Energy operation planing device and energy operation planing method

Abstract

To enable creation of an adequate energy operation plan taking into account the influence of a surrounding environment of a power device such as communication delay, performance such as output response, and external factors.SOLUTION: An energy operation planing device acquires power device information related to power device performance and / or power device state information related to operation state, and creates an operation plan for each power device. On the basis of the power device performance and operation state, it then generates a response of power output of the power device for a transmitted planned command value as a device output responce model including the influence of a surrounding environment of the power device. While predicting the response of the power output of the power device to the planned command value on the basis of the device output responce model, the energy operation planing device generates the plan command value and transmits to each power device so as to compensate for the influence of the surrounding environment of the power device at shorter time intervals than with the operation plan for the power device and satisfy an operation restriction with the operation plan.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to an energy operation planning device and an energy operation planning method.

Background Art

[0002] Business operators having a plurality of energy facilities, business operators capable of adjusting the operation plan of energy facilities, etc. formulate an energy operation plan. This energy operation plan creates an operation plan indicating the guidelines for future operation and power generation output of power equipment based on predicted values of future situations in order to realize the optimal operation of power equipment connected to the power grid.

[0003] That is, the energy operation plan determines the operation / stop state and power generation output of power equipment while satisfying the operation constraints of each generator and power grid based on the predicted values of future situations at each time during the planned period. Thereby, the operation cost can be minimized or the profit from power generation can be maximized. The time interval of this plan is generally in 30-minute increments, and the plan is created on the day before the operation period targeted by the plan and adjusted according to the situation on the day thereafter. As an example of the method for creating the plan, there is the technology described in Non-Patent Document 1. In addition, when connecting power equipment to the power grid, since there is an obligation to report the operation state of the power equipment to the power grid operator in advance, the created plan is sent to the power grid operator by the day before the operation date. Note that fines are imposed as imbalance charges as the power output such as the power generation amount deviates from the sent plan. Therefore, in order to achieve economical operation, it is necessary to operate the power equipment according to the power generation amount in the plan.

[0004] The power generation amount of the plan created in 30 - minute increments is treated as a target value for the average value of the actual power generation output over 30 minutes when operating power equipment according to the plan. On the other hand, in order to make the 30 - minute average value of the actual power generation amount of the power equipment the same as the output according to the plan value, it is necessary to constantly calculate the planned command value at short - time increments based on the 30 - minute plan value and use it as the command value for the power equipment that is operated moment by moment. Since the power equipment generates power output according to this planned command value at short - time increments, the planned command value not only needs to have a 30 - minute average value equal to the plan but also needs to meet more detailed equipment specifications compared to the response speed of the power equipment, etc. In this way, by utilizing the power operation plan and the planned command value, the power equipment is always properly operated.

[0005] In recent years, in renewable energies such as solar power generation and wind power generation that have been actively introduced, since the power generation output varies according to the weather, the actual power generation amount of the power equipment may not be the same as the planned value submitted to the power grid operator in advance. For this reason, in response to the output fluctuations of renewable energies, a virtual power plant (VPP) has been introduced, which adjusts the power generation output of generators by using power storage batteries, fuel cells, etc. whose power generation output is controllable, so that the total power output of multiple power equipment is as planned.

[0006] As a method of obtaining the desired power by configuring with multiple power equipment, for example, the technology described in Patent Document 1 is known. When controlling each power equipment according to the planned command value, the technology described in Patent Document 1 corrects the command value of each power equipment from the point of deviation to the future according to the priority order of the equipment when the planned command value deviates from the power output of a specific power equipment. According to the technology described in this Patent Document 1, the planned command value is updated regularly or at the time of deviation, so that it always becomes an appropriate planned command value according to the situation, and it becomes possible to operate the actual power generation amount of the power equipment as planned.

Prior Art Documents

Non - Patent Documents

[0007]

Non-Patent Document 1

Patent Documents

[0008]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0009] In the technology described in Patent Document 1, when the power output of a specific power device deviates from the planned command value, the period average value of the total power output of a plurality of power devices is made to be as planned by correcting the planned command value after the deviation occurs. On the other hand, when the output of renewable energy fluctuates steeply and unexpectedly, it is necessary to secure an output margin for power devices capable of high-speed output response so that the steep fluctuations can be compensated in advance. In particular, when making the period average value of the power output equal to the planned value, if a steep change occurs at the final point within the period, due to the lack of time allowance until the final point, there is a possibility that the imbalance charge will be levied because it does not reach the planned value.

[0010] In self-dispatch and virtual power plants, which have been increasingly introduced in recent years, power devices in remote locations are also targeted. In such cases, a communication delay occurs in the process of transmitting the planned command value to the remote device, and due to the transmission delay of the planned command value, the power output of the remote device may increase. If the delay is large, the power generation amount of the actual power device may not reach the planned command value, so the period average value of the power output may not be equal to the planned value.

[0011] The present invention has been made in consideration of the above points, and is capable of creating an appropriate energy operation plan including the influence of the surrounding environment of power equipment such as transmission delay, performance such as output responsiveness, or other external factors. An object of the present invention is to provide an energy operation planning device and an energy operation planning method.

Means for Solving the Problems

[0012] In order to solve the above problems, for example, the configuration described in the claims is adopted. This application includes a plurality of means for solving the above problems. For example, as an energy operation planning device, a planning information section that stores power equipment information related to power equipment performance and / or power equipment status information related to the operation state, and information necessary for creating a plan in the planning information section as input, an operation plan creation section that creates an operation plan for each power equipment, a device output response model section that generates, as a device output response model including the influence of the surrounding environment of the power equipment, the response of the power output of the power equipment to the transmitted planned command value based on the power equipment performance and operation state in the planning information section, and while predicting the response of the power output of the power equipment to the planned command value based on the device output response model, compensates for the influence of the surrounding environment of the power equipment at time intervals shorter than the operation plan of the power equipment, and generates a planned command value so as to satisfy the operation constraints with the operation plan and transmits it to each power equipment, and an operation plan command section.

Effects of the Invention

[0013] According to the present invention, for example, even when affected by the surrounding environment of power equipment such as transmission delay or when the output of some power equipment fluctuates, it is possible to generate a planned command value that satisfies the operation constraints. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0014]

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Embodiments for Carrying Out the Invention

[0015] Hereinafter, preferred embodiment examples of the present invention will be described. Note that the following description is merely an embodiment example and is not intended to limit the invention itself to the specific content described below. In addition, in each of the following embodiment examples, the same or similar elements and processes are denoted by the same reference numerals, and redundant descriptions are omitted. Further, in and after the second embodiment example, only the differences from the already described embodiment examples are described, and redundant descriptions are omitted. In addition, the descriptions of the following embodiment examples and the configurations and processes shown in the respective drawings show the outlines of the embodiment examples to the extent necessary for understanding and implementing the present invention, and are not intended to limit the embodiments according to the present invention. Also, each embodiment example and each modification example can be partially or wholly combined within the scope of not departing from the gist of the present invention and being mutually consistent.

