Energy operation planning device and energy operation planning method

Abstract

To provide an energy operation planning device capable of creating an energy operation plan by selecting a scenario, which does not exert influences on a result, without reducing simulation accuracy of uncertainty even if uncertainty scenarios are reduced.SOLUTION: A power operation planning device 1 comprises: a scenario-based plan approximation unit 31 for creating an approximation plan, in which a power operation plan is approximated, by inputting information required for creating a power operation plan to an estimation model as one or more scenarios; a similarity evaluation unit 321 for a plan result feature amount which evaluates similarity between the scenarios based on a result of the approximated plan; a scenario selection unit 322 for selecting a scenario in such a manner that the scenario does not become similar based on the evaluation of the similarity evaluation unit 321 for the plan result feature amount; and an uncertainty plan calculation unit 33 for creating the power operation plan using the selected scenario.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] In order to realize power operation, a plan showing the guidelines for future operation of equipment and control command values based on predicted values of future situations is created as a power operation plan. As an example of this power operation plan, there is an operation plan for a generator. The operation plan for a generator is based on the predicted power demand values at each time in the planned period, and satisfies the operational constraints of each generator and the power system, and then determines the operating or stopped state of the generator according to the power demand, and the output of the generator so that the total generation cost is minimized. Also, when implementing power trading in the power market, the operation plan may be determined so as to maximize the profit including power trading as well as the total generation cost based on the predicted value of the power market price.

[0003] Such a method for operating a generator is disclosed in, for example, Non-Patent Document 1. Non-Patent Document 1 discloses a technique for calculating an operation plan so that the total generation cost is minimized while satisfying operational constraints of each generator and the power system, such as the supply-demand balance where power demand and supply match, and the minimum continuous startup time and minimum continuous stop time where a generator maintains its state for a certain period of time after startup or shutdown. In the calculation of this operation plan, even if there is only one generator, considering the two states of startup and shutdown of the generator for all of the time cross-sections n of the operation plan, an extremely large number of combinations of operation plans, on the order of 2 n can be considered. Therefore, an optimization method for quickly determining an operation plan that minimizes the total generation cost or maximizes the profit from among an extremely large number of combinations of operation plans has been essential.

[0004] On the other hand, for renewable energy sources such as solar power, whose output depends on weather conditions, forecasts of future power generation are subject to forecast errors, which are discrepancies between forecasts and actual power generation during operating hours. Further increases in the amount of renewable energy introduced are planned for the future. However, the impact of forecast errors will also increase as the amount of renewable energy introduced increases. As a result, the discrepancy between actual power generation and forecast values ​​will also increase, making it difficult to meet operational constraints such as the balance of electricity supply and demand. Furthermore, the forecast errors for renewable energy also increase the forecast error, which is the discrepancy between the forecast value and the actual contract price of electricity in the market.

[0005] If operations are modified after they have been implemented based on an operational plan, due to prediction errors in renewable energy, it may lead to an increase in total power generation costs and a deterioration in profitability. In such uncertain situations where actual operational conditions cannot be accurately predicted, planning techniques that simulate uncertainties such as renewable energy generation amounts and electricity market prices using a vast number of prediction scenarios have been proposed in Non-Patent Documents 2 and 3. According to these techniques, operational plans are created by considering not just a single assumed scenario, but also scenarios with different predicted values. Therefore, if the deviation of actual power generation is small in any of the scenarios, uncertainties such as prediction errors are assumed, making it possible to reduce the impact of uncertainty on the operational plan. [Prior art documents] [Patent Documents]

[0006] [Non-Patent Document 1] M. Carrion and JM Arroyo, "A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem," in IEEE Transactions on Power Systems, vol. 21, no. 3, pp. 1371-1378, Aug. 2006 [Non-Patent Document 2] S. Takriti, JR Birge and E. Long, "A stochastic model for the unit commitment problem," in IEEE Transactions on Power Systems, vol. 11, no. 3, pp. 1497-1508, Aug. 1996 [Non-Patent Document 3] Yao Zhang, et al., “Chance-Constrained Two-Stage Unit Commitment under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition”, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 32, NO. 5(2017) [Non-Patent Document 4] Yoav Goldberg, “A Primer on Neural Network Models for Natural language Processing,” Journal of Artificial Intelligence Research 57, pp.354-357(1016) [Overview of the project] [Problems that the invention aims to solve]

[0007] As mentioned above, in creating power operation plans, uncertainty scenarios were assumed in advance to minimize the impact of uncertainties such as prediction errors in future conditions. Uncertainty scenarios include not only the fluctuations in renewable energy generation mentioned above, but also scenarios caused by short-term weather changes, such as a brief period of extreme heat despite it being the rainy season. Furthermore, if the plan period is long, it will also include scenarios caused by long-term factors, such as a mild winter contrary to the forecast.

[0008] The more uncertain scenarios anticipated in advance, the larger the computational scale required to create the operational plan becomes. In this case, there was a concern that the process of creating the operational plan would require an enormous amount of computation time, making it impossible to apply in actual operations.

[0009] Therefore, in order to reduce the computation time required to create the operational plan, it is necessary to reduce the number of uncertainty scenarios. However, simply reducing the number of uncertainty scenarios will also decrease the accuracy of uncertainty simulation, resulting in insufficient assumptions about uncertainty at the time of creating the operational plan. If an unexpected uncertainty scenario occurs after the operational plan has been created, the operational plan must be revised and the generators operated accordingly. As a result, there are concerns about an increase in total power generation costs and a deterioration in profitability.

[0010] This invention was made in view of these circumstances, and aims to create an energy operation plan by selecting scenarios in which the results are not affected even if scenarios that assume uncertainty are removed. [Means for solving the problem]

[0011] The energy operation planning device according to the present invention provides information necessary for creating an energy operation plan. multiple The system includes: an estimation plan creation unit that inputs the scenarios into an estimation model and creates an estimated plan that approximates the energy operation plan; an evaluation unit that evaluates the similarity between scenarios based on the results of the estimated plan; a scenario selection unit that selects scenarios from among those input into the estimation model that are not similar based on the similarity evaluation by the evaluation unit; and a planning unit that creates an energy operation plan using the selected scenarios. [Effects of the Invention]

[0012] According to the present invention, by selecting scenarios in which the results are not affected even if scenarios that assume uncertainty are removed, it becomes possible to create an energy operation plan that can improve economic efficiency while suppressing the effects of uncertainty within a practical computation time. Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

Brief Description of the Drawings

[0013] [Figure 1] It is a block diagram showing a functional configuration example of a power operation planning device according to a first embodiment of the present invention. [Figure 2] It is a block diagram showing an internal configuration example of a determination variable evaluation and scenario selection unit for an approximate plan according to a first embodiment of the present invention. [Figure 3] It is a diagram showing a configuration example of a power system according to a first embodiment of the present invention and a hardware configuration example of a power operation planning device according to the first embodiment. [Figure 4] It is a flowchart showing an example of a process in which a power operation planning device according to a first embodiment of the present invention selects an uncertainty scenario and creates a power operation plan. [Figure 5] It is a diagram showing an example of a screen display of an operation cost distribution with respect to uncertainty according to a first embodiment of the present invention. [Figure 6] It is a graph showing the relationship between the number of scenarios according to a first embodiment of the present invention and the accuracy of a plan result for a plurality of scenarios simulating uncertainty. [Figure 7] It is a diagram showing a functional configuration example of a power operation planning device according to a second embodiment of the present invention. [Figure 8] It is a block diagram showing an internal configuration example of a determination variable evaluation and scenario selection unit for an approximate plan according to a second embodiment of the present invention. [Figure 9] It is a flowchart showing an example of a process of an arithmetic unit of a power operation planning device according to a second embodiment of the present invention. [Figure 10] It is a conceptual diagram of a loss risk according to a second embodiment of the present invention.

