JP7274871B2 - Contingent Failure Analyzer - Google Patents

Description

The present invention relates to a contingency analysis apparatus and a contingency analysis method capable of analyzing contingencies in the operation and operation plan of a power system.

In the electric power system, reliability standards are established so that power can be stably supplied even if accidents such as ground faults, short circuits, and transmission line disconnections occur in the electric power system, and facility planning and operation are carried out. According to Non-Patent Document 1, the concept of reliability criteria is defined as follows.

When the equipment is in good condition, the tidal current must not exceed the capacity of the equipment at all times, the voltage must be properly maintained, and the generator must be able to operate stably. In the event of equipment failure (N-1 failure), as a general rule, supply disruption should not occur and power generation should be limited to a limited extent. In the event of a facility failure (N-2 failure, etc.), partial power outages and supply disruptions should be tolerated. However, if the scale of the supply disruption is large and there are concerns about social impact, consider taking countermeasures. That's what I'm saying. Here, the N-1 failure is a failure of one transmission line, one transformer, and one generator. An N-2 failure is a failure accompanied by the simultaneous loss of equipment at two locations, such as a failure of two transmission lines.

On the other hand, in Japan, power system reform and the spread of renewable energy power generation are progressing, and facilities are aging and natural disasters are occurring more frequently. becoming. For example, in the wide-area operation of electric power systems promoted by electric power system reform, nationwide operation that goes beyond the conventional concept of areas is required. Along with this, the contingency failures to be managed by the operator increase with the increase in the areas to be managed and the number of devices.

In addition, as renewable energy power generation spreads, sudden changes in power output due to weather are expected. A large-scale sudden change in power output affects the reliability of a power system to the same extent as an equipment failure. Furthermore, due to aging facilities and natural disasters, there is a risk of multiple failures exceeding the conventionally assumed N-1 failures and N-2 failures.

For this reason, in order to maintain reliability in future electric power systems, it is desirable to expand the scope of assumptions and operate without limiting to conventional N-1 and N-2 failures.

For example, Patent Literature 1 discloses an accident spillover prevention system that generates a sudden change pattern in the event of a sudden change in the power flow state in addition to a system model during normal times, and determines a stabilization control amount for each of them when a contingency occurs. ing.

JP 2015-159644 A

Research Committee for Natural Resources and Energy, Energy Conservation and New Energy Subcommittee, New Energy Subcommittee, System Working Group (1st Meeting) Document 4 “Current Status of Interregional Interconnection Line Operation Rules, etc.”

However, the accident spillover prevention system disclosed in Patent Document 1 does not take advance measures against the increase in the amount of calculation due to the setting of the sudden change pattern, and the increase in the number of combinations delays the update of the control table, causing the power system to be disrupted. There was a fear that it could not be controlled stably. Alternatively, by limiting the number of combinations, there is a risk of a large power outage when an unexpected event occurs. In addition, there was a possibility that it would not be economically operated because it was not possible to consider cooperation with the introduction of phase modifying equipment, etc., which would take a long time to complete the implementation of the countermeasures.

The present invention has been made in view of the above circumstances, and its object is to provide a contingency analysis apparatus and a contingency analysis method capable of managing the reliability of a power system while coping with an increase in contingencies. be.

In order to achieve the above object, a contingency analysis apparatus according to a first aspect includes a prediction unit for predicting a system state of a power system based on plan information related to the operation of the power system; an analysis unit that analyzes KPI (Key Performance Indicator) in the system state at the time of occurrence of the contingency, and a classification unit that classifies the contingency based on the KPI.

ADVANTAGE OF THE INVENTION According to this invention, the reliability of a power system can be managed, coping with the increase of the contingency failure.

FIG. 1 is a block diagram showing the hardware configuration of the contingency analysis device according to the embodiment. FIG. 2 is a block diagram showing a functional configuration of the contingency analysis device according to the embodiment; FIG. 3 is a flow chart showing the processing of the medium-term foresight cross section generation unit of FIG. FIG. 4 is a diagram showing an example of demand plan values and power generation plan values used in the process of FIG. FIG. 5 is a diagram showing an example of controllability transaction information used in the processing of FIG. FIG. 6 is a diagram showing an example of medium-term demand-based plan values and medium-term power generation base plan values generated by the process of FIG. FIG. 7 is a diagram showing an example of the results of linking retail balancing groups and substations used in the processing of FIG. 3 . FIG. 8 is a diagram showing an example of the relationship between the actual substation demand value and the average substation demand value used to generate the medium-term substation demand plan value of FIG. FIG. 9 is a diagram showing an example of a distribution of substation actual demand values used to generate the medium-term substation demand plan value of FIG. FIG. 10 is a diagram showing an example of medium-term substation demand plan values and medium-term power plant power generation plan values generated by the process of FIG. FIG. 11 is a diagram showing an example of a mid-term foreseeing cross section generated by the process of FIG. FIG. 12 is a diagram showing an example of a contingency assumed by the contingency assumption unit in FIG. FIG. 13 is a flowchart showing a process of calculating KPIs used for contingency classification. FIG. 14 is a diagram showing an example of a PV curve before and after a failure used for calculating KPIs. FIG. 15 is a diagram showing the relationship between reliability of the power system and KPI. 16 is a diagram showing an example of KPIs analyzed by the KPI analysis unit of FIG. 2. FIG. FIG. 17 is a diagram showing an example of contingency classification results based on KPIs after countermeasures. FIG. 18 is a flow chart showing classification processing of the contingency classification unit of FIG. FIG. 19 is a diagram showing an example of classification based on KPIs before countermeasures and classification based on KPIs after countermeasures for contingency failures. FIG. 20 is a diagram showing contingency classification results based on the transition pattern from the classification before countermeasures to the classification after countermeasures in FIG. FIG. 21 is a diagram showing an example of a short-term preview slice generated by the short-term preview slice generator in FIG. FIG. 22 is a diagram showing an example of the state of the power system before a failure occurs in the power system of FIG. FIG. 23 is a diagram showing an example of the system state when the contingency of the power system in FIG. 22 is classified into contingency classification 1 in FIG. FIG. 24 is a diagram showing an example of the system state when the contingency of the power system in FIG. 22 is classified into contingency classification 2 or 3 in FIG. FIG. 25 is a diagram showing a calculation example of KPIs before and after taking countermeasures against contingency failures in the power system of FIG. 24 . FIG. 26 is a diagram showing an example of countermeasures taken when the contingency of the electric power system in FIG. 24 is classified into contingency classification 2 in FIG. 20 and voltage stability after the countermeasures. FIG. 27 is a diagram showing an example of countermeasures taken when the contingency of the power system in FIG. 24 is classified into contingency classification 3 in FIG. 20 and voltage stability after the countermeasures. FIG. 28 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 4 in FIG. FIG. 29 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 5 in FIG. FIG. 30 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 6 in FIG. FIG. 31 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 7 in FIG.

Embodiments will be described with reference to the drawings. In addition, the embodiments described below do not limit the invention according to the claims, and all of the elements described in the embodiments and their combinations are essential to the solution of the invention. Not necessarily.

FIG. 1 is a block diagram showing the hardware configuration of the contingency analysis device according to the embodiment.
In FIG. 1, the contingency analysis device 10 is configured by, for example, a computer system or computer. The contingency analysis device 10 includes a central control device 11 , an input device 12 , an output device 13 , a display device 14 , a communication device 15 , a main storage device 16 and an auxiliary storage device 17 . Central control device 11 , input device 12 , output device 13 , display device 14 , communication device 15 , main storage device 16 and auxiliary storage device 17 are connected via bus 18 .