[0016] [First Embodiment Example] <Configuration of Power Operation Planning Device> First, a functional configuration example of the power operation planning device 10 according to the first embodiment example will be described with reference to FIGS. 1 and 2. FIG. 1 is a block diagram showing a functional configuration example of a power operation planning device according to the first embodiment. A power operation planning device 10, which is an example of an energy operation planning device, performs processing for a power operation plan. To perform this processing for the power operation plan, the power operation planning device 10 includes a plan information unit 11, a power operation planning unit 12, a result storage unit 13, an operation plan command transmission unit 14, and a display unit 21.

[0017] The plan information unit 11 stores, as a database (DB), information required for creating a power operation plan and state information of detected power equipment. For example, the plan information unit 11 stores a power equipment information DB2, a power equipment state information DB3, a regular inspection information DB4, a demand prediction information DB5, a power market information DB6, and a renewable energy information DB7. That is, the plan information unit 11 performs storage processing of various information such as power equipment information storage processing and power equipment state information storage processing. These information DBs 2 to 7 are also referred to as plan information.

[0018] The power equipment information DB2 is information such as equipment constants indicating equipment characteristics such as the specifications of each power equipment. The power equipment state information DB3 is information storing the results of detecting the states of each power equipment. The regular inspection information DB4 is information for maintenance inspection that stops the operation of power equipment or limits its output. The demand prediction information DB5 is information on prediction errors such as the predicted value of power demand, which is the required power generation amount, and the confidence interval of the predicted value, for each base where power equipment is connected.

[0019] The power market information DB6 is information on the power market, including power commodities that can be bought and sold for each type of power market and the predicted value of the power market price, in a power market where power can be bought and sold as a commodity. The renewable energy information DB7 is information on prediction errors such as the characteristics, power generation amount, predicted value, and the confidence interval of the predicted value of equipment that uses renewable energy among power equipment.

[0020] Next, the configuration and functions of the power operation planning department 12 will be described. Note that the detailed processing in each configuration will be described with reference to the flowchart regarding the processing described later. The power operation planning department 12 includes an operation plan creation section 12a, a device output response model section 12b, and an operation plan command section 12c.

[0021] The operation plan creation section 12a performs operation plan creation processing. That is, the operation plan creation section 12a takes, as input, information necessary for creating a power operation plan from the plan information section 11, and creates a plan indicating the power output of each power device at each time during a specific period such as one day while satisfying the constraints on power operation so that the operation cost is minimized. As information necessary for creating the power operation plan here, for example, the power device information DB2, the power device status information DB3, the demand prediction information DB5, the power market information DB6, etc. are used.

[0022] The device output response model section 12b performs device output response model generation processing. That is, the device output response model section 12b generates a device output response model indicating the state and performance of a power device, including the influence of the surrounding environment of the power device such as communication delay, based on the power device information DB2 and the power device status information DB3 obtained from the plan information section 11.

[0023] When fluctuations occur in power demand or the like, the operation plan command unit 12c creates plan command values at shorter time intervals than the plan values created by the operation plan creation unit 12a, on the premise that operation constraints can also be satisfied by updating the plan command values. At this time, it is assumed that the power demand or the like is as predicted from the demand prediction information DB5, the renewable energy information DB7, etc. of the plan information unit 11, and that the prediction fluctuates within the prediction confidence interval.

[0024] Here, the operation plan command unit 12c predicts the future power output of each power device according to the plan command values from the current state to the future while creating plan command values that satisfy the operation constraints from the current state to the future within the planning period, so that each power device can output power as per the plan command values. When creating this plan command value, the operation plan command unit 12c also refers to the device output model generated by the device output response model unit 12b and the power device information DB2 and the power device status information DB3 of the plan information unit 11.

[0025] The result storage unit 13 stores the power operation plan created by the operation plan creation unit 12a, the plan command value created by the operation plan command unit 12c, and the constants of the device output response model created by the device output response model unit 12b as the power operation plan database DB1, and sends the plan command value to the operation plan command transmission unit 14. The display unit 21 displays the data stored by the result storage unit 13. The operation plan command transmission unit 14 transmits an operation plan command including the plan command value created by the operation plan command unit 12c to the group of a plurality of power devices 130 connected to the power system. Also, the plan information unit 11 acquires the device status in the power device group 130 and updates the various statuses of the power devices.

[0026] <Hardware Configuration of Power Operation Planning Device> FIG. 2 is a diagram showing a configuration example of a power system 100 to which power devices are connected and a hardware configuration example of the power operation planning device 10 according to the first embodiment example.

[0027] Above Figure 2, a configuration example of the power system 100 is shown. The power system 100 is a system in which power equipment 130 such as renewable energy, generators, storage batteries, fuel cells, and bases 160 such as consumers or factories where the power equipment is installed are connected by a network. The power demands 150 at each base 160 are coordinated with each other via a bus (node) 110, a transformer 120, a transmission line 140, etc. Information on the state of the power equipment detected by the power equipment 130 and the power demand 150 within a plurality of bases 160 in the power system 100 is stored in the power operation plan DB1 and the plan information DB2 to DB7 shown on the lower side of Figure 2 via the communication network 300.

[0028] Also, as systems in the power system, there are a power market that operates a market for buying and selling electricity and a central power station that stably operates the power system. The market price (market information) in the power market and the power command from the central power station are also stored in the power operation plan DB1 and the plan information DB2 to DB7 via the communication network 300. Among these pieces of information, the information related to the operation state of the power equipment is stored in the database DB3 of the power equipment state information, and the rest is stored in the power operation plan database DB1.

[0029] In Figure 2, various measuring instruments for the purposes of protection, control, and monitoring of the power system 100 are appropriately installed on the bus 110. The signals detected by the measuring instruments are transmitted to the communication unit 23 of the power operation plan device 10 via the communication network 300. The dashed lines in the figure represent the state where the power equipment 130 and the power demand 150 wirelessly transmit the signals detected by the measuring instruments to the communication network 300.

[0030] The lower side of Figure 2 shows a hardware configuration example of the power operation plan device 10 for the power system 100. The power operation plan device 10 is composed of a computer system. This power operation plan device 10 includes a display unit 21, an input unit 22, a communication unit 23, a CPU (Central Processing Unit) 24, a memory 25, and various databases (power operation plan DB1, plan information DB2 to DB7), and are each connected to the bus line 26.

[0031] The display unit 21 is, for example, a display device. Note that the display unit 21 may be configured to use, for example, a printer device or a voice output device instead of or together with the display device. The input unit 22 is configured to include at least one of, for example, a pointing device such as a keyboard or a mouse, a touch panel, a voice instruction device, and the like.

[0032] The communication unit 23 includes a circuit and a communication protocol for connecting to the communication network 300. Further, the communication unit 23 also communicates with an aggregator or a power generation company that monitors and controls a plurality of distributed power sources and consumers such as a weather system, a power market, and a VPP (Virtual Power Plant).

[0033] The CPU 24 cooperates with the memory 25 to execute a program to realize each part of the power operation planning device 1, issue an instruction for image data to be displayed, search for data in various databases, and the like. The CPU 24 may be configured as one or more semiconductor chips, or may be configured as a computer device such as a computing server.

[0034] The memory 25 is configured as, for example, a RAM (Random Access Memory), stores a computer program, stores calculation result data, image data, etc. necessary for each process. Further, the memory 25 may use a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a non-volatile memory, or the like. In this case, the memory 25 is used as an example of a computer-readable non-transitory storage medium that stores a program executed by the power operation planning device 1.

