctuation factor information, and calculates the forecasted demand. The demand forecasting unit 21 inputs the calculated forecast demand to the procurement optimization unit 23.

The electric power price prediction unit 22 predicts the electric power price in the target period based on the past electric power price. For the past electric power price, for example, the transaction price in the past JPEX can be used. Further, regarding the relative power source, the electric power price prediction unit 22 uses the price determined by the relative contract. The electric power price prediction unit 22 predicts the electric power price for the target period using a predetermined algorithm, a prediction model, or the like, and inputs the electric power price to the procurement optimization unit 23.

The procurement optimization unit 23 is targeted by using the optimization model described below based on the actual demand, the forecast demand acquired from the demand forecast unit 21, the electricity price acquired from the electricity price forecast unit 22, and the procurement conditions. Create a power procurement plan for the period. The procurement optimization unit 23 is an example of the model generation means and the determination means of the present invention.

[Optimized model]
Next, the optimization model used by the procurement optimization unit 23 will be described. The procurement optimization unit 23 repeatedly generates a power procurement plan using the optimization model on the premise of replanning for the target period, for example, every 30 minutes. Now, assuming that the procured power x is an instrumental variable and the demand d is an uncertain variable, the power procurement plan can be shown by the following optimization model.

Here, Eq. (1) shows the objective function to be minimized by the optimization model. “T” indicates the time corresponding to the target period, “c” indicates the cost for procuring electric power, and “x (t)” indicates the amount of electric power to be procured at time t. That is, the equation (1) gives an optimization problem of minimizing the cost of the amount of electric power to be procured at time t = 1 to T.

Equations (2) and (3) indicate constraints. In the formula (2), "n" is a number indicating the power procurement means. Equation (2) assumes that there are N electric power procurement means, and "x _n ^(t) " indicates the amount of electric power to be procured by the nth electric power procurement means at time t. “D ^(t) ” indicates the predicted demand for electric power at time t. Therefore, the equation (2) shows that, as the first constraint condition, the supply and demand (procurement amount) of electric power match at each time t.

Equation (3) shows, as the second constraint condition, a constraint condition mainly related to power procurement by a power source such as a generator. In the formula (3), "l (x ^(t) )" indicates the lower limit of the power procurement amount at time t, and "u (x ^(t) )" indicates the upper limit value of the power procurement amount at time t. That is, in equation (3), the amount of power procured by the power source is limited to the range of the upper limit and the lower limit of the amount of power procured at the previous time, in other words, the amount of power procured by the power source suddenly increases. It shows the constraint that it cannot be changed. Equation (3) is an equation for all electric power procurement means, and each electric power procurement means n is represented by the following equation (4).

なお、式（４）は、電力調達手段が電源である場合の制約条件であり、電力調達手段がスポット市場、時間前市場、インバランス調整である場合の制約条件としては、各時刻における電力調達量の上限値及び下限値はいずれも予め決められた固定値となる。

Equation (4) is a constraint condition when the power procurement means is a power source, and a constraint condition when the power procurement means is a spot market, a pre-hour market, or an imbalance adjustment is power procurement at each time. Both the upper limit value and the lower limit value of the amount are predetermined fixed values.

In the present embodiment, as described above, as the power procurement means, a relative power source including a constant backup power source, a spot market, a pre-hour market, and an imbalance adjustment are assumed. ^{The power procurement amount x (t)} and the cost c (x ^(t) ) when these power procurement means are used are given by the following equations (5) and (6).

Here, the subscripts "g1 to gN" indicate the relative power supply, the subscript "S" indicates the spot market, the subscripts "I1 to IT" indicate the time frame of the pre-hour market, and the subscript "Im" indicates the time frame. Indicates imbalance adjustment. Thus, equation (5) is a power sourcing amount ^{x (t)} power procurement of the N power _x ^{g1 t} _{~x gN} t, and the spot market power procurement _x ^{S t,} the time before at each time frame It is shown that it is given as a vector of the amount of electricity procured in the market x _I1 ^{t to} _{x IT} ^t and the amount of electricity procured by imbalance adjustment x _Im ^t.

