JP2023074886A - Energy operation planning device - Google Patents

Abstract

To provide an energy operation planning device capable of realizing an improvement of operation time caused by the reduction of the number of scenarios, while guaranteeing a stably operation in consideration of severe operation conditions.SOLUTION: A power operation planning device 1 has a plan information part 11 storing power operation plan relevant information including device information concerning a plan object, uncertainty information, and an initial scenario previously assumed, a scenario preparing part 112 newly preparing an additional uncertainty scenario which leads to an operation restriction violation in a power operation plan and a cost increase and in which an occurrence probability is equal to or more than a predetermined occurrence probability threshold value, different from the initial scenario based on the power operation plan relevant information, and a scenario evaluation part 116 generating a power operation plan respectively based on each of a plurality of uncertainty scenarios comprising the initial scenario and the additional uncertainty scenario.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present invention relates to an energy operation planning device that supports drafting of an energy operation plan.

As an example of an energy operation planning device that assists in drafting an energy operation plan, there is an electric power operation planning device for the electric power business. In the electric power business, a plan is created that indicates future operation guidelines and control command values for equipment based on predicted values of future conditions in order to realize electric power operation. An example of this power operation plan is a generator operation plan. The generator operation plan is based on the forecast value of power demand at each time during the planning period, and starts and stops the generator according to the power demand so as to satisfy the operational constraints of each generator and the power system. It determines the state and output.

For example, methods disclosed in Non-Patent Document 1 and Non-Patent Document 2 are known as such a generator operation planning method. In these operation planning methods, each power generation has its own characteristics, such as the supply and demand balance, in which the supply and demand of electricity match, and the minimum continuous start-up time and minimum continuous stop time, in which the generator maintains its state for a certain period of time after starting or stopping. The operation plan is calculated to minimize the total power generation cost while satisfying the operational constraints of the generator and the power system. In calculating this operation plan, for example, even if the number of generators is one, if the two states of starting and stopping of the generator are considered for all the time sections n of the operation plan, there are 2 ⁿ kinds of huge numbers. A combination operation plan is conceivable. Therefore, an optimization method is required to quickly determine an operation plan that minimizes the total power generation cost from among a huge number of operation plans.

On the other hand, regarding renewable energy such as photovoltaic power generation whose output depends on the weather, prediction of future power generation yields a prediction error, which is a discrepancy from the actual power generation at the time of actual operation. The amount of renewable energy introduced is expected to increase further in the future, but as the amount of renewable energy introduced increases, the impact of forecast errors will increase, so the amount of deviation between the actual amount of power generation and the predicted value will also increase, and the supply and demand balance will be disturbed. difficult to fulfill. In such an uncertain situation where the actual operation status cannot be predicted with accuracy, it is possible to create a large number of scenarios of future renewable energy power generation amount in advance and create an electric power operation plan. Non-Patent Literatures 3 and 4 report that it is possible to estimate the divergence between the amount of power generation and the predicted value and suppress the influence of uncertainty.

Mototsune Yoshikawa, Toshiyuki Sawa, Hiroshi Nakajima, Mitsuo Kinoshita, Yoshiyuki Kurebayashi, Yuji Nakata: "Methods for Creating Operation Plans for Thermal and Pumped-storage Power Plants", IEEJ, Vol.114, No.12 (1994) Toshiyuki Sawa, Yasuo Sato, Mitsuo Tsurugi, Tsukasa Onishi, "Preparation of integrated next-day operation plan for thermal power, pumped storage, hydraulic power and interchange considering tidal current constraints", IEEJ Trans.PE, Vol.128, No.10 (2008) Takayuki Shiina, "Probabilistic Programming", Asakura Shoten, pp99-110 (2015) Yao Zhang, et al., "Chance-Constrained Two-Stage Unit Commitment under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition", IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 32, NO. 5 (2017)

As mentioned above, in order to consider the impact of uncertainty, operation plans are created with a huge number of uncertainty scenarios in advance. become enormous. On the other hand, if the number of uncertainty scenarios is reduced, the increase in computation time can be improved, but there is a possibility that the accuracy of uncertainty evaluation in the operation plan will decrease. Also, since the power operation plan is not taken into account when creating uncertainty scenarios in advance, there is no guarantee that severe scenarios are taken into account in power operation.

An energy operation planning apparatus according to an aspect of the present invention includes: a plan information unit storing energy operation plan-related information including energy equipment information, uncertainty information, and a presupposed first uncertainty scenario regarding planning targets; Based on the energy operation plan related information, unlike the first uncertainty scenario, a second uncertainty that leads to operation constraint violations and cost increases in the energy operation plan and has an occurrence probability equal to or higher than a predetermined occurrence probability threshold. a scenario creation unit that newly creates a scenario; an operation plan creation unit that creates a first energy operation plan based on each of a plurality of uncertainty scenarios including the first and second uncertainty scenarios; Prepare.

ADVANTAGE OF THE INVENTION According to this invention, the improvement of the calculation time accompanying the reduction of the number of scenarios can be aimed at, ensuring stable operation|movement considering severe operating conditions.

FIG. 1 is a block diagram showing the functional configuration of the power operation planning device according to the first embodiment. FIG. 2 is a diagram showing the hardware configuration of the power operation planning device. FIG. 3 is a diagram showing a schematic image of a power system. FIG. 4 is a diagram for explaining how demand is handled in an optimization problem. FIG. 5 is a flow chart showing a processing procedure in the plan and scenario calculation unit. FIG. 6 is a block diagram showing an example of a power operation planning device according to the second embodiment. FIG. 7 is a diagram for explaining the concept of alteration processing in the partial scenario alteration unit. FIG. 8 is a flowchart for explaining the processing procedure in the detailed calculation unit.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings. The following description and drawings are examples for explaining the present invention, and are appropriately omitted and simplified for clarity of explanation. Also, in the following description, the same or similar elements and processes are denoted by the same reference numerals, and redundant description may be omitted. It should be noted that the contents described below merely show an example of the embodiment of the present invention, and the present invention is not limited to the following embodiment, and can be implemented in various other forms. It is possible. In the following, as an example of an energy operation planning apparatus that supports energy operation planning, a power operation planning apparatus for electric power business will be described as an example, but the following embodiments are not limited to power operation planning apparatuses. energy operation planning device.