[0035] The data stored in the memory 25 is sent to the display unit 21 for display, or commands and information necessary for the operation of power equipment, such as operation plan command values, are transmitted to the power equipment at each site 160 via the communication network 300. Since each power equipment is connected to and operated in the power system 100 including the communication network 300, the power operation planning device 10 needs to generate operation plan command values for various power equipment so as to respond to changes in the surrounding environment within these power systems 100 and to satisfy the constraints in system operation.

[0036] <Process of creating operation plan command values for power equipment in the power operation planning department> Next, the process of creating operation plan command values in the power operation planning department 12 of the power operation planning device 10 will be described with reference to FIG. 3.

[0037] FIG. 3 is a flowchart showing an example of the process of creating a power operation plan and creating operation plan command values based on the operation plan by the power operation planning department 12 in the power operation planning device 1 according to the first embodiment. Hereinafter, each process related to the creation of the power operation plan and the creation of operation plan command values based on the operation plan will be described in order using this flowchart.

[0038] First, the power operation planning department 12 extracts necessary information such as power equipment information and power equipment status information from the planning information department 11 in order to create an operation plan and operation plan command values (step S10). Next, the operation plan creation unit 12a creates an operation plan that shows when the power equipment starts and stops and how much power it outputs in about 30-minute intervals for one day using the extracted information as input (step S11). As an example of a method for creating a power operation plan, there is a technique described in Non-Patent Document 1. Since it is necessary to report the operation plan to the power system operator in advance when connecting the power equipment to the power system, the power operation plan is created on the day before the operation day. After that, the plan is slightly modified according to the situation on the operation day.

[0039] Next, when creating the planned command value to be transmitted to the power equipment, the equipment output response model unit 12b generates an equipment output response model showing the current output response performance according to the planned command value of the power equipment (step S12). Note that since the output response performance with respect to the planned command value also changes depending on the external environment of the power equipment such as transmission delay, the influence of not only the main body of the power equipment but also the surrounding external environment is included in the output response model. Examples of this output response model include a discrete-time state equation model and a non-linear model, and examples of these models will be described below.

[0040] · Discrete-time state equation model As shown in Equation [1], the discrete-time state equation model is a model that shows the equipment state x[k + 1] of the power equipment at the next time point k + 1 based on the planned command value u[k] transmitted to the power equipment at time point k and the state variable x[k] of the power equipment. Here, A, B, and C are coefficient matrices, y is the power output of the power equipment, and it is output by multiplying the coefficient matrix C by the state variable x[k] of the power equipment. Equation [1] is a difference equation in which the state at the next time point k + 1 is represented by the previous time points x[k] and u[k], and the response of the change in power output y from time point k can be simulated by appropriately determining the coefficient matrices A and B. Note that as a method for determining A, B, and C, it can be calculated by applying a system identification method such as the subspace method.

[0041] [Equation 1] x[k + 1]=Ax[k]+Bu[k] y[k]=Cx[k]

[0042] · Non-linear model (machine learning) As shown in Equation [2], the non-linear model is a model that predicts the future equipment state or power output of the power equipment based on the variable z including the planned command value u and the state variable x of the power equipment. The value obtained by multiplying the vector z by the weight matrix W and adding the bias coefficient b is used as the input to the non-linear function g. The output h of the non-linear function g is arranged in parallel with h1 having the same configuration in the same layer 1 and used as the input to h2 in the upper layer 2. By multi-layering, the model is configured. This model is a type of machine learning and is called a neural network. It is said that high prediction accuracy can be obtained by appropriately setting the coefficient matrices W and b in the model. Note that, unlike the discrete-time state equation model, z may include x and u at multiple time points in the past from time point k. Also, a non-linear function may be set so that a non-linear model can be assumed in the optimization problem described later.

[0043] [Equation 2] h 1 = g 1 (zW 1 + b 1 ) h 2 = g 2 (h 1 W 2 + b 2 ) y = g 3 (h 2 W 3 )

[0044] [Equation 2] Each item in the equation is defined as follows. g (n) : Activation function of the nth layer W (n) : Weight coefficient of the nth layer b (n) : Bias coefficient of the nth layer z: Input information, a vector composed of x[k] and u[k] y: Output information, x[k + 1] or y[k]

[0045] The discrete-time state equation model and non-linear model (machine learning) described above are models that show the characteristics of the temporal change of the output with respect to the input based on the data of u, x, and y detected so far. The parameters such as the coefficient matrix in the model are adjusted based on the data that can be obtained in advance or during operation so that the relationship between the input and the output can be simulated. Note that when the model is adjusted while operating the device, it is possible to simulate the characteristics of a more up-to-date model.

[0046] In these models, if the planned command value transmitted from the power operation planning device is used as the input and the power equipment status information received by the power operation planning device is used as the output, the characteristics between transmission and reception from the power operation planning device can be modeled. That is, the response of the power output to the planned command value of the power equipment, including the surrounding environment of the power equipment model such as transmission delay and the influence outside the assumed specifications, is modeled.

[0047] Note that if the input of the model is the command value transmitted from the power operation planning device and the output of the model is the status of the equipment received by the power operation planning device, modeling is possible even if the details of the power equipment are unknown. For example, in a demand response that transmits a power consumption reduction command to a customer, it is possible to simulate even if the details of the customer are unknown. Especially when the response of the demand response varies depending on the customer, the total value of the power output of multiple customers may be the target of modeling.

[0048] Returning to the explanation of the flowchart in FIG. 3, after generating the equipment output response model in step S12, the equipment output response model unit 12b constitutes a prediction model that predicts the future power output of the power equipment when the planned command value is input by the generated equipment output response model. In the prediction model created in step S12, as shown in the following [Equation 3], in x[k + 1]=f(x[k], u[k]), it is possible to predict x[k + n] at a future time point by repeatedly substituting the state equation of the past x[k]. In particular, it shows that if the current state x[k] and the input u[k + n] from the current time point k to the future time point k + n can be determined, the future states x[k + 1]…x[k + n] can be predicted.

[0049] [Equation 3] x[k + 1]=f(x[k], u[k]) x[k + 2]=f(x[k + 1], u[k + 1])= f(f(x[k], u[k]), u[k + 1]) x[k + 3]=f(x[k + 2], u[k + 2])= f(f(f(x[k], u[k]), u[k + 1]), u[k + 2])

[0050] For the time from the current time k to the future time k + n, when the planned command values are changed from u[k] to u[k + n], the future power output of the power equipment can be predicted to be from x[k] to x[k + n]. Here, the operation plan is an operation plan shown in about 30 - minute intervals for one day. For the planned values for each 30 - minute period, the time - average value of the power output of the power equipment needs to match the planned value. Assuming that these 30 - minute time - average values are considered and the changes in future situations are addressed in advance, in order for the power equipment to operate according to the plan, the operation plan creation unit 12a predicts the future situation up to [k + n] of about 2 hours and creates the planned command values. Note that the planned command value, which is the target value of the power output, is set in short time intervals of about several minutes because the power equipment operates moment by moment.