Equation (6), the cost c ^{(x (t))} and the cost _c ^{g1 t} _{~c gN} t of N relative power, and cost _c ^{S t} of the spot market, the time in each time frame for power sourcing It is shown that it is given as a vector of _{the costs c I 1} ^{t to} c _IT ^{t of the} previous market and the cost c _Im ^t due to the imbalance adjustment. The second term on the right side of the equation (6) is a regularization term for imbalance adjustment, and the square of the imbalance amount multiplied by the non-negative constant γ is regarded as a cost and added. Basically, since the cost c (x ^(t) ) for power procurement can be considered as the total cost by a plurality of power procurement means, only the first term on the right side of the equation (6) may be used as the objective function. However, if optimization is performed using only the first term on the right side of Eq. (6) as the objective function, there is a possibility that a procurement plan that intentionally generates a lot of imbalances in order to reduce costs may be generated. Supply and demand adjustment by the power transmission and distribution business operator works for the imbalance generated by the electric power retailer, but this does not mean that you can put out as much imbalance as you like, and each electric power retailer is imbalanced as much as possible. It is basic to procure electricity so as not to generate electricity. In this regard, by providing the above regularization term, as a solution to the optimization problem, a solution that suppresses the occurrence of imbalance while suppressing the power procurement cost can be generated. Further, the value of the constant "γ" is input by the user as a fixed value, whereby the trade-off between the power procurement cost and the imbalance amount can be adjusted. Therefore, it is preferable to include the above regularization term in the equation (6).

The procurement optimization unit 23 uses equations (5) and (6) to solve an optimization problem that minimizes the objective function of equation (1) under the constraints of equations (2) and (3). , Each time t, that is, a power procurement plan for each target period is generated. By repeatedly generating a power procurement plan for each target time using the above optimization model, it is possible to perform optimization considering uncertainty for all power demand scenarios.

[Power procurement plan creation process]
FIG. 3 is a flowchart of the power procurement plan creation process by the power procurement plan creation device 10. This process is realized by the processor 13 shown in FIG. 1 executing a program prepared in advance. First, various information such as the actual demand and the electric power price are input to the electric power procurement plan creating device 10 (step S11). The demand forecasting unit 21 of the power procurement plan creating device 10 predicts the demand d ^(t) based on the input actual demand, information on variable factors, and the like (step S12). Further, the electric power price prediction unit 22 predicts the electric power price based on the past electric power price (step S13). As a result, the above cost c (x ^(t) ) can be obtained. Then, the procurement optimization unit 23 generates a procurement plan in the target period by solving the above optimization model by a mathematical optimization method (step S14).

[Optimization example]
Next, an example in which the procurement optimization unit 23 obtains the power procurement amount using the above optimization model will be described. In the following description, for convenience, it is assumed that all the power procurement means are generators. As shown in FIG. 4, the procurement optimization unit 23 includes a discretization unit 24 and an optimization unit 25.

(Optimized model)
The optimal power procurement plan is a power generation plan that meets the power demand at each time and minimizes the cost. Further, since the power generation amount of each generator depends on the power generation amount one hour ago and cannot be significantly changed from the power generation amount one hour ago, the power generation amount of each generator is restricted. Based on this, the relationship between the power demand and the power generation plan, and the restrictions on the amount of power generated by each generator can be expressed as the above equations (1) to (3).

Here, when the power demand is uniquely determined, the power generation plan can be calculated based on the above equations (1) to (3). However, future power demand cannot be uniquely determined because it is determined by multiple factors. Specifically, the power demand is determined by a plurality of factors such as temperature, weather, and prediction error. Therefore, the power demand is set with an uncertain data range (uncertainty range) determined by a plurality of factors.

By the way, the uncertainty of power demand increases as the time precedes the present. For example, the temperature and weather one day later can be predicted to some extent from the current situation. Therefore, the uncertainty of the power demand after one day can be reduced. On the other hand, for example, it is difficult to predict the temperature and weather after one week from the current situation, so it is also difficult to determine the power demand. Therefore, the uncertainty of power demand after one week becomes large. Thus, the uncertainty of power demand increases as the time precedes the present. Therefore, in order to reduce the uncertainty of power demand, it is necessary to make a decision to replan the power generation plan at a certain timing.

