Supply and demand planning apparatus and method

Abstract

To provide a supply-demand planning device and method for enabling an operation plan and a countermeasure in a rare event occurrence to be considered.SOLUTION: Provided is a supply-demand planning method to be executed by a supply-demand planning device that designs a supply-demand plan that satisfies various restriction conditions for a power system, the method including steps of: acquiring restriction information including one or more restriction conditions set in advance and restriction extension information in which changing ranges of respective set values for the restriction conditions are respectively defined; and respectively generating a plurality of verification scenarios composed of a combination of set values obtained by changing the set values of the respective restriction conditions in the acquired restriction information within the changing ranges in accordance with the restriction extension information.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present invention relates to a supply and demand planning apparatus and method, and is suitable for application to a supply and demand planning apparatus that formulates a supply and demand plan that satisfies various constraint conditions, for example, with respect to a power system.

Background Art

[0002] A power system is composed of many generators, loads, power transmission and distribution equipment, and control devices. In order to operate the power system stably and at low cost, it is necessary to formulate a supply and demand plan to maintain the balance of the power supply amount with respect to the power demand that varies every moment. In order to realize highly efficient system operation while introducing a large amount of renewable energy power sources whose output varies greatly depending on the weather, the transition to market-oriented power system operation is being promoted in each country.

[0003] As necessary functions for realizing such power system operation, there are a security-constrained unit commitment (SCUC) function and a security-constrained economic dispatch (SCED) function implemented in the next-generation central power dispatching center. With these functions, it is possible to formulate a generator startup and shutdown plan and an output distribution that satisfy the wide-area merit order and various operation constraints. In these SCUC functions and SCED functions, in the optimization problem for obtaining a power generation plan, the operation cost minimization is set as the objective function, and the generator unit constraints (output upper and lower limits, output change rate, minimum startup and shutdown time, etc.) and system constraints (power flow constraints, etc.) are set as the operation constraints to be complied with.

[0004] Currently, supply and demand are tight (kWh constraints) due to insufficient fuel reserves and unexpected demand fluctuations, as are prolonged reductions in renewable energy output caused by sudden weather changes such as Dark Doldrums (DDD) and wind turbine freezing. Furthermore, with the increasing adoption of renewable energy, distributed power sources, EVs (Electric Vehicles), and BESS (Battery Energy Storage Systems), it is becoming difficult to predict electricity consumption trends and fluctuations in advance, and the impact of accidents is becoming more complex.

[0005] Against this backdrop, the person responsible for the long-term stable operation of the power grid (in Japan, this is the general transmission and distribution operator) needs to formulate plans that consider long-term constraints of monthly, yearly, or longer, and to verify pre-emptive measures by formulating plans that take into account various unforeseen fluctuations, in order to fulfill their investment in and accountability for long-term stable operation.

[0006] Here, "verification scenario" refers to information that defines the constraints that the power system must satisfy. For each constraint that the power system must satisfy, a verification scenario representing its content is generated, and a supply and demand plan is formulated to satisfy the constraints defined by the generated verification scenario. In the following, the verification scenario will simply be referred to as a scenario.

[0007] Against this backdrop, supply and demand planning technologies have been devised to enable operators of power grids to determine the optimal power grid operation method after considering various possible grid conditions. For example, Patent Document 1 discloses a method for calculating the grid impact of each piece of equipment under evaluation in order to appropriately calculate the priority of equipment replacement or new construction. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2021-191112 [Overview of the project] [Problems that the invention aims to solve]

[0009] In recent years, renewable energy sources, such as solar and wind power generation systems, have been introduced into the power grid on a large scale. The renewable energy output from these sources is accompanied by steep and unpredictable fluctuations in power generation output, which could result in significant changes in the power supply and demand situation.

[0010] Furthermore, in the operation of thermal power generators, the amount of electricity that can be generated is affected from the perspective of kWh constraints due to the political situation in resource-producing countries such as natural gas and oil, troubles at fuel transportation and storage facilities, soaring fuel prices, and difficulties in procuring fuel. In addition, there is a possibility that a supply-demand tightness may occur due to insufficient power supply capacity caused by an increase in electricity demand due to unexpected weather conditions that deviate from weather forecasts, coinciding with the timing of generator shutdowns.

[0011] Considering the operational status of the power grid as described above, proactive measures based on comprehensive scenario verification are necessary. However, mechanically setting scenarios may lead to an increase in calculation time for planning due to an excessive number of scenarios relative to the objective, or to the failure to verify important scenarios due to an insufficient number of scenarios. From this perspective, it is necessary to generate and extract scenarios that are neither excessive nor insufficient according to the verification objective.

[0012] In this regard, the technology described in Patent Document 1 evaluates the impact of whether or not a single piece of equipment fails, and therefore cannot evaluate unspecified system conditions such as combinations of multiple equipment failures or severe events other than equipment failures. On the other hand, attempting to specify all such combinations of multiple equipment failures and severe events other than equipment failures would require a great deal of time and effort. Thus, the technology disclosed in Patent Document 1 has the problem of making it difficult to verify comprehensive scenarios.

[0013] The present invention has been made in consideration of the above points, and its first objective is to propose a supply and demand planning device and method that can generate comprehensive verification scenarios that also take into account operational constraints associated with rare events, thereby enabling the consideration of operational plans and countermeasures when rare events occur. The second objective of the present invention is to propose a supply and demand planning device and method that enables the generation of comprehensive verification scenarios while reducing the processing time for plan formulation caused by an excessive number of verification scenarios. [Means for solving the problem]

[0014] To solve the above problems, the present invention provides a supply and demand planning device for a power grid that formulates a supply and demand plan that satisfies various constraints, comprising: a constraint information acquisition unit that acquires constraint information including one or more of the constraints set in advance, and constraint extension information which defines the range of change for each setting value of the constraints; and a scenario generation unit that generates a plurality of verification scenarios consisting of combinations of setting values ​​obtained by changing each of the setting values ​​of the constraints in the constraint information acquired by the constraint information acquisition unit within the change range according to the constraint extension information. Based on the constraints included in the verification scenario, the planning department formulates the supply and demand plan that takes these constraints into account. We decided to implement this.