Modes for Carrying Out the Invention

[0014] The following describes embodiments preferred for carrying out the present invention. It should be noted that the following are merely examples of embodiments, and the invention itself is not intended to be limited to the specific details described below. Furthermore, in the following description, the same or similar elements and processes are denoted by the same reference numerals, and redundant explanations are omitted. Also, in later embodiments, only the differences from previously described embodiments are explained, and redundant explanations are omitted. In addition, the following descriptions of embodiments and the configurations and processes shown in each figure are intended to provide an overview of the embodiments to the extent necessary for understanding and implementing the present invention, and are not intended to limit the manner in which the present invention can be implemented. Furthermore, each embodiment and each modification can be combined in whole or in part within a range that is consistent with each other and does not depart from the spirit of the present invention.

[0015] This specification describes an energy operation planning device that assists in the development of energy operation plans by operators with multiple energy facilities or operators capable of coordinating the operation plans of energy facilities. Below, an example of a power operation planning device for a power business to which the present invention applies will be described.

[0016] [First Embodiment] <Functional Configuration of Power Operation Planning System 1> First, an example of the functional configuration of the power operation planning device 1 will be explained with reference to Figures 1 and 2. Figure 1 is a block diagram showing an example of the functional configuration of the power operation planning device 1 according to the first embodiment. The power operation planning device 1 (an example of an energy operation planning device) comprises a power generation and grid information unit 2, a calculation unit 3, and a screen display and result storage unit 4, and performs processing for power operation planning.

[0017] The power generation and grid information unit 2 has the function of storing information necessary for creating a power operation plan, as the grid information database DB1 and the power operation plan database DB2 shown in Figure 3, which will be described later. This power generation and grid information unit 2 stores, for example, power generation equipment information, periodic inspection information, grid information, supply and demand information, adjustment capacity information, renewable energy information, assumed accident information, and scenario information.

[0018] Power generation equipment information includes information such as equipment constants that indicate the characteristics of each generator. Periodic inspection information refers to information regarding the shutdown or restriction of the generator's output for maintenance and inspection purposes. System information includes information such as the maximum transmission capacity of interconnection lines in the power grid. Supply and demand information includes electricity demand, which is the required amount of power generation, and the probability distribution range of demand, including forecast errors.

[0019] Adjustment capacity information is information used to adjust for deviations from assumed conditions, such as power operation plans. Renewable energy information includes information such as the characteristics of renewable energy sources, the amount of electricity generated, and the probability distribution range of the amount of electricity that can be generated, including prediction errors. The anticipated accident information includes details about generators and equipment that will be shut down due to malfunctions preventing them from operating. Scenario information refers to information about scenarios assumed by the operator, or scenarios statistically created from a range of possible probability distributions, including supply and demand information and prediction errors in renewable energy.

[0020] Next, we will describe an example of the functional configuration of the arithmetic unit 3. The calculation unit 3 includes a scenario-based plan estimation unit 31, a decision variable evaluation and scenario selection unit 32 for the estimated plan, and an uncertainty plan calculation unit 33.

[0021] The scenario-based planning and estimation unit 31 (an example of a preliminary planning unit) receives information necessary for creating a power operation plan, such as generator equipment information, supply and demand information, and scenario information, from the power generation and grid information unit 2. multipleThe scenario is input into an approximate estimation model. This estimation model is a model that shows the relationship between the scenario and the planned result. For example, the estimation model is a model that simulates the relationship between the information necessary to create a power operation plan and the power operation plan created by strictly solving the optimization problem in equation (1) described later, and is composed of a different method than the method used to create the power operation plan. Based on this simulated relationship, the estimation model outputs a planned result (also called a "preliminary plan") that approximates the power operation plan from the input. The estimation model is, for example, a relaxed optimization problem which is a relaxed version of the optimization problem in equation (1) for creating a power operation plan. Alternatively, the estimation model may be a machine learning model that has learned the relationship between the information necessary to create a power operation plan and a power operation plan that assumes uncertainty. Details of the relaxed optimization problem and the machine learning model will be described later.

[0022] The scenario-based planning estimation unit 31 outputs the output of the estimation model as an estimated plan that takes into account the uncertainty of the power operation plan. Here, the scenario-based planning estimation unit 31 takes as input the information necessary for creating the power operation plan, such as generator equipment information, supply and demand information, and scenario information, as a scenario. As a scenario, for example, there are multiple scenarios in which the predicted value of power demand (the power that people need) is incorrect, in order to consider when the predicted value of power demand is incorrect. For these scenarios, the power operation plan is a plan that determines when and how much power to generate in order to decide the operation of the generators on that day. The planning result of this plan is information (also called "planning result") that shows when and how much power to generate according to the power operation plan. In other words, the planning result is the output derived from the plan, and by showing the operator when to start or stop the generators, it becomes a basis for the operator's decision to decide the operation of the generators on that day.

[0023] Furthermore, the planning results include elements that the operator does not currently consider important, as these can be adjusted by the operator according to future circumstances. Here, if the operator anticipates multiple different situations, there may be multiple scenarios. However, if the differences between scenarios are small, deleting one of the scenarios with small differences may not change the elements that the operator considers important in the power operation plan results. For this reason, extracting all scenarios stored in the power generation and grid information unit 2 would include redundant information. In order to shorten the calculation time required to create the power operation plan described later, it is necessary to extract the necessary scenarios by excluding redundant ones, based on the estimated plan output by the scenario-based planning estimation unit 31.

[0024] Here, an example of the functional configuration of the decision variable evaluation and scenario selection unit 32 for the preliminary plan will be explained with reference to Figure 2. Figure 2 is a block diagram showing an example of the internal configuration of the decision variable evaluation and scenario selection unit 32 for the preliminary plan.

[0025] The preliminary plan determination variable evaluation and scenario selection unit 32 includes a similarity evaluation unit 321 for plan result features and a scenario selection unit 322.

[0026] The similarity evaluation unit 321 of the planning result features evaluates the similarity between scenarios based on the results of the estimated plans created by the scenario-based planning estimation unit 31. Here, the similarity evaluation unit 321 of the planning result features evaluates the similarity based on the results of the estimated plans, assuming that the effects on the energy operation plan are similar for each scenario. For example, if the same planning result is estimated for different scenarios, then the planning result that is not an estimate for the scenario on which the estimated planning result was based is also evaluated as similar. Here, when proceeding with operations according to the plan, the planning result includes elements that, even if the content of the plan is modified during operation, do not result in violations of operational constraints or additional costs and do not affect operations. When evaluating the planning result, instead of all elements of the planning result, only the decision variables that would result in violations of operational constraints or additional costs if not decided during planning, excluding elements that do not affect operations, may be subject to similarity evaluation.