The central control unit 11 executes computer programs, searches data in various databases stored in the auxiliary storage device 17, instructs display of processing results, performs contingency analysis of the power system 20, and the like. The central control unit 11 may be a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). Central controller 11 may be a single-core processor or a multi-core processor. The central controller 11 may include a hardware circuit (for example, FPGA (Field-Programmable Gate Array) or ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing. Central controller 11 may comprise a neural network. Central controller 11 may be configured as one or more semiconductor chips, or may be configured as a computer device such as a calculation server.

The input device 12 inputs various conditions and various instructions for operating the contingency analysis device 10 . The input device 12 is, for example, a keyboard, mouse, touch panel, card reader, voice input device, or the like.

The output device 13 outputs the results of processing in the contingency analysis device 10 and the like. The output device 13 is, for example, an output interface.

The display device 14 displays processing results and the like in the contingency analysis device 10 . The display device 14 is, for example, a liquid crystal monitor, an organic EL (Electro Luminescence) display, or a graphic card.

The communication device 15 has circuits and communication protocols for connecting to the communication line 101a. The communication line 101a may be a WAN (Wide Area Network) such as the Internet, a LAN (Local Area Network) such as WiFi or Ethernet (registered trademark), or a mixture of WAN and LAN. may

The main storage device 16 is configured as, for example, a RAM (Random Access Memory), stores computer programs and calculation result data, and provides the central control device 11 with a work area required for each process.

The auxiliary storage device 17 is a storage device having a large storage capacity, such as a hard disk device or an SSD (Solid State Drive). The auxiliary storage device 17 can store various databases 17A and various programs 17B. The various programs 17B may be software that can be installed in the contingency analysis apparatus 10, or may be incorporated in the contingency analysis apparatus 10 as firmware. The various programs 17B include a contingency analysis program that causes the central controller 11 to perform contingency analysis of the power system 108 .

The contingency analysis device 10 is connected to a cross-regional organization (cross-regional power management promotion organization) system 102, a wholesale power market system 103, a supply and demand adjustment market system 104, and an area central supply (central load dispatch center) system 105 via a communication line 101a. can be connected to The area central feeding system 105 can be connected to the measuring instrument 106 and the control target device 107 via the communication line 101b. The contingency device 1 may be directly connected to the cross-regional organization system 102 , the wholesale power market system 103 , the balancing market system 104 or the area central supply system 105 . In addition, although only one area central feeding system 105 is shown in FIG. 1, there may be more than one.

The measuring instrument 106 measures the output state of the generator installed in the electric power system 108, the power flow state of the power transmission and transformation equipment, and the equipment condition of the equipment to be controlled 107 such as phase modifying equipment and switches. The equipment to be controlled 107 is a phase modifying equipment installed in the power system 108, a generator, a converter, a storage battery, and equipment capable of changing output, such as consumer equipment, and equipment status such as a transformer with a tap and a circuit breaker. is a modifiable device.

The contingency analysis apparatus 10 can access the cross-regional organization system 102, the wholesale power market system 103, or the supply and demand adjustment market system 104 via the communication line 101a to acquire plan information related to the operation of the power system 108. In addition, the contingency analysis apparatus 10 can access the area central feeding system 105 via the communication line 101a and acquire measurement information measured by the measuring instrument 106 .

The central control unit 11 reads the contingency analysis program into the main storage device 16 and executes the contingency analysis program to foresee the system state of the power system 108 based on the plan information related to the operation of the power system 108. It is possible to assume a contingency based on the system state thus obtained, analyze the KPI in the system state at the time of occurrence of the contingency, classify the contingency based on the KPI, and output the classification result. The plan information related to the operation of the power system 108 is, for example, supply and demand plan information acquired from the cross-regional organization system 102, power transaction information acquired from the wholesale power market system 103, and control power transaction information acquired from the supply and demand adjustment market system 104. be.

Here, when the plan information related to the operation of the electric power system 108 is used, the far future system state can be predicted compared to the case of using the measurement information measured by the measuring instrument 106 . For example, in the case of using the plan information related to the operation of the electric power system 108, the system state two hours ahead is foreseen at intervals of 10 minutes. On the other hand, when the measurement information measured by the measuring device 106 is used, for example, the state of the system 10 minutes ahead is foreseen at intervals of 1 minute.

For this reason, it is possible to classify contingencies in advance while coping with an increase in the number of combinations of sudden output changes and other contingencies, and to plan countermeasures according to the classification. For this reason, it is possible to expand the range of assumptions and stably operate the power system 108 without limiting it to only N-1 failures and N-2 failures, and prevent large power failures when unexpected events occur. can be done. In addition, it is possible to consider cooperation with the installation of phase modifying equipment, etc., which takes a long time to complete the implementation of countermeasures.

FIG. 2 is a block diagram showing a functional configuration of the contingency analysis device according to the embodiment; In the following explanation, when the subject of action is described as "Part XX", the central control unit 11 reads Part XX, which is a program, from the auxiliary storage device 17, loads it into the main storage device 16, and executes Part XX. It means to realize the function of

In FIG. 2 , the contingency analysis device 10 has an online system 21 and a real-time system 22 . The online system 21 classifies contingency failures of the power system 108 based on plan information related to operation of the power system 108 . For example, the online system 21 classifies contingencies and plans countermeasures at the gate closing stage one hour before the actual operation. The real-time system 22 formulates countermeasures against contingencies based on the contingency and contingency classification results based on the measurement information of the power system 108 . For example, the real-time system 22 analyzes contingency failures and formulates countermeasures in the actual operation section immediately before the actual operation.

The online system 21 includes a system linking unit 201 , a medium-term forecast cross section generation unit 202 , a KPI analysis unit 203 and a contingency classification unit 204 .

The system cooperation unit 201 acquires supply and demand plan information 221 from the cross-regional organization system 102, acquires electricity transaction information 222 from the wholesale electricity market system 103, acquires controllability transaction information 223 from the supply and demand adjustment market system 104, Output to the foresight cross section generation unit 202 .

The supply and demand plan information 221 and the power trading information 222 are demand plan values and power generation plan values for every 30 minutes. The reserve trading information 223 is the trading result of the reserve in the demand and supply market.

The medium-term forecast cross section generation unit 202 generates a medium-term forecast cross section 227 based on the supply and demand plan information 221, the power trading information 222, the control power trading information 223, the system equipment data 224, the actual power generation/demand data 225, and the balancing group information 226. and output to the KPI analysis unit 203 , the medium-term forecast cross-section database 26 and the contingency assumption unit 27 .

The system equipment data 224 indicates the types/parameters of system equipment and connection relationships between system equipment. In general, the bus-branch model is often used, so the bus-branch model may be used, but the node-breaker model is preferable from the viewpoint of the complexity of management associated with changes in the bus line configuration. The actual power generation/demand data 225 is time-series measurement data of past power generation and demand for each renewable energy power plant and substation. Balancing group information 226 indicates correspondence between power generation balancing groups and power plants, and correspondence between retail balancing groups and supply regions and sizes.

The medium-term forecast section 227 is a forecast result regarding the system state of the power system 108 forecast based on the plan information regarding the operation of the power system 108 . The medium-term forecast cross section 227 shows, for example, the equipment status and power flow status of the power system 108 every 10 minutes up to two hours ahead. Although the medium-term forecast section 227 indicates the system status every 10 minutes up to two hours ahead, it is not necessary to limit it to every 10 minutes up to two hours ahead.