On the other hand, for example, assuming that the generator is a thermal power generator, the thermal power generator requires about 12 hours to start. Therefore, it is necessary to determine a power generation plan that takes into account the start-up time of the thermal power generator. In other words, it is also necessary to make an early decision to replan the power generation plan. Therefore, it is important to make decisions that determine when to replan.

Therefore, in the present embodiment, even when an uncertain constraint is given to the power demand, the robust optimization problem premised on replanning is formulated, and the optimum solution of the formulated robust optimization problem is obtained. Consider determining the optimal power generation plan for the input power demand.

First, at each time, when an uncertain constraint is given to the power demand (a range of uncertainty is set), it is formulated to formulate a plan for that time based on the replanning. do. At this time, since the range of uncertainty is set for the power demand, the formulation will include this content as well. When this content is formulated, it can be expressed as the following equations (7) to (10).

The above equation (7) is an equation showing an optimization problem for determining an optimized power generation plan based on replanning, and is an equation showing an optimization model. The optimization model is a model in which the optimization problem for determining the optimum power generation plan for the demand amount up to a predetermined time (time T) is formulated by the above equations (7) to (10).

The variable of equation (7) corresponds to the variable of equation (1). Equation (7) determines the power demand at time 2 based on the power generation plan at time 1, determines the power generation plan at time 2 based on the power demand at time 2, ..., Power at time T. It is a formula that formulates the determination of the power generation plan at time T based on the demand.

Equations (8) and (9) correspond to equations (2) and (3), and the variables of equations (8) and (9) are the variables of equations (2) and (3). handle.

Next, equation (10) shows the relationship between the power demand at time t + 1 and the power demand from time 1 to time t, and U (d ⁽¹⁾ , ..., d ^(t) ) is the power. A function that determines the extent of demand uncertainty. That is, the equation (10) shows that the range of uncertainty of the power demand at time t + 1 is determined by using the power demand from time 1 to time t as a variable.

The function U of the equation (10) is a function determined according to an existing prediction model that determines the power demand, and is also called a predictor. That is, the function (predictor) U is a known function determined according to the prediction model.

Here, the function U will be described. For example, for the power demand (d ⁽¹⁾ , d ⁽²⁾ , ..., d ^(t) ) at each time, U (d ⁽¹⁾ , d ⁽²⁾ , ..., d ^{(t)). )} ) Suppose you want to calculate the area. Assuming that the power demand forecast model is an autoregressive model for normal noise, it can be expressed as in Eq. (11).

At this time, U (d ⁽¹⁾ , d ⁽²⁾ , ..., d ^(t) ) can be designed as a trust region of, for example, 3σ (99.8%), in which case the function U can be designed by the equation (1). It can be defined as 12).

As described above, the function (predictor) U of the equation (10) can be determined according to the prediction model for determining the power demand, as shown in the equation (12). The above is an example, and the function U can be set based on the prediction model to be used and the reliability to be set.

(Calculation processing of the optimum solution of the optimization model)
Next, the process of calculating the optimum solution of the optimization problem (optimization model) formulated by the equations (7) to (10) will be described. In order to determine the optimum power generation plan for the electric power demand (demand amount), it is necessary to find the optimum solution of the optimization model (optimization problem) formulated by the equations (7) to (10). Therefore, consider finding the optimum solution of the optimization model.

As shown in the equation (10), since the power demand at time t + 1 has a range of uncertainty, there are an infinite number of transition patterns of power demand between time t and time t + 1. In the following description, the transition pattern of electric power demand will be referred to as a “demand scenario”.

Specifically, since the range of uncertainty is set for the power demand at time t, the value of the power demand that can be taken is not finite. Similarly, since the range of uncertainty is set for the power demand at time t + 1, the possible power demand values are not finite. Therefore, the demand scenarios at time t and time t + 1 are continuous and exist infinitely. Therefore, it is generally extremely difficult to find the optimum solution of the optimization problem formulated by the equation (7), and the optimization problem cannot be solved as it is. If this optimization problem can be solved, a robust plan assuming replanning can be calculated. In other words, it is possible to determine the optimum power generation plan that guarantees the power demand at all times.

Therefore, in order to solve the optimization problem formulated by the equation (7), the demand scenario is discretized based on the graph. The discretization unit 24 implements the discretization of the demand scenario.