[0027] The scenario selection unit 322 selects scenarios from those input to the estimation model that are not similar, based on the similarity evaluation of the plan result features by the similarity evaluation unit 321. For example, the scenario selection unit 322 deletes similar scenarios based on the similarity evaluation of the plan result features by the similarity evaluation unit 321 and selects the remaining scenarios. In this case, the scenario that forms the basis of the estimated plan result that was evaluated as similar is considered a duplicate scenario. The scenario selection unit 322 determines that one of the duplicate scenarios is unnecessary, removes the duplicate scenario, and selects the remaining scenarios. The scenarios selected by the scenario selection unit 322 are output to the uncertainty plan calculation unit 33.

[0028] Here, the scenario selection unit 322, based on the scenarios input by the scenario-based plan estimation unit 31, selects scenarios from the remaining ones by prioritizing the deletion of scenarios with a low probability of occurrence and a probability similarity evaluation index, which decreases as the similarity with other scenarios increases. Therefore, even if the scenario selection unit 322 deletes a scenario, its probability of occurrence is low, minimizing the impact on the results of the power operation plan created from the remaining scenarios.

[0029] Returning to Figure 1, we continue the explanation. The uncertainty planning calculation unit 33 (an example of a planning unit) calculates a power operation plan that takes uncertainty into account using the scenarios selected in the preliminary plan determination variable evaluation and scenario selection unit 32. The uncertainty planning calculation unit 33 outputs the uncertainty scenarios and power operation plan to the screen display and result saving unit 4.

[0030] The screen display and result storage unit 4 displays the uncertainty scenario and power operation plan output from the uncertainty plan calculation unit 33 on the screen of the display unit 21 shown in Figure 3, which will be described later. The screen display and result storage unit 4 also saves the results of the power operation plan to the power operation plan database DB2 shown in Figure 3, which will be described later.

[0031] <Hardware configuration of power operation planning device 1> Figure 3 shows an example of the configuration of the power system 100 and an example of the hardware configuration of the power operation planning device 1 according to the first embodiment.

[0032] The upper part of Figure 3 shows an example of the configuration of the power system 100. The power system 100 is a system in which multiple generators 130 and loads 150 are interconnected via busbars (nodes) 110, transformers 120, transmission lines 140, etc. The system information representing the configuration of the power system 100 is stored in the system information database DB1 shown at the bottom of Figure 3.

[0033] The power operation planning device 1 acquires various information from the power system 100, including power generation equipment information stored in the power generation and system information unit 2 shown in Figure 1, and multiple measurement data such as load 150. The power operation meter information acquired by the power operation planning device 1 from the power system 100 via the communication network 300 is stored in the power operation planning database DB2 shown at the bottom of Figure 3.

[0034] In Figure 3, various measuring instruments are appropriately installed on the busbar 110 for the purpose of protecting, controlling, and monitoring the power system 100. Signals detected by the measuring instruments are transmitted to the communication unit 23 of the power operation planning device 1 via the communication network 300. The dashed lines in the figure represent how the generator 130 and load 150 transmit signals detected by the measuring instruments wirelessly to the communication network 300.

[0035] The lower part of Figure 3 shows an example of the hardware configuration of the power operation planning device 1 for the power system 100. The power operation planning device 1 is composed of a computer system. This power operation planning device 1 includes a display unit 21, an input unit 22, a communication unit 23, a CPU 24, a memory 25, and various databases (system information database DB1, power operation planning database DB2), and each unit is connected to a bus line 26.

[0036] The display unit 21 is, for example, a display device. Alternatively, the display unit 21 may be configured to use, for example, a printer device or an audio output device, either in place of or in conjunction with a display device.

[0037] The input unit 22 is configured to include, for example, at least one of the following: a pointing device such as a keyboard or mouse, a touch panel, or a voice instruction device.

[0038] The communication unit 23 is equipped with circuits and communication protocols for connecting to the communication network 300. The communication unit 23 also communicates with weather systems, electricity market systems, and aggregators that monitor and control multiple distributed power sources and consumers such as VPPs (Virtual Power Plants).

[0039] The CPU 24 works in cooperation with the memory 25 to execute programs to implement each part of the power operation planning device 1, to issue instructions for image data to be displayed, and to search for data in various databases. The CPU 24 may be configured as one or more semiconductor chips, or as a computer device such as a computing server.

[0040] Memory 25 is configured, for example, as RAM (Random Access Memory) and stores computer programs, calculation result data and image data necessary for each process, etc. Alternatively, memory 25 may be ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), non-volatile memory, etc. In this case, memory 25 is used as an example of a non-transient storage medium readable by a computer that stores programs executed by the power operation planning device 1. The data stored in memory 25 is saved in a database, sent to the display unit 21 for display, or transmitted via the communication network 300 as operation control commands to each piece of equipment such as generators. Since each piece of equipment is operated based on the operation control command value, the control command value must be a value that satisfies the operation constraints.

[0041] <Process for selecting uncertainty scenarios and creating power operation plans> Next, the process of selecting uncertainty scenarios for the power operation planning device 1 and creating a power operation plan will be explained with reference to Figure 4.

[0042] Figure 4 is a flowchart showing an example of the process by which the power operation planning device 1 according to the first embodiment selects an uncertainty scenario and creates a power operation plan. The power operation planning device 1 has a function to input the input information necessary for creating a power operation plan into an estimation model and to select a scenario based on the estimated plan output from the estimation model. Subsequently, the power operation planning device 1 creates a power operation plan that takes uncertainty into account based on the selected scenario. The process from step S11 shown in Figure 4 is described below.

[0043] In step S11 of Figure 4, the planning estimation unit 31, based on the scenario shown in Figure 1, extracts information necessary for creating a power operation plan, such as power generation equipment information, supply and demand information, and scenario information, from the power generation and grid information unit 2 as an initial scenario. The scenario information extracted as an initial scenario includes scenarios that show cases where the assumptions for power demand and renewable energy generation differ between the forecast and the actual operation due to prediction errors, or a range of possible probability distributions that include prediction errors.

[0044] Furthermore, the process by which the scenario-based planning and estimation unit 31 extracts the initial scenario may use a scenario desired by the operator of the power operation planning device 1, such as past power demand data, as the initial scenario. Alternatively, the operator of the power operation planning device 1 may use past time-series data with similar conditions, such as the same season and period, as the initial scenario.

[0045] Furthermore, if the scenario information is a range of possible probability distributions including prediction errors, the scenario-based planning estimation unit 31 may create an uncertainty scenario that shows the prediction error based on the probability distribution. This uncertainty scenario can be created using statistical scenario creation methods such as Monte Carlo simulation based on the probability distribution range for each time point. In this way, the scenario-based planning estimation unit 31 extracts information necessary for creating a power operation plan that takes uncertainty into account from the power generation and grid information unit 2.

[0046] In step S12 of Figure 4, the scenario-based planning estimation unit 31 estimates the power operation plan based on the scenario using an estimation model that shows the relationship between the input information necessary for creating the power operation plan extracted in step S11 and the results of the power operation plan. Alternatively, when estimating the power operation plan, the estimation model may be used for each scenario to estimate the plan results corresponding to that scenario.

[0047] Two methods are envisioned as examples of this estimation model: (1) a method utilizing a relaxation optimization problem, and (2) a method utilizing a pre-trained machine learning model. These two methods are described below. In the following, estimation of the planning result means that the scenario-based planning calculation unit 31 calculates a power operation plan based on the scenario.