[0015] As a result, the supply and demand planning device of the present invention can generate comprehensive verification scenarios that also take into account operational constraints associated with rare events.

[0016] Furthermore, the present invention includes a scenario evaluation unit that calculates evaluation indicators related to each of the verification scenarios generated by the scenario generation unit, and a scenario selection unit that selects some of the verification scenarios from among the verification scenarios generated by the scenario generation unit based on the evaluation indicators of each of the verification scenarios calculated by the scenario evaluation unit.

[0017] As a result, the supply and demand planning device of the present invention can generate and extract verification scenarios that are neither excessive nor insufficient.

[0018] Furthermore, in the present invention, there is a supply-demand planning method executed by a supply-demand planning device that formulates a supply-demand plan that satisfies various constraint conditions for an electric power system. The method includes: a first step of acquiring constraint information including one or more of the previously set constraint conditions, and constraint extension information in which the change range of each set value for the constraint condition is defined; and a second step of generating a plurality of verification scenarios each consisting of a combination of the set values obtained by changing the set values of each of the constraint conditions in the acquired constraint information within the change range according to the constraint extension information. The third step is to formulate the supply and demand plan that takes into account the constraints included in the verification scenario, is provided.

[0019] As a result, according to the supply-demand planning method of the present invention, it is possible to generate comprehensive verification scenarios that also take into account operation constraint conditions associated with rare events.

[0020] Furthermore, in the present invention, In the third step described above, the supply and demand planning device, calculate evaluation indicators respectively related to each of the generated verification scenarios death , and based on the evaluation indicators of each of the calculated verification scenarios, select some of the verification scenarios from among the verification scenarios. Based on the constraints included in the selected verification scenario, a supply and demand plan is formulated that takes these constraints into account. is provided.

[0021] As a result, according to the supply-demand planning method of the present invention, it is possible to generate and extract verification scenarios without excess or deficiency.

Effects of the Invention

[0022] According to the present invention, it is possible to generate comprehensive verification scenarios that also take into account operation constraint conditions associated with rare events, and thereby realize a supply-demand planning device and method that can consider operation plans and countermeasures when rare events occur.

[0023] Also, according to the present invention, it is possible to realize a supply-demand planning device and method that can reduce the processing time for formulating a plan due to an excessive number of verification scenarios while enabling the generation of comprehensive verification scenarios.

Brief Description of the Drawings

[0024] [Figure 1] This is a block diagram showing the configuration of the supply and demand planning device and power system according to this embodiment. [Figure 2] This is a block diagram showing the logical configuration of the supply and demand planning device according to this embodiment. [Figure 3] This is a diagram illustrating an example of the structure of constraint information. [Figure 4] (A) and (B) are diagrams illustrating examples of the structure of constraint extension information. [Figure 5] (A) and (B) are diagrams illustrating examples of the structure of past information. [Figure 6] Flowchart showing the processing steps for formulating supply and demand plans. [Figure 7] This is a diagram illustrating an example of the structure of scenario information. [Modes for carrying out the invention]

[0025] An embodiment of the present invention will be described in detail with respect to the following drawings. It should be noted that the embodiment described below is merely an example, and is not intended to limit the present invention itself.

[0026] (1) Configuration of the supply and demand planning device according to this embodiment In Figure 1, 1 represents a supply and demand planning device according to this embodiment. This supply and demand planning device 1 is a device that formulates a supply and demand plan for the power system 2 under management and operates and manages the power system 2 in accordance with the formulated supply and demand plan.

[0027] Each of these power systems 2 comprises first and second generators 10A and 10B, a substation 11, phase-shifting equipment 12, and a power load 13, as well as a plurality of measuring devices 14A, 14B, 14C, 14D, and 14E. Each of the measuring devices 14A to 14E is connected to the supply and demand planning device 1 via an information and communication network 15.

[0028] In the following, unless there is a need to distinguish between the first and second generators 10A and 10B, they will be referred to as generator 10. Similarly, unless there is a need to distinguish between the measuring devices 14A to 14E, they will be referred to as measuring devices 14.

[0029] The first generator 10A is a large-scale generator, including thermal, hydroelectric, or nuclear power plants, installed on the high-voltage side of substation 11 in the power system 2, and transmits system state quantities, including the amount of power generated, to the supply and demand planning device 1 via the measuring device 14A and the information and communication network 15. The first generator 10A also changes the system state quantities, including the amount of power generated, according to the control command information transmitted from the supply and demand planning device 1 via the information and communication network 15 and the measuring device 14A.

[0030] The second generator 10B is a small to medium-sized generator, including solar power generation, wind power generation, biomass power generation, tidal power generation, and cogeneration, installed on the low-voltage side of substation 11 in the power system 2, and transmits grid status information, including power generation amount, to the supply and demand planning device 1 via measuring device 14B and information and communication network 15.

[0031] Substation 11 is installed between transmission lines within the power system 2, transforms the power transmitted from the high-voltage side where the first generator 10A is installed, and transmits it to the low-voltage side where the power load 13 is installed. Phase-shifting equipment 12, such as power capacitors and shunt reactors, are connected to substation 11.

[0032] The phase-shifting equipment 12 controls the voltage distribution within the power system 2 by changing the reactive power in the power system 2, and includes power capacitors, shunt reactors, STATCOM (Static Synchronous Compensator: self-excited reactive power compensation device), or SVC (Static Var Compensator). Some of the phase-shifting equipment 12 changes system state quantities, including the amount of power generated, in response to control command information transmitted from the supply and demand planning device 1 via the measuring device 14C and the information and communication network 15.

[0033] The power load 13 consists of electric motors, lighting fixtures, and other devices that consume power in facilities such as homes, factories, and buildings. The measuring device 14 consists of a PMU (Phasor Measurement Unit) and other components, and periodically measures system state quantities such as active power flow, reactive power flow, system voltage, system current, and / or voltage phase at measurement points in the power system 2, and transmits the time-series system state quantities obtained from the measurements to the supply and demand planning device 1 via the information and communication network 15.

[0034] The information and communication network 15 is a network capable of transmitting data bidirectionally. The information and communication network 15 is composed of, for example, a wired network, a wireless network, or a combination thereof. The information and communication network 15 may be the so-called Internet, or it may be a dedicated line network.