[0051] Here, the equipment output response model unit 12b constructed a prediction model through the processes of step S12 and step S13. On the other hand, in machine learning, it is possible to directly configure the relationship between u[k]…u[k + n] and x[k]…x[k + n] as the prediction model without going through step S12. In this case, the prediction model of step S13 may be configured without going through step S12.

[0052] The operation plan command unit 12c utilizes the prediction model configured in step S13 to generate the planned command values so that the cost required for operation is minimized (step S14). Here, while assuming x[k]…x[k + n] for u[k]…u[k + n], the planned command values u[k]…u[k + n] are generated such that the operation - related constraints regarding x[k]…x[k + n] are satisfied and the cost required for operation is minimized. Also, when the power equipment includes renewable energy, since the output may change unexpectedly according to the weather, it is necessary to ensure that the operation - related constraints can be satisfied even when the output changes.

[0053] When the operation planning command unit 12c considers the output change according to the weather included in the renewable energy for the power equipment, it generates a plurality of scenarios assuming the output change. Even when any of these scenarios occurs, the operation planning command unit 12c can cope with the output change according to the weather included in the renewable energy by generating the planned command values u[k]…u[k+n] as described below so that the operation constraints can be satisfied. Here, the scenario assuming the output change of the renewable energy is created based on the measured values of the past power generation amounts of the renewable energy, where the specific weather conditions and the assumed time are the same.

[0054] Figure 4 shows an example of the scenario creation method. The horizontal axis in Figure 4 represents time, and the vertical axis represents the power output of the renewable energy. As shown in Figure 4, from the measured values with the same conditions, the measured values with the maximum increase or decrease in power output during a specific period such as 30 minutes from the start of the fluctuation are extracted. In this way, a fluctuation scenario is created by adding the increase or decrease amount of the measured value to the predicted value of the current renewable energy. In the case of solar power generation, it is assumed that the output of solar power generation at a local point decreases due to the shadow of locally generated clouds. In such a case, the movement trend of the clouds near the solar power generation may be added as a condition to the specific weather conditions.

[0055] Returning to Figure 3 and continuing the explanation, next, the operation planning command unit 12c generates planned command values that allow for output fluctuations of u[k]…u[k+n] based on the prediction model configured in step S13 and the power output fluctuation scenario of the above power equipment (step S14). In creating the planned command values in step S14, as follows, in addition to the operation constraints of the power equipment, u[k]…u[k+n] is generated by solving an optimization problem that includes the prediction model and the fluctuation scenario. Here, an example of the optimization problem is shown below.

[0056] · Example of the optimization problem in the operation plan assuming uncertainty Objective function: Minimization of the total power generation cost of all power equipment during the planning period

[0057]

Number

[0058] [Number 4] Each item of the formula is defined as follows. S: The number of scenarios or a set of scenarios. As an example of a scenario, assume a predicted value scenario of renewable energy or a scenario during output fluctuation. s: Scenario number T end : End time of the plan N gen : Number of power equipment a i , b i , c i : Power output cost coefficient of power equipment i u iks : Planned command value u[k] at scenario s, power equipment i, and time k P iks : Predicted power output value of scenario s, power equipment i, and time k for the planned command value u PNL ts : Penalty associated with violation of operation constraints for the number of scenarios

[0059] The constraint conditions here are as follows. These constraint conditions are defined for each scenario. · Mathematical formula of the prediction model that outputs the predicted value of the power equipment output for the planned command value · Maximum and minimum output (the predicted value of the power output of each power equipment is within the range from the maximum output to the minimum output) · Agreement between the 30 - period average value of the sum of the predicted values of the power outputs of all power equipment and the planned value · Agreement of the planned command values between each scenario before the fluctuation of the power output of power equipment such as renewable energy

[0060] In the above optimization problem, the objective function is set to generate a planned command value that minimizes the sum of the cost associated with the power output of each power equipment in each scenario S and the amount of violation of operation constraints. Also, as shown in FIG. 4, renewable energy assumes both an increasing variation and a decreasing variation as predicted, and constraint conditions are defined so that operational constraints can be satisfied even when each scenario occurs. By solving the optimization problem, it is possible to generate planned command values that satisfy the constraint conditions while minimizing these objective functions.

[0061] Here, among the constraint conditions, according to the prediction model constructed in step S13, from the current time k to the future time T end up to u iks …u iTend s for P iks …P iTend s is predicted. By this prediction of the power output, even when there are effects due to the surrounding environment such as communication delay and response delay of the power output to the planned command value, it is possible to satisfy the operation constraints while considering these effects. In particular, when a prediction model based on machine learning with the non-linear function g as a function that can be implemented in the optimization problem (for example, ReLU: Rectified Linear Unit) is selected, more accurate and complex predictions become possible during optimization.

[0062] Note that the above optimization problem also assumes output fluctuations of renewable energy due to multiple scenarios. As shown in FIG. 4, before the start of the fluctuation of renewable energy, since it is unknown whether the fluctuation will actually occur, constraint conditions are given such that the planned command values u iks are the same in all assumed scenarios. This optimization problem is an optimization problem called a quadratic programming problem or a mixed-integer programming problem, and can be solved by using a general commercial optimization solver.

[0063] Next, the operation planning command unit 12c determines whether an operation constraint violation that cannot be satisfied occurs in step S14 (step S15). In step S15, if an operation constraint violation occurs (no in step S15), the content and amount of the constraint violation are reported, and the operation plan creation unit 12a updates the operation plan for one day so as to ensure a margin of power output to avoid the constraint violation (step S16). After updating the operation plan in step S16, the process returns to the generation process of the planned command value in step S14.

[0064] When updating in step S16, since the operation plan creation unit 12a creates a plan for one day with a time step of 30 minutes, short-term operation constraints may not be taken into account. Also, in step S14, the planned command value with a short time step is generated by predicting up to a few hours in the future, so there is a possibility of detecting these short-term operation constraint violations a few hours before the constraint violation occurs. Therefore, the operation plan command unit 12c utilizes the generation result of the planned command value in step S14 to detect a constraint violation in advance, and updates the operation plan for one day so as to ensure a margin of power output to avoid the constraint violation. By coordinating this planned command value generation and the update of the operation plan, it becomes possible to operate the power equipment while satisfying both long-term and short-term constraints, with short-term constraints being handled in the generation of the planned command value in step S14 and the operation plan being updated.

[0065] In step S15, if no operation constraint violation occurs (yes in step S15), the process proceeds to step S16. That is, the result storage unit 13 stores the power operation plan created by the operation plan creation unit 12a, the planned command value created by the operation plan command unit 12c, and the predicted value of the power output of the power equipment with respect to the constants of the equipment output response model generated by the equipment output response model unit 12b and the planned command value (step S17). The data stored by the result storage unit 13 is displayed by the display unit 21.

[0066] The operation plan command value saved by the result storage unit 13 is transmitted from the operation plan command transmission unit 14 to each power device 130 (step S18). Each power device 130 is controlled so that the power output becomes the planned command value based on the transmitted planned command value. Here, in this embodiment example, since the planned command value is generated in advance according to the current state and characteristics of the power device, the power device can output without violating the planned command value.