[0048] (1) Methods that utilize relaxation optimization problems The method of estimating the planning results using a relaxed optimization problem involves approximately solving the optimization problem in power operation planning that assumes the uncertainty shown in equation (1). The objective function of the optimization problem in equation (1) is to minimize the total power generation cost of all generators during the planning period. The definitions of the variables that make up equation (1) and the constraints on equation (1) are also described below.

[0049]

number

[0050] Tend: End time of the plan Ngen: Number of generators ai, bi, ci: Power generation cost coefficients for generator i Pits: Scenarios, generator i, power output at time t uits: Scenario s, generator i, discrete variables of 0 and 1 indicating start and stop at time t. Δuits: Scenario s, generator i, discrete variables of 0 and 1 indicating the start of operation at time t. SUCi: Generator i startup cost S: Number of assumed scenarios Prs: Probability of scenario occurrence PNLts: Penalties for violations of operational constraints

[0051] <Restrictions> • Maximum and minimum generator output (the output of each generator is within the range from the maximum output to the minimum output) • Supply and demand balance (the shared demand matches the total power generation output) • Minimum continuous operating time and minimum continuous shutdown time (restarting or restarting will occur after the minimum continuous time has elapsed). • Operating period and shutdown period (the generator will be stopped or operation will continue during the specified period) • Operating reserve capacity and necessary adjustment capacity (the capacity to correct the discrepancy between actual operation and supply and demand plans)

[0052] Here, if the scenario-based planning and estimation unit 31 attempts to solve equation (1) exactly, it will take a very long time to perform the calculations. This is because it is necessary to solve equation (1) for all scenarios. Furthermore, it searches for the optimal values ​​and combinations of discrete variables from a vast number of solution combinations for the numerous discrete variables included in equation (1). Therefore, the scenario-based planning and estimation unit 31 estimates the planning results by solving a relaxed optimization problem that simplifies equation (1). By simplifying the difficult characteristics of equation (1), the relaxed optimization problem can be solved in less time than solving the original optimization problem.

[0053] The method for solving this relaxed optimization problem involves solving a relaxed optimization problem in which all the complex discrete characteristics of equation (1) are approximated as continuous, rather than strictly solving the optimization problem for power operation planning in equation (1).

[0054] The discrete variable uits, which represents a vast number of combinations depending on which generator i is 1 or 0 at which time t, is approximated by a continuous variable such that 0 < uits_con < 1, thus eliminating discrete combinations of uits. Therefore, the scenario-based planning and estimation unit 31 can solve the relaxed optimization problem in a short time. The approximate power operation plan obtained by solving this relaxed optimization problem is treated as the estimated plan.

[0055] (2) How to utilize a pre-trained machine learning model In the method of estimating planning results using a pre-trained machine learning model, the relationship between the input information for creating the power operation plan and the created power operation plan is pre-trained in the machine learning model. By inputting the input information into this pre-trained machine learning model, the machine learning model estimates the planning result, that is, creates an approximate power operation plan.

[0056] One method for estimating planning results based on machine learning is to utilize the neural network shown in equation (2) in Non-Patent Document 4. In this case, the machine learning model is expressed as shown in equation (2) below.

[0057]

number

[0058] g (1) Activation function W (n) : n-level weighting coefficients b (n) : Bias coefficient of n levels x: Input information y: Output information

[0059] Equation (2) simulates the relationship between input information x and the target y to be estimated. In equation (2), for input information x such as a scenario, a nonlinear function g is applied to (wx+b), which is the weighted sum wx with a bias b. (n)w and b are adjusted so that the output information y, to which the formula has been applied, becomes the power operation plan that is the target of estimation.

[0060] When the scenario-based planning and estimation unit 31 estimates the planning result from the input information x, it is assumed that an example of the target to be estimated (planning result) corresponding to the input information x is prepared in advance, and the learning model of equation (2) is trained with this example to adjust w. The scenario-based planning and estimation unit 31 can estimate the planning result corresponding to the input x by inputting the input information x to the trained equation (2). This estimated planning result is treated as a rough plan.

[0061] As described above, the scenario-based planning unit 31 can estimate planning results by approximating the power operation plan using methods such as methods based on relaxation optimization problems or methods based on machine learning. Here, the operator may be notified of the estimated power operation plan results that take uncertainty into account by displaying these estimated plans on the screen. Compared to strictly solving equation (1), which is an optimization problem for power operation planning that assumes uncertainty, obtaining planning results in a short time can improve the efficiency of the operator's work.

[0062] When outputting the estimated plan to the screen, the scenario-based plan estimation unit 31 calculates the probability of each estimated plan occurring and the operating cost index for the estimated plan. An example of the screen display based on these occurrence probabilities and operating cost indexes is shown in Figure 5. Figure 5 is a graph showing the operating cost distribution against uncertainty in the power operation plan, with the horizontal axis representing operating costs and revenues, and the vertical axis representing frequency. Revenues are shown on the right side of the horizontal axis, and losses are shown on the left side. The operating cost distribution shown in Figure 5 is displayed on the display unit 21 shown in Figure 3 by the screen display and result saving unit 4.

[0063] The scenario-based planning and estimation unit 31 can report to the operator whether operating costs will fluctuate due to uncertainty or whether there is a possibility of loss by showing the distribution of operating costs to the operator through the screen display and result saving unit 4. For example, if the frequency of a certain scenario is skewed towards losses, the operator can determine, when creating a power operation plan, that there is a high probability of loss if that scenario occurs in actual operation.

[0064] Returning to the explanation of the flowchart shown in Figure 4. Here, the estimated plan result in step S12 is an approximate plan result, and therefore deviates from the plan result created by strictly solving the power operation plan optimization problem in equation (1). Since these approximate plan results do not provide the accuracy of a plan that can actually operate power equipment, in the following steps (S13 to S15), the approximate plan results are used as criteria for scenario selection. In particular, based on the approximate plan result estimated in step S12, scenarios with similar and overlapping plan results are excluded. The scenario selection unit 322 selects the minimum number of scenarios necessary to accurately simulate uncertainty. The scenario selection based on these approximate plans is shown in the following steps S13 to S15.

[0065] In step S13 of Figure 4, the similarity evaluation unit 321 of the planning result features shown in Figure 2 performs a similarity evaluation to determine whether the planning results are similar and overlapping, based on the estimated planning results in step S12. The similarity evaluation unit 321 of the planning result features uses, for example, the index in equation (3), which extracts elements of each scenario s for the objective function in equation (1), as a criterion for determining whether the estimated planning results are similar and overlapping. Equation (3) assumes the total power generation cost for each scenario.

[0066]

number

[0067] Furthermore, the similarity evaluation unit 321 of the planning result features may also use specific elements as evaluation indicators in addition to the elements of each scenario s described above. For example, in the planning results, there are elements that can be adjusted according to the situation, such as generator output, which can be adjusted in a short time, even if any of the scenarios actually occur. In contrast to these elements that can be adjusted according to the situation, there are also elements that cannot be adjusted according to the situation after the plan is created due to constraints on generator operation, such as slow adjustments, or due to policies and systems for power generation operation. Since elements that cannot be adjusted according to the situation are more important to operators than elements that can be adjusted according to the situation, these elements that cannot be adjusted according to the situation after the plan is created due to constraints on generator operation or systems for power generation operation may also be used as evaluation indicators.