Here, the discretization of the demand scenario based on the graph will be described with reference to FIGS. 5 and 6. 5 and 6 are diagrams illustrating the discretization of demand scenarios. Specifically, FIG. 5 is a diagram showing a state before the discretization of the demand scenario. FIG. 6 is a diagram showing a state after discretization of the demand scenario.

First, FIG. 5 will be described. The horizontal axis is the axis related to time, and the vertical axis is the axis related to demand. ^{Let d 1} , d ² , and d ³ be the power demands of time 1 to time 3, respectively. It is assumed that the maximum value of the power demand at time 2 is β _v , the minimum value is α _v , the maximum value of the power demand at time 3 is β _u , and the minimum value is α _u .

The discretization unit 24 regards the value of the power demand or the data range of the power demand as each node so that the relationship of the power demand at each time can be understood, and generates the relationship information between the nodes. Specifically, the discretization unit 24 ^{regards the power demand value d 1} at time 1 as the node r, the power demand d ² at time 2 as the node v, and the power demand d ³ at time 3 as the node u. .. Then, the discretization unit 24 generates the relationship information between the nodes, for example, rv-u.

As shown in FIG. 5, the power demand d ² at time 2 has a wider data range than the power demand d ^{1 at time 1.} This is due to the uncertainty of the power demand, ^{and the width set in the power demand d 2} is a data range indicating the range of uncertainty. Next, the power demand d ³ at time 3 has a wider data range than the power demand d ^{2 at time 2.} In this way, the data range grows with time.

FIG. 5 shows the state of the power demand of the equation (10), but as described above, the optimization problem of the equation (7) cannot be solved as it is. Therefore, in order to solve the optimization problem of the equation (7), the demand scenario is discretized as shown in FIG.

FIG. 6 is a diagram showing a state after discretization of the demand scenario. Similar to FIG. 5, the horizontal axis is the axis related to time, and the vertical axis is the axis related to demand. As shown in FIG. 5, since the uncertainty of power demand propagates with time, the data range expands with time. Therefore, in order to narrow the data range indicating the range of uncertainty of power demand, the dispersal unit 24 sets the time (time 2) one time before the time (time 3) at which the power generation plan (optimal solution) is desired to be obtained. The data range in is divided into a predetermined number. In the present embodiment, it is assumed that the data range at time 2 is divided into two.

In the present embodiment, it is assumed that the data range at time 2 is divided into two, but as the number of divisions of the data range is increased, it becomes possible to divide the data range into fine-grained demand scenarios. That is, if the data range can be made infinitely fine, it is equivalent to solving the optimization problem of the equation (7). However, by increasing the number of divisions, it takes a long time to calculate the optimum solution, which is a trade-off between the calculation time and the quality of the calculated solution. Therefore, the number of divisions can be appropriately determined by the user, and may be appropriately changed.

When the discretizing unit 24 divides the data range at time 2, two divided data ranges are generated as shown at time 2 in FIG. The discretization unit 24 sets the node v and the node u in order from the data range at time 2 in which the power demand is relatively small. That is, the discretization unit 24 sets v and u for each data range in order from the bottom of the graph. In the following description, the data range in which the node v is set is referred to as the data range v, and the data range in which the node u is set is referred to as the data range u.

Further, the discretization unit 24 sets indexes for each of the data ranges at time 2 in order from the bottom of the graph. That is, the index 1 is set for the data range v, and the index 2 is set for the data range u.

Next, the discretization unit 24 determines (sets) the data range at time 3 using the function U for each of the data range v and the data range u, and sets a node for each data range. do. Of the data range at time 3, the node w is set in the data range determined from the data range v, and the data range is set as the data range w. Of the data range at time 3, the node q is set in the data range determined from the data range u, and the data range is set as the data range q. Then, the discretization unit 24 generates the relationship information between the nodes. The relationship information between the nodes is shown in the lower figure in the graph of FIG.

By branching the demand scenario as shown in FIG. 6, the discretization unit 24 needs to consider the demand scenario of the electric power demand only for the branched scenario. Therefore, the discretization unit 24 branches the demand scenario as described above.