[0035] Furthermore, an external power system 16 is connected to power system 2 via an interconnection line. External power system 16 is an external power system that cannot be controlled from the supply and demand planning device 1.

[0036] The supply and demand planning device 1 is a computer device that has the function of generating control command information in accordance with a pre-formulated supply and demand plan based on system state quantities transmitted from each measuring device 14 in the power system 2 via the information and communication network 15. The supply and demand planning device 1 transmits the generated control command information to the first generator 10A and the phase adjustment equipment 12 of the substation 11 via the information and communication network 15 and measuring devices 14A and 14B.

[0037] This supply and demand planning device 1 is comprised of a CPU (Central Processing Unit) 21, memory 22, storage device 23, display device 24, input device 25, and communication device 26, all of which are interconnected via an internal bus 20.

[0038] The CPU 21 is a processor that controls the operation of the entire supply and demand planning device 1. The memory 22 consists of non-volatile semiconductor memory such as RAM (Random Access Memory) and is used as the working memory of the CPU 21. The storage device 23 consists of a non-volatile, high-capacity storage device such as a hard disk drive or SSD (Solid State Drive) and stores various programs and data that needs to be stored for a long period of time.

[0039] When the supply and demand planning device 1 is started or when necessary, the program stored in the storage device 23 is read from the storage device 23 to the memory 22, and the CPU 21 executes the program read from the memory 22, thereby executing various processes for the supply and demand planning device 1 as a whole, as described later.

[0040] The display device 24 is composed of, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is used to display information such as the system status calculated by the CPU 21, as well as various screens generated by the CPU 21.

[0041] The input device 25 consists of a mouse and keyboard, and is used by the user of the supply and demand planning device 1 (the administrator of the power system 2) when operating the supply and demand planning device 1. The user can appropriately set various thresholds and other parameters through the user interface screen displayed on the display device 24, and can also use the input device 25 to select the type of data they want to check and display the contents of that data on the display device 24.

[0042] The communication device 26 is composed of, for example, a NIC (Network Interface Card) and performs protocol control during communication with the generator 10, the phase-shifting equipment 12, and each measuring device 14. The communication device 26 also transfers various time-series system state quantities obtained through communication with each measuring device 14 to the CPU 21 via the internal bus 20. The CPU 21 then generates control command information based on these transferred system state quantities and transmits the generated control command information to the first generator 10A of the power system 2 and the phase-shifting equipment 12 of the substation 11, thereby controlling the amount of power generated by the first generator 10A and the amount of phase-shifting performed by the phase-shifting equipment 12.

[0043] (2) Supply and demand planning function according to this embodiment Next, we will explain the supply and demand planning function installed in this supply and demand planning device 1. This supply and demand planning function expands on constraint information created in advance by the user (details will be described later) and utilizes information from past supply and demand plans to create more scenarios. At the same time, it filters the created scenarios and formulates a supply and demand plan using only realistic scenarios. This supply and demand planning function prevents the oversight of critical scenarios due to insufficient scenarios, while suppressing the increase in supply and demand plan formulation time caused by having too many scenarios for the purpose.

[0044] As a means to realize the supply and demand planning function according to this embodiment, the supply and demand planning device 1 is provided with a constraint information database 31, a constraint extension information database 32, and a past information database 33 stored in an information storage unit 30, as shown in Figure 2, and a constraint information acquisition unit 35, a past information acquisition unit 36, a past information analysis unit 37, a scenario generation unit 38, a scenario evaluation unit 39, a scenario selection unit 40, and a planning unit 41 which constitute the supply and demand planning unit 34. The information storage unit 30 is a logical storage area composed of the memory 22 and storage device 23 described above in Figure 1, and the supply and demand planning unit 34 is a functional unit that is realized when the CPU 21 (Figure 1) executes a program stored in the memory 22.

[0045] The constraint information database 31 is a database that stores information (hereinafter referred to as constraint information) that represents the specific details of several items (hereinafter referred to as constraint items) that are constraints in operating the power system 2. The constraint information 50 (Figure 3) is created in advance by the user and registered in the constraint information database 31.

[0046] Specifically, as shown in Figure 3, the necessary items 50A to 50E from the items 50A, 50B, 50C, 50D, and 50E for the restricted equipment, the upper limit of the restricted quantity, the lower limit of the restricted quantity, the specified quantity of the restricted quantity, and the restricted period are set. In this case, items 50A and 50E for the restricted equipment and the restricted period are set for all constraint items, while for items 50B to 50D for the upper limit of the restricted quantity, the lower limit of the restricted quantity, and the specified quantity, one or more of these items 50B to 50D are set.

[0047] The constrained equipment represents the equipment subject to the corresponding constraint item. The constrained equipment can be any of the following within power system 2: generator 10, transmission lines, substation 11, power load 13, interconnection point nodes, or a combination thereof.

[0048] The constraint period represents the period to which the constraint item applies, and a period is set for determining whether the constraint can be complied with. The constraint period may include a period encompassing multiple time zones. Alternatively, for constraint items where individual constraint values ​​are set for individual time zones, the constraint period may be set for each individual time zone. Furthermore, the constraint period may be set to a period that satisfies certain conditions. Such conditions can include the year, season, day of the week, weekday / holiday type, and time.

[0049] The constraint limit represents the upper limit for the constrained equipment in the corresponding constraint item. The constraint limits apply to active power (kW), reactive power (kvar or Mvar), active energy (kWh or MWh), system voltage (V or kV), power factor (%), current phase or voltage phase (deg or rad), etc.

[0050] Furthermore, the constraint lower limit represents the lower limit value for the constrained equipment in the corresponding constraint item. The constraint lower limit applies to active power (kW), reactive power (kvar or Mvar), active energy (kWh or MWh), system voltage (V or kV), power factor (%), current phase or voltage phase (deg or rad), etc.

[0051] The constraint quantity represents the specified quantity for the equipment subject to the constraint in the corresponding constraint item. Here, the specified quantity is the value that the object for which the constraint quantity is set must always maintain. The objects subject to the constraint quantity include active power ([kW]), reactive power ([Kvar] or [Mvar]), active energy ([kWh] or [MWh]), system voltage ([V] or [kV]), power factor ([%]), current phase or voltage phase ([deg] or [rad]), etc.