-First Embodiment-
1 to 3 are diagrams illustrating an example of the power operation planning device 1. FIG. FIG. 1 is a block diagram showing the functional configuration of the power operation planning device 1, and FIG. 2 is a diagram showing the hardware configuration of the power operation planning device 1. As shown in FIG. The power operation planning apparatus 1 includes a plan creation unit 10, a plan information unit 11 as a database, and a result presentation unit 12 having functions as a display device, a storage unit, an output unit, and the like. The plan creation unit 10 includes an initial scenario extraction unit 100 and a plan and scenario calculation unit 110 . Plan and scenario calculation unit 110 includes scenario creation unit 112 , management unit 114 , and scenario evaluation unit 116 . Details of each part will be described later.

As shown in FIG. 2, the power operation planning apparatus 1 is composed of a computer system, and includes a display unit 21, an input unit 22, a communication unit 23, a CPU 24, a memory 25, a system information database DB1, and a power operation planning database DB2. , and they are connected to a bus line 26 . The display unit 21 may be configured to use a display device, a printer device, an audio output device, or the like, or may be configured to use a printer device, an audio output device, or the like together with the display device. The input unit 22 can include at least one of, for example, a keyboard switch, a pointing device such as a mouse, a touch panel, and a voice instruction device. The communication unit 23 has circuits and communication protocols for connecting to the communication network 300 . The system information database DB1 in FIG. 2 corresponds to the plan information unit 11 in FIG. 1, for example. Further, the display unit 21 corresponds to the result presentation unit 12, for example.

The CPU 24 cooperates with the memory 25 to execute a program, realize each part of the plan creation part 10 shown in FIG. etc. CPU 24 may be configured as one or more semiconductor chips, or may be configured as a computing device such as a computing server. The memory 25 is configured as, for example, a RAM (Random Access Memory), and stores computer programs, calculation result data, image data, and the like required for each process. The data stored in the memory 25 are stored in the databases DB1 and DB2, sent to the display unit 21 and displayed, and furthermore, sent to each device such as a generator in the electric power system via the network 300 as an operation control command. sent. Since each device in the electric power system is operated based on the operation control command value, the control command value needs to be a value that satisfies the operation constraints.

FIG. 3 is a diagram showing a schematic image of the power system 200. As shown in FIG. Information such as generator equipment information in the power system 200 and a plurality of measurement data such as the load 150 are stored in the system information database DB1 (that is, the plan information unit 11) via the network 300. FIG. The power system 200 is a system in which a plurality of generators 230 and loads 250 are interconnected via busbars (nodes) 210, transformers 220, transmission lines 240, and the like. Although not shown, various measuring instruments are appropriately installed on the bus 210 for the purpose of protection, control, and monitoring of the electric power system 200. It is transmitted to the communication unit 23 of the planning device 1 . In addition to the power system 200, the communication unit 23 also communicates with a plurality of distributed power sources such as a weather system, a power market system, a VPP (Virtual Power Plant), and an aggregator that monitors and controls consumers.

<Detailed description of each part of the power operation planning device 1>
Returning to FIG. 1, details of each part of the power operation planning device 1 will be described. Information such as generator equipment information in the power system 200 and a plurality of measurement data such as the load 150 are stored in the system information database DB1 (that is, the plan information unit 11) via the network 300. FIG. The plan information unit 11 stores information about the power system 200 that is the target of the plan. For example, generator equipment information such as equipment constants that indicate the characteristics of each generator, periodic inspection information such as operation stop and output limit for maintenance inspection, system information such as maximum transmission capacity of interconnection lines of the power system are required Demand forecast information such as demand information corresponding to the amount of power generation and its possible probability distribution range, adjustment capability information to adjust fluctuations from the assumed state such as operation plan, renewable energy information such as power generation amount and characteristics of renewable energy, The plan information unit 11 also stores information necessary for the plan creation unit 10 to create an uncertainty scenario and a power operation plan, such as initial scenario information, which is information on a scenario assumed by an operator.

The initial scenario extraction unit 100 of the plan creation unit 10 extracts from the plan information unit 11 information necessary for scenario creation and power operation plan creation, such as demand forecast information, power generator information, and initial scenario information. As described above, the initial scenario information is information on a scenario assumed by the operator, and the operator stores a plurality of initial scenarios in advance as initial scenario information in the plan information section 11 .

As described above, conventionally, a large number of exhaustive scenarios are prepared in advance when creating an electric power operation plan. On the other hand, in the power operation planning apparatus 1 of the present invention, based on the information about the power system 200 stored in the plan information unit 11, an additional uncertainty scenario considering the power system 200 is planned and the scenario is generated as described later. It is created by the calculator 110 . Therefore, the operator does not need a huge number of scenarios as initial scenarios prepared as initial scenario information as in the conventional case.

If the plan information unit 11 does not store an initial scenario, the initial scenario extraction unit 100 extracts a statistical scenario such as a Monte Carlo simulation from the probability distribution of demand at each time stored as the demand forecast information. Depending on the creation method, a demand fluctuation scenario based on probability distribution may be created as an initial scenario. Alternatively, an initial scenario may be created from time-series data for the past few years using data with similar conditions such as the same season or the same time. Hereinafter, the initial scenario extracted or created by the initial scenario extraction unit 100 is used as an uncertainty scenario for creating the power operation plan, so the initial scenario may be referred to as an uncertainty scenario hereinafter. An initial scenario as an uncertainty scenario extracted or created by the initial scenario extraction unit 100 is input to the plan and scenario calculation unit 110 .