[0068] Here, the similarity evaluation unit 321 of the planned result features treats elements that cannot be adjusted according to the situation after the creation of the power operation plan as evaluation indicators and as decision variables determined in the power operation plan. These decision variables are elements whose values ​​determined in the power operation plan cannot be changed during the operation of the power operation plan. A concrete example of a decision variable is the start and stop of generators, which cannot be adjusted according to the situation even if the prediction error changes immediately before operation on the day after the power operation plan is created the day before the day of operation. In addition, the trading volume of the electricity market, which is finalized the day before and cannot be adjusted on the day of operation, also becomes a decision variable. its In this case, if the power operation planning device 1 creates the power operation plan the day before the actual operation day, the variables for the few hours immediately preceding operation become the decision variables in equation (1). The reason for this is that, even if a power operation plan for a generator has been created, if the generator is already operating according to the plan, and the generator scheduled to start up in the near future is the one that will be operated soon, it is not possible to change the plan, such as starting up a different generator than planned. Note that in the similarity evaluation unit 321 of the plan result features, "plan result features" refers to the decision variables.

[0069] Furthermore, if the amount of fuel purchased before operation begins, which is necessary for power generation, is used as the determining variable, then instead of the determining variable, variables that influence the determining variable, such as fuel consumption in each scenario, may be used as evaluation indicators. Here, in equation (1), the decision variables were defined for each scenario in order to evaluate the planning results according to each scenario. However, after the creation of the power operation plan, the decision variables are elements that cannot be adjusted according to the occurrence of each scenario. For this reason, when creating the power operation plan that takes uncertainty into account in step S16 described later, it is assumed that the decision variables are the same across all scenarios.

[0070] The similarity evaluation unit 321 of the planning result features evaluates the similarity and overlap of the planning results based on the evaluation index for each scenario s in the objective function and the decision variables determined from the power operation plan results in the planning results estimated for each scenario. In this process, the evaluation method of equation (4) below is used.

[0071]

number

[0072] s: Similarity and scenarios to be evaluated (scenarios to be deleted) s': Comparison with scenario s Prs: Probability of scenario occurrence ys: A vector of normalized evaluation metrics (In each scenario s, the evaluation metrics and decision variables in the objective function are normalized and combined into a single vector).

[0073] The similarity evaluation unit 321 of the planned result features evaluates the scenario s to be evaluated using equation (4) based on the proximity of the scenario s to the closest scenario s', either by the small probability of occurrence of scenario s, Prs, or by the closest distance (||·|| Euclidean norm) of the evaluation index y. Here, the evaluation value in equation (4) is used as the probability similarity evaluation index.

[0074] Probability of scenario s occurring Pr sIf the value is small, it is considered unlikely to occur in reality. Removing such scenarios s will have little impact on the planning results. Also, if the distance to the closest scenario s' is small, it means that there are scenarios that will result in similar planning results. Therefore, the similarity evaluation unit 321 of the planning result features calculates the similarity evaluation index of equation (4) for each scenario as an evaluation of the similarity of the planning results according to the scenario. The similarity evaluation index calculated for each scenario is used as the similarity evaluation value.

[0075] In step S14 of Figure 4, the scenario selection unit 322 selects a scenario based on the similarity evaluation value calculated in step S13. The scenario selection unit 322 removes the scenario that is evaluated as overlapping due to having the smallest similarity evaluation value and excludes it from scenario s or scenario s'. The probability of occurrence of the scenario removed by the scenario selection unit 322 is added to the probability of occurrence of the most similar scenario in equation (4).

[0076] Here, the relationship between the number of scenarios and the accuracy of the uncertainty simulated by multiple scenarios will be explained with reference to Figure 6. Figure 6 is a graph showing the relationship between the number of scenarios and the accuracy of the planning results for multiple scenarios that simulate uncertainty. In the graph shown in Figure 6, the horizontal axis represents the number of scenarios, and the vertical axis represents the deterioration of the accuracy of the planning results according to the number of scenarios. Here, the deterioration of the planning results is evaluated, for example, by the expected value of the total power generation cost calculated using equation (1) based on the estimated planning results of all scenarios, and is the amount of change in the expected value that changes as the number of scenarios decreases from the total number of scenarios.

[0077] The initial state corresponds to scenario number A0, which is at the far right of the horizontal axis in the graph shown in Figure 6. Since scenario number A0 is the initial state with the largest number of scenarios, the degradation of the accuracy of the planning results is minimal. However, as duplicate scenarios are deleted by the process in step S14 in Figure 4, the accuracy of the planning results gradually deteriorates, as indicated by the white arrows in the figure.

[0078] The scenario selection unit 322 evaluates the degradation of the accuracy of the plan result due to scenario exclusion, as shown in Figure 6, each time it excludes a scenario that has been evaluated as highly similar based on the similarity evaluation index of equation (4). As long as the degradation of accuracy is within the threshold a%, the scenario is excluded, and the remaining scenarios are selected. In the above, the degradation of the accuracy of the plan result was evaluated using the expected value cost calculated based on the scenario and the estimated plan result, but instead of the expected value cost, the expected value of the probabilistic similarity evaluation index shown by the similarity evaluation of the plan result features with other scenarios by the similarity evaluation unit 321 may be used as the evaluation value. Alternatively, multiple elements may be treated as a single vector, as in ys in equation (4), and the evaluation may be based on the expected value of the distance between each scenario (||·|| Euclidean norm). By doing so, the accuracy of the power operation plan results, which was created assuming uncertainty, can be guaranteed by excluding duplicate scenarios while keeping the degradation of the plan result within the threshold.

[0079] In step S15 of Figure 4, the scenario selection unit 322 evaluates whether the accuracy of the planning results shown in step S14 is maintained within the threshold of a%. The scenario selection unit 322 evaluates that it is within the threshold if the error representing the deterioration of the accuracy of the planning results is within a% of the specified error shown in Figure 6 (YES in S15). In this case, there is no problem with the accuracy of the planning results, so the scenario selection unit 322 returns to step S13 and repeats the deletion of scenarios with overlapping planning results. As the process from step S13 to S15 is repeated, the accuracy deteriorates from the initial state each time the number of scenarios decreases, as shown in Figure 6.

[0080] The scenario selection unit 322 evaluates that the error representing the deterioration of the accuracy of the plan result is not within the threshold if it exceeds a% of the specified error (NO in S15). In this case, since there is a problem with the accuracy of the plan result, the scenario selection unit 322 stops the scenario deletion process. Subsequently, the scenario selection unit 322 outputs the remaining scenarios that were not deleted to the uncertainty plan calculation unit 33.

[0081] In step S16 of Figure 4, the uncertainty planning unit 33 creates a power operation plan that takes uncertainty into account. Here, the uncertainty planning unit 33 creates a power operation plan by solving an optimization problem that takes uncertainty into account using equation (1) for scenarios in which the plan results selected in step S15 do not overlap.

[0082] Unlike the approximate planning result in step S12, the uncertainty planning calculation unit 33 solves equation (1) without approximating its characteristics. In this case, the uncertainty planning calculation unit 33 obtains an exact solution by applying a commercial optimization solver to solve the optimization problem in equation (1). Subsequently, the uncertainty planning calculation unit 33 outputs the created power operation plan to the screen display and the result storage unit 4.

[0083] In step S17 of Figure 4, the screen display and result saving unit 4 displays the scenarios deleted in steps S13 to S15 due to duplicate plan results, the selected scenarios with no duplicate plan results, and the power operation plan results created in step S16 on the screen of the display unit 21 shown in Figure 3, and saves them to the power operation plan database DB2 shown in Figure 3.