[0052] Therefore, in the example in Figure 3, it is shown that "Constraint Item 1" states that the lower limit of the total amount of electricity generated by the four first generators, "Generator a-1," "Generator a-2," "Generator b-1," and "Generator b-2," over a period of "1.5 years" from "May 2023 to November 2024" is "10 [TWh]." Also in Figure 3, it is shown that "Constraint Item 2" states that the upper limit of the amount of electricity flowing through the two transmission lines, "Transmission Line A-1" and "Transmission Line A-2," at each time is "300 [MW]," and "Constraint Item 3" states that the amount of electricity generated by the first generator, "Generator a-1," at each time should always be "100 [MW]."

[0053] The constraint extension information database 32 is a database in which parameters used to extend the constraint information 50 are stored as constraint extension information 51 (Figure 4). The constraint information 50 is extended by increasing or decreasing the number of restricted equipment, increasing or decreasing the upper and lower constraint limits set for the restricted equipment, changing the restricted period, etc. In this case, the parameters that define the extent to which the extension should be performed are the constraint extension information 51. The constraint extension information 51 is created in advance by the user and registered in the constraint extension information database 32.

[0054] Figure 4(A) shows an example of constraint extension information 51 for constraint conditions related to the equipment under constraint. In this example in Figure 4(A), parameters are defined that indicate how the set values ​​of the constraint conditions related to the equipment under constraint are extended. Specifically, for the equipment under constraint, the upper limit of the constraint, and the lower limit of the constraint, parameters are defined for three items 51AA, 51AB, and 51AC: the lower limit when the quantity increases or decreases, the upper limit when the quantity increases or decreases, and the change step when the quantity increases or decreases.

[0055] For example, in Figure 4(A), for the constrained equipment, the lower limit when increasing or decreasing the constrained equipment ("lower limit when increasing or decreasing quantity") is 50% of the base constraint, the upper limit ("upper limit when increasing or decreasing quantity") is the total number of constrained equipment in power system 2 (all interconnected equipment in the target system), and the amount of change when increasing or decreasing ("increment when increasing or decreasing quantity") is 1 equipment. The base constraint here refers to the number of constrained equipment set by the user as item 50A in Figure 3 in the constraint information 50 mentioned above.

[0056] Furthermore, regarding the upper limit of the constraint, it is indicated that the lower limit when increasing or decreasing the upper limit of the constraint is 50% of the base constraint, the upper limit is 200% of the base constraint, and the amount of change when increasing or decreasing is 10% of the constraint amount of the base constraint. The base constraint here refers to the upper limit of the constraint set by the user as item 50B in Figure 3 in constraint information 50.

[0057] Furthermore, regarding the lower limit of the constraint, it is shown that when increasing or decreasing the lower limit of the constraint, the lower limit is 80% of the base constraint, the upper limit is 120% of the base constraint, and the amount of change when increasing or decreasing is 5% of the constraint amount of the base constraint. The base constraint here refers to the lower limit of the constraint set by the user as item 50C in Figure 3 in constraint information 50.

[0058] Therefore, in the example of Figure 4(A), for example, if we expand "Constraint Item 1" in Figure 3 by increasing or decreasing only the "Constrained Equipment," since there are 4 "Constrained Equipment" items, the lower limit of the Constrained Equipment is "2," which is "50%" of that number. If the upper limit is a total of "9" Constrained Equipment items within power system 2, then multiple expansion patterns can be generated, each increasing or decreasing the number of Constrained Equipment items by "1" in the range of "2" to "9."

[0059] Furthermore, if we extend "Constraint Item 1" by only increasing or decreasing the "Lower Limit of the Constraint," since the base constraint is "10[TWh]," the lower limit becomes "80%" of "10[TWh]," which is "8[TWh]," and the upper limit becomes "120%" of "10[TWh]," which is "12[TWh]." Within this range (8[TWh]~12[TWh]), we can generate multiple extension patterns by increasing or decreasing the lower limit of the constraint by "5%" (i.e., 0.5[TWh]) of "10[TWh]."

[0060] Furthermore, for example, if we extend "Constraint Item 2" in Figure 4 by only increasing or decreasing the "Constraint Upper Limit," since the base constraint is "300[MW]," the lower limit becomes "150[MW]," which is "50%" of "300[MW]," and the upper limit becomes "600[MW]," which is "200%" of "300[MW]." Within this range (150[MW]~600[MW]), we can generate multiple extension patterns by increasing or decreasing the "Constraint Upper Limit" by "10%" (i.e., "30[MW]") of "300[MW]."

[0061] On the other hand, Figure 4(B) shows an example of constraint extension information related to changes in the constrained period. In this example of Figure 4(B), parameters are defined that indicate how the settings for the constrained period are extended. Specifically, parameters are defined for each item 51BA, 51BB, 51BC, 51BD, 51BE, and 51BF, which are the lower and upper limits of the extension range for the start time in the constrained period, the lower and upper limits of the extension range for the end time in the constrained period, and the type of the constrained period.

[0062] For example, in the example in Figure 4(B), it is shown that the start time of the restricted period can be changed in "1-month" increments within the range of "start time of the entire verification period" to "5 years after the start time of the entire verification period," assuming that the verification period is set in advance by the user. Similarly, it is shown that the end time of the restricted period can be changed in "1-month" increments within the range of "3 years before the end time of the entire verification period" to "end time of the entire verification period." Figure 4(B) also shows that the types of restricted periods that can be applied are "all day," "holidays only," or "weekdays outside of 8am-6pm."

[0063] Therefore, the number of extension patterns for the constrained period is equal to the total number of combinations of each extension pattern where the start time of the constrained period is changed by one month at a time, each extension pattern where the end time of the constrained period is extended by one month at a time, and each extension pattern where one of the following types of constrained period is applied: "all day", "holidays only", and "weekdays other than 8am-6pm".

[0064] Therefore, for example, for "Constraint Item 1" in Figure 4, the same number of expanded constraint items will be generated as there are combinations of all expansion patterns for the constrained equipment, all expansion patterns for the constraint lower limit, and all expansion patterns for the constrained period. Similarly, for "Constraint Item 2," the same number of expanded constraint items will be generated as there are combinations of all expansion patterns for the constrained equipment, all expansion patterns for the constraint upper limit, and all expansion patterns for the constrained period. And for "Constraint Item 3," the same number of expanded constraint items will be generated as there are combinations of all expansion patterns for the constrained equipment and all expansion patterns for the constrained period.