The scenario creation unit 112 of the plan and scenario calculation unit 110 stores the uncertainty scenarios extracted or created by the initial scenario extraction unit 100 and the uncertainty scenario group (multiple uncertainties) selected by the management unit 114 described later. certainty scenario) is entered. The scenario creation unit 112 does not overlap with the input uncertainty scenario based on the input uncertainty scenario and the demand forecast information, power generation equipment information, etc. from the plan information unit 11, and the power system 200 Create additional uncertainty scenarios with high severity above thresholds that lead to operational constraint violations and increased operational costs. Note that the threshold for the probability of occurrence is set in advance. Details on how to create additional uncertainty scenarios are provided below. The uncertainty scenario created by scenario creating section 112 is input to management section 114 .

Management unit 114 receives the uncertainty scenario created by scenario creation unit 112 and the uncertainty scenario (initial scenario) from initial scenario extraction unit 100 . The management unit 114 evaluates the degree of overlap, which indicates the degree of similarity between scenarios, for all the input uncertainty scenarios, and selects one of a set of uncertainty scenarios whose degree of overlap is equal to or greater than a preset threshold value. delete one of the

The degree of duplication of uncertainty scenarios is, for example, an index that measures the degree to which the tendency of the demand curves becomes the same when there is a demand curve as an uncertainty scenario. It takes the value 1 if the two uncertainty scenarios are identical and completely overlapped, and the value of the degree of overlap becomes less than 1 as the degree of overlap decreases. Examples of multiplicity include statistical techniques such as correlation and covariance. For example, the correlation coefficient is represented by a value between +1 and -1, and the closer it is to 0, the lower the correlation.

The management unit 114 selects any two uncertainty scenarios from a plurality of uncertainty scenarios and calculates the degree of redundancy for all combinations of the plurality of uncertainty scenarios. Then, combinations whose redundancy is equal to or greater than a predetermined threshold are extracted from among the plurality of combinations, and one of the extracted combinations, for example, the uncertainty scenario with the lower probability of occurrence, is deleted from the uncertainty scenario group. Then, the management unit 114 outputs an uncertainty scenario group consisting of a plurality of uncertainty scenarios with a low degree of duplication remaining after the deletion process.

Scenario evaluation unit 116, for example, by the method described in Non-Patent Document 3 and Non-Patent Document 4, based on the uncertainty scenario output from management unit 114, to create a power operation plan that takes into account the uncertainty. do. Since the uncertainty scenario group output from management unit 114 includes a plurality of uncertainty scenarios as described above, a power operation plan is created for each of the plurality of uncertainty scenarios. In addition, when creating a power operation plan, if the number of generators or the number of scenarios increases and the calculation time becomes enormous, the formula of the power operation plan is corrected as in the method described in Non-Patent Document 5 below. , the computation time may be speeded up by approximation. For example, the number of generators can be reduced by simulating a plurality of generators with a single generator based on the constraints of power generation efficiency and operational constraints.
(Non-Patent Document 5)
Bryan S. Palmintier, et al., "Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling", IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 3 (2014)

Furthermore, the scenario evaluation unit 116 creates a total cost based on the plurality of created power operation plans. The total cost here is an expected value of the cost calculated by calculating the cost based on each power operation plan and weighting each cost by the occurrence probability of the corresponding uncertainty scenario.

Uncertainty scenario group consisting of uncertainty scenarios with a low degree of overlap output from the management unit 114, and electric power operation created by the scenario evaluation unit 116 for each uncertainty scenario of the uncertainty scenario group The plan and each of the above-mentioned total costs are temporarily stored in the memory 25 and then stored in the power operation plan database DB2. Then, the uncertainty scenarios and the power operation plans created for them are presented to the operator by the result presentation unit 12 .

As a form of presentation by the result presentation unit 12, the results may be displayed on the display unit 21 provided in the power operation planning device 1. Data on the uncertain scenario and the power operation plan may be output to the terminal. By presenting the results to the operator in this way, the operator is alerted to an operational abnormality such as a violation of operational constraints and the reason, and the operator can adjust the control of the generator, etc. before the abnormal state of power operation occurs. can do. This makes it possible to maintain stable power operation.

<Method of Creating Uncertainty Scenario in Scenario Creation Unit 112>
When adding uncertainty scenarios, in order to reduce computation time and ensure operational stability, create scenarios that are of high importance leading to operational violations and operational cost increases, and that do not overlap with existing uncertainty scenarios. It is important to Therefore, a scenario is created while satisfying these by solving the following optimization problem. In addition, in the following optimization problem, demand is used as information with uncertainty.

ただし、
T_end：計画の終端時刻 N_gen：発電機台数 F_i( )：発電コスト関数
p_it：発電出力 u_it：起動停止を示す0,1の離散変数
PNL( )：違反量に応じたペナルティコスト関数
Δd_t：需給バランスの違反量などの運用制約違反量
・制約条件
需給バランス

変化レベル
Dmin(t, dlv_sec) ≦ d_t ≦ Dmax(t, dlv_sec) …（３）
重複度
R(ddlv, ddlv₁, ddlv₂,…ddlv_s) ≦ α …（４）
発生閾値
Pr(dlv₁, dlv₂, dlv₃,…dlv_sec) ＞ β …（５）
その他の制約条件は非特許文献１などと同一とする。
dlv _sec：需要の範囲の選択変数 sec：区間
Dmin( )：選択された範囲の下限値 d_t：作成する需要シナリオ
Dmax( )：選択された範囲の上限値
R( )：重複度評価関数であり、既出のシナリオとの重複した割合を示す。
重複度の例として、相関や共分散がある。
α：許容可能な重複の閾値とする。
ddlv：需要の範囲選択のベクトルであり、dlv₁ … dlv_secのベクトルとなる。
Pr( )：発生確率 ddlv_s：既出のシナリオsの需要の範囲選択ベクトルddlv (Optimization problem for scenario creation)
・Objective function Total power generation cost within planned time, operation constraint violation cost

however,
T _end : Plan end time N _gen : Number of generators F _i ( ): Generation cost function
p _it : Power output u _it : 0, 1 discrete variable indicating start/stop
PNL( ): Penalty cost function according to the amount of violations Δd _t : Violation amount of operational constraints such as the amount of violations of supply-demand balance and constraints Supply-demand balance

change level
Dmin(t, dlv _sec ) ≤ _dt ≤ Dmax(t, dlv _sec ) (3)
Multiplicity
R(ddlv, _ddlv1 , _ddlv2 ,... _ddlvs ) ≤ α...(4)
Occurrence threshold
Pr(dlv ₁ , dlv ₂ , dlv ₃ ,…dlv _sec ) > β … (5)
Other constraints are the same as in Non-Patent Document 1 and the like.
dlv _sec : Selection variable for the range of demand sec: Interval
Dmin( ): Lower limit of selected range d _t : Demand scenario to be created
Dmax( ): Upper limit of selected range
R( ): Multiplicity evaluation function, indicating the ratio of duplication with previous scenarios.
Examples of multiplicity are correlation and covariance.
α: Threshold for allowable overlap.
ddlv: A vector of range selections for demand, resulting in a vector of dlv ₁ … dlv _sec .
Pr( ): Occurrence probability ddlv _s : Demand range selection vector ddlv for scenario s already mentioned