[0084] <Effects of the First Embodiment> In the power operation planning device 1 according to the first embodiment described above, when creating a power operation plan that takes into account uncertainties such as prediction errors, the relationship between a vast number of scenarios that simulate uncertainty and the plan results created based on those scenarios is evaluated in advance. Based on this evaluation, scenarios whose effects on the plan results overlap are reduced, and the remaining scenarios are selected. The power operation planning device 1 then creates a power operation plan that takes uncertainty into account for the selected scenarios. In this way, the power operation planning device 1 deletes only the scenarios whose plan results overlap, that is, scenarios whose effects on the plan overlap, and selects scenarios that affect the plan results. Furthermore, even when deleting overlapping scenarios, the accuracy of the plan results is guaranteed by evaluation, so even if the number of scenarios is reduced, the accuracy of the uncertainty simulation does not deteriorate, and the computation time required to create the power operation plan is reduced.

[0085] Here, in evaluating the effect of scenarios on the planning results, the power operation planning device 1 constructed an estimation model that shows the relationship between scenarios and planning results. As this estimation model, it estimates planning results according to the scenario using methods such as solving an approximate relaxation problem of the optimization problem used in power generation plan creation, or using a machine learning model that has been pre-trained on the relationship between scenarios and planning results. Therefore, the power operation planning device 1 can estimate planning results faster than if it were to create a power generation plan by rigorously solving the optimization problem related to power generation planning, and can evaluate the similarity of scenarios based on the estimated planning results.

[0086] Furthermore, the power operation planning device 1 uses as decision variables in the plan the elements whose modification during planned operation after plan creation is restricted by equipment and operational regulations in the approximate planning results output from the estimation model. These decision variables and the objective function value of the plan are evaluated, and scenarios with a low probability of occurrence and a high degree of overlap are deleted while evaluating the impact before and after deletion. In this way, since the power operation planning device 1 deletes only scenarios in which the planning results overlap, it can ensure optimal accuracy of the elements to be determined in the plan while also speeding up the computation time to create a power operation plan that takes uncertainty into account.

[0087] In conventional technology, when creating a plan that anticipates uncertainty, the impact of scenarios on the plan results was not considered. As a result, reducing the number of uncertainty scenarios assumed in the plan resulted in lower optimal accuracy of the plan results and the decision variables in the plan. On the other hand, increasing the number of scenarios resulted in an enormous computation time for plan creation. In contrast, the power operation planning device 1 according to the first embodiment carefully selects scenarios while evaluating their impact on the plan results, based on the approximate plan results and decision variables estimated by the estimation model, and creates a power operation plan. As a result, it becomes possible to achieve both improved optimality and computation speed, which is not possible with conventional technology.

[0088] In the embodiments described above, electricity was used as an example of energy, and the present invention was applied to the power operation planning device 1. However, gas, wind power, hydropower, biomass, etc., may be used as examples of energy instead of electricity, and the present invention may be applied to the operation planning of these energies.

[0089] [Second Embodiment] Next, an example of the configuration and processing of the power operation planning device 1A according to the second embodiment of the present invention will be described with reference to Figures 7 to 10. Note that explanations that overlap with those described in the first embodiment will be omitted.

[0090] <Functional Configuration of Power Operation Planning Device 1A> Figure 7 shows an example of the functional configuration of the power operation planning device 1A according to the second embodiment.

[0091] The calculation unit 3A of the power operation planning device 1A includes, in addition to the scenario-based plan estimation unit 31, the estimation plan determination variable evaluation and scenario selection unit 32A, and the uncertainty plan calculation unit 33 provided in the power operation planning device 1 according to the first embodiment, a unit 34 for evaluating the impact of unselected scenarios.

[0092] The unselected scenario impact evaluation unit 34 takes the planning results (power operation plan considering uncertainty) calculated by the uncertainty planning calculation unit 33 as input and evaluates the situation in which the scenario selection unit 322 did not select a scenario. The unselected scenario impact evaluation unit 34 outputs an evaluation value that evaluates the planning results for scenarios that were not assumed when the uncertainty planning calculation unit 33 created the plan. Scenarios that were not assumed when creating the plan are scenarios that were not selected by the scenario selection unit 322.

[0093] The preliminary plan determination variable evaluation and scenario selection unit 32A re-selects scenarios based on evaluation values ​​input from the unselected scenario impact evaluation unit 34. For example, after creating a power operation plan by fixing the determination variables for scenarios not selected by the scenario selection unit 322, if the unselected scenario impact evaluation unit 34 evaluates that the scenarios not selected by the scenario selection unit 322 will have an impact on operating costs, the preliminary plan determination variable evaluation and scenario selection unit 32A adds the scenarios not selected by the scenario selection unit 322 to the scenarios selected by the scenario selection unit 322. The uncertainty plan calculation unit 33 calculates an uncertainty power operation plan by adding the scenarios not selected by the scenario selection unit 322.

[0094] Figure 8 is a block diagram showing an example of the functional configuration of the decision variable evaluation and scenario selection unit 32A for the preliminary plan.

[0095] The preliminary plan determination variable evaluation and scenario selection unit 32A includes a risk scenario selection unit 323 in addition to the similarity evaluation unit 321 and scenario selection unit 322 of the first embodiment of the plan result features. Furthermore, evaluation values ​​are input to the risk scenario selection unit 323 from the unselected scenario impact evaluation unit 34.

[0096] The Risk Scenario Selection Unit 323 receives the evaluation values ​​of the planning results for the scenarios selected by the Scenario Selection Unit 322 and for the scenarios that were not anticipated during planning, as evaluated by the Unselected Scenario Impact Evaluation Unit 34. Based on the scenarios selected by the Scenario Selection Unit 322 and the estimated plans for those scenarios, the Risk Scenario Selection Unit 323 selects only those scenarios that affect the statistical risk evaluation indicators based on the operating cost distribution, and selects these scenarios as risk scenarios. In this case, the Risk Scenario Selection Unit 323 selects scenarios that fall within a specific distribution area of ​​the operating cost distribution calculated based on the estimated plan for each scenario as risk scenarios. A specific distribution area is, for example, the area corresponding to the loss risk 50 shown in Figure 10, which will be described later. Alternatively, the Risk Scenario Selection Unit 323 may extract scenarios that correspond to statistical evaluation indicators from the operating cost distribution and select these scenarios as risk scenarios. These risk scenarios are then added to the Unselected Scenario Impact Assessment Unit 34, which evaluates scenarios that were not anticipated during the planning process and are determined to have an impact on operating costs based on the evaluation values ​​of the planning results. These scenarios are then output to the Uncertainty Plan Calculation Unit 33.

[0097] As shown in Figure 7, the uncertainty planning calculation unit 33 creates a power operation plan that takes uncertainty into account, based on the scenarios selected by the risk scenario selection unit 323 that affect the statistical risk assessment indicators. The created power operation plan is stored in the power operation plan database DB2 shown in Figure 3 and output to the screen display and result saving unit 4.

[0098] The screen display and result saving unit 4 displays the uncertainty scenarios and power operation plans output from the uncertainty plan calculation unit 33 and the risk scenario selection unit 323, and saves the planning results to the power operation plan database DB2.

[0099] <Hardware configuration of power operation planning device 1A> Since it is the same as the first embodiment, a detailed explanation will be omitted.

