WO2022249786A1

WO2022249786A1 - Electric power system operation plan generation device, and method for generating electric power system operation plan

Info

Publication number: WO2022249786A1
Application number: PCT/JP2022/017902
Authority: WO
Inventors: 哲嗣小野; 勉河村
Original assignee: 株式会社日立製作所
Priority date: 2021-05-26
Filing date: 2022-04-15
Publication date: 2022-12-01
Also published as: JP2022181250A

Abstract

Provided are an electric power system operation plan generation device and an electric power system operation plan generation technology with which it is possible to derive an operation plan that satisfies three requirements, specifically a short calculation time, a robustness to missed prediction, and the consideration of restraints crossing over time slots. Provided is an electric power system operation plan generation device for defining, in consideration of uncertainty parameters in an electric power system, an operation plan variable that should be determined in preliminary stages and an on-day control variable that can be determined in real time, and producing an operation plan for the electric power system, the electric power system operation plan generation being characterized by comprising: an operation plan generation unit for acquiring information pertaining to an operation plan variable and information pertaining to an on-day control variable in the electric power system and creating a proposed operation plan with respect to an objective function value in the case of a severest scenario assuming severe states of the electric power system; a severest scenario generation unit for deriving a severest scenario and an objective function value in that case from uncertainty parameter variability information, the proposed operation plan, and the objective function value of the on-day control variable; and an on-day control quantity generation unit for determining an operation plan using the system information, demand information, and severest scenario of the electric power system.

Description

POWER SYSTEM OPERATION PLAN GENERATION DEVICE AND POWER SYSTEM OPERATION PLAN GENERATION METHOD

The present invention relates to a power system operation plan generation device and a power system operation plan generation method.

In power systems, the introduction of renewable energy power sources that do not emit greenhouse gases is progressing. The output of photovoltaic power generation and wind power generation, which are representative renewable energy sources, fluctuates depending on weather conditions. In addition, with the spread of electric vehicles, the spread of quick charging stations and the increase in capacity are progressing, and this power demand may affect the power system. There is a demand for system operation that takes into account uncertain parameters (hereinafter referred to as uncertain parameters), such as the output of renewable energy power sources and charging demand for electric vehicles.

On the other hand, in system operation, there are two types of variables: variables that should be determined in advance such as the day before (hereinafter referred to as operation plan variables) and variables that can be determined in real time (hereinafter referred to as control variables on the day). For example, as operation planning variables in a distribution system, the tap position of a tap adjustment device (e.g., LRT: Load Ratio Control Transformer, SVR: Step Voltage Regulator), the distributed energy owned by consumers such as private generators, storage batteries, and electric vehicles The reserved amount of the adjustment margin of the source (DER: Distributed Energy Resource) and the like can be mentioned. In addition, as control variables for the day, SVC (: Static Var Compensator), distributed energy sources DER such as storage batteries for grids owned by system operators, and command to activate adjustment margin of distributed energy sources DER owned by reserved consumers quantity, etc.

The first possible method for determining the operation planning variables is to assume that the prediction of the uncertain parameters is correct, and to combine values that result in the best system operation KPI (e.g. voltage tolerance, line capacity violation amount, response cost). is a method of choosing This method does not take into account the case where the prediction is wrong, and depending on the fluctuation pattern of uncertain parameters, the system operation KPI will deteriorate significantly (e.g., violation of constraints such as voltage tolerance and line capacity, extreme cost increases, etc.) may occur.

Therefore, even if the prediction of uncertain parameters fluctuates, the system operation KPI (e.g. voltage tolerance, line capacity violation amount, response cost) will not deteriorate significantly (hereinafter referred to as robust). Planning variables need to be determined.

As one of the techniques for determining operation plan variables for the purpose of robust system operation, Patent Document 1 describes "an operation plan formulation device, an operation plan formulation method, and an operation plan formulation program". In Patent Document 1, in order to handle the uncertainty of the output of renewable energy power sources in the optimization problem for solving the start-up and shutdown plan of a generator, a method of expressing this uncertainty in multiple scenarios is used. there is

JP 2016-63609 A

In general, increasing the number of scenarios increases the computation time of the optimization problem. For this reason, for example, when there is a limit on computation time, a sufficient number of scenarios cannot be used, and the reliability of the obtained solution is lowered.