FIG. 4 shows the treatment of demand in the above optimization problem. FIG. 4 is a diagram relating to demand scenarios. The demand forecast information of the plan information unit 11 includes a probability distribution at each time indicating possible values of demand, and the possible range of demand in FIG. 4 is determined from the probability distribution. In FIG. 4, the range between the upper dashed line and the lower dashed line is the possible range of demand. In the example shown in FIG. 4, the range that this demand can take is divided into upper and lower parts. According to equation (3), when the demand range selection variable dlv _sec is dlv _sec = 1, the upper divided range is selected, and when dlv _sec = 0, the lower divided range is selected. and the assumed demand dt is inherent in the selected division range.

In the above optimization problem, in order to create a scenario that does not overlap with the existing scenarios, whether or not the demand patterns overlap (that is, similar) is evaluated by the degree of overlap in Equation (4), and the degree of overlap is Duplication is restricted so that it is equal to or less than the threshold α. _Note that the overlap evaluation uses ddlv=[dlv ₁ . This is because the degree of overlap of demand is evaluated based on trends in time intervals, such as whether demand is large or small in a specific time interval, rather than evaluating the degree of overlap based on local changes in time. . Also, we need to evaluate the high severity scenarios and not consider the scenarios that have very low probability and do not occur in reality. Therefore, scenarios are limited to those with occurrence probabilities equal to or greater than the threshold value β, as in Equation (5).

Note that equations (2) to (5) are only examples, and any method that can avoid redundancy and probability that are influenced by local changes in time may be used. For example, a filter using a difference equation A method of excluding local changes by, for example, may be used.

Regarding the above optimization problem, the objective function of equation (1) adjusts the power generation output p _it and start/stop u _it so that the power generation cost and violation cost are minimized while satisfying the above constraints, and the worst case is To create a scenario, adjust d _t and dlv _t so that the objective function (power generation cost and violation cost) is maximized.

(Simplification of optimization calculation)
If the computation time becomes an issue, approximation computation may be performed by relaxing (linear relaxation) some variables such as start/stop u _it from discrete variables to linear variables. In the solution of the optimization problem, the linearly relaxed discrete variables do not necessarily give the solution of the original discrete variables. can be approximated to For example, if the linearly relaxed 0-1 discrete variable solution is 0.9, let 0.9≈1.

Approximating some variables affects other variables, such as changing the optimal solution. can be calculated. In the above optimization problem, in order to create an uncertainty scenario that maximizes the violation of operational constraints, etc., the time and place of occurrence of violation of operational constraints and cost increase, and the difference between the existing scenario and the additional scenario in terms of the occurrence time This makes it possible to understand the reasons for operational constraint violations and cost increases (differences in scenarios).

In the explanation of FIG. 1 mentioned above, the processing in each part of the plan creating part 10 has been explained. In the plan and scenario calculation unit 110 of FIG. will be repeated.

FIG. 5 is a flow chart showing the procedure of repeated processing. The control program of the flow chart of FIG. 5 is stored in the memory 25 of FIG. 1 and executed by the CPU 24 . First, in step S11, the initial scenario extraction unit 100 extracts an initial scenario. The extracted initial scenario is input to the plan and scenario calculator 110 as an uncertainty scenario. In step S12, the scenario creation unit 112 creates an additional uncertainty scenario.

Note that when the process proceeds from step S11 to step S12, the creation of the additional uncertainty scenario in the scenario creation unit 112 is based on the uncertainty scenario input from the initial scenario extraction unit 100 and the uncertainty scenario from the plan information unit 11. This is done based on demand forecast information and power generation equipment information. On the other hand, as will be described later, when returning from step S15 to step S12, the uncertainty scenario group (a plurality of uncertainty scenarios) selected by the management unit 114 and the demand forecast information and power generation from the plan information unit 11 Additional uncertainty scenarios are created based on equipment information, etc.

In step S13, the management unit 114 selects an uncertainty scenario group. Specifically, for all the input uncertainty scenarios, the degree of overlap, which indicates the degree of similarity between scenarios, is evaluated, and one of a set of uncertainty scenarios with a degree of overlap greater than or equal to a threshold is selected. The deletion process is executed. In step S14, the scenario evaluation unit 116 creates a power operation plan for each of the plurality of uncertainty scenarios selected by the management unit 114 and calculates the total cost.

In step S15, the total cost calculated in step S14 is compared with the previously calculated total cost held in the memory 25, and the cost difference between them (="current total cost"-"previous total cost" ) is equal to or less than a predetermined threshold. By the way, the additional uncertainty scenario created in step S12 is a severe scenario that leads to violation of operational constraints and increased operational costs in the power system 200 . Therefore, the value of the total cost calculated in step S14 increases each time the addition of the uncertainty scenario in step S12 is repeated, and the magnitude of the increase in the total cost (that is, the cost difference) By repeating the addition, it gradually becomes smaller.

If it is determined in step S15 that "cost difference≦threshold", the process of step S16 is executed and the series of processes is terminated. In step S16, the uncertainty scenario group (a plurality of uncertainty scenarios) selected by the management unit 114, the power operation plan for each uncertainty scenario included in the uncertainty scenario group, and the calculated total cost Each is stored in the power operation plan database DB2.