[0100] <Processing by the calculation unit of the power operation planning device 1A> Figure 9 is a flowchart showing an example of processing by the calculation unit 3A of the power operation planning device 1A according to the second embodiment. The functions of power operation planning and impact evaluation of unselected scenarios performed by the calculation unit 3A will be explained using the flowchart in Figure 9.

[0101] The processes from steps S11 to S15 are the same as those in the first embodiment shown in Figure 4.

[0102] In step S15, the scenario selection unit 322 evaluates whether the accuracy of the planning results is maintained within the threshold range. If the accuracy of the planning results is not within the threshold range (NO in S15), the risk scenario selection unit 323 selects a risk scenario (S15B-1). Here, the risk scenario selection unit 323 evaluates the scenario selected by the scenario selection unit 322 and its approximate planning results (rough plan) using CVaR (Conditional Value at Risk), a statistical risk assessment index that indicates loss risk. The risk scenario selection unit 323 selects only scenarios that correspond to loss risk as risk scenarios. Now, with reference to Figure 10, the method of selecting scenarios according to risk will be explained.

[0103] Figure 10 is a conceptual diagram of loss risk. Figure 10 is similar to the example screen display of the operating cost distribution for uncertainty shown in Figure 5. Here, we will explain how to select a scenario according to the risk based on this conceptual diagram of loss risk.

[0104] In simulating the uncertainty of power operation planning under multiple scenarios, the operating costs and revenues are expressed by equation (3) above, based on the estimated plans for each scenario shown in Example 1. Each scenario also has a frequency (probability of occurrence Prs). The horizontal axis of Figure 10 shows the operating costs or revenues when a particular scenario occurs, and the vertical axis shows the frequency at which these operating costs or revenues occur. Based on the frequency and operating costs for each scenario, these can be represented by histograms or probability density functions.

[0105] Fund managers have requirements such as wanting to avoid situations where operating costs result in losses rather than generating profits. In this case, fund managers may want to evaluate only risk scenarios that fall under the loss risk of 50 in Figure 10. The aforementioned CVaR is used as an index to evaluate such loss risk of 50.

[0106] The risk scenario selection unit 323 evaluates the loss risk using CVaR and selects scenarios corresponding to the loss risk 50, which corresponds to the CVaR in the operating cost distribution shown in Figure 10. By evaluating the scenarios that correspond to the loss risk, the risk scenario selection unit 323 can delete scenarios that do not correspond to the loss risk, thereby reducing the number of scenarios required for evaluation and reducing the computation time required for the uncertainty plan calculation unit 33 to create the plan.

[0107] Furthermore, the uncertainty planning calculation unit 33 can create a power operation plan that takes uncertainty into account while suppressing loss risk by targeting only scenarios that correspond to loss risk. The uncertainty planning calculation unit 33 can minimize costs by targeting scenario s that correspond to loss risk in the objective function of equation (1).

[0108] In the processing of the risk scenario selection unit 322 described above, the operator selects scenarios corresponding to the risks they wish to evaluate in advance. As an example of selection, the example of selecting scenarios corresponding to loss risks has been explained. However, the risk scenario selection unit 323 may also select scenarios in the area specified by the operator in the operating cost distribution as risk scenarios. For example, the power operation planning device 1A creates a histogram based on the scenarios and the estimated approximate planning results. The operator specifies the distribution area to be evaluated for this histogram. Scenarios in this specified distribution area are selected as risk scenarios.

[0109] In step S16 of Figure 9, the uncertainty planning unit 33 creates a power operation plan that takes uncertainty into account, based on the risk scenario selected in step S15B-1.

[0110] In step S16B-1 of Figure 9, the unselected scenario impact assessment unit 34 evaluates the impact of operating the equipment according to the power operation plan created in step S16. In particular, since scenarios other than the selected scenario may occur during operation, the impact of the occurrence of scenarios other than the selected scenario is evaluated. That is, the unselected scenario impact assessment unit 34 evaluates the impact of scenarios other than the risk scenario selected by the risk scenario selection unit 323 in step S15B-1, or the scenario selected by the scenario selection unit 322 in step S14, occurring during operation.

[0111] As described above, the scenario selection unit 322 and the risk scenario selection unit 323 select scenarios based on approximate planning results estimated by the scenario-based planning estimation unit 31. Therefore, if the estimation accuracy of the approximate plan is low, the risk scenario selection unit 323 may not extract scenarios that affect the planning results. To avoid insufficient extraction of important scenarios that affect the planning results, the unselected scenario impact evaluation unit 34 evaluates the impact of applying scenarios other than those extracted by the scenario selection unit 322 and the risk scenario selection unit 323 to the planning results. Furthermore, the unselected scenario impact evaluation unit 34 evaluates whether the operating costs shown in equation (3) become excessive due to constraint violations or other reasons.

[0112] In particular, it is possible that the scenarios not selected above may actually occur only immediately before operation begins after the plan has been created. Therefore, when evaluating the unselected scenarios, the uncertainty plan calculation unit 33 fixes the variables that cannot be changed during operation after the plan has been created (decision variables defined in the first embodiment) to their planned values. Then, using the remaining variables, the uncertainty plan calculation unit 33 recreates the power operation plan for the scenarios not selected by the scenario selection unit 322. Based on the results of this replanning, the unit evaluates the impact of what would happen if the unselected scenarios occurred during operation.

[0113] Compared to the power operation plan created according to the first embodiment, in the power operation plan recreated with fixed decision variables to assume operation after the plan is created, the decision variables cannot be changed, so there are fewer adjustment elements. For this reason, depending on the scenario that occurs, the operational constraints may be violated and the operational cost in equation (3) may become excessive. The unselected scenario impact evaluation unit 34 evaluates whether the scenario not selected by the scenario selection unit 322 will cause the operational cost in equation (3) to become excessive due to a violation of the operational constraints, and outputs an evaluation value.

[0114] In step S16B-2 of Figure 9, the unselected scenario impact evaluation unit 34 determines whether the impact of the unselected scenario is within the threshold. Here, it is evaluated whether the evaluation value in step S16B-1, which assumes that a scenario other than the selected scenario occurs during operation, becomes excessive due to a constraint violation.

[0115] If the impact of the unselected scenarios exceeds the threshold (NO in S16B-2), the unselected scenario impact assessment unit 34 extracts scenarios in which the operating costs exceed a pre-set threshold. In this case, the scenarios that exceed the threshold are scenarios other than the risk scenarios selected in S15B-1. The process then returns to step S15B-1 and continues.

[0116] In other words, the risk scenario selection unit 323 adds the scenarios that the unselected scenario impact evaluation unit 34 determined to cause a constraint violation in step S16B-2 based on an evaluation assuming operational conditions, to the scenario selected in step S15B-1, and outputs this to the uncertainty plan calculation unit 33. The uncertainty plan calculation unit 33 again creates a power operation plan that takes uncertainty into account in step S16, and the unselected scenario impact evaluation unit 34 evaluates the impact of the unselected scenarios. This process is repeated until there are no more constraint violations in step S16B-2.

[0117] Here, if the impact of the unselected scenarios is within the threshold (YES in S16B-2), the uncertainty plan calculation unit 33 outputs the power operation plan considering the uncertainties created in step S16 to the screen display and result storage unit 4. In addition, the scenario selection results in step S14, the risk scenario selection results in step S15B-1, and the scenarios added in the impact evaluation of the unselected scenarios in step S16B-1 are also output in the same manner.