The KPI analysis unit 203 calculates a system state KPI 229 when a contingency 228 occurs in the medium-term forecast cross section 227 and outputs the calculated KPI 229 to the contingency classification unit 204 . The KPI 229 includes, for example, the probability of occurrence of the contingency 228, the reliability of the power system 108 when the contingency 228 occurs, the blackout range when the contingency 228 occurs, the urgency of countermeasures against the contingency 228, and the countermeasures against the contingency 228. This is the impact on the market economy caused by

A contingency 228 is a failure that is assumed to occur in the power system 108 . Specifically, the contingency failures 228 include the parallel off of each generator in the mid-term forecast cross section 227, the ground fault in each transmission and transformation facility, the shutdown due to a short circuit or disconnection, and the renewable energy that was not assumed when the mid-term forecast cross section 227 was generated. N-1 generated assuming sudden large-scale output changes in power generation, suspension of multiple facilities due to stormy weather such as typhoons, simultaneous disconnection of generator groups due to large-scale earthquakes, simultaneous suspension of transmission and transformation facilities, and combinations of these List of faults, N-2 faults, N-1-1 faults, N-2-1 faults, Nk faults, Nk-1 faults and Nk-2 faults.

The N-1 failure and N-2 failure are as defined above. The Nk failure assumes the simultaneous stoppage of facilities due to stormy weather such as a typhoon or a large-scale earthquake. The N-1-1, N-2-1, Nk-1 and Nk-2 faults are contingencies 228 due to a combination of N-1, N-2 and Nk faults. We assume that both events occur simultaneously and that occur with a certain amount of time lag. These assumptions themselves are determined in advance based on information such as past system failures and disasters. Assume a contingency 228 .

The contingency classification unit 204 classifies the contingency 228 based on the KPI 229 , formulates a classification result 230 of the contingency 228 , and outputs it to the classification result database 28 and the display/output unit 29 .

The real-time system 22 includes a middle supply system cooperation unit 211 , a short-term forecast cross-section generation unit 212 , and a reliability analysis/measure planning unit 213 .

The intermediate supply system linking unit 211 acquires the measurement information 231 from each area intermediate supply system 105 via the communication line 101 a and outputs it to the short-term forecast profile generation unit 212 . The measurement information 231 is information measured by each measuring device 106 .

The short-term prediction cross section generation unit 212 generates a short-term prediction cross section 232 based on the measurement information 231, the system equipment data 224, and the medium-term prediction cross section 227, and outputs it to the contingency assumption unit 27 and the reliability analysis/measure planning unit 213. .

For example, the short-term forecast cross-section generation unit 212 generates the current power flow cross-section by processing such as state estimation using the measurement information 231 and the system equipment data 224 . The short-term forecast cross section generation unit 212 generates the short-term forecast cross section 232 by linearly interpolating the power generation/demand data of the medium-term forecast cross section 232 at 10-minute intervals based on the current power flow cross section. The short-term forecast slice 232 is more information about the near future than the medium-term forecast slice 232 .

The reliability analysis/countermeasure planning unit 213 generates a real-time control table 233 based on the short-term predictive cross section 232 , the contingency 228 and the classification result 230 and outputs it to the display/output unit 29 . The real-time control table 233 shows information regarding control for stabilizing the power system 108 . For example, the real-time control table 233 shows information about countermeasures for the contingency 228 that take less time to complete, and post-event control such as a system stabilization system.

The contingency analysis apparatus 10 also includes a system equipment database 23, a power generation/demand record database 24, a balancing group information database 25, a mid-term forecast section database 26, a contingency assumption unit 27, a classification result database 28, and a display/output unit 29. Prepare.

The system equipment database 23 stores system equipment data 224 . The power generation/demand record database 24 stores power generation/demand record data 225 . The balancing group information database 25 stores balancing group information 226 . The medium-term foresight cross-section database 26 stores medium-term foresight cross-sections 227 .

The contingency assumption unit 27 generates a contingency 228 for the power system 108 based on the medium-term forecast cross section 227 and outputs the contingency 228 to the KPI analysis unit 203 . Further, the contingency assumption unit 27 generates a contingency 228 of the power system 108 based on the short-term forecast cross section 232 and outputs it to the reliability analysis/measure planning unit 213 . Classification results database 28 stores classification results 230 of contingencies 228 of power system 108 .

The display/output unit 29 displays or outputs the KPI 229 for the contingency 228 of the power system 108 and the classification result 230 of the contingency 228 . The display/output unit 29 also displays the real-time control table 233 and transmits it to the area central feeding system 105 via the communication line 101a.

By referring to the real-time control table 233 displayed on the display/output unit 29, the user can take countermeasures against the contingency 228, 223, a control command 241 or a control table is transmitted to the device 107 to be controlled.

In the above-described embodiment, as an output destination of the control command 241 or the control table from the contingency analysis device 10, the area central supply system 105 is taken as an example. It may be sent directly to the grid stabilization system. Further, although an example in which the real-time system 22 is provided in the contingency analysis device 10 has been shown, it may be provided outside the contingency analysis device 10 such as the area central feeding system 105, and the real-time system 22 and the real-time system 22 may be connected via the communication line 101a. You can exchange information.

Hereinafter, the operation of the contingency analysis apparatus 10 shown in FIG. 2 will be described in detail using specific numerical examples.
FIG. 3 is a flow chart showing the processing of the medium-term foresight cross section generation unit of FIG.
In step S11 of FIG. 3, the medium-term forecast cross section generation unit 202 acquires the supply and demand plan information 221, the power transaction information 222, the control power transaction information 223, the system equipment data 224, the actual power generation/demand data 225, and the balancing group information 226. .

FIG. 4 is a diagram showing an example of demand plan values and power generation plan values used in the process of FIG.
In FIG. 4, the supply and demand plan information 221 and the power trading information 222 respectively include a demand plan value 221A and a power generation plan value 222A every 30 minutes. For example, assuming that the current time is 10:00, the demand plan value 221A and the power generation plan value 222A are calculated for the current time of 10:00 and the assumed times of 10:30, 11:00, 11:30, : 00, . . . kWh values included.

Since the demand plan value 221A is submitted for each balancing group, it becomes a plan value for every 30 minutes for each balancing group. For example, each retailer RA-RC plans a demand of 350 kWh, 600 kWh and 850 kWh at the 11:00 cross-section.

The power generation plan value 222A is also a plan value for every 30 minutes for each balancing group, but the power generation plan value 222A may be associated with, for example, the power plant or generator owned by the power generation company. For example, the power plant JA-1 of the power producer JA is planning to generate 250 MWh of power in each section from 10:00 to 12:00. Here, assuming that the gate closes (deadline for submission of supply and demand plans from system users (power generators and retailers) to system operators) is one hour before the actual operation cross section, at 10:00, The plan up to the cross section at 11:00 has been finalized. On the other hand, the cross section after 11:30 is subject to change due to market transactions. Therefore, the status of the power generation plan value 222A is finalized up to the cross section at 11:00 and unconfirmed for the cross section after 11:30.

FIG. 5 is a diagram showing an example of controllability transaction information used in the process of FIG.
In FIG. 5, the controllability trading information 223 includes information on the controllability supplier, the procurement amount, and the price for each power transmission company when the controllability trading frame is set in units of 30 minutes. For example, at 10:00, power transmission company 1 procures 250 MW of control power from generator G1 at a ΔkW price of 1.00 yen/kW and a kWh price of 2.0 yen/kWh. I understand.