Although FIG. 6 shows only times 1 to 3, it goes without saying that there may be times 4 or later. For example, when it is desired to obtain a power generation plan (optimal solution) at time t, the demand scenario may be discretized for each data range at time t-1. That is, the discretization unit 24 divides each data range at time t-1 into a predetermined number, and uses the function U for each of the divided data ranges to set the range of uncertainty at time t. Set the data range to be shown. Then, the discretization unit 24 sets nodes for the data range at time t-1 and the data range at time t, and generates relationship information between the nodes. When this process is referred to as a discretization process, the discretization unit 24 repeats the discretization process until a predetermined time (time T).

When the above contents are generalized and expressed as a mathematical formula at time T, they can be expressed as the following equations (13) to (15). Equation (13) is an equation describing a demand scenario branched to a node v (data range v). ch (r) indicates that the node v is a child node of the node r. Although not described here, in the following description, P (v) indicates the parent node of the node v.

In the above, the discretization unit 24 divides the data range and branches the demand scenario. As a result, the data range at time 2 and time 3 can be narrowed from the state shown in FIG. 5, and the equation (7) can be transformed into the equation (13). However, since the configuration of the equation (13) is a configuration in which the maximization problem and the minimization problem are mixed as in the equation (7), it is not possible to find the optimum solution for the optimization problem at present. Can not.

Therefore, the discretization unit 24 discretizes the possible power demand values into a predetermined number of demand values for the divided data range. By doing so, it becomes possible to solve the optimization problem represented by the original equation (7). Specifically, the discretizing unit 24 discretizes the value of the power demand that can be taken in the data range set by using the function U from each of the divided data ranges to the end points (upper limit value and lower limit value).

Next, the optimization unit 25 calculates the optimum solution for the optimization problem of the equation (13). Specifically, the optimization unit 25 optimizes the equation (13) with respect to the end points (upper limit value and lower limit value) that discretize the power demand values that the discretization unit 24 can take in each data range. Calculate the optimal solution to the problem.

Here, the lower end point (lower limit value) in each data range is α, and the upper end point (upper limit value) is β. For example, in the case of the data range v (node v), the lower end point (lower limit value) is α _v , and the upper end point (upper limit value) is β _v . In the data range u (node u), the lower end point (lower limit value) is α _u , and the upper end point (upper limit value) is β _u . As shown in FIG. 6, the data range v and the data range u are continuous data ranges, and the end points above the data range v and the end points below the data range u have the same value, so β The relationship of _v = α _{u holds.}

Next, the variable corresponding to the lower end point (lower limit value) is defined as the variable y, and the variable corresponding to the upper end point (upper limit value) is defined as the variable z. The variable y is a variable for determining the power generation plan corresponding to the lower end point. The variable z is a variable for determining the power generation plan corresponding to the upper endpoint. The power generation plan for the variables y and z can be expressed as follows. y _{v and n} indicate the amount of power generated by the generator n corresponding to the lower end point of the power demand of the data range v (node v). z _{v and n} indicate the amount of power generated by the generator n corresponding to the upper end point of the power demand of the data range v (node v).

The discretizing unit 24 divides the data range of the power demand, and discretizes the possible values of the data range set by using the function U for each of the divided data ranges at the upper and lower end points of the data range. It became. Therefore, as in the above equation (16), the equations (13) and (7) can be replaced with the minimization problem, and the optimization unit 25 can determine the optimum solution (power generation plan).

However, the above equations (16) to (19) alone may not be a power generation plan that satisfies the constraints for all demand scenarios at time 3. Therefore, at time 2, it is necessary to calculate a power generation plan that satisfies the constraints for all demand scenarios. Therefore, a variable for determining the optimum power generation plan in consideration of the case where the power generation plan is most changed from the power generation plans at the upper end point (upper limit value) and the lower end point (lower limit value) of the data range v at time 2. Constraints are given to y and the variable z.

Here, with reference to FIG. 7, the constraints given to the variable y and the variable z to be set will be described. FIG. 7 is a diagram for explaining the constraints given to the variables y and z. Further, FIG. 7 is a diagram illustrating a constraint between the power generation output at time 2 and the power generation output at time 3. The horizontal axis of FIG. 7 is an axis representing the time, and the vertical axis is a diagram showing the amount of power generated by the generator n.