In order to solve the above problem, Patent Document 1 assumes that the optimization problem can be simplified to be independent for each time period, with the aim of expressing uncertainty with a small number of scenarios. However, tap adjustment devices used in power distribution systems are subject to restrictions across time zones, such as an upper limit on the number of tap position adjustments per day. Therefore, it cannot be simplified to be independent for each time zone.

The present invention has been made in view of the above, and is capable of obtaining an operation plan that satisfies three points: short calculation time, robustness against misprediction, and consideration of constraints across time zones. An object of the present invention is to provide a plan generation device and a power system operation plan generation technology.

From the above, in the present invention, "taking into account the uncertainty parameters in the power system, determining the operation plan variables that should be determined in advance and the control variables that can be determined in real time, and planning the operation of the power system A power system operation plan generating device that obtains information on operation plan variables and information on current day control variables in the power system, and proposes an operation plan for objective function values in the most severe scenario assuming the severe state of the power system. , the most severe scenario generation unit that obtains the most severe scenario and the objective function value at that time from the fluctuation information of the uncertainty parameter, the operation plan proposal, and the objective function value of the control variable on the day, and the electricity A power system operation plan generation device characterized by comprising a current control amount generation unit that determines an operation plan using system information, demand information, and the most severe scenario of the system.

In addition, in the present invention, "taking into account the uncertainty parameters in the power system, determining the operation planning variables to be determined in advance and the control variables on the day that can be determined in real time, and planning the operation of the power system. A plan generation method in which information on operation plan variables in the electric power system and information on control variables for the day are obtained, and an operation plan proposal for the objective function value in the most severe scenario assuming the severe state of the electric power system is created. Regarding the power system operation planning problem, the power system operation planning problem is divided into the main problem, the operation plan decision problem, and the subordinate problem. A power system operation plan generation method for performing an operation plan for a power system as a solution to a triple structure power system operation plan problem.

According to the present invention, it is possible to obtain an operation plan that satisfies three points: short calculation time, robustness against misprediction, and consideration of constraints across time zones.

The figure which described the processing content in the calculating part of a power system operation plan production|generation apparatus as the representative processing function part. The figure which shows that the operational plan proposal xp is obtained as a solution of the operational plan decision problem. The figure which shows the example of uncertain parameter fluctuation|variation information. The figure which shows the example of a severest scenario. A processing flow of a power system operation plan generation method. The figure which shows the difference of an effect when this invention and a conventional system are compared.

Below, embodiments of the power system operation plan generation device and the power system operation plan generation technology according to the present invention will be described in detail based on the drawings. In addition, this invention is not limited by this Example. Further, each embodiment can be appropriately combined within a range that does not contradict the processing contents.

In the following description, the power system operation plan generation device will be described in the first embodiment, the concept of power system operation plan generation in the second embodiment, and the power system operation plan generation method in the third embodiment.

In the first embodiment of the present invention, a power system operation plan generation apparatus will be described, but this application example is targeted at a distribution system including a tap adjustment device and a customer-owned DER, and a robust operation plan against fluctuations in uncertain parameters We will describe the power system operation plan generation device that formulates the

First, the configuration of the power system operation plan generation device will be explained. FIG. 1 is a diagram showing, as a representative processing function unit, the processing contents of the operation unit of the power system operation plan generation device realized using a computer device.

As shown in FIG. 1, the robust operation plan generation device 10 has an operation plan generation unit 20, a most severe scenario generation unit 30, and a control amount generation unit 40 for that day. However, in addition to the functional units shown in FIG. 1, the operation plan formulation device 10 has, for example, a display unit for displaying various information, an input unit for inputting input data, a communication interface unit for communicating with other terminals, and the like. You may

The robust operation plan generation device 10 obtains tap adjustment equipment information D1, customer-owned DER information D2, uncertain parameter variation information D3, demand information D4, and system information D5 as inputs from the outside in order to perform its processing.

The operation plan generation unit 20 in the robust operation plan generation device 10 obtains the tap adjustment device information D1 and the customer-owned DER information D2 as input data from the outside, and the objective function obtained by the most severe scenario generation unit 30 A value ηp and sensitivity information b1 are input, an operation plan decision problem is generated based on these input data, and the solved operation plan proposal xp is output to the most severe scenario generator 30 .

Among the external inputs used in this case, the tap adjustment device information D1 includes, for example, the device type (for example, LRT, SVR, etc.) of each tap adjustment device, the installed bus ID, the number of tap stages, the tap width for each stage, the tap reference Including position, speed of response to control commands, tap adjustment limit per day, facility cost, installation cost, etc.