Returning to FIG. 3 , in step S12, the medium-term forecast cross-section generation unit 202 calculates the medium-term demand for every 10 minutes based on the demand plan value 221A and the power generation plan value 222A for every 30 minutes in the supply and demand plan information 221 and the power trading information 222. Generate base plan value and medium-term generation base plan value. At this time, the kWh values of the demand plan value 221A and the power generation plan value 222A are converted into kW values. The kWh value is obtained by integrating the kW value with time as shown in the following formula (1). However, E is the kWh value, P(t) is the kW value, and t is the time. Here, if P(t) is the average value of P(t) during the integration time Δt, E can also be expressed by the following equation (2). That is, the kW value as the average value Pave can be obtained by dividing the kWh value by the integration time Δt. For example, given a kWh value for one frame of 30 minutes, the kWh value is obtained by doubling the kWh value. The medium-term forecast cross-section generating unit 202 can generate the medium-term demand-based plan value and the medium-term power generation base plan value by using linear interpolation or the like to set the kW value thus calculated to a value for each 10 minutes.

E=∫P(t)dt (1)

E=Pave×Δt (2)

FIG. 6 is a diagram showing an example of medium-term demand-based plan values and medium-term power generation base plan values generated by the process of FIG.
In FIG. 6, medium-term demand-based plan value 221B and medium-term generation-based plan value 222B are obtained as kW values for every 10 minutes for each balancing group. MW values may be used instead of kW values.

Returning to FIG. 3, in step S13, the medium-term forecast cross section generator 202 generates medium-term power plant/substation base plan values. At this time, the medium-term forecast cross section generation unit 202 generates medium-term demand for the substation on the power system 108 based on the medium-term demand base plan value 221B, the medium-term power generation base plan value 222B, the balancing group information 226, and the system equipment data 224. The base plan value 221B is linked, and the medium-term power generation base plan value 222B is linked to the power plant. Since the power generation plan value 222A used to generate the medium-term power generation base plan value 222B normally includes information for each power plant, the correspondence between the power plant and the medium-term power generation base plan value 222B is determined on a one-to-one basis. On the other hand, the correspondence between retail balancing groups and substations is not determined on a one-to-one basis. There may be

FIG. 7 is a diagram showing an example of the results of linking retail balancing groups and substations used in the processing of FIG. 3 .
In FIG. 7, the medium-term demand base plan value 221B for each retail balancing group in FIG. For example, the supply destination of the retailer RA spans substations H1 to H3, and the ratio is 50:30:20. Using this ratio, the demand-based plan value for each substation is calculated by allocating the kW value of the medium-term demand-based plan value 221B. Note that if the correspondence relationship between the equipment on the power system 108 and each planned value is already determined, it may be determined accordingly.

In this way, the medium-term forecast cross section generation unit 202 generates the medium-term power plant/substation base plan values, and converts the medium-term demand base plan value 221B and the medium-term power generation base plan value 222B for each balancing group in FIG. By assigning to the upper power station and substation, it becomes possible to execute system analysis processing such as power flow calculation.

Returning to FIG. 3, in step S14, the medium-term forecast cross-section generator 202 generates medium-term power plant power generation/substation demand plan values. For example, the medium-term forecast cross section generation unit 202 calculates the fluctuation range of renewable energy power generation and demand in each 10-minute frame based on the actual power generation/demand data 225, and adds it to the medium-term power plant/substation base plan value. , subtraction, etc., to generate medium-term power plant generation and substation demand planning values.

Here, the medium-term forecast cross-section generation unit 202 formulates the medium-term power plant power generation/substation demand plan value including fluctuations with respect to the medium-term power plant/substation base plan value, so that the demand or renewable energy power generation These fluctuations can be assumed even if the medium-term power plant/substation base plan values are not achieved due to fluctuations in power generation due to

FIG. 8 is a diagram showing an example of the relationship between the actual substation demand value and the average substation demand value used to generate the medium-term substation demand plan value of FIG.
In FIG. 8, when calculating the fluctuation range of the substation actual demand value VB, the medium-term forecast cross section generation unit 202 obtains the average value VA of the substation actual demand value VB for 10 minutes, and calculates the substation A deviation of actual demand value VB is obtained. The maximum deviation of the substation actual demand value VB from the average value VA may be used as the variation width.

FIG. 9 is a diagram showing an example of a distribution of substation actual demand values used to generate the medium-term substation demand plan value of FIG.
In FIG. 9, the substation actual demand value VB may be approximated as a normal distribution, and 2σ (standard deviation) of the normal distribution may be used as the fluctuation width of the substation actual demand value VB.

FIG. 10 is a diagram showing an example of medium-term substation demand plan values and medium-term power plant power generation plan values generated by the process of FIG.
In FIG. 10, the medium-term substation demand plan value 221C is a combination of the medium-term substation base plan value and the fluctuation range of the demand amount. For example, in the medium-term substation demand plan value 221C, substation H1 is assumed to have an average demand of 283 kW and a fluctuation of ±20 kW at a cross section of 10:20.

The medium-term power plant power generation plan value 222C is a combination of the medium-term power plant base plan value and the fluctuation range of the power generation amount. As for power plants, assuming fluctuations is not necessary for controllable power plants such as thermal power plants, only planned values are used like power plant JA-1. On the other hand, renewable energy power plants need to assume output fluctuations due to weather. For example, power plant JA-2 is expected to have an average output of 500 MW and fluctuations of ±20 MW at a cross section of 10:20. . If there is already information on renewable energy power generation and demand fluctuation range, there is no need to newly calculate it.

Returning to FIG. 3, in step S15, the medium-term foreseeing cross-section generating unit 202 generates a mid-term foreseeing provisional cross-section. For example, the medium-term forecast cross-section generation unit 202 generates a plurality of power flow calculation input data based on the medium-term power plant power generation/substation demand plan values and the system equipment data 224 . As power flow calculation input data, parameters such as the output of each power plant, the demand of substations, the connection relationship of power transmission and distribution equipment, and the impedance are required. The output of each power plant and the demand of the substation can be determined using medium-term power plant generation and substation demand plan values. At this time, the medium-term power plant generation and substation demand plan values include base plan values and fluctuation range information. , a value obtained by adding the fluctuation range to the base planned value and a value obtained by subtracting the fluctuation range from the base planned value are prepared, and a medium-term forecast provisional section is exhaustively generated from all combinations thereof. Therefore, a plurality of intermediate-term forecast provisional cross sections exist for one time cross section.

In step S16, the medium-term forecast cross section generation unit 202 compensates for the supply-demand imbalance caused by the pattern of the combination of power generation and demand in the medium-term forecast provisional cross section and the supply-demand imbalance caused by the power transmission loss by using the adjustment power. 227. Specifically, the supply and demand imbalance amount in the medium-term forecast provisional section is calculated. When calculating the amount of supply and demand imbalance caused by the pattern of combination of power generation and demand, the total amount of demand may be subtracted from the total amount of power generation, or the power flow calculation may be performed and the input/output power at the swing bus at that time may be calculated. and the set value. The supply-demand imbalance amount due to transmission loss may also be calculated by power flow calculation, or if the transmission loss rate is already defined, that value may be used.

The control power of the control power transaction information 223 is activated in descending order of the kWh price until it reaches the same amount as the supply and demand imbalance amount calculated in this way. For example, when the amount of power generation is insufficient, it is assumed that an increase of the adjustability will be activated, and the output of the adjustability resource is increased by that amount to obtain the medium-term forecast cross section 227 . If the amount of power generation is excessive, it is assumed that the down control capacity will be activated, and the output of the control capacity resource is reduced by that amount to obtain the medium-term forecast cross section 227 .