[0118] In step S17 of Figure 9, the screen display and result saving unit 4 saves the power operation plan, which takes uncertainty into account, to the power operation plan database DB2 and displays it on the screen. At this time, the power operation plan database DB2 stores the scenario selection results from step S14, the risk scenario selection results from step S15B-1, and the scenarios added in the impact assessment of the unselected scenarios in step S16B-1, and the selection results are displayed on the screen.

[0119] The power generation and grid information unit 2 may be configured as a storage device or cloud server located outside the power operation planning device 1. In this case, the calculation unit 3 of the power operation planning device 1 will acquire the necessary information from the storage device or cloud server as appropriate when executing processing. The uncertainty plan calculated by the calculation unit 3 may be stored on the storage device or cloud server.

[0120] <Effects of the second embodiment> In the power operation planning device 1A according to the second embodiment described above, in addition to scenario selection according to the first embodiment, an operational cost distribution (revenue) is created based on the estimated planning results according to the scenario, showing the frequency of occurrence of the scenario and the operational cost assumed for that scenario. From this operational cost distribution, the power operation planning device 1A selects scenarios that fall within a specific area of ​​the distribution specified by the operator, such as the risk of revenue and loss, and creates a power operation plan that takes uncertainty into account. As a result, the power operation planning device 1A has the effect of reducing the number of scenarios outside the specific area, prioritizing the improvement of risks that the operator considers important to evaluate when creating the power operation plan, and speeding up the calculation time for plan creation.

[0121] Here, scenario selection is performed based on the estimation of planning results according to the scenario, as shown in the first embodiment. If the estimation accuracy of the estimated planning results is low, the overlap of planning results may not be properly evaluated, and scenarios that affect the planning results may not be sufficiently extracted in the scenario selection. Therefore, in order to avoid insufficient extraction of important scenarios, the power operation planning device 1A evaluates the occurrence of scenarios that were not selected during the actual operation of the power operation plan that takes uncertainty into account and is created based on the selected scenarios.

[0122] During operation after the plan is created, there are decision variables that cannot be adjusted immediately before operation. Therefore, in evaluations simulating operation, these decision variables are set to their planned values, limiting the adjustment elements. By adding scenarios in the evaluation simulating operation where costs become excessive due to constraint violations, etc., to the newly selected scenarios, the aforementioned lack of scenario extraction can be corrected, and the accuracy of uncertainty simulation can be improved with high precision.

[0123] It should be noted that the present invention is not limited to the embodiments described above, and various other applications and modifications can be taken as long as they do not deviate from the gist of the present invention as described in the claims. For example, the embodiments described above are detailed and specific explanations of the configuration of the apparatus and system in order to clearly illustrate the present invention, and are not necessarily limited to having all the configurations described. Furthermore, it is possible to replace some of the configurations of the embodiments described here with the configurations of other embodiments, and it is also possible to add the configurations of other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace some of the configurations of each embodiment with other configurations. Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of symbols]

[0124] 1,1A…Power operation planning device, 2…Power generation and grid information unit, 3…Calculation unit, 4…Scenario display and result storage unit, 31…Scenario-based plan estimation unit, 32…Decision variable evaluation and scenario selection unit for estimated plan, 33…Uncertainty plan calculation unit, 34…Out-of-selection scenario impact evaluation unit, 321…Similarity evaluation unit for plan result features, 322…Scenario selection unit, 323…Risk scenario selection unit, DB1…Grid information database, DB2…Power operation planning database

Claims

1. A preliminary plan creation unit inputs information necessary for creating an energy operation plan as multiple scenarios into an estimation model and creates a preliminary plan that approximates the energy operation plan, An evaluation unit that evaluates the similarity between the scenarios based on the results of the aforementioned budget plan, A scenario selection unit selects scenarios from among the scenarios input to the estimation model such that the scenarios are not similar, based on the evaluation of similarity by the evaluation unit. The system includes a planning unit that creates the energy operation plan using the selected scenario. Energy utilization planning device.

2. The evaluation unit evaluates the similarity of the effects on the energy operation plan for each scenario based on the results of the preliminary plan, The scenario selection unit deletes similar scenarios based on the similarity evaluation by the evaluation unit and selects the remaining scenarios. The energy utilization planning device according to claim 1.

3. The aforementioned estimated plan creation unit creates an operational cost distribution consisting of the probability of the scenario occurring and the operational cost of the estimated plan, based on the estimated plan. The energy utilization planning device according to claim 2.

4. The system includes a risk scenario selection unit that, based on the scenarios selected by the scenario selection unit and the estimated plan, selects scenarios that affect the statistical risk assessment indicators based on the operating cost distribution as risk scenarios. The energy utilization planning device according to claim 3.

5. The estimation model is a model that simulates the relationship between the information necessary for creating the energy operation plan and the estimated plan which is estimated assuming uncertainties, and is composed of a method different from the method used to create the energy operation plan. An energy utilization planning device according to any one of claims 1 to 4.

6. The evaluation unit evaluates the similarity of overlapping scenarios using decision variables, which are elements whose values ​​cannot be changed when the energy operation plan created by the planning unit is put into operation. The energy utilization planning device according to claim 2.

7. The system includes an unselected scenario impact evaluation unit that evaluates situations in which the scenario selection unit did not select a scenario, The planning unit creates the energy operation plan by fixing the decision variables for the scenarios not selected by the scenario selection unit, and then, if the unselected scenario impact evaluation unit evaluates that the scenarios not selected by the scenario selection unit will have an impact on operating costs, it adds the scenarios not selected by the scenario selection unit to the scenarios selected by the scenario selection unit. The energy utilization planning device according to claim 6.

8. The scenario selection unit, based on the scenarios entered in the estimated plan creation unit, prioritizes deleting scenarios with a small probability of occurrence and a probability similarity evaluation index, where the value decreases as the similarity with other scenarios increases. It then extracts the remaining scenarios. The energy utilization planning device according to claim 2.

9. When the scenario selection unit deletes the scenario and extracts the remaining scenarios, it selects a scenario in which the expected value of the energy operation plan based on the scenario and its estimated plan, or the change in the probability similarity evaluation index shown by the evaluation unit in the similarity evaluation with other scenarios, is within a threshold before and after the deletion of the scenario. The energy utilization planning device according to claim 8.

10. The system includes a display unit that shows the aforementioned operating cost distribution. The energy utilization planning device according to claim 3.

11. The estimation model is a relaxed optimization problem, which is a relaxed version of the optimization problem for creating the energy operation plan. The energy utilization planning device according to claim 5.

12. The estimation model is a machine learning model that has learned the relationship between the information necessary for creating the energy operation plan and the estimated plan. The energy utilization planning device according to claim 5.

13. A display unit that displays at least one of the deleted scenario and the selected scenario. The energy utilization planning device according to claim 2.

14. An energy operation planning method executed by an energy operation planning device comprising an estimated plan creation unit, an evaluation unit, a scenario selection unit, and a plan creation unit, The aforementioned preliminary plan creation unit inputs information necessary for creating the energy operation plan as multiple scenarios into an estimation model and creates a preliminary plan that approximates the energy operation plan, The evaluation unit performs the step of evaluating the similarity between the scenarios based on the results of the estimated plan, The scenario selection unit performs the step of selecting scenarios from among the scenarios input to the estimation model such that the scenarios are not similar, based on the evaluation of similarity. The planning unit includes the step of creating the energy operation plan using the selected scenario. Energy utilization planning method.

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