[0067] Furthermore, after transmitting the operation plan command value in step S18, after a period shorter than the planned command value assuming the future time point k + n from the current time point k has elapsed (k = k + 1), each power device 130 extracts the current state and planned information of the latest state (step S19). Thereafter, the device state information of the plan information unit 11 is updated to the latest state information, and the process returns to step S12. As a result, the generation of the planned command value is periodically updated based on the latest state of the power device in a short period, and even if an error occurs between the planned command value generated at time point k and the power output of the power device 130, it can be corrected at time point k + 1.

[0068] <Effect of the First Embodiment> In the power operation plan device 10 according to the first embodiment of the present invention described above, the response of the power output of the power device 130 to the planned command value including the influence of the surrounding environment such as power device performance and communication delay is modeled as a device output response model. By generating the planned command value while predicting the power output of each power device 130 with respect to the planned command value using these device output response models of the power devices 130, it is possible to compensate for the influence of the surrounding environment and output performance of the power devices 130 and obtain the effect of satisfying the operation constraints.

[0069] Also, by obtaining the device output response model, the following effects can be obtained. That is, in the exemplary embodiment, in the optimization problem for generating the planned command value, a machine learning model (non-linear model) using a definable or implementable non-linear function is adopted. As a result, by modeling the influence of the ambient environment and output performance of the power equipment with higher accuracy, a more appropriate planned command value can be generated by solving the optimization problem.

[0070] Also, based on the planned command value transmitted to the power equipment and the power output of the power equipment, while operating, the learning (adjustment) of the equipment output response model is carried out simultaneously, so that an equipment output response model applied to the latest situation can be obtained. As a result, a more appropriate planned command value can be generated by solving the optimization problem. In particular, in the case of demand response assuming equipment failures and consumers during operation, although the details of the command target are unknown, it is possible to grasp an appropriate model, that is, the output response characteristics while operating.

[0071] In addition, when generating the planned command value, a scenario based on the measured values of past renewable energy and the measured values with the same specific weather conditions and assumed time may be considered. This scenario may be a scenario in which the measured values change only by the increase or decrease amount during a specific period such as 30 minutes due to local weather conditions such as the influence of local clouds from the predicted values of renewable energy. By assuming that the planned command value is the same for each scenario before the start of the variation, including these variation scenarios, on the premise that the variation cannot be expected before the start of the variation, it is possible to generate a planned command value that allows variation even in the presence of variation factors such as the output variation of renewable energy.

[0072] Also, when calculating the planned command value while predicting the future situation of the power output of the power equipment by the equipment output response model, there may be cases where the operating constraints regarding the planned value and the planned command value cannot be satisfied in the future situation. In such cases, the plan is updated to ensure an operating margin that allows the planned command value to be satisfied. By the cooperation of this plan and the planned command value, it becomes possible to satisfy both constraints in the generation of a plan with a long time period and the generation of a planned command value with a short time period.

[0073] In the above-described first embodiment example, the power operation planning device 10 was taken as an example with power as the energy. In contrast, gas, wind power, hydropower, biomass, etc. may be taken as an example of energy instead of power, and the operation planning of these energies may be executed by an energy operation planning device that performs the same processing as the power operation planning device 10.

[0074] [Second Embodiment Example] Next, the energy operation planning device according to the second embodiment example of the present invention will be described with reference to FIGS. 5 to 8. In FIGS. 5 to 8, the same reference numerals are given to the same parts as those in FIGS. 1 to 4 described in the first embodiment example, and redundant description will be omitted.

[0075] [Configuration of Power Operation Planning Device] FIG. 5 shows an example of the functional configuration of the power operation planning device 10 according to the present embodiment example. The power operation planning unit 12' of the power operation planning device 10 according to the present embodiment example includes an assumed performance correction unit 12d and an external command scenario generation unit 12e in addition to the components included in the power operation planning unit 12 of the power operation planning device 10 shown in FIG. 1. These assumed performance correction unit 12d and external command scenario generation unit 12e perform necessary processing assuming, for example, bidding in the supply-demand adjustment market and application to the operation of power equipment after a contract.

[0076] Here, the supply-demand adjustment market treats the speed of the power output responsiveness of power equipment as an adjustment power commodity and enables it to be bought and sold in the market. When an output command with a fast-changing speed corresponding to the sold adjustment power commodity is received from the outside during the operation of power equipment after power sales in the power market, it is necessary to adjust the output of the power equipment according to the command.

[0077] Note that after selling the adjustment power commodity, it is necessary to ensure a margin of power output necessary for the response to an external power command. However, since a power output command is transmitted from the operator of the external power system according to the situation of the power system, it is not always possible to receive a power command. Here, as an example, an explanation will be given for the case of targeting this supply-demand adjustment market. However, the functions described in this exemplary embodiment are not limited to the supply-demand adjustment market.

[0078] The assumed performance correction unit 12d shown in FIG. 5 compares the assumed specifications, which are the specifications of power equipment such as power equipment information extracted from the plan information unit 11, with the measured performance based on the equipment output response model generated by the equipment output response model unit 12b. When there is a difference in this comparison, the assumed performance correction unit 12d updates the power equipment information, which is the assumed specification, and also updates the setting values necessary for the operation planning unit, such as the available supply amount in the power market, based on the updated values. The setting values updated by the assumed performance correction unit 12d are output to the operation plan creation unit 12a.

[0079] The external command scenario generation unit 12e pre-generates an assumed scenario when an adjustment command occurs from outside the power operation planning device 10 for the power output of the power equipment, and outputs it to the operation plan command unit 12c. The operation plan command unit 12c generates a planned command value that can respond to future external commands by generating a planned command value that ensures an output margin capable of responding to the external command scenario. Note that the hardware configuration of the power operation planning device 10 shown in FIG. 5 is the same as the hardware configuration of FIG. 2 described in the first exemplary embodiment.

[0080] <Processing in the power operation planning device> FIG. 6 is a flowchart showing an example of processing related to the creation of a power operation plan and the creation of an operation plan command value, including the functions of the assumed performance correction unit 12d and the external command scenario generation unit 12e, in the power operation planning unit 12' of the power operation planning device 10 according to the second exemplary embodiment. Hereinafter, with reference to FIG. 6, the processing of the assumed performance correction unit 12d and the external command scenario generation unit 12e and the differences from the first exemplary embodiment will be mainly described.

[0081] First, the power operation planning unit 12' extracts necessary information such as power equipment information and power equipment status information from the plan information unit 11 in order to create an operation plan and an operation plan command value (step S10). Next, in the operation plan creation unit 12a, using the extracted information as input, an operation plan is created that shows, for each half-hour increment throughout the day, when the power equipment starts and stops on the operation day and how much power it outputs (step S11). At this time, in addition to the processing described in the flowchart of FIG. 3, the operation plan creation unit 12a creates an operation plan assuming the supply-demand adjustment market.

[0082] For each regulation power product in the supply-demand adjustment market, the required output responsiveness varies depending on the product, and the requirements such as the response time to the external command value, the output response speed, and the output duration are different. Therefore, based on the specifications of the power equipment, it is necessary to calculate the available supply volume of the regulation power product so as to meet the requirements and grasp the maximum volume that can be bid in the market. For example, in the case of a generator, the response time to the external command value is affected by waste time such as the output response speed and communication time. In addition, in the case of a storage battery, the output duration is affected by the remaining charge amount. The operation plan creation unit 12a calculates the bidable volume of each product according to these assumed specifications.