[0065] The historical information database 33 is a database that stores historical information 52 (Figure 5) which includes at least one of the following: information on past supply and demand plans (hereinafter referred to as historical plan information) and constraint information related to past supply and demand plans (hereinafter referred to as historical constraint information). The historical information 52 is registered in the historical information database 33 in advance.

[0066] Figure 5(A) shows an example of past planning information 52A included in past information 52. This past planning information 52A specifies the amount of power generated by each first generator 10A within the power system 2 for each time period. However, power flow rates at various facilities other than the first generators 10A (transmission lines, substations 11, power loads 13, interconnection point nodes) may also be included in the past planning information 52A.

[0067] Figure 5(B) shows an example of past constraint information 52B. This Figure 5(B) defines the validity / invalidity of four constraints in past supply and demand plans: "kWh constraint for generator group A," "planned shutdown constraint for generator b-1," "planned shutdown constraint for generator b-2," and "kWh constraint for generator group B," for each 30-minute time period from "2022 / 7 / 1 0:00 to 2022 / 7 / 1 2:00." In Figure 5(B), a check mark 52C is displayed when the corresponding constraint is valid, and not displayed when it is invalid.

[0068] However, the past constraint information 52B may include, in addition to the validity / invalidity of the constraint conditions, the constraint amounts set for each constraint item in Figure 4 (constraint item 1, constraint item 2, ...), i.e., the upper constraint amount, the lower constraint amount, or the specified constraint amount. Furthermore, for constraint items involving multiple grid equipment, the past constraint information 52B may also include which grid equipment is subject to each constraint item during each time period.

[0069] On the other hand, the constraint information acquisition unit 35 (Figure 2) is a functional unit that has the function of acquiring constraint information 50 (Figure 3) and constraint extension information 51 (Figure 4) from the constraint information database 31 and the constraint extension information database 32. The constraint information acquisition unit 35 outputs the acquired constraint information 50 and constraint extension information 51 to the scenario generation unit 38.

[0070] The past information acquisition unit 36 ​​is a functional unit that has the function of acquiring past information 52 (Figure 5) from the past information database 33.

[0071] Furthermore, the historical information analysis unit 37 is a functional unit that calculates constraint information (hereinafter referred to as historical constraint information) and statistical information in past supply and demand plans based on the historical information 52 acquired by the historical information acquisition unit 36, and calculates constraint extension information (hereinafter referred to as historical constraint extension information) based on the calculated historical constraint information and statistical information. The historical information analysis unit 37 outputs the calculated historical constraint information and historical constraint extension information to the scenario generation unit 38. The historical constraint information has the same data structure as the constraint information shown in Figure 4, and the historical constraint extension information has the same data structure as the constraint extension information shown in Figure 5.

[0072] The scenario generation unit 38 generates multiple scenarios defined by the setting values ​​for each constraint item, based on the constraint information 50 and constraint extension information 51 provided by the constraint information acquisition unit 35 and the past constraint information and past constraint extension information provided by the past information analysis unit 37, and outputs the generated scenarios to the scenario evaluation unit 39 and the scenario selection unit 40, respectively.

[0073] The scenario evaluation unit 39 calculates the priority of each scenario provided by the scenario generation unit 38 based on evaluation indicators, and outputs the calculation results as evaluation results to the scenario selection unit 40.

[0074] Based on the evaluation results notified by the scenario evaluation unit 39, the scenario selection unit 40 selects a scenario from among the scenarios provided by the scenario generation unit 38 that should be considered for further investigation, and outputs the selected scenario to the planning unit 41.

[0075] The planning unit 41 is a functional unit that has the function of formulating a supply and demand plan that takes into account the constraint information 50 included in each scenario selected by the scenario selection unit 40. As a formulating method here, known mathematical programming methods called security-constrained generator start-up / shut-down planning (SCUC) and security-constrained economic distribution (SCED) functions can be used. In this case, the objective function should be the minimization of operating costs, and the operational constraints to be observed should be power generation unit constraints (upper and lower limits of output, output change rate, minimum start-up / shut-down time) and grid constraints (power flow constraints). Furthermore, the objective function and operational constraints in this case are not limited to these, and various objective functions and operational constraints applicable to the current operation of the power system 2 may be set.

[0076] (3) Processing related to the supply and demand planning function according to this embodiment Figure 6 shows the flow of a series of processes (hereinafter referred to as the supply and demand planning process) executed in the supply and demand planning device 1 in relation to the supply and demand planning function of this embodiment.

[0077] This supply and demand planning process is initiated when a user performs a predetermined operation on the supply and demand planning device 1. First, the constraint information acquisition unit 35 acquires constraint information 50 that needs to be considered in formulating the demand plan from the constraint information database 31, and outputs the acquired constraint information 50 to the scenario generation unit 38 (S1). The method for acquiring the constraint information 50 at this time can be manual input by the user using analysis tools / software, importing a file containing the constraint information 50, or database linkage with other systems.

[0078] The constraint information acquisition unit 35 also acquires constraint extension information 51 from the constraint extension information database 32 and outputs the acquired constraint extension information 51 to the scenario generation unit 38 (S2). Regarding the method of acquiring the constraint extension information 51 at this time, similar to step S1, one of the following can be employed: manual input by the user using analysis tools / software, import of a file containing the constraint extension information 51, or database linkage with other systems.

[0079] Next, the historical information acquisition unit 36 ​​acquires historical information 52 from the historical information database 33 and outputs the acquired historical information 52 to the historical information analysis unit 37 (S3). The method for acquiring the historical information 52 at this time can be manual input by the user using analysis tools / software, importing a file containing the historical information 52, or database linkage with other systems. Alternatively, the user may specify in advance any of the following conditions: data period, acquisition date and time, target equipment, and / or target area, and the historical information acquisition unit 36 ​​may acquire historical information 52 that satisfies these conditions.