On the other hand, if "cost difference>threshold" is determined in step S15, the total cost held in the memory 25 is replaced with the total cost calculated in step S14, and the process returns to step S12. It should be noted that the total cost is not stored in the memory 25 when the process proceeds from step S11 to step S12 and the process of step S15 is performed for the first time. In that case, the previous total cost is set to zero, and it is determined in step S15 that "cost difference>threshold".

When the process of FIG. 5 is completed, the calculation results are input from the plan creation section 10 to the result presentation section 12, and the result presentation section 12 presents the calculation results to the operator. For example, the calculation result is displayed on the display unit 21 in FIG. At that time, by presenting the operational constraint violation and cost increase and the reason indicated by the additional uncertainty scenario in step S12 to the operator, the operator is alerted to the operational abnormal state and reason such as the operational constraint violation. In addition, the operator can perform control adjustment of the generator and the like before an abnormal state of power operation. This makes it possible to maintain stable power operation.

In the above-described first embodiment, additional uncertain scenarios are created while evaluating overlaps with the existing initial scenarios, probability of occurrence, violation of constraints, and increases in power generation costs. Scenarios with high degrees are deleted. Then, based on the remaining uncertain scenarios, we created a power operation plan that takes uncertainty into consideration. In the scenario creation optimization problem, as mentioned above, the constraints and costs of the power operation plan, including the operation of the generator, are taken into account, so a severe uncertainty scenario that is prone to operational violations can be obtained. . Also, in the management unit 114, only one of the uncertainty scenarios with a high duplication degree is left, so that the number of uncertainty scenarios for examining power operation planning can be reduced, and the calculation time can be reduced. can be shortened.

Furthermore, by repeating the above-described series of processes as shown in FIG. 5, severe scenarios in the power operation plan can be created one after another. As a result, it is possible to sufficiently ensure the accuracy of uncertainty evaluation in the operation plan without preparing a huge number of initial scenarios. Therefore, it is possible to speed up the computation time by simplifying the optimization computation, avoiding duplication of uncertainty scenarios, and eliminating duplication.

In addition, in evaluating the degree of overlap, as shown in Fig. 4, by dividing the range of uncertainty for each time interval and judging by which divided range it is inherent, it is possible to identify specific uncertainties instead of short-term instantaneous changes. Multiplicity can be evaluated as a trend over time. By setting the time interval according to the time length of the uncertain phenomenon to be evaluated, it is possible to create an uncertainty scenario according to the length of the phenomenon.

-Second Embodiment-
FIG. 6 is a diagram showing an example of the energy operation planning device of the second embodiment, and is a block diagram showing the functional configuration of the power operation planning device 1B. The same components as those of the power operation planning apparatus 1 shown in FIG. 1 are denoted by the same reference numerals, and overlapping descriptions are omitted. The power operation planning device 1B includes a plan creation unit 10B, a plan information unit 11 and a result presentation unit 12. The plan information section 11 and the result presentation section 12 have the same configurations as those shown in FIG. The hardware configuration of the power operation planning device 1 is not shown, but it has the same configuration as that shown in FIG.

The plan creation unit 10B includes an initial scenario extraction unit 100, a plan and scenario calculation unit 110, and a detailed calculation unit 120. The initial scenario extraction unit 100 and the plan and scenario calculation unit 110 have the same configurations as those shown in FIG. The plan and scenario calculation unit 110 performs the same processing as in the first embodiment, and inputs the calculation result to the detailed calculation unit 120 . However, in the second embodiment, the scenario creation unit 112 of the plan and scenario calculation unit 110 simplifies the optimization calculation described in the first embodiment, and shortens the calculation time. do.

The detailed calculation unit 120 includes a partial scenario modification unit 122, a management unit B124, and a scenario evaluation unit B126. The scenario partial modification unit 122 stores the uncertainty scenario group (multiple uncertainty scenarios) output from the planning and scenario calculation unit 110 and the uncertainty scenario group (multiple uncertainty scenario) is entered. The partial scenario modification unit 122 generates a new uncertainty scenario by modifying the input uncertainty scenario.

FIG. 7 is a diagram for explaining the concept of alteration processing in the partial scenario alteration unit 122. As shown in FIG. FIG. 7 is a diagram similar to the demand scenario shown in FIG. In FIG. 7, a plurality of sections (sections 2, 3, and 4) that are modification target periods are selected for the plurality of sections sec in FIG. 4, and sections 2, 3, and 4 are shortened To divide. Below, the section after division is called a division section. In the example shown in FIG. 7, section 2 is divided into two divided sections 2-1 and 2-2, section 3 is divided into two divided sections 3-1 and 3-2, section 4 is divided into two divided sections It is divided into 4-1 and 4-2.

The partial scenario modification unit 122 does not use the optimization calculation simplification (for example, an approximation method such as linear relaxation) performed by the scenario creation unit 112 of the plan and scenario calculation unit 110 in the previous stage for this modification target period. Then, by re-solving the optimization problem with a strict optimization calculation method and updating only the uncertainty scenario modification target period, an additional uncertainty scenario with higher accuracy is created. At this time, from the viewpoint of obtaining detailed results from the rough calculation results, the initial condition of the start time and the terminal condition of the end time of the modification target are divided range dlv _sec in the uncertainty range of the scenario, the starting state of the generator etc. are the same.

As will be described later, when the processing of the partial scenario modification unit 122 is repeated, the modification target period is repeatedly updated for the uncertainty scenario, and the previously mentioned uncertainty scenario is transformed into a more detailed scenario. going to be updated. The modification target period is changed each time the modification process is repeated. For example, in the modification process subsequent to the modification process for the modification target on the left side of FIG. 7, the modification target period on the right side of the figure is set as the modification target period.

Another example of partial modification of the scenario by the partial scenario modification unit 122 is as follows. If specific conditions set by the operator, such as a low demand zone in a specific season, are applicable to the scenario to be modified, the occurrence of specific events such as maintenance of a specific generator will be added as an additional condition of uncertainty. Create sexual scenarios.