Among the same external inputs, the consumer-owned DER information D2 includes, for example, the device type (eg, private power generator, electric vehicle, etc.), installed bus ID, installed capacity, response speed to control commands, and the like.
The consumer-owned DER information 110 may be input individually for each distributed energy source DER, or may be input as a total value of a plurality of distributed energy sources DER.

Here, the objective function value η represents the objective function when the most severe scenario determination problem is solved by the most severe scenario generator 30, that is, the value of equation (14) described later. The sensitivity information b1 is the amount of change in the objective function value ηp when the operation plan xp is slightly changed.

The operation plan draft xp is obtained as a solution to the operation plan decision problem. This example will be described with reference to FIG. Each row in FIG. 2 represents a time step 221 and each column represents a tap adjuster ID 222 and a customer owned DER ID 223 . For each tap adjuster ID 222, the tap position 224 at each time step 221 is stored. In addition, for the ID 223 of each customer-owned DER, the reservation amount 225 of the adjustment margin of each time step is stored. Since the reservation amount 225 of the adjustment margin can take both values of demand increase and decrease, it can also take a negative value as shown in FIG.

Returning to FIG. 1, the most severe scenario generation unit 30 in the power system operation plan generation device 10 inputs the uncertain parameter fluctuation information D3 as input data from the outside, and the operation plan proposal obtained by the operation plan generation unit 20 x, the objective function value φp ^KPI obtained by the control amount generation unit 40 for the current day, and the sensitivity information b2 are input, the most severe scenario determination problem is generated based on these input data, and the most severe scenario UP that is solved is controlled for the current day. The objective function value φp ^KPI and the sensitivity information b2 are output to the most severe scenario generator 30 .

An example of the uncertain parameter fluctuation information D3, which is an external input used in this case, is shown in the graph of FIG. The graph considers PV output as an example of an uncertain parameter. The horizontal axis represents the time of day and 24 hours, and the vertical axis represents the photovoltaic power output. The part represented by the gray range is the fluctuation information 121 of the photovoltaic power generation output. It is assumed that the photovoltaic output may fluctuate within this gray range. The range of the fluctuation information 121 may be determined based on actual values, or may be determined based on a weather forecast or the like.

The objective function value φp ^KPI represents the objective function when solving the most severe scenario determination problem, that is, the value of equation (15) described later. The sensitivity information b2 is the amount of change in the objective function value φp ^KPI when the most severe scenario u is slightly changed.

The graph in Fig. 4 shows an example of the most severe scenario up. The graph considers PV output as an example of an uncertain parameter. The horizontal axis represents the time of day and 24 hours, and the vertical axis represents the photovoltaic power output. The part represented by the gray range is the fluctuation information 121 of the photovoltaic power generation output. A thick line 241 represents the most severe scenario up. The most severe scenario up is determined by solving the most severe scenario determination problem. Also, the thick line 241 falls within the gray range 121 at any time step.

Returning to FIG. 1 again, the current control amount generation unit 40 in the power system operation plan generation device 10 obtains the customer-owned DER information D2, the demand information D4, and the system information D5 as input data from the outside. The most severe scenario up obtained by the severe scenario generation unit 30 is input, the current day control amount determination problem is generated based on these input data, and the solved objective function value φp ^KPI and sensitivity information b2 are sent to the most severe scenario generation unit. 30.

Further, as the final solution obtained as a result of repeated processing in the processing of the operation plan generation unit 20, the severest scenario generation unit 30, and the today's controlled variable generation unit 40, the operation plan proposal xp and the objective function value ηp (=system operation theoretical worst value of KPI) is output from the power system operation plan generation device 10 .

In the second embodiment, regarding the concept of power system operation plan generation, the device shown in FIG. 1 will be used to explain in detail that a robust operation plan can be generated using mathematical formulas. In order to clarify the characteristics of the operation planning method according to the present invention, it is better to compare it with the normal operation planning method, so the normal operation planning method will be explained first.

The characteristic of the normal operation planning method is that the user inputs in advance a group of scenarios that express uncertainty, and the operation plan is obtained by solving a mathematical programming problem with the optimization of the system operation KPI as the objective function. be. More specifically, the above mathematical programming problem is represented by the following equations (1) and (2).