[0080] Next, the past information analysis unit 37 calculates the past constraint information and past constraint extension information from the past information 52, and outputs the calculated past constraint information and past constraint extension information to the scenario generation unit 38 (S4).

[0081] If the past information 52 includes past constraint information or past constraint extension information, that past constraint information or past constraint extension information may be used in the processing from step S4 onward.

[0082] Alternatively, indicators (features) related to various constraint items may be calculated from past information 52, and the calculation results may be used as past constraint information or past constraint extension information. For example, for constraint items related to the kWh constraint, the cumulative value of power generation energy over a certain period may be calculated using past information 52, and the cumulative value of this energy may be calculated for multiple periods or conditions. A feature may be calculated that includes one or more quantiles (boundary points) based on a pre-set threshold using statistical information for the set of energy cumulative values ​​for multiple periods, i.e., the mean, median, variance, standard deviation, cumulative frequency in the frequency distribution, or relative frequency. The calculated feature may then be used as the upper or lower constraint limit for the kWh constraint to set the past constraint information and past constraint extension information.

[0083] Similarly, for constraints related to planned shutdowns, the shutdown period for equipment due to pre-set maintenance, etc., may be extracted from historical information 52, and historical constraint information and historical constraint extension information may be set as the upper or lower constraint limits for the work shutdown constraints based on statistical information using the same energy integration values ​​as described above. For constraint items other than these, statistical analysis or machine learning methods may be applied to the historical information 52 related to each constraint to calculate features related to the constraint information, and historical constraint information and historical constraint extension information may be generated based on the calculated features.

[0084] Next, the scenario generation unit 38 selects one or more constraint items to be expanded in the constraint information 50 during scenario generation (S5). As a method for selecting constraint items here, manual input by the user using analysis tools or software can be adopted. In this case, the scenario generation unit 38 will select the constraint items specified by the user as constraint items to be expanded.

[0085] However, the scenario generation unit 38 may be configured to automatically select constraint items such that, when the setting value of the constraint item is varied within a specified range, the impact on the supply and demand plan calculated based on each scenario is greater than or equal to a pre-set threshold, or that when the constraint items are permuted in descending order based on their impact, the number of items is less than or equal to a pre-set threshold.

[0086] Next, the scenario generation unit 38 generates multiple scenarios using the constraint items selected in step S5, based on the constraint information 50 and constraint extension information 51 provided by the constraint information acquisition unit 35 and the past constraint information and past constraint extension information provided by the past information analysis unit 37, and outputs the information of each generated scenario as scenario information to the scenario evaluation unit 39 and the scenario selection unit 40, respectively (S6).

[0087] Specifically, the scenario generation unit 38 calculates possible combinations of constraint setting values ​​for each constraint item selected in step S5, based on the variation range (upper limit when increasing or decreasing, lower limit when increasing or decreasing, and step size when increasing or decreasing) of each constraint item included in the constraint extension information acquired in step S2. It then selects one of the combinations of constraint setting values ​​for all constraint items selected in step S5 and generates a scenario by defining all combination conditions of the setting values.

[0088] Figure 7 shows an example of the configuration of each scenario generated in this way. In the example in Figure 7, each row corresponds to one scenario 53. Each scenario 53 includes the following information 53A to 53E: name ("scenario name"), whether each constraint item is applied ("kWh constraint" and "planned shutdown constraint"), equipment subject to constraint for each constraint item ("kWh constraint target" and "planned shutdown constraint target"), constraint amount for each constraint item ("kWh constraint amount"), and constraint period for each constraint item ("kWh constraint period" and "planned shutdown constraint period"), as well as the following information 53F to 53H: "scenario generation conditions", one or more "evaluation indicators", and "scenario selection results". Figure 7 is an example where the kWh constraint and planned shutdown constraint are selected as constraint items in step S5. Note that the "planned shutdown constraint" is the same as "Constraint Item 3" in Figure 3, but with the constraint amount value set to "0 [MV]".

[0089] The "scenario generation conditions" are information about what operations were applied to the base constraints set by the user based on the constraint information 50 and constraint extension information 51 to set the constraint conditions in that scenario 53. In the example in Figure 7, the scenario generation conditions are shown to include information such as the increase or decrease in the constraint amount in the base constraint and which of the multiple constraint items were combined to set the constraint conditions in that scenario 53.

[0090] The "scenario selection result" indicates whether or not the corresponding scenario 53 will be processed in subsequent steps. The scenario selection result is information added during the scenario 53 selection process in step S8, which will be described later, and this information 53H does not exist at step S6. The evaluation indicator is information added during the process described later in step S7. The specific details of the evaluation indicator will be described later.

[0091] Next, the scenario evaluation unit 39 evaluates each scenario based on the scenario information provided by the scenario generation unit 38 using evaluation indicators (S7). In this case, evaluation indicators can be applied that prioritize scenarios with a large impact on grid operation. For example, various grid stability levels, power outage duration, power outage scope, and power restoration time can be applied as evaluation indicators.

[0092] Furthermore, known calculation methods for estimating various evaluation indicators such as stability, power outage duration, and power outage extent can be used, including system stability analysis and assumed failure calculations that simulate N-1, N-2, or Nx events (events in which one, two, or x system equipment out of all system equipment under consideration becomes inoperable).

[0093] In this process, power flow calculations are performed by applying multiple constraints included in the scenario to multiple predefined grid cross-sectional data sets using power demand, power generation, and various grid operation parameters (such as the tap position of the substation 11, connection information of the phase-shifting equipment 12, and the amount of reactive power controlled) in the grid equipment. If the grid constraint exceeds the judgment threshold, or if the following equation is used...

number

[0094] The above equation (1) is a determination formula that shows the relationship between the calculation result in the power flow calculation of the transmission line L and the determination threshold, where α represents a predetermined constant parameter. The determination condition is not limited to the transmission line power flow, but can use one or more of the following: power flow, voltage stability, or transient stability in various system equipment.

[0095] Another method for evaluating scenarios is to apply evaluation metrics that prioritize scenarios with a high frequency of events that meet predefined conditions. In this case, such events can be defined as events in which various evaluation metrics for system stability, power outage duration, power outage scope, or power restoration time exceed pre-set thresholds, and the frequency of such events can be applied as an evaluation metric.