In the management unit B124 of FIG. 6, the uncertainty scenario input to the partial scenario modification unit 122 and the additional uncertainty scenario created by the partial scenario modification unit 122 are subjected to the uncertainty scenario by the management unit 114 described above. Processing similar to the scenario group selection processing is performed. That is, for all of the input uncertainty scenarios, the degree of overlap, which indicates the degree of similarity between scenarios, is evaluated, and one of a set of uncertainty scenarios whose degree of overlap is greater than or equal to a threshold is deleted. is executed. However, if a scenario is created with additional conditions such as occurrence of a specific event under specific conditions set by the operator during the processing of the partial scenario modification unit 122, the specific event will occur under the same type of specific conditions. The processing of the management unit B124 is performed only for a certain uncertainty scenario.

The scenario evaluation unit B126 performs the same processing as the scenario evaluation unit 116 in FIG. 1 on the result of the management unit B124. That is, for a plurality of uncertainty scenarios included in the uncertainty scenario group output from the management unit B124, a power operation plan is created in consideration of the uncertainty based on them.

FIG. 8 is a flowchart for explaining a processing procedure by the detailed calculation unit 120. As shown in FIG. In step S<b>21 , the uncertainty scenario and the power operation plan, which are the calculation results, are read from the plan and scenario calculator 110 . The read uncertainty scenario is input to the partial scenario modification unit 122. At step S22, the partial scenario modification unit 122 modifies the input uncertainty scenario to create a new uncertainty scenario. At step S23, the above-described management unit B124 selects a group of uncertainty scenarios. In step S24, the scenario evaluation unit B126 creates a power operation plan based on the uncertainty scenario and calculates the total cost.

In step S25, determination processing similar to that in step S15 shown in FIG. 5 is executed based on the total cost calculated in step S24. Then, when it is determined that "cost difference>threshold", the process returns to step S22, and the processing from step S22 to step S24 is executed based on the uncertainty scenario of the uncertainty scenario group selected by the management section B124. . On the other hand, if it is determined in step S25 that “cost difference≦threshold”, the series of processes is terminated, and in step S26, the uncertainty scenario group selected by the management unit B124 and each uncertainty of the uncertainty scenario group The power operation plan for the certain scenario and the calculated total cost are each stored in the power operation plan database DB2.

When the process of FIG. 8 is completed, similarly to the case of the first embodiment, the calculation result is input from the plan creation unit 10B to the result presentation unit 12, and the calculation result is presented to the operator by the result presentation unit 12. . Also, at that time, the operator may be presented with the operational constraint violation, the cost increase, and the reason shown in step S22.

In the above-described second embodiment, the uncertainty scenario created by the plan and scenario calculation unit 110 is reoptimized for a specific time interval as a modification target, or By adding additional conditions when there are specific conditions and specific events, we partially modified the scenario and created an additional scenario. At that time, the scenario creation unit 112 of the plan and scenario calculation unit 110 simplifies the optimization calculation described in the first embodiment to shorten the calculation time.

In addition, when creating a power operation plan, if the number of generators or the number of scenarios increases and the calculation time becomes enormous, the formula of the power operation plan is corrected as in the method described in Non-Patent Document 5 below. , the computation time may be speeded up by approximation. For example, the number of generators can be reduced by simulating a plurality of generators with a single generator based on the constraints of power generation efficiency and operational constraints.

In the second embodiment, the plan and scenario calculation unit 110 extracts uncertainty scenarios involving constraint deviations and cost increases, and the scenario partial modification unit 122 improves the increments and accuracy of uncertainty trend changes. I tried to fix it in detail. In this way, by performing scenario creation and scenario detailing separately, it is possible to reduce the scale of calculation and complete the process in a shorter time.

(Modification)
In the second embodiment, a plan and scenario calculation unit 110 and a detailed calculation unit 120 are provided. Although it was made to correct, the following modifications are also possible. That is, in the configuration of FIG. 1 of the first embodiment, the concept of modification processing of the partial scenario modification unit 122 is adopted in the scenario creation unit 112, and FIG. 7 is used instead of FIG. to create

According to the embodiment of the present invention described above, the following effects are obtained.

(C1) As shown in FIGS. 1 to 3, the power operation planning device 1 as an energy operation planning device assumes device information, uncertainty information, and uncertainty scenarios in advance regarding the power system 200 to be planned. Based on the plan information unit 11 storing information related to the power operation plan including the initial scenario, and the information related to the power operation plan, unlike the initial scenario assumed in advance, it leads to violation of operational constraints in the energy operation plan and an increase in cost. A scenario creation unit 112 that newly creates an uncertainty scenario whose probability of occurrence is equal to or greater than a predetermined threshold, and an energy operation plan based on each of the initial scenario and the uncertainty scenario created by the scenario creation unit 112, respectively. and a scenario evaluation unit 116 as an operation plan generation unit.

In the past, a huge number of uncertainty scenarios (including the initial scenario assumed above) were exhaustively based on demand information, without considering violations of operational constraints in energy operation plans and situations that lead to cost increases. equivalent) were prepared in advance, and an operation plan was created for the enormous number of uncertainty scenarios. Therefore, there is a problem that the calculation time becomes enormous.
On the other hand, in the power operation planning device 1, in addition to the assumed initial scenario, based on the energy operation plan related information, unlike the assumed initial scenario, the operation constraint violation and cost increase in the energy operation plan will occur, and the probability of occurrence will increase. A new uncertainty scenario is created that is greater than or equal to a predetermined threshold. The uncertainty scenario newly created by the scenario creation unit 112 takes into consideration the constraints and costs of the power operation plan that takes into consideration the operation of the power generator, etc. You get a scenario. As a result, it is possible to sufficiently ensure the accuracy of uncertainty evaluation in the operation plan without preparing a huge number of initial scenarios. Also, by reducing the number of scenarios to be considered, it is possible to reduce the calculation time.