FIG. 11 is a diagram showing an example of a mid-term foreseeing cross section generated by the process of FIG.
In FIG. 11, for example, in the mid-term forecast section 227A at 10:30, the kW value of the demand and output of each substation and power station is described as the base, and the demand or output increased with respect to the base as patterns F1 and F2 kW values and reduced kW values are given. The same is true for the 10:40 mid-term foresight cross-section 227B and the 10:50 mid-term foresight cross-section 227C.

It is desirable that the medium-term forecast section 227 includes system topology information in addition to information on demand at the substation and power generation at the power station, and the possibility of changing the system topology in subsequent processing such as planning of countermeasures. It is better to assume and follow the node breaker model. In addition, the demand plan value and power generation plan value only have information on active power. Therefore, the demand reactive power follows the past actual values, and the states of the generated reactive power, phase modifying equipment, transformer taps, system configuration, etc., follow the schedule if they are in scheduled operation. Otherwise, the current state may be assumed, or past performance may be followed. However, these methods are only examples, and the present invention is not limited to these methods.

The medium-term foresight cross section 227 generated by the medium-term foresight section generator 202 is stored in the medium-term foreseeable cross section database 26 and is also output to the KPI analysis section 203 and the contingency assumption section 27 . The contingency assumption unit 27 assumes a contingency 228 of the power system 108 based on the medium-term forecast section 227 .

FIG. 12 is a diagram showing an example of a contingency assumed by the contingency assumption unit in FIG.
In FIG. 12, contingencies 228 include a contingency definition 228A that defines contingency 228 and a combined contingency 228B that consists of a combination of contingencies defined in contingency definition 228A. For example, in the contingency definition 228A, contingency No. 1 defines a single line three-phase ground fault in the transmission line A-1 as the N-1 fault. 2, as the N-2 failure, disconnection of two transmission lines A-1 and A-2 is defined. In Contingency No. 3, the N-2 fault is defined as a reduction in the output of the generator G5 and disconnection of one line of the transmission line A-5. 4, simultaneous disconnection of generators G1, G2 and G3 is defined as an Nk fault. In the combined contingency 228B, for example, the contingency No. In M1, contingency failure No. is assumed as Nk-1 failure. 4 and assumed failure No. 1 combinations are defined. Further, in the contingency definition 228A and the combined contingency 228B, the occurrence probability assumed for each contingency is set.

In general, faults such as ground faults and short circuits are automatically removed by protective relays, so the process up to that point is often regarded as a series of contingency faults, and this embodiment also conforms to this.

FIG. 13 is a flowchart showing a process of calculating KPIs used for contingency classification.
In step S21 of FIG. 13, the KPI analysis unit 203 acquires the medium-term forecast cross section 227 and the contingency 228. FIG.

In step S22, the KPI analysis unit 203 analyzes the reliability of the power system 108 when a contingency occurs. The reliability of the power system 108 includes, for example, security such as overload, transient stability, steady-state stability, voltage stability, and frequency stability of transmission and transformation equipment, and adequacy of whether power can be supplied sufficiently to meet demand. is. Note that the reliability analysis in step S22 may be executed by the reliability analysis/measure planning unit 213 in FIG.

The overload of the transmission and transformation equipment is evaluated, for example, by executing the power flow calculation with the configuration excluding the failed equipment and subtracting the heat capacity of the transmission and transformation equipment from the power flow at that time according to the following equation (3).

Thermal _i =P _i -P _{lim, i} (3)

However, Thermal _i represents the overload amount of the transmission and transformation equipment i, P _i represents the active power flow in the transmission and transformation equipment obtained from the result of the power flow calculation, and P _lim,i represents the heat capacity limit of the transmission and transformation equipment.

Transient stability is obtained by transient analysis by time-series calculation, energy function method, or BCU Evaluate by a direct method represented by the method. When the power system 108 is transiently unstable, the synchronous generator greatly accelerates or decelerates under the influence of a contingency failure, and synchronous operation cannot be continued, resulting in step-out.

Steady-state stability is evaluated by linearly approximating the oscillation equation after the occurrence of a contingency around the operating point, performing eigenvalue analysis, and evaluating whether the tendency of divergence or attenuation is based on the sign of the vibration component determined from the eigenvalue. When the electric power system 108 is statically unstable, a generator or the like fluctuates due to a minute change such as a demand fluctuation, and the fluctuation diverges, making it impossible to continue synchronous operation, leading to step-out.

Voltage stability is evaluated by generating the PV curve of the power system 108 before and after the occurrence of a contingency, whether a high solution can be maintained, and voltage changes by time-series simulation based on the operation of voltage control equipment and loads. do.

FIG. 14 is a diagram showing an example of a PV curve before and after a failure used for calculating KPIs.
In FIG. 14, the PV curve is a graph obtained by plotting the bus voltage while changing the total demand, with the horizontal axis representing the total demand and the vertical axis representing the bus voltage. The PV curve is divided into areas where the bus voltage has two solutions for one total demand value, points with only one solution, and areas with no solutions. In an area where the bus voltage has two solutions, the solution with the higher voltage is called the voltage boosting solution, and the voltage boosting solution can maintain the voltage stably. On the other hand, the solution with the lower voltage is called the voltage-lowering solution, and the voltage-lowering solution further drops in voltage and collapses, resulting in an unstable solution. The boundary, the point with only one solution, is the stability limit.

In general, when a failure occurs in the power system 108, the PV curve PV1 before the failure occurs becomes a PV curve PV2 shifted to the left. For example, assuming that the power system 108 was operated at the operating point PU of the PV curve PV1 before the failure occurred, the PV curve PV2 after the failure occurred at the same total demand TD as before the failure occurred. There is no solution and it becomes unstable, leading to voltage collapse if no countermeasures are taken. Static evaluation of voltage stability and evaluation of stability in a steady state before and after a failure can be made to some extent only with the PV curve, but actual voltage changes must be evaluated by time-series simulation.

Returning to FIG. 13, the frequency stability is evaluated by time series simulation based on the oscillation equation for the frequency after the occurrence of contingency failure. Therefore, frequency stability may be evaluated together with transient stability. If the power system 108 is frequency unstable, the frequency of the power system 108 after the occurrence of a contingency will increase or decrease significantly. As a result, a protection relay for protecting the equipment operates, and the equipment is disconnected from the power grid 108 .

If these security evaluations result in the disconnection of power generation facilities and transmission and transformation facilities, and it becomes impossible to supply sufficient power to meet demand, there will be a lack of adequacy.

In step S23, the KPI analysis unit 203 calculates KPI for each contingency based on the reliability analysis result.

FIG. 15 is a diagram showing the relationship between reliability of the power system and KPI.
In FIG. 15, for overload, for example, the amount of overload of each transmission and transformation equipment shown in the equation (3) should be integrated for all equipment. Transient stability and steady-state stability are, for example, the total capacity of generators that are out of step due to contingency failures. Voltage stability is the total capacity of generators and the demand that is disconnected by low-voltage relays as a result of voltage collapse. The frequency stability is the total capacity of the generator and the demand that is paralleled off by the frequency protection relay.

On the other hand, the adequacy KPI is the total amount of demand that cannot be supplied due to disconnection of generators and demand. In reality, these security violations may occur in combination, so it is necessary to perform reliability analysis in combination. In addition to this, the KPI can be calculated by the user inputting the KPI calculation method.