[0096] Furthermore, a comprehensive evaluation index may be calculated by weighting and adding the evaluation results of each scenario related to the frequency of such events and their impact on grid operation, and the calculated comprehensive evaluation index may be used as the evaluation result of the scenario. In this case, power flow calculations are performed by applying multiple constraint conditions included in the scenario to multiple grid cross-section data sets defined in advance using power demand, power generation amount, and various grid operation parameters (tap positions of substation 11, connection status of phase adjustment equipment 12, amount of reactive power control, etc.) in the grid equipment, and equipment where the grid constraint exceeds the judgment threshold, or the following equation

number

[0097] Equation (2) above is a determination formula that shows the relationship between the calculation result in the power flow calculation of the transmission line L and the determination threshold, where β represents a parameter of a predetermined constant value. The determination conditions are not limited to transmission line power flow, but can use one or more of the following: power flow, voltage stability, or transient stability in various system equipment.

[0098] As another method, the above-mentioned system cross-sectional data, to which multiple constraints included in the scenario are applied, may be subjected to transient analysis simulations to apply one or more assumed failure conditions that have been predefined as system failures. The simulation results may then be used to calculate whether generator interruptions and load interruptions occur and which targets they affect, and the resulting number of power outages or the rated capacity of the equipment that experiences the outages may be used as the system impact for evaluation.

[0099] The scenario evaluation unit 39 adds the evaluation results for each scenario to the scenario information. These evaluation results can include the frequency of events and the degree of impact on grid operation (grid impact) for each scenario. In the example in Figure 7, the frequency of power flow constraint violations is added as the frequency of events, and an evaluation index based on the assumed power outage time is added as the grid impact.

[0100] Next, the scenario selection unit 40 selects a scenario to be used in formulating the power generation plan (S8). In this case, one selection method is to display the evaluation results of each scenario by the scenario evaluation unit to the user using analysis tools / software, and then have the scenario selection unit 40 select the scenario selected by the user through manual input.

[0101] Furthermore, the scenario selection unit 40 may select a scenario from among multiple scenarios selected by the user such that the setting values ​​of the deviations for various constraint items fall within a predetermined range. In addition, the scenario selection unit 40 may automatically select a scenario so that the scenarios are arranged in descending order according to the evaluation results of the scenario evaluation unit 39, and the total number of selected scenarios is less than or equal to a pre-set threshold (threshold for the total number of selectable scenarios), and or the sum of the estimated processing times in subsequent processing is less than or equal to a pre-set threshold (threshold for estimated processing times related to the selected scenario).

[0102] Furthermore, the scenario evaluation unit 39 may calculate a weighted sum using pre-set coefficients for multiple evaluation indicators calculated for each scenario, and the scenario selection unit 40 may select a scenario by referring to the calculated weighted sum. For example, if there are a total of m evaluation indicators, the following equation

number

[0103] The scenario selection unit 40 adds the selection results (selected or not selected) for each scenario, as described above, to the scenario information of the corresponding scenario as shown in Figure 7. The scenario selection unit 40 then outputs the scenario information of each scenario to the planning unit 41, to which the scenario selection results have been added.

[0104] Next, the planning unit 41 formulates a supply and demand plan (power generation plan) using each scenario selected by the scenario selection unit 40 (S9). Specifically, the planning unit 41 formulates an optimization problem that reflects the constraints defined in each scenario selected by the scenario selection unit 40, and calculates a power generation plan including the start-stop state and power output of each first generator 10A (Figure 1) in the power system 2 based on the calculation results of the formulated optimization problem. In this case, the optimization problem is, for example, the following equation

number

[0105] In equation (4), t is the index representing the time, i is the index representing the first generator 10A, C i (p) is the operating cost relative to the power output p of generator i, SUC i The startup cost of generator i is D i (r) represents the operating cost of the adjustment force contribution r of generator i, and PC represents the penalty cost associated with the constraint violation.

[0106] Furthermore, the constraints include one or more of the following (a) to (f): (a) Supply and demand constraint; the electricity demand subject to the allocation matches the total power output. (b) Generator output upper and lower limit constraints; each first generator 10A takes an output value within the range from the lower output limit to the upper output limit. (c) Generator output change rate constraint; the range of change in the output value of each first generator 10A between different time points must be within the upper limit of the output change rate. (d) Minimum start-up time / minimum stop-down time constraints; the first generator 10A shall be operated so that its continuous start-up time or stop-down time is equal to or greater than the specified minimum time. (e) Designated start / stop period; the operator (user) will forcibly stop or start the first generator 10A during the period specified by the operator (user). (f) Operating reserve and required adjustment capacity constraints; ensuring the necessary amount of generator surplus to correct the error between actual operating conditions and supply and demand plans for electricity demand.

[0107] However, the planning unit 41 may also set constraints in the optimization problem that are not specified in each scenario selected by the scenario selection unit 40. For example, the planning unit 41 may extract a certain period from past information, extract constraints other than those specified in each scenario from the constraint information set during the extracted period, and use those set values ​​to set the constraints in the optimization problem.

[0108] (4) Effects of this embodiment As described above, the supply and demand planning device 1 of this embodiment generates multiple scenarios by expanding the constraint information 50 according to the constraint expansion information 51. This makes it possible to generate comprehensive scenarios that also take into account operational constraints associated with infrequent events, and thus it becomes possible to consider operational plans and countermeasures when infrequent events occur.

[0109] Furthermore, according to this supply and demand planning device 1, the scenario evaluation unit 39 calculates evaluation indicators related to each scenario generated by the scenario generation unit 38, and the scenario selection unit 40 selects the scenario to be used in formulating the supply and demand plan based on the calculated evaluation indicators for each scenario, thereby enabling the generation and extraction of scenarios without excess or deficiency. This reduces the processing time for formulating the demand plan that would otherwise result from an excess of verification scenarios.

[0110] (5) Other embodiments In the above-described embodiment, the constraint information database 31, the constraint extension information database 32, and the past information database 33 were described in a case where they are held within the supply and demand planning device 1. However, the present invention is not limited to this, and these databases may be held by a device other than the supply and demand planning device 1, and the constraint information acquisition unit 35 and the past information acquisition unit 36 ​​of the supply and demand planning device 1 may acquire the information stored in these databases from that device.