For the sake of simplicity, it is assumed that the upper limit of the amount of power generation at time 2 is y _{p (v), n} and the lower limit of the amount of power generation at time 2 is z _{p (v), n.} do. In reality, the variables that give the maximum value of the amount of power generated by the generator n may be y _{p (v), n} , or z _{p (v), n} . For example, it is conceivable that the amount of power generated by the generator n is the lowest and the amount of power generated by the other generators is the highest. On the contrary, it is also possible that the amount of power generated by the generator n is the highest and the amount of power generated by the other generators is the lowest. That is, it is unknown whether the lower and upper limits of the amount of power generation at time 2 are y _{p (v), n} and z _{p (v), or n.}

Continuing the explanation, it is considered that the constraint on the amount of power generation at time 3 is set so as to satisfy the upper and lower limit constraints for any amount of power generation at time 2 and not to depend on the amount of power generation at time 2. To achieve this, the upper limit of the amount of power generated at time 3 is set to the value when the amount of power generated is the highest from the lower limit of the amount of power generated at time 2, and the lower limit of the amount of power generated at time 3 is set at time 2. The value when the amount of power generation is the lowest from the upper limit of the amount of power generation may be set.

The solid line in FIG. 7 shows the possible amount of power generation that satisfies the restrictions on the amount of power generation shown in the equations (9) and (15) at time 3 with respect to the lower limit of the amount of power generation at time 2. The alternate long and short dash line in FIG. 7 shows the possible amount of power generation that satisfies the power generation amount constraints shown in the equations (9) and (15) at time 3 with respect to the upper limit value of the power generation amount at time 2. ..

Here, in FIG. 7, with respect to the upper limit value and the lower limit value of the power generation amount at the time 2, the possible power generation amount satisfying the restrictions of the power generation amount shown by the equations (9) and (15) at the time 3. There is a range where When the power generation amount at time 3 is included in this overlapping range, the constraint for the case where the power generation amount at time 2 is the upper limit value and the constraint for the case where the power generation amount at time 2 is the lower limit value are satisfied. Become. Therefore, the amount of power generated by the generator that satisfies the condition (constraint) that the amount of power generated at time 3 is included in this overlapping range is determined.

Separately from this, as shown in FIG. 6, the divided data range at time 3 is a data range divided from the same parent node, and is an adjacent (continuous) divided data range. It is designed to share one end. For one end to be shared, it is necessary to have the same power generation plan. Therefore, at time 3, among the continuous divided data ranges divided from the same parent node, the optimum solution for the upper limit value of the first data range and the optimum solution for the lower limit value of the second data range. It is also set as a condition (constraint) that, and match. The first data range is a data range in which the lower limit value is lower than the lower limit value of the second data range.

When the above conditions (constraints) are expressed as mathematical expressions, the following equations (20) to (22) are obtained. As described above, the upper limit of the generator n may be y _{p (v), n} , or z _{p (v), n} , so it is generalized and described. Further, since the following equations are conditions (constraints) set for obtaining the above equations (16) to (19), the equations (16) to (19) are also described.

In equations (20) and (21), the amount of power generation when the amount of power generation at time t is the lowest from the upper limit of the amount of power generation at time t-1, and the lower limit of the amount of power generation at time t-1. It is an expression that expresses the constraint that it is included between the value and the amount of power generation when the amount of power generation is raised the most. That is, in the equations (20) and (21), the power generation amount at time t is the minimum value within the range of the power generation amount constraint with respect to the upper limit value of the power generation amount at time t-1, and the power generation amount at time t-1. It is an expression showing that it is included between the maximum value within the range of the constraint of the amount of power generation with respect to the lower limit value of. In other words, it is necessary to determine the amount of power generated at time t, which satisfies all of equations (20) and (21) at consecutive times up to time t.

Further, the equation (22) shows the amount of power generated with respect to the upper limit of the first data range having the smaller index number among the continuous divided data ranges divided from the same parent node at time t, and the second. It is an equation showing that the amount of power generation with respect to the lower limit of the data range matches. Note that k in equation (22) indicates the index of the divided data range.