[0083] Based on this bidable volume, the operation plan creation unit 12a determines whether to sell electricity in the supply-demand regulation power market from the perspective of minimizing the operation cost and creates an operation plan. If the assumed specifications are different from the actual measured values, if a set of multiple power sources combined like a virtual power plant is regarded as one power equipment, or if the demand side does not necessarily respond to the power command such as demand response, the situation may not conform to the assumed specifications. In such a case, the operation plan creation unit 12a updates to a value closer to the measured value by the processing in steps S21 and S22 described later.

[0084] The process of creating the device output response model in step S12 and the process of generating the device power output prediction model in step S13 are the same as the processes described in the flowchart of FIG. 3. After creating the device output response model in step S12, the assumed performance correction unit 12d compares the assumed specifications, which are the specifications of the power equipment such as power equipment information, with the actual measured performance based on the device output response model generated by the device output response model unit 12b (step S21).

[0085] Figures 7 and 8 show examples of comparisons performed by the assumed performance correction unit 12d. Figure 7 shows a comparative example based on the step response of the power equipment 130. The vertical axis in Figure 7 represents power output, and the horizontal axis represents time. In Figure 7, the response of the equipment output response model is shown by a solid line, and the response of the assumed model based on the assumed specifications of the power equipment is shown by a dashed line, and the respective steady-state values are obtained.

[0086] In this example based on the step response, the response until reaching the steady-state value when the planned command value is changed stepwise is shown. As a result, the assumed performance correction unit 12d can confirm the response time and output response speed based on the time until reaching the steady-state value, and can correct the response time and output speed when there are differences between the assumed value and the equipment output response model.

[0087] Figure 8 shows a comparative example based on the frequency response of the power output with respect to the planned command value. In the upper part of Figure 8, the vertical axis represents the gain and the horizontal axis represents the frequency ω, and in the lower part of Figure 8, the vertical axis represents the phase and the horizontal axis represents the frequency ω. When the equipment output response model for the planned command value shown by the solid line in Figure 8 is obtained, the assumed model based on the assumed specifications of the power equipment is assumed as shown by the dashed line in Figure 8.

[0088] In the example based on the frequency response shown in the lower part of Figure 8, it is assumed that the specifications of the equipment are represented by a transfer function or the discrete-time state equation described in the first embodiment example. As shown in Figure 8, when the solid-line planned command value changes at the frequency ω, the assumed value shown by the dashed line indicates the magnification (gain) and delay (phase) of the amplitude of the power output with respect to the planned command value. Regarding the break frequency at which the gain rapidly decreases and the phase delay at that frequency based on this frequency response, the assumed value and the equipment output response model are compared, and when there are differences, the response time and output speed can be corrected based on the frequency response.

[0089] In the case of regarding a set that combines a plurality of power sources, such as a virtual power plant, as one power device, the frequency response may be calculated for the sum of the step responses of each power device, or the sum total of the transfer functions or discrete-time state equations of all power devices. Also, when the speed of the power output response of a power device, such as in a supply-demand adjustment market, is used as an adjustment power product, in the frequency response, the available amount may be calculated based on the gain and phase delay at the frequency corresponding to each product item.

[0090] Returning to the explanation of the flowchart in FIG. 6, in the comparison in step S21, if there is a deviation, the assumed performance correction unit 12d updates the assumed specifications (step S22). Here, the assumed performance correction unit 12d calculates the available amount of the adjustment power product so as to meet the requirements based on the updated assumed specifications, and calculates the maximum amount that can be bid in the market. Similar to the operation plan in step S11, for example, in the case of a generator, the response time to the external command value depends on the wasted time such as the output response speed and communication time. In addition, in the case of a storage battery, the output duration depends on the remaining charge amount. Based on these updated assumed specifications, the assumed performance correction unit 12d calculates whether it is possible to respond to each adjustment power product and the response amount, and calculates the maximum amount that can be bid in the market. Then, the assumed performance correction unit 12d outputs the calculated result to the operation plan creation unit 12a, and the operation plan creation unit 12a updates the operation plan based on the updated assumed values. When the operation plan setting value is updated in step S22, the process returns to the creation process of the operation plan in step S11.

[0091] Furthermore, after creating the device output response model in step S12, in addition to generating the prediction model of the device power output in step S13, scenario generation assuming an external model is performed (step S23). In this step S23, it is assumed that the external command scenario generation unit 12e receives an external adjustment command for power output based on each commodity traded in the supply-demand adjustment market. The external command scenario generation unit 12e generates, in advance, the maximum adjustment command scenario corresponding to the agreed quantity so as to satisfy the operational constraints and the adjustment command, and creates a planned command that satisfies the adjustment command by considering the generated scenario in step S14A described below. In this maximum adjustment command scenario, scenarios assuming the maximum increased or decreased state as described in FIG. 4 may be created respectively within the regulations such as the response time for each agreed commodity.

[0092] When the prediction model of the equipment power output is generated in step S13 or the scenario assuming the external model is generated in step S23, the operation planning command unit 12c performs the process of step S14A. In this step S14A, the operation planning command unit 12c performs a process of creating a planned command considering the scenario generated in step S23 in addition to the process in step S14 (FIG. 4) of the first embodiment example. Thereby, even when an adjustment command from the outside is received, it becomes possible to satisfy the adjustment command.

[0093] Steps S15 and S16 are the same as the processes in steps S15 and S16 of the first embodiment example, and thus the description is omitted. In step S17, the result storage and display described in the first embodiment example are performed. However, in the case of this embodiment example, when a deviation is detected in the comparison result between the equipment output response model used as the measured value and the assumed value in step S21 and the assumed specification is updated, the screen display includes the updated content. Steps S18 and S19 are the same as the processes described in the first embodiment example.

[0094] <Effects of the Second Embodiment Example> In the second embodiment example, based on the equipment output response model created based on the measured values and the step response and frequency response in the assumed specifications, when there are differences between the two, the market bidable volume corresponding to the output response speed, response time, or the response speed of power output is updated. The speed of these output responses and the response time change not only depending on the power equipment but also on the surrounding environmental conditions such as transmission delays. Therefore, by updating various constants using the model based on the measured values, it is possible to calculate a more accurate bidable volume, and it is possible to improve the accuracy of the operation plan and actual operation, such as improving the electricity sales revenue through the power market, etc.

[0095] Also, when performing external commands such as adjustment commands transmitted during operation when a contract is made in the market, etc., scenarios at the maximum increase and maximum decrease within the rules such as the contracted products are assumed at the time of generating the planned command value. As a result, it is possible to secure the remaining output capacity in advance, and even when an actual external command is received, it becomes possible to adjust the power output as per the command.

[0096] [Third Embodiment Example] Next, the energy operation planning device of the third embodiment example of the present invention will be described with reference to FIGS. 9 to 11. In FIGS. 9 to 11, the same parts as those in FIGS. 1 to 8 described in the first and second embodiment examples are denoted by the same reference numerals, and duplicate explanations are omitted.