Returning to FIG. 13, in step S24, the KPI analysis unit 203 formulates countermeasures against the contingency 228 that deteriorates the KPI 229 due to occurrence. Countermeasures include shutting down the generator or demand by the system stabilization system, introducing phase modifying equipment, changing the terminal voltage of the generator, and changing the output of the generator. As a method of determining the targets and amounts of countermeasures, a list may be generated in advance and the effects of the countermeasures may be sequentially evaluated by reliability analysis, or may be obtained using an optimization method such as optimum power flow calculation. Note that the countermeasure planning in step S24 may be executed by the reliability analysis/countermeasure planning unit 213 in FIG.

In step S25, the KPI analysis unit 203 calculates the KPI when the measures planned in step S24 are implemented. The KPI 229 analyzed by the KPI analysis unit 203 includes information on the KPI before countermeasures, the KPI after countermeasures, and the probability of occurrence of countermeasures and contingency failures.

16 is a diagram showing an example of KPIs analyzed by the KPI analysis unit of FIG. 2. FIG. In addition, in the example of FIG. 16, contingency No. of the contingency definition 228A of FIG. KPI229 for 1 was shown.
In FIG. 16, the KPI 229 is, for example, contingency No. 1 includes information on KPIs before countermeasures, KPIs after countermeasures, countermeasures, and probability of occurrence of contingency failures. Countermeasures include operations and time required for countermeasures to be implemented. In addition, the KPI 229 is analyzed for each demand-supply pattern in which fluctuations in the demand-supply amount are assumed.

KPI 229 output by KPI analysis unit 203 in FIG. 2 is input to contingency classification unit 204 . The contingency classifier 204 generates classification results 230 based on the KPIs 229 .

FIG. 17 is a diagram showing an example of contingency classification results based on KPIs after countermeasures.
In FIG. 17, in the contingency classification, for example, a classification definition 230D is set in advance, and the threshold in the classification definition 230D is compared with the KPI after countermeasures in the contingency 228, the time to the assumed cross section, and the occurrence probability. Classify contingencies.

The parameters K1, K2, K3, K4, T2, T3, P2, P3, and P4 are thresholds set in advance. Kmax, Kave, Kmin, T, and P are values included in the KPI229 or values calculated from the values included in the KPI229. Kmax is the maximum value of KPIs after countermeasures in various demand-supply patterns of target time sections. Kave is the average value of KPI after countermeasures in various supply and demand patterns. Kmin is the minimum value of KPI after countermeasures in various supply and demand patterns. T is the time from the current time to the assumed cross section. P is the probability of occurrence of a contingency failure.

FIG. 18 is a flow chart showing classification processing of the contingency classification unit of FIG. Note that FIG. 18 shows a method of classifying the contingency 228 based on the classification definition 230D of FIG.
In step S31 of FIG. 18, the contingency classification unit 204 determines whether Kmax is equal to or less than K1. If Kmax is equal to or less than K1, the contingency classification unit 204 proceeds to step S32 and classifies the contingency 228 into category 1. If Kmax is not equal to or less than K1, proceed to step S33.

In step S33, the contingency classification unit 204 determines whether Kave is less than or equal to K2 and Kmax is greater than K2. If Kave is less than or equal to K2 and Kmax is greater than K2, the process proceeds to step S34; otherwise, the process proceeds to step S37.

In step S34, the contingency classification unit 204 determines whether T is equal to or greater than T2. If T is equal to or greater than T2, the contingency classification unit 204 proceeds to step S35 and classifies the contingency 228 into category 2. If T is not equal to or greater than T2, the contingency classification unit 204 proceeds to step S36 and classifies the contingency 228 into category 3.

In step S37, the contingency classification unit 204 determines whether Kmin is K4 or less and P is P4 or less. If Kmin is less than or equal to K4 and P is less than or equal to P4, the contingency classification unit 204 proceeds to step S38 and classifies the contingency 228 into Class 4. If the condition that Kmin is equal to or less than K4 and P is equal to or less than P4 is not satisfied, the contingency classification unit 204 proceeds to step S39 to proceed to other classification determination processing as others.

The contingency classification unit 204 saves the classification result 230 generated by the process of FIG. 18 in the classification result database 28 . Further, the contingency classification unit 204 displays the KPI 229 and the classification result 230 to the user through the display/output unit 29 and outputs them to the area central supply system 105 . Based on the KPI 229 and the classification result 230 , the area central feeding system 105 gives priority to countermeasures that take a long time, and transmits a control command 241 to the control target device 107 .

FIG. 19 is a diagram showing an example of classification based on KPIs before countermeasures and classification based on KPIs after countermeasures for contingency failures.
In FIG. 19, in the pre-measure classification based on the pre-measure KPI, for example, the classification definition 230A is set in advance, and the threshold in the classification definition 230A, the pre-measure KPI in the contingency failure, the time to the assumed cross section, and the occurrence probability are compared. Classify the contingency by

In the post-measure classification based on the post-measure KPI, for example, the classification definition 230B is set in advance, and the contingency is classified by comparing the threshold in the classification definition 230B with the post-measure KPI for the contingency.

FIG. 20 is a diagram showing contingency classification results based on the transition pattern from the classification before countermeasures to the classification after countermeasures in FIG.
In FIG. 20, in the contingency classification based on the transition pattern, for example, the classification definition 230C is set in advance, and the classifications A1 to A7 before countermeasures defined by the classification definition 230A in FIG. 19 and the countermeasures defined by the classification definition 230B By combining the post-classifications B1 to B3, contingencies are classified.

In this classification definition 230C, countermeasure examples and costs are set for each contingency classification 1-8. Examples of countermeasures can be divided into countermeasures that do not require payment and countermeasures that require payment. Payment-free measures are measures that do not require payment to other business operators. The payment-free measure corresponds to, for example, the operation of system equipment such as phase modifying equipment. A payment-requiring measure is a measure that requires payment to another business operator. Measures that require payment include, for example, redistribution of generator output. Changes in generator terminal voltage and interruptions by the grid stabilization system are categorized as non-payment or payment-required depending on the type of contract. If it can be implemented free of charge, it is a measure that does not require payment, and if it is necessary to pay for it, it is a measure that requires payment. The cost parameter C1 is a preset threshold. C is a value included in the KPI229 or a value calculated from the value included in the KPI229.

Here, by classifying the contingency based on the transition pattern from the classification before the countermeasure to the classification after the countermeasure, it is possible to classify the contingency based on the KPI before the countermeasure and also based on the KPI before the countermeasure. It is possible to reflect the viewpoint of whether or not sufficient countermeasures are taken against contingencies classified as

FIG. 21 is a diagram showing an example of a short-term preview slice generated by the short-term preview slice generator in FIG.
In FIG. 21, short-term forecasting slice 232 is information about the near future than medium-term forecasting slice 227 . For this reason, in the example of FIG. 21, only a single change is assumed without setting a fluctuation range due to uncertainty, but as with the medium-term forecast cross section 232, a fluctuation range is set and a plurality of combinations can be assumed.

For example, in medium-term forecast sections 227A, 227B, and 227C in FIG. 11, kW values of substations H1 to H3 and power stations JA-1 and JA-2 more than 30 minutes ahead from the current time 10:00 are shown at 10-minute intervals. be able to. On the other hand, in the short-term forecast section 232 in FIG. 21, the kW values of the substations H1 to H3 and power stations JA-1 and JA-2 from the current time 10:00 onward can be shown at one-minute intervals.

A specific example of measures applied to the power system 108 based on the classification result 230 of FIG. 2 will be described below.
FIG. 22 is a diagram showing an example of the state of the power system before a failure occurs in the power system of FIG.
At the current time 10:00, it is assumed that the system status 2270 of area EA and area EB is given as shown in FIG. Power stations G1, G2, G3, phase modifying facility SC1, and buses #1 to #5 are provided in area EA. Power stations G4 and G5, a phase modifying facility SC2, and buses #6 to #10 are provided in area EB. It is assumed that power plants G1 to G4 are thermal power plants, and power plant G5 is a solar energy power plant.