As described above, the equations (7) to (10) can be changed to the equations (16) to (22) by discretizing the demand scenario and the value of the electric power demand. Equation (16) is a minimization problem that minimizes ω, that is, it is a problem that can be solved because the maximization problem and the minimization problem are no longer mixed. By solving the above equations (16) to (22), the optimization unit 25 calculates the optimum solution for the upper limit value and the lower limit value of each divided data range at each time.

As described above, the optimum solution for the upper and lower limits of each divided data range can be calculated, but for the optimal solution for the power demand between the upper and lower limits of each divided data range. Also need to be calculated. Therefore, the optimization unit 25 calculates the ratio of the distance between the power demand for which the optimum solution is to be obtained and the upper limit value of each divided data range, and the distance between the power demand for which the optimum solution is to be obtained and the lower limit value. .. Then, the optimization unit 25 calculates the optimum solution for the power demand for which the optimum solution is to be obtained by using the optimum solution for the upper limit value and the lower limit value of each divided data range and the calculated ratio. As a result, the optimization unit 25 can calculate the optimum solution of the optimization model formulated by the equations (7) to (10).

For example, the ratio of the distance from the power demand for which the optimum solution is to be obtained to the upper limit of each divided data range and the distance from the power demand for which the optimum solution is to be obtained to the lower limit is 1-γ: γ. Suppose there is. Further, it is assumed that the optimum solution for the upper limit value of each divided data range is z _{v, n} , and the optimum solution for the upper limit value is y _{v, n.} Then, the solution to power demand to be determined an optimal solution is calculated _{(1-γ) y v,} n + γz v, by _n.

[Display example of power procurement plan]
FIG. 8 shows a display example of the power procurement plan generated by the power procurement plan creation device 10. This display example is displayed, for example, on the display unit 18 of the power procurement plan creation device 10. Further, the power procurement plan creation device 10 may output data indicating the displayed graph. As shown in the figure, the electric power procurement plan shows the amount of electric charge procured by a plurality of electric power procuring means at each time. A demand scenario showing a predicted change in power demand may be displayed at the same time.

[Modification example]
In the above embodiment, relative power supply, spot market, pre-hour market, constant backup power supply, imbalance adjustment, etc. are mentioned as power procurement means, but pre-hour market and constant backup power supply are optional and always. Not always used. Therefore, as a minimum combination, the power procurement plan creation device 10 may generate a power procurement plan by relative power supply, spot market, and imbalance adjustment.

In addition, a part or all of each of the above embodiments (including modifications, the same shall apply hereinafter) may be described as in the following appendix, but is not limited to the following.

(Appendix 1)
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. A model generation means for generating an optimized model having a constraint on the amount of power that can be procured from the power procurement means, and a model generation means.
By solving the optimization model by a mathematical optimization method, a determination means for determining an electric power procurement plan indicating the amount of electric power to be procured from each of the plurality of electric power procurement means, and a determination means.
Information processing device equipped with.

(Appendix 2)
The information processing device according to Appendix 1, wherein the determination means generates the power procurement plan for each time-continuous target period.

(Appendix 3)
The information processing apparatus according to Appendix 2, wherein the optimization model further has a constraint condition regarding supply and demand that the total amount of electric power procured from each of the plurality of electric power procurement means matches the predicted demand in the target period.

(Appendix 4)
The plurality of power procurement means include imbalance adjustment.
The information processing apparatus according to any one of Supplementary note 1 to 3, which is given as the sum of the total cost and the regularization term for imbalance adjustment.

(Appendix 5)
The information processing apparatus according to any one of Supplementary note 1 to 4, wherein the plurality of electric power procurement means includes a relative power source, a spot market, and imbalance adjustment.

(Appendix 6)
Regarding the relative power source, the constraint condition regarding the amount of power that can be procured includes the condition that the amount of power that can be procured in a certain target period is within the range of the amount of power that can be procured in the immediately preceding target period. The information processing device described.

(Appendix 7)
Regarding the spot market and the imbalance adjustment, the constraint condition regarding the amount of power that can be procured is that the amount of power that can be procured in a certain target period is within the range of the amount of power determined for each target period. The information processing apparatus according to Appendix 5 or 6, which includes.

(Appendix 8)
The information processing apparatus according to any one of Supplementary note 5 to 7, wherein the plurality of power procurement means further include a time-before market and a constant backup power source.