[0097] <Configuration of Power Operation Planning Device> FIG. 9 shows a functional configuration example of the power operation planning device 10 of this embodiment example. The power operation planning device 10 shown in FIG. 9, as the power operation planning unit 12″, in addition to the components provided in the power operation planning unit 12 of the power operation planning device 10 shown in FIG. 1, includes an equipment set output response model unit 12f. Also, the power operation planning device 10 shown in FIG. 9 includes an external operation planning cooperation unit 15.

[0098] The power operation planning device 10 of this embodiment example assumes that power generators and aggregators connected via the communication network 300 shown in FIG. 2, for example, integrate and control the power equipment by the power operation planning device in the same way as their own power equipment. By being able to handle it in the same way as the company's own power equipment, power generators and aggregators can control a plurality of power equipment with different characteristics via a power operation planning device without modifying the system. In order to be able to handle it in the same way as the power equipment owned by power generators and aggregators, the power operation planning device controls the characteristics of the aggregated power equipment to be the same as the specifications of the power equipment owned by or already controllable by power generators and aggregators. The configuration of the power operation planning device 10 in the example of the present embodiment shown in FIG. 9 is such that these functions are executed.

[0099] In the power operation planning device 10 of the example of the present embodiment, the planning information unit 11 stores the device aggregated characteristic information DB8. The device aggregated characteristic information DB8 stores the characteristics of the power equipment controllable in the systems of power generators and aggregators and the output of the device aggregated output response model unit 12f described later. The device aggregated output response model unit 12f extracts the types of power equipment that can be simulated by integrating the output characteristics of each power equipment output by the device output response model unit 12b among the types of a plurality of power equipment controlled by the aforementioned power generators and the like. Then, the device aggregated output response model unit 12f determines the model constants for the extracted types of power equipment based on the characteristics obtained by integrating the output characteristics of each power equipment output by the device output response model unit 12b. Further, the device aggregated output response model unit 12f outputs a device aggregated output response model composed of the type of this power equipment and the model constants to the operation planning command unit 12c.

[0100] The operation planning command unit 12c generates a planned command value so that the characteristics of the total power output of the plurality of power equipment are the same as the characteristics of the device aggregated output response model. The planned command value generated by the operation planning command unit 12c is stored in the result storage unit 13 and displayed on the display unit 21. The external operation plan cooperation unit 15 transmits the equipment set output response model stored by the result storage unit 13 to an external aggregator or power generation company. Further, the external operation plan cooperation unit 15 outputs an operation plan for the equipment set output response model created by the aggregator or power generation company to the operation plan creation unit 12a.

[0101] The operation plan creation unit 12a creates an operation plan for each power equipment so that the power output in the operation plan obtained by summing the power outputs of a plurality of power equipment is the same as the operation plan for the equipment set output response model received by the external operation plan cooperation unit. Note that the hardware configuration of the power operation plan device 10 in this embodiment example is the same as the hardware configuration shown in FIG. 2 described in the first embodiment example.

[0102] <Processing in the power operation plan device> FIG. 10 is a flowchart showing an example of processing related to the equipment set output response model unit 12f and the external operation plan cooperation unit 15 in the power operation plan device 10 according to the third embodiment example. Note that the processing described in FIG. 3 of the first embodiment example is executed after step S35 in FIG. 10.

[0103] First, the power operation plan unit 12″ extracts, from the plan information unit 11, information on power equipment that can be controlled in the systems of external power generation companies or aggregators as the equipment set characteristic information DB8 (step S31). The information here may be items of the types and performance specifications of power equipment that can be handled by the systems of external companies. For example, they may be storage batteries, fuel cells, or generators, and parameter items such as the output change rate and the maximum and minimum chargeable amounts indicating their performance may be used.

[0104] Next, based on the device output response model output by the device output response model unit 12b, the device set output response model unit 12f obtains the types of power devices controlled by the power operation planning device 10 from the power device information DB2 of the power operation planning unit 11. Based on these information and the device output response model, the device set output response model unit 12f integrates the power devices controlled by the power operation planning device 10 among the types of power devices controllable by the external operator, and selects which types can be simulated (step S32).

[0105] For example, assume that the device controllable by the external system extracted in step S31 is a generator capable of high-speed output change, and the power devices controlled by the power operation planning device 10 are batteries with excellent high-speed responsiveness and generators with low-speed responsiveness arranged at multiple bases. In this case, the battery outputs the change in power output that cannot be followed by the low-speed generator, and corrects it with the battery until the power output of the low-speed generator changes. Thus, the combination of the battery and the low-speed generator can be regarded as a high-speed generator.

[0106] Similarly, even if the generator controlled by the power operation planning device 10 has complex device specifications, when there is a complex change in power output, by correcting with other power devices, from the perspective of the external system, the complex device specifications of the generator can be ignored. In this way, the device set output response model unit 12f selects the devices that can be simulated by combining any devices. Here, the device set output response model unit 12f shows a combined example, but the process of solving the optimization problem of Equation [4] in the operation planning command unit 12c may also be utilized. In this case, instead of the agreement between the 30-day period average value and the planned value of the sum of the predicted values of the power outputs of all power devices in the constraint conditions, the sum of the predicted values of the power outputs of all power devices may be set to match the output planned value assuming the devices controllable by the external system. In this case, if the sum of the power outputs cannot be output according to the device performance, a constraint violation PNL ks will occur. Whether or not this constraint violation occurs may be used to select whether simulation is possible.

[0107] Next, the equipment set output response model unit 12f integrates the equipment output response models indicating the performance of each power equipment output from the equipment output response model unit 12b for the items of their performance specifications in the type of power equipment selected in step S32, and calculates the integrated output characteristics of all the power equipment (step S33). Here, for the items of the performance specifications of the type of power equipment selected in step S32, these integrated output characteristics are applied as model constants. Note that in the calculation of the integrated output characteristics, the same processing as in step S21 in the assumed performance correction unit 12d in the second embodiment example (Fig. 6) is performed.

[0108] Furthermore, the external operation plan cooperation unit 15 transmits the selection result of the power equipment that can be simulated by the power operation planning device 10 and the model constants to the external operator from among the power equipment that can be handled by the system of the external operator selected in steps S32 and S33 (step S34). Then, the external operator creates an operation plan including its own power equipment based on the received power equipment and its model constants. From these operation plans, the plan regarding the received power equipment is transmitted to the power operation planning device 10.

[0109] The operation plan creation unit 12a creates the operation plan and operation plan command values for each power equipment so as to satisfy the operation plan received from the external power generation company (step S35). Here, the operation plan creation unit 12a generates the operation plan and plan commands for each power equipment so as to satisfy the constraint conditions regarding the equipment set. Here, it is assumed to satisfy the operation plan received from the external power generation company and to have the same characteristics as the equipment set output model created in step S34. Except for adding the constraint conditions regarding this equipment set, the method for creating the operation plan created by the operation plan creation unit 12a and the method for generating the operation plan commands calculated by the operation plan command unit 12c are the same as the processing in the first embodiment example. Specifically, when executing the processing described in the flowchart of Fig. 3, the above-mentioned constraint conditions are added in steps S11 and S14.