Power station G1 is connected to bus #1. Power stations G2 and G3 are connected to bus #3. Consumer L1 is connected to bus #4. Phase modifying facility SC1 is connected to bus #5. Consumer L2 is connected to bus #6. Consumer L3 is connected to bus #9. Power plants G4, G5 and consumer L4 are connected to bus #10. Bus lines #1 to #10 are connected by transmission lines. The transmission line between buses #2 and #8 is open.

Here, the output of each power plant G1 to G5 and the demand of each consumer L1 to L4 are given in MW value, and the demand of each consumer L1 to L4 is calculated based on the output from each power plant G1 to G5. shall be satisfied. At this time, the total value of the outputs from the power plants G1 to G5 and the total value of the demands of the consumers L1 to L4 are each 3500 MW.

FIG. 23 is a diagram showing an example of the system state when the contingency of the power system in FIG. 22 is classified into contingency classification 1 in FIG.
In FIG. 23, in the mid-term forecast cross section 2271 at the assumed time of 10:30, No. of the contingency definition 228A of FIG. 1 contingency is assumed. In this contingency, it is assumed that a three-phase ground fault occurs in transmission line A-1 between buses #6 and #7 and that transmission line A-1 is released. At this time, even if the transmission line A-1 is released, power can be transmitted from the area EA to the area EB via the transmission line A-2 between the bus lines #6 and #7, and each of the power plants G1 to G5 It is possible to satisfy the demand of each consumer L1 to L4 based on the output from the.

Therefore, since the contingency shown in FIG. 23 does not cause a supply failure, countermeasures are not required, and it is classified as contingency classification 1 in FIG. At this time, it is assumed that the short-term foresight cross section generation unit 212 in FIG. Here, the fact that the transmission line A-1 is in a closed state means that there is a possibility that it will be released due to a contingency. When the reliability analysis/countermeasure planning unit 213 acquires the contingency 228 generated based on the short-term forecast cross section 232 from the contingency assumption unit 27, the contingency 228 is identified as a contingency by referring to the classification result database 28. It can be judged that it is classified into Category 1. Therefore, the reliability analysis/countermeasure planning unit 213 can determine that countermeasures are not required for the contingency failure 228 assumed from the medium-term forecast cross section 2271 without conducting reliability analysis, and starts reliability analysis and countermeasure planning. load can be reduced.

FIG. 24 is a diagram showing an example of the system state when the contingency of the power system in FIG. 22 is classified into contingency classification 2 or 3 in FIG.
In FIG. 24, in the mid-term forecast cross section 2272 at the assumed time of 10:30, No. of the contingency definition 228A of FIG. 3 contingencies are assumed. In this contingency, for example, due to a sudden change in weather, a three-phase ground fault occurs in the transmission line A-1 between buses #6 and #7, the transmission line A-1 is released, and the output of the generator G5 is reduced. expected to decline sharply.

Since the output of the generator G5 has suddenly decreased, the output of the generators G1, G2, and G3 in the area EA is increased to meet the demand required in the area EB via the transmission line A-2 between the buses #6 and #7. An attempt is made to transmit power from area EA to area EB. However, if the power transmitted from area EA to area EB is increased, voltage instability will occur. At this time, if countermeasures are not taken after the contingency failure occurs, a voltage collapse will occur, resulting in a power outage at the customer L3. In order to prevent a supply disruption to consumer L3, the KPI analysis unit 203 in FIG. Assume that M2 plans to introduce the phase-modifying equipment SC2.

FIG. 25 is a diagram showing a calculation example of KPIs before and after taking countermeasures against contingency failures in the power system of FIG. 24 .
In FIG. 25, the KPI analysis unit 203 in FIG. 2 calculates that the KPI before the countermeasure is 1500 MW because the consumer L3 has a power outage before the countermeasure. The KPI analysis unit 203 calculates that all KPIs after the countermeasures are 0 MW because there will be no disruption of supply to the customer L3 after the countermeasures are taken. However, it takes about 15 minutes to complete the closing of the transmission line between the buses #2 and #8 of the measure M1, and it takes about one minute to complete the closing of the phase modifying equipment SC2 of the measure M2.

In addition, the KPI analysis unit 203 uses the reliability analysis to determine the PV curve PV11 when the measure M1 is implemented as shown in FIG. 26 and the P-V curve PV11 when the measure M2 is implemented as shown in FIG. Generate a V-curve PV12. From the PV curves PV11 and PV12, the KPI analysis unit 203 can confirm that the voltage collapse can be avoided regardless of whether the countermeasures M1 or M2 are implemented.

In this case, there is plenty of time until the assumed time slice. Therefore, the contingency of FIG. 24 is classified into contingency classification 2 of FIG. 24, the real-time system 22 in FIG. The computational load can be reduced while maintaining

On the other hand, for example, if there is only 10 minutes until the assumed time slice, the contingency in FIG. 24 is classified into contingency classification 3 in FIG. At this time, the contingency analysis apparatus 10 can maintain the reliability of the electric power system 108 by implementing a time-saving countermeasure M2 based on the analysis result of the real-time system 22 .

FIG. 28 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 4 in FIG.
In FIG. 28, in the mid-term forecast cross section 2273 at the assumed time of 10:30, a three-phase ground fault occurred on the transmission line A-1 between the buses #6 and #7 while the demands L1, L2, and L3 exceeded. , the power line A-1 is released, the output of the generator G5 drops sharply, and it is assumed that even if countermeasures are taken, a partial supply failure will occur.

At this time, it is assumed that the KPI analysis unit 203 in FIG. 2 plans to close the transmission line between buses #2 and #8 as a measure M11 and load shedding as a measure M12 based on the medium-term forecast cross section 2273 . However, it takes about 15 minutes to complete the closing of the transmission line between the buses #2 and #8 of the countermeasure M11, and the load shedding of the countermeasure M12 can be implemented immediately after the failure occurs.

In this case, there is plenty of time until the assumed time slice. Therefore, the contingency in FIG. 28 is classified into contingency classification 4 in FIG. At this time, by implementing countermeasure M11 for the contingency shown in FIG. 28, the real-time system 22 shown in FIG. can be mitigated.

FIG. 29 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 5 in FIG.
In FIG. 29, in the mid-term forecast section 2274 at the assumed time 10:05, a three-phase ground fault occurs in the transmission line A-1 between the buses #6 and #7, the transmission line A-1 is released, and power generation It is assumed that the output of machine G5 will drop sharply, and even if countermeasures are taken, partial supply disruption will occur.

At this time, it is assumed that the KPI analysis unit 203 of FIG. 2 plans the introduction of the phase modifying facility SC2 as the measure M21 and the load shedding as the measure M22 based on the medium-term forecast cross section 2274 . However, it is assumed that it takes about one minute to turn on the phase modifying equipment SC2 of the countermeasure M21, and the load shedding of the countermeasure M22 can be performed immediately after the occurrence of the failure.

In this case, the time to the assumed time cross section is short. Therefore, the contingency in FIG. 29 is classified into contingency classification 5 in FIG. In this case, the real-time system 22 in FIG. 2 needs to analyze whether it is necessary to implement countermeasure M21 or countermeasure M22 for the contingency failure in FIG.