(C2) As shown in FIGS. 1 to 3, the power operation planning device 1 calculates a degree of overlap representing a degree of similarity between a pair of uncertainty scenarios with respect to a plurality of uncertainty scenarios, and the calculated degree of overlap is Select an uncertainty scenario with a lower probability of occurrence from a pair of uncertainty scenarios that exceed a preset threshold (first multiplicity threshold), and delete the selected uncertainty scenario from multiple uncertainty scenarios. A scenario management unit 114 for outputting the remaining uncertainty scenarios as a plurality of operational plan uncertainty scenarios, and the scenario evaluation unit 116 generates a plurality of operational plans in place of the plurality of uncertainty scenarios described above. A power operation plan based on each of the plurality of operational plan uncertainty scenarios is generated using the operational uncertainty scenarios.

Since one of the uncertainty scenarios with a high degree of similarity that is equal to or greater than the first multiplicity threshold is removed from the multiple uncertainty scenarios, the remaining multiple uncertainty scenarios are uncertainties with low similarity. This makes it possible to shorten the calculation time while preventing deterioration in the evaluation accuracy of the prepared power operation plan.

(C3) As shown in FIGS. 1 to 3, the additional uncertainty scenario corresponding to the second uncertainty scenario generated by the scenario creation unit 112 is the first uncertainty scenario assumed in advance. Since the degree of overlap with the corresponding initial scenario is low similarity below the preset second degree of overlap threshold, it is possible to prevent similar uncertainty scenarios from being generated.

(C4) As shown in FIGS. 1 to 4, the temporal change trend of uncertainty information is expressed as characteristics for each of a plurality of time intervals (interval 1, interval 2, . . . , interval sec) having a predetermined time width. The scenario generator 112 generates additional uncertainty scenarios based on the uncertainty information expressed as characteristics for each time interval. For example, as shown in Fig. 4, the demand dt is set to belong to either the range of dlv _sec = 1 or the range of dlv _sec = 0 for each section such as section 1, so the local demand line differences do not affect the multiplicity determination. As a result, the global similarity (redundancy) of demand lines can be determined without being affected by local differences.

(C5) As shown in FIGS. 1 and 7, the scenario creation unit 112 reflects the uncertainty information based on detailed uncertainty information obtained by adding additional conditions to the uncertainty information, or reflects the uncertainty information with higher accuracy. to create additional uncertainty scenarios. As a result, uncertainty scenarios can be created with higher accuracy.

As an example of reflecting uncertainty information with higher accuracy, as shown in FIG. In the target period, uncertainty information is reflected with higher accuracy than in other time intervals. In addition, it is possible to use an optimization method with a higher degree of optimality, or to refine the simulation accuracy of the optimization problem. In addition, as a method based on detailed uncertainty information that adds additional conditions to uncertainty information, for example, specific conditions set by the operator, such as a low demand zone in a specific season, are applied to the scenario to be modified. Where applicable, create uncertainty scenarios with the additional condition that certain events occur, such as maintenance of certain generators.

(C6) As shown in FIGS. 1 and 5, the power operation planning device 1 includes a scenario evaluation unit 116 as a first total cost calculation unit that calculates a total cost, which is an expected cost value for a plurality of power operation plans; Repeated control is executed to repeat the processing of the scenario creation unit 112, the processing of the scenario management unit 114, and the processing of the scenario evaluation unit 116 in order. and a plan and scenario calculation unit 110 as a first control unit that stops repetitive control. A plurality of operational planning uncertainty scenarios output from 114 are used.

As described above, by repeating a series of processes as shown in FIG. 5, severe scenarios in the power operation plan can be created one after another. As a result, it is possible to ensure sufficient accuracy in evaluating uncertainties in operation plans without preparing a huge number of exhaustively created initial scenarios as in the past, and it is possible to save computation time. can be shortened.

(C7) As shown in FIGS. 1 and 2, the power operation planning device 1 calculates the power operation plan created by the scenario evaluation unit 116 and the operation constraint violations and costs in the uncertainty scenario created by the scenario creation unit 112. It further includes a result presenting unit 12 that presents information about the increase.

By presenting the operational constraint violation and the cost increase and the reason to the operator in the uncertainty scenario created by the scenario creation unit 112, the operator is alerted to the operational abnormal state such as the operational constraint violation and the reason, and the power operation is stopped. Before an abnormal state occurs, the operator can perform control adjustment of the generator and the like. This makes it possible to maintain stable power operation. As the result presenting unit 12, display of the result by the display unit 21 in FIG.

(C8) As shown in FIGS. 6 to 8, the power operation planning device 1B adds additional conditions to the uncertainty information to obtain more accurate information, or makes the uncertainty information more accurate. A partial scenario modification unit that reflects and modifies the uncertainty scenario group (a plurality of uncertainty scenarios) when the repetitive control by the plan and scenario calculation unit 110 is stopped, thereby creating post-modification uncertainty scenarios respectively. 122 and a plurality of post-modification uncertainty scenarios, each of which calculates the degree of redundancy representing the degree of similarity between a pair of post-modification uncertainty scenarios, and the pair whose degree of redundancy is equal to or greater than the second redundancy threshold Select the modified uncertainty scenario with the lower probability of occurrence from the modified uncertainty scenarios, and delete the modified uncertainty scenario selected from the multiple modified uncertainty scenarios. a scenario management unit B124 that outputs a plurality of uncertainty scenarios for the post-modification operation plan, and a scenario evaluation unit B126 that creates a power operation plan based on each of the plurality of uncertainty scenarios for the post-modification operation plan. And further comprising.

In this way, the plan and scenario calculation unit 110 extracts uncertainty scenarios that involve constraint deviations and cost increases, and then the scenario partial modification unit 122 modifies them into highly accurate uncertainty scenarios. By separating scenario creation and scenario detailing, the scale of computation can be reduced and the process can be completed in a shorter period of time.

(C9) As shown in FIGS. 6 to 8, the power operation planning device 1B performs the processing of the scenario evaluation unit B126 that calculates the total cost, which is the expected value of the costs related to a plurality of power operation plans, and the partial scenario modification unit 122. A second step in which iterative control is executed to repeatedly perform the processing of the scenario management unit B124 and the processing of the scenario evaluation unit B126 in order, and the iterative control is stopped when the change in the repeatedly calculated total cost for each iteration is equal to or less than a predetermined threshold value. and a detailed calculation unit 120 as a control unit. Modify multiple uncertainty scenarios output from B124.