(Appendix 9)
Demand forecasting unit means for obtaining the forecasted demand based on the history of past power demand, and
Electric power price prediction means that predicts electric power prices based on the history of past electric power prices,
The information processing apparatus according to any one of Appendix 1 to 8.

(Appendix 10)
The information processing apparatus according to any one of Supplementary note 1 to 9, further comprising a display means for displaying procurement plans determined for a plurality of target periods in chronological order.

(Appendix 11)
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
An information processing method for determining a power procurement plan indicating the amount of power procured from each of the plurality of power procurement means by solving the optimization model by a mathematical optimization method.

(Appendix 12)
The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
A recording medium recording a program that causes a computer to execute a process of determining a power procurement plan indicating the amount of power procured from each of the plurality of power procurement means by solving the optimization model by a mathematical optimization method.

Although the invention of the present application has been described above with reference to the embodiment, the invention of the present application is not limited to the above embodiment. Various changes that can be understood by those skilled in the art can be made within the scope of the present invention in terms of the structure and details of the present invention. That is, it goes without saying that the invention of the present application includes all disclosure including claims, and various modifications and modifications that can be made by those skilled in the art in accordance with the technical idea. In addition, each disclosure of the above-mentioned patent documents cited shall be incorporated into this document by citation.

10 Electric power procurement planning device 21 Demand forecasting department 22 Electric power price forecasting department 23 Procurement optimization department 24 Discretization department 25 Optimization department

Claims (11)

The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. A model generation means for generating an optimized model having a constraint on the amount of power that can be procured from the power procurement means, and a model generation means.
By solving the optimization model by a mathematical optimization method, a determination means for determining an electric power procurement plan indicating the amount of electric power to be procured from each of the plurality of electric power procurement means, and a determination means.
Equipped with a,
The plurality of power procurement means include imbalance adjustment.
The objective function is an information processing device given as the sum of the total cost and a regularization term related to imbalance adjustment. The information processing device according to claim 1, wherein the determination means generates the power procurement plan for each time-continuous target period. The information processing apparatus according to claim 2, wherein the optimization model further has a constraint condition regarding supply and demand that the total amount of electric power procured from each of the plurality of electric power procurement means matches the predicted demand in the target period. .. The information processing apparatus according to any one of claims 1 to 3 , wherein the plurality of electric power procurement means include a relative power source and a spot market. For the relative power, constraints on the procurable amount of power, according to claim 4 is procurable electric energy in the target period, including the condition that it is within the scope of procurable electric energy in the immediately preceding period The information processing device described in. Regarding the spot market and the imbalance adjustment, the constraint condition regarding the amount of power that can be procured is that the amount of power that can be procured in a certain target period is within the range of the amount of power determined for each target period. The information processing apparatus according to claim 4 or 5, which includes. The information processing apparatus according to any one of claims 4 to 6 , wherein the plurality of electric power procurement means further include an hour-ahead market and a constant backup power source. Demand forecasting unit means for obtaining the forecasted demand based on the history of past power demand, and
Electric power price prediction means that predicts electric power prices based on the history of past electric power prices,
The information processing apparatus according to any one of claims 1 to 7. The information processing apparatus according to any one of claims 1 to 8 , further comprising a display means for displaying procurement plans determined for a plurality of target periods in chronological order. The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
By solving the optimization model by a mathematical optimization method, a power procurement plan indicating the amount of power to be procured from each of the plurality of power procurement means is determined .
The plurality of power procurement means include imbalance adjustment.
The objective function is an information processing method given as the sum of the total cost and a regularization term related to imbalance adjustment. The amount of power procured from each of the plurality of power procurement means is used as an operating variable, and the value including the total cost required to procure the amount of power corresponding to the predicted demand using the plurality of power procurement means is used as the objective function. An optimization model having constraints on the amount of power that can be procured from the power procurement means is generated.
By solving the optimization model by a mathematical optimization method, a computer is made to execute a process of determining a power procurement plan indicating the amount of power to be procured from each of the plurality of power procurement means .
The plurality of power procurement means include imbalance adjustment.
The objective function is a program given as the sum of the total cost and the regularization term for imbalance adjustment.

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