FIG. 30 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 6 in FIG.
In FIG. 30, it is assumed that the area EA is further provided with a generator G6, and the generator G6 is connected to the bus #2. In the mid-term forecast cross section 2275 at the assumed time of 10:30, for example, it is assumed that generators G1, G2, and G3 are disconnected simultaneously due to a large-scale earthquake. In this case, the total output of the generators G4 and G5 is 1000 MW, while the total demand of the consumers L1 to L4 is 3500 MW. Therefore, if no countermeasures are taken, consumers L3 and L4 will be disconnected from the power system 108 by the frequency protection relay, leading to power failure.

At this time, the KPI analysis unit 203 in FIG. 2 determines, based on the medium-term forecast cross section 2275, the closing of the transmission line between buses #2 and #8 as a measure M31, the start-up of the generator G6 as a measure M32, and the load reduction as a measure M33. It shall be assumed that the interruption has been planned.

In this case, countermeasures can be taken to minimize the supply disruption, but the countermeasures are very costly and the probability of occurrence is low. Therefore, the contingency in FIG. 30 is classified into contingency classification 6 in FIG. In this case, it is up to the user to decide whether or not to take countermeasures against the contingency.

Contingent failures classified into contingency classification 6 are not analyzed by the real-time system 22, but are analyzed in advance by the online system 21 and displayed to the user, so that the power outage area after normal countermeasures are implemented can be grasped. can. By knowing at least the power outage areas in advance, users can judge whether or not power outages will occur in important facilities, and use it as information for making investment decisions by comparing the economic impact of power outages and the cost of countermeasures. You can

FIG. 31 is a diagram showing an example of the system state and countermeasures when the contingency of the power system in FIG. 24 is classified into contingency classification 7 in FIG.
In FIG. 31, in the mid-term forecast cross section 2276 at the assumed time of 10:30, for example, due to a large-scale earthquake, generators G1, G2, and G3 are simultaneously disconnected and the three-phase ground of the transmission line between buses #5 and #6 is broken. It is assumed that the release due to the fault and the sudden decrease in the output of the generator G5 are assumed.

In this case, the output of the power generator G4 is 500 MW, while the total demand of the consumers L1 to L4 is 3500 MW. As a result, the consumers L1, L3, and L4 are disconnected from the power system 108 by the frequency protection relay, resulting in a power outage.

At this time, it is assumed that the KPI analysis unit 203 in FIG. 2 plans the closing of the transmission line between buses #2 and #8 as the measure M41 and the load shedding as the measure M42 based on the medium-term forecast cross section 2276 .

In this case, the probability of occurrence is low, but even if countermeasures are taken, supply disruption will occur over a wide area, resulting in enormous costs. Therefore, the contingency in FIG. 31 is classified into contingency classification 7 in FIG. In this case as well, the user decides whether or not to take countermeasures against the contingency.

Contingent failures classified into contingency classification 7 are not analyzed by the real-time system 22, but are analyzed in advance by the online system 21 and displayed to the user, so that the power outage area after normal countermeasures are implemented can be grasped. can. By knowing at least the power outage areas in advance, users can judge whether or not power outages will occur in important facilities, and use it as information for making investment decisions by comparing the economic impact of power outages and the cost of countermeasures. You can

As described above, according to the above-described embodiments, by pre-classifying a huge number of combinations of hypothetical events, including sudden changes in power flow conditions and multiple failures, based on the KPI 229, the necessary In addition to being able to take appropriate countermeasures, it is also possible to quantitatively evaluate the range of power outages in advance for events where power outages cannot be avoided. In addition, the operation margin of transmission lines can be reduced by implementing various measures. As a result, it is possible to achieve both the reliability of the power system 108 and the economy of power trading.

1 contingency analysis device, 11 central control device, 12 input device, 13 output device, 14 display device, 15 communication device, 16 main storage device, 17 auxiliary storage device, 17A various databases, 17B various programs, 21 online system, 22 Real-time system, 23 System equipment database, 24 Power generation/demand record database, 25 Balancing group information database, 26 Medium-term forecast cross-section database, 27 Contingent failure assumption unit, 28 Classification result database, 29 Display/output unit, 101 Communication line, 102 Wide area Institution system 103 Wholesale power market system 104 Supply and demand adjustment market system 105 Area middle supply system 106 Measuring instrument 107 Control target device 108 Electric power system 201 System cooperation unit 202 Mid-term forecast cross section generation unit 203 KPI analysis Section 204 Contingent Failure Classification Section 211 Intermediate Supply System Coordination Section 212 Short Term Prediction Cross Section Generation Section 213 Reliability Analysis/Countermeasure Planning Section 221 Demand and Supply Planning Information 222 Electricity Trading Information 223 Controlling Capacity Trading Information 224 System Equipment data, 225 power generation/demand record data, 226 balancing group information

Claims (5)

a prediction unit for predicting a system state indicating a facility state and a power flow state of the power system based on plan information related to the operation of the power system;
an assumption unit that assumes contingency failures based on the system state;
an analysis unit that analyzes a KPI (Key Performance Indicator) in the system state when the contingency occurs;
a classification unit that classifies the contingency based on the KPI ;
The plan information includes supply and demand plan information, which is the supply and demand plan value, power trading information, which is the power generation plan value, and control power trading information, which is the trading result of control power in the supply and demand control market,
The contingency includes disconnection of each generator in the system state, stoppage due to ground fault, short circuit or disconnection in each transmission and transformation facility, large-scale sudden change in renewable energy power generation that was not assumed when the system state was generated. , suspension of multiple facilities due to stormy weather, simultaneous disconnection of generator groups due to large-scale earthquakes, simultaneous suspension of transmission and transformation facilities, and combinations of these N-1 failures, N-2 failures, N - contains a list of 1-1 failures, N-2-1 failures, Nk failures, Nk-1 failures and N-1 failures,
The KPIs are the probability of occurrence of the contingency, the reliability of the power system when the contingency occurs, the power outage range when the contingency occurs, the urgency of countermeasures when the contingency occurs, and the market economy caused by the countermeasures. selected from at least one of the effects on sexuality,
The contingency analysis device, wherein the classification unit classifies the contingency based on the classification based on the KPI before the countermeasure for the contingency and the classification based on the KPI after the countermeasure. 2. The contingency analysis device according to claim 1, wherein the prediction unit predicts the system state of the power system based on the supply and demand plan information, the power trading information, and the controllability trading information. 2. The cooperation unit according to claim 1 , further comprising a linking unit that acquires the supply and demand plan information from a cross-regional organization system, acquires the electricity transaction information from a wholesale electricity market system, and acquires the controllability transaction information from a supply and demand adjustment market system. Contingent Failure Analyzer. The foreseeing unit
a first foreseeing unit for foreseeing the system state ;
a second prediction unit that predicts a system state indicating the equipment state and the power flow state in a shorter period than the first prediction unit;
Planning a countermeasure against the contingency based on the system state foreseen by the second prediction unit, the contingency based on the system state foreseen by the second prediction unit, and the classification result of classifying the contingency. In addition, we have a planning department ,
2. The contingency analysis apparatus according to claim 1, wherein the countermeasures include shutting down the generator or demand by the system stabilization system, turning on phase modifying equipment, changing the terminal voltage of the generator, and changing the output of the generator. Further comprising an area power supply system that acquires measurement information including the output state of the generator installed in the power system, the power flow state of the transmission and transformation equipment, and the equipment state of the equipment to be controlled,
The second prediction unit predicts the system state based on the measurement information acquired from the area central feeding system,
5. The contingency analysis apparatus according to claim 4 , wherein the planner calculates a control table related to control for stabilizing the power system based on the system state, the contingency, and the classification result .

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