[0111] Furthermore, in the above-described embodiment, we have described a case in which all of the functional units of the constraint information acquisition unit 35, past information acquisition unit 36, past information analysis unit 37, scenario generation unit 38, scenario evaluation unit 39, scenario selection unit 40, and plan formulation unit 41 are located within the supply and demand planning device 1. However, the present invention is not limited to this, and these functional units may be distributed and arranged across multiple computer devices constituting a distributed computing system (i.e., the supply and demand planning device may be composed of multiple computer devices constituting a distributed computing system).

[0112] Furthermore, in the above-described embodiment, we have described a case where the constraint information 50 is configured as shown in Figure 3, the constraint extension information 51 is configured as shown in Figure 4, and the past information 52 is configured as shown in Figure 5. However, the present invention is not limited to this, and various other configurations can be widely applied to the constraint information 50, constraint extension information 51, and past information 52. [Industrial applicability]

[0113] The present invention can be applied to a power grid supply and demand planning device that formulates a supply and demand plan that satisfies various constraints. [Explanation of symbols]

[0114] 1... Supply and demand planning device, 2... Power system, 10, 10A, 10B... Generator, 11... Substation, 12... Phase adjustment equipment, 13... Power load, 14, 14A~14E... Measurement device, 15... Information and communication network, 21... CPU, 31... Constraint information database, 32... Constraint extension information database, 33... Historical information database, 35... Constraint information acquisition unit, 36... Historical information acquisition unit, 37... Historical information analysis unit, 38... Scenario generation unit, 39... Scenario evaluation unit, 40... Scenario selection unit, 41... Plan formulation unit, 50... Constraint information, 51... Constraint extension information, 52... Historical information, 53... Scenario.

Claims

1. In a power grid, a supply and demand planning device that formulates a supply and demand plan that satisfies various constraints, A constraint information acquisition unit acquires constraint information including one or more pre-set constraint conditions, and constraint extension information which defines the range of changes for each setting value of the constraint conditions. A scenario generation unit generates multiple verification scenarios, each consisting of a combination of setting values ​​obtained by changing the setting value of each constraint condition in the constraint information acquired by the constraint information acquisition unit within the change range according to the constraint extension information, Based on the constraints included in the verification scenario, the planning department formulates the supply and demand plan that takes these constraints into account. A supply and demand planning device characterized by comprising the following features.

2. The constraint information acquisition unit, The constraint information is obtained, which includes at least one of the constraint items relating to the constraint conditions for the kWh constraint and the constraint items relating to the constraint conditions for the planned shutdown constraint. The supply and demand planning device according to feature 1.

3. The constraint information acquisition unit, The constraint extension information is obtained, which includes at least one of the following: upper and lower limits and change amounts for the increase or decrease of the set values ​​of each of the constraint conditions relating to the equipment; upper and lower limits and change ranges for the start and end times of the period subject to the constraint and the period subject to the constraint; and the type of the period subject to the constraint. The supply and demand planning device according to feature 1.

4. A historical information acquisition unit that acquires historical information including at least one of past supply and demand plans and constraint information related to said supply and demand plans, The system further comprises a historical information analysis unit that analyzes the aforementioned historical information and generates the aforementioned constraint information and the aforementioned constraint extension information in past supply and demand plans, The scenario generation unit, Based on the constraint information and constraint extension information acquired by the constraint information acquisition unit and the constraint information and constraint extension information generated by the past information analysis unit, a plurality of verification scenarios are generated. The supply and demand planning device according to feature 1.

5. The aforementioned past information acquisition unit, Acquire the aforementioned historical information that meets the conditions of a pre-specified data period, acquisition date and time, target equipment, and / or target area. The supply and demand planning device according to feature 4.

6. The aforementioned historical information analysis unit, Statistical analysis or machine learning methods are applied to the aforementioned past information to calculate features related to the constraint information, and based on the calculated features, the constraint information and constraint extension information in the past supply and demand plan are generated. The scenario generation unit, The verification scenario is generated based on the constraint information and constraint expansion information in the past supply and demand plan generated by the historical information analysis unit. The supply and demand planning device according to feature 4.

7. The aforementioned historical information analysis unit, Using the aforementioned historical information, the system calculates a feature that includes one or more boundary points based on a pre-set threshold using the mean, median, variance, standard deviation, cumulative frequency in the frequency distribution, or relative frequency, and generates the constraint information and constraint extension information for the past supply and demand plan based on the calculated feature. The supply and demand planning device according to feature 6.

8. A scenario evaluation unit calculates evaluation indicators related to each of the verification scenarios generated by the scenario generation unit, The system further comprises a scenario selection unit that selects some of the verification scenarios from among the verification scenarios generated by the scenario generation unit, based on the evaluation index of each verification scenario calculated by the scenario evaluation unit. The supply and demand planning device according to feature 1.

9. The aforementioned scenario evaluation unit, The evaluation index is calculated based on one or more of the pre-set event frequency or impact on grid operation in each of the aforementioned verification scenarios. The supply and demand planning device according to feature 8.

10. The aforementioned scenario selection unit, The verification scenario is selected based on one or more of the following: the evaluation index, a threshold for the total number of selectable verification scenarios, and the estimated processing time associated with the selected verification scenario. The supply and demand planning device according to feature 8.

11. A supply and demand planning method implemented by a supply and demand planning device that formulates a supply and demand plan that satisfies various constraints with respect to the power grid, A first step of obtaining constraint information including one or more pre-set constraint conditions, and constraint extension information in which the range of changes for each setting value of the constraint conditions is defined, A second step is to generate a plurality of verification scenarios, each consisting of a combination of setting values ​​obtained by changing the setting value of each constraint condition in the acquired constraint information within the range of modification according to the constraint extension information, A third step is to formulate the supply and demand plan that takes into account the constraints included in the verification scenario, A supply and demand planning method characterized by comprising the following:

12. In the third step, the supply and demand planning device, For each of the generated verification scenarios, calculate the corresponding evaluation metrics. Based on the evaluation indicators for each of the calculated verification scenarios, select some of the verification scenarios from among them. Based on the constraints included in the selected verification scenario, a supply and demand plan is formulated that takes these constraints into account. The supply and demand planning method according to feature 11.

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