In this way, by repeating the processing of the scenario part modification unit 122, the processing of the scenario management unit B124, and the processing of the scenario evaluation unit B126 in order, the modification of the uncertainty scenario is repeated, and a more optimal uncertainty scenario can be obtained. can be created.

Each embodiment described above is merely an example, and the present invention is not limited to these contents as long as the features of the invention are not impaired. Other aspects conceivable within the scope of the technical idea of the present invention are also included in the scope of the present invention. Moreover, the above embodiments and modifications can be combined in whole or in part without departing from the gist of the present invention and within a mutually compatible range.

DESCRIPTION OF SYMBOLS 1... Power operation planning apparatus, 10, 10B... Plan preparation part, 11... Plan information part, 12... Result presentation part, 21... Display part, 22... Input part, 23... Communication part, 24... CPU, 25... Memory, 26 Bus line 100 Initial scenario extraction unit 110 Planning and scenario calculation unit 112 Scenario creation unit 114 Management unit 116 Scenario evaluation unit 120 Detailed calculation unit 122 Partial scenario modification unit 124 Management unit B 126 Scenario evaluation unit B 200 Power system 300 Communication network DB1 System information database DB2 Power operation plan database

Claims (9)

a plan information unit storing energy operation plan-related information including energy equipment information, uncertainty information, and a pre-assumed first uncertainty scenario regarding planning targets;
Based on the energy operation plan related information, unlike the first uncertainty scenario, a second uncertainty that leads to operation constraint violations and cost increases in the energy operation plan and has an occurrence probability equal to or higher than a predetermined occurrence probability threshold a scenario creation unit that creates a new sex scenario;
and an energy operation planning device, comprising: an operation plan generation unit that generates a first energy operation plan based on each of a plurality of uncertainty scenarios including the first and second uncertainty scenarios. In the energy operation planning device according to claim 1,
Calculate a degree of overlap representing a degree of similarity between a pair of uncertainty scenarios for each of the plurality of uncertainty scenarios, and generate from the pair of uncertainty scenarios whose calculated degree of overlap is greater than or equal to a first redundancy threshold Selecting an uncertainty scenario with a lower probability, and outputting a residual uncertainty scenario obtained by deleting the selected uncertainty scenario from the plurality of uncertainty scenarios as a plurality of operational planning uncertainty scenarios. further comprising a first scenario management unit,
The operation plan generation unit uses the plurality of operation plan uncertainty scenarios instead of the plurality of uncertainty scenarios, and performs first energy operation based on each of the plurality of operation plan uncertainty scenarios. An energy operation planning device, each generating a plan. In the energy operation planning device according to claim 1,
The energy operation planning device, wherein the second uncertainty scenario has a degree of overlap representing a degree of similarity with the first uncertainty scenario that is equal to or less than a predetermined second degree of overlap threshold. In the energy operation planning device according to claim 1,
The temporal change trend of the uncertainty information is expressed as characteristics for each of a plurality of time intervals having a predetermined time width,
The energy operation planning device, wherein the scenario creation unit creates the second uncertainty scenario based on the uncertainty information expressed as characteristics for each time interval. In the energy operation planning device according to claim 4,
The scenario creation unit creates the second uncertainty scenario based on detailed uncertainty information obtained by adding additional conditions to the uncertainty information, or by reflecting the uncertainty information with higher accuracy. An energy operation planning device that creates In the energy operation planning device according to claim 2,
a first total cost calculation unit that calculates a first total cost that is an expected value of costs related to the plurality of first energy operation plans generated by the operation plan generation unit;
Execution of a first iterative control for sequentially repeating the processing of the scenario creation unit, the processing of the first scenario management unit, the processing of the operation plan generation unit, and the processing of the first total cost calculation unit, and repeatedly calculating a first control unit that stops the first iteration control when a change in each iteration of the first total cost that is calculated is less than or equal to a predetermined first cost change threshold;
In the first repetitive control, the scenario creation unit uses the plurality of operation plan uncertainty scenarios instead of the first uncertainty scenario included in the energy operation plan related information. Device. In the energy operation planning device according to claim 1,
The energy operation planning device, further comprising a presentation unit that presents the first energy operation plan and information about the operation constraint violation and cost increase in the second uncertainty scenario. In the energy operation planning device according to claim 6,
Stopping the first repetition control by the first control unit based on the information that is made more accurate by adding an additional condition to the uncertainty information, or by reflecting the uncertainty information with higher accuracy a scenario partial modification unit that generates post-modification uncertainty scenarios by modifying the plurality of operational planning uncertainty scenarios at the time;
For each of the plurality of post-modification uncertainty scenarios, a degree of overlap representing a degree of similarity between a pair of post-modification uncertainty scenarios is calculated, and the calculated degree of redundancy is equal to or greater than a second redundancy threshold value of the pair of After selecting the modified uncertainty scenario with the lower probability of occurrence from the modified uncertainty scenarios, and deleting the selected modified uncertainty scenario from the plurality of modified uncertainty scenarios a second scenario management unit that outputs uncertainty scenarios as a plurality of post-modification operational plan uncertainty scenarios;
An energy operation planning device, further comprising: a detailed plan creation unit that creates a second energy operation plan based on each of the plurality of post-modification operation plan uncertainty scenarios. In the energy operation planning device according to claim 8,
a second total cost calculation unit that calculates a second total cost, which is an expected value of costs related to the plurality of second energy operation plans created by the detailed plan creation unit;
executing a second repetition control for sequentially repeating the processing of the partial scenario modification unit, the processing of the second scenario management unit, the processing of the detailed plan creation unit, and the processing of the second total cost calculation unit; a second control unit that stops the second iterative control when a change in the calculated second total cost for each iteration is equal to or less than a predetermined second cost change threshold;
In the second iterative control, the partial scenario modifying unit modifies the plurality of post-modification operational plan uncertainty scenarios instead of modifying the plurality of operational plan uncertainty scenarios. Device.

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