[0110] Here, when proceeding with the process according to the flowchart of FIG. 3, similar to the screen display and result saving of the device output response model in the first embodiment example, the predicted value of the total power output of the power equipment may be displayed on the screen. Here, the predicted value of the total power is the predicted value of the total power output of the power equipment when the power operation planning device is regarded as a power equipment according to the device collective output response model.

[0111] A model integrating a plurality of power equipment controlled by the power operation planning device 10 is created and transmitted externally. An external operator or the power operation planning device creates an operation plan based on the received integrated model and returns the plan. Based on the operation plan received from the outside, the power operation planning device 10 creates an operation plan and a planned command value for each power equipment and controls each equipment. By utilizing this function, as shown in FIG. 11, for example, a large-scale power generation operator can control a plurality of power operation planning devices. The first power operation planning device and the second power operation planning device can be controlled in cooperation by the power generation operator. Alternatively, in FIG. 11, as shown in the configuration of the first power planning device, the first power planning device can control the second power operation planning device at its lower layer as one of the power equipment. Similarly, the fourth power planning device and the fifth power planning device at the lower layer of the second power operation planning device can be controlled as one of the power equipment. This means that a hierarchical device function in which a power operation planning device controls another operation planning device can be realized, enabling the control of a huge number of power equipment.

[0112] <Effect of the Third Embodiment Example> According to the third embodiment example, when cooperating with an external operator, without modifying the system, the external operator can indirectly control the power equipment controlled by the power operation planning device in the same way as its own equipment. In addition, when the number of devices to be controlled becomes extremely large, there is a concern about processing delays due to communication delays and large-scale computations. However, the power operation planning device can control a large number of power devices while compensating for communication delays by controlling other power operation planning devices in addition to the power devices.

[0113] [Modification Example] Note that the present invention is not limited to the above-described embodiments, and it is needless to say that various other application examples and modification examples can be adopted without departing from the gist of the present invention described in the claims. For example, in each of the above-described embodiments, power is taken as an example of energy, and the present invention is applied to the power operation planning device 10. On the other hand, when gas, wind power, hydraulic power, biomass, etc. are taken as an example of energy instead of power and the operation plan for these energies is performed, the present invention may be applied.

[0114] In addition, each of the above-described embodiments has described the configuration of the device and the system in detail and specifically in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to having all the configurations described. Also, it is possible to replace a part of the configuration and processing of the embodiment described here with the configuration and processing of another embodiment, and further, it is possible to add the configuration of another embodiment to the configuration of a certain embodiment. Also, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration. In addition, the control lines and information lines show those considered necessary for explanation, and not necessarily all the control lines and information lines are shown on the product. In reality, it may be considered that almost all the components are interconnected.

Explanation of Reference Numerals

[0115] 10…Power operation planning device, 11…Planning information section, 12, 12′, 12″…Power operation planning sections, 12a…Operation plan creation section, 12b…Equipment output response model section, 12c…Operation plan command section, 12d…Assumed performance correction section, 12e…External command scenario generation section, 12f…Equipment set output response model section, 13…Result storage section, 14…Operation plan command transmission section, 15…External operation plan cooperation section, 21…Display section, 22…Input section, 23…Communication section, 24…CPU, 25…Memory, 26…Bus line, 100…Power system, 110…Bus, 120…Transformer, 130…Power equipment, 140…Transmission line, 150…Power demand, 160…Base, 300…Communication network, DB1…Power operation planning database, DB2…Power equipment information, DB3…Power equipment status information, DB4…Regular inspection information, DB5…Demand prediction information, DB6…Power market information, DB7…Renewable energy information, DB8…Equipment set characteristic information

Claims

1. A planning information section that stores power equipment information related to the performance of power equipment and / or power equipment status information related to the operating state; An operation plan creation section that creates an operation plan for each power equipment by using, as an input, information necessary for creating a plan in the planning information section; A device output response model section that generates, as a device output response model including the influence of the surrounding environment of the power equipment, the response of the power output of the power equipment to a planned command value based on the performance and operating state of the power equipment in the planning information section; An operation plan command section that, while predicting the response of the power output of the power equipment to a planned command value based on the device output response model, compensates for the influence of the surrounding environment of the power equipment at time intervals shorter than the operation plan of the power equipment, and generates a planned command value so as to satisfy the operation constraints with the operation plan, and transmits the generated planned command value to each power equipment, the energy operation planning device comprising: An energy operation planning device.

2. The device output response model section: transmits the planned command value generated by the operation plan command section to the power equipment, detects the power output of the power equipment, and adjusts the device output response model while the power equipment is in operation, thereby estimating the output response characteristics of the power equipment even when the detailed performance or state of the power equipment is unknown The energy operation planning device according to claim 1.

3. The operation plan creation section: when generating a planned command value while predicting the future power output of the power equipment, if it is determined that the operation constraints will be violated at a future time, updates the operation plan so as to secure a margin corresponding to the amount of violation of the operation constraints The energy operation planning device according to claim 2.

4. The operation plan command section: when the power output fluctuates in any of a plurality of power equipment, assumes a scenario in which the power output increases or decreases during the target period of the planned command value, sets the planned command value before the occurrence of the fluctuation during the period to be the same in each fluctuation scenario, and generates a planned command value so as to satisfy the operation constraints for each of the assumed scenarios The energy operation planning device according to claim 1.

5. An assumed performance correction section that corrects the assumed specifications of the power equipment and / or the set values in the power market based on the device output response model in the device output response model section; An external command scenario generation section that generates an adjustment command scenario at the maximum or minimum adjustment within the adjustment regulations; and the operation plan command section generates a planned command in consideration of the adjustment command scenario The energy operation planning device according to claim 2.

6. An external operation plan cooperation unit that grasps the types and / or characteristics of devices that can be controlled by an external system; A device set output response model unit that selects a model type that can be simulated by integrating and controlling the power devices handled by the energy operation planning device from among the types of devices that can be controlled by the external system, and sets model constants according to the selected model type; Controlling the power devices so as to conform to the output response model calculated by the device set output response model The energy operation planning device according to claim 5.

7. Regarding the energy operation planning device and the plurality of power devices integrated and controlled by the energy operation planning device, they are regarded as power devices that respond according to the device set output response model generated by the device set output response model unit, and one or more energy operation planning devices are controlled by a higher-level energy operation planning device or an external operator's system. The energy operation planning device according to claim 6.

8. The device output response model unit constructs a device output response model by utilizing a non-linear function that can define the device output response model in an optimization problem. The energy operation planning device according to claim 1.

9. The model constants of the device output response model or the power output of the power device with respect to the planned command value utilizing the device output response model are displayed on the screen. The energy operation planning device according to claim 1.

10. A power device state information storage process for storing power device information regarding power device performance and / or power device state information regarding the operation state; An operation plan creation process for creating an operation plan for each power device using, as input, the information necessary for creating a plan in the power device state information storage process; A device output response model generation process for generating, as a device output response model including the influence of the surrounding environment of the power device, the response of the power output of the power device to the planned command value to be transmitted based on the power device performance and operation state in the power device state information; An operation plan command process for compensating for the influence of the surrounding environment of the power device at time intervals shorter than the operation plan of the power device while predicting the response of the power output of the power device to the planned command value based on the device output response model, and generating a planned command value that satisfies the operation constraints with the operation plan and transmitting it to each power device. An energy operation planning method.

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