In the equations (1) and (2), a lower case letter s represents a scenario, and a capital letter S represents a set of input scenarios. A small letter t represents time, a capital letter T represents a set of times t included in the planning period, and φ ^KPI represents system operation KPI. The subscript of min represents a decision variable, x is a vector of operation plan variables (e.g., tap position control amount, adjustment margin reservation amount of customer-owned DER), y is a vector of control variables for the day (e.g., reserved adjustment Activation command amount of reserve power) respectively. It should be noted that these are vectors, and equation (2) is a constraint expression indicating the system constraint conditions for obtaining equation (1).

Here, the operation plan variable vector x must be determined at a stage where the risk of the uncertain parameter fluctuating from the predicted value remains. Therefore, the same operation plan x is determined for all scenarios S input by the user in advance. be. On the other hand, the intraday control variable vector y is a variable that can be adjusted in real time after the uncertain parameters become clear (for example, after the actual photovoltaic output is measured). Control y is determined. A typical operation planning technique simultaneously optimizes the above decision variables x and y in a single mathematical programming problem.

Also, as a constraint expression of the mathematical programming problem shown in expression (1), the system constraint of expression (2) is set.
The system constraints include the energy balance formula for each bus, the voltage calculation formula for each bus, the power distribution loss calculation formula for each line, the upper limit of the number of tap position adjustments per day for the tap adjustment device, and the adjustable amount of the consumer DER device. A lower limit etc. are mentioned. Note that A1, A2, and C in equation (2) are matrices or vectors representing system constraints, and the constraint equations in equation (2) are matrix notations of system constraints.

This mathematical programming problem can be solved with a mathematical programming solver such as CPLEX or Gurobi as a mixed integer programming (hereinafter referred to as MILP) or a mixed integer second-order cone programming (hereinafter referred to as MISOCP). As a result of finding the solution, an operation plan x that minimizes (=best) the system operation KPI in the planning period T for the input scenario s is obtained. With this operation plan x, it can be expected that, for the input scenario s, extreme deterioration of the system operation KPI due to violation of the allowable voltage range or the like will not occur.

However, when the scenario group S that is input does not include the scenario u that makes the system operation KPI worst (hereafter referred to as the most severe scenario), there is a possibility that the system operation KPI will be extremely deteriorated in some cases. Therefore, in order to obtain an operation plan x that is robust against any variation pattern, it is necessary to include the most severe scenario u in the input scenario group S. However, it is difficult to appropriately select the most severe scenario u in advance for the following two reasons.

The first reason is that there are a huge number of candidates for the most severe scenario u. In particular, when the number of uncertain parameters increases due to the spread of renewable energy sources and electric vehicles, the number of candidates increases explosively.

The second reason is that it is difficult to appropriately select the most severe scenario u before determining the operation plan x, because which scenario u will be the most severe scenario varies depending on the operation plan x. .

On the other hand, on the other hand, it is possible to consider a method of inputting all possible candidates for the most severe scenario u. have a nature.

Due to the above problems in the normal operation planning method, the following operation planning method is proposed in the present invention. The proposed operation planning method (hereinafter referred to as the proposed method) is superior to the normal operation planning method in the following two points.

The first point is that in the normal operation planning method, it is necessary to prepare a group of scenarios that express uncertainty, but in the proposed method, it is only necessary to input the fluctuation range of the uncertain parameter, and the scenario group No preparation is required.

The first point is that the normal operation planning method can only ensure robustness against the input scenario group, but the proposed method can guarantee robustness against any fluctuation pattern within the fluctuation range.

The mathematical programming problem (hereinafter referred to as robust operation planning problem) solved by the proposed method according to the present invention is shown in Equations (3) and (4) below. In addition, explanation may be omitted about the matter which has already been explained.

In equation (3), u is a vector of uncertain parameters and, like x and y, is a decision variable of the power system operation planning problem. As for the objective function and the constraint formula, the system operation KPI, φ ^KPI , and formula (4) are set as the system constraint in the same manner as in the normal operation planning method.

A feature of this power system operation planning problem is that the objective function has a three-layer structure of min and max. (3) In order from the outside of the formula, the part that determines the operation plan x that minimizes (=optimizes) the system operation KPI, the value of the uncertainty parameter u that maximizes (=worst) the system operation KPI (=maximum severe scenario), and the part that determines the same-day control y that minimizes the system operation KPI.

By solving this, the most severe scenario u, in which the system operation KPI deteriorates the most, is determined among the fluctuation patterns within the fluctuation range. At the same time, the operation plan x and the day control y that optimize the system operation KPI in the most severe scenario u are determined. Also, the value of the system operation KPI at this time becomes the theoretical worst value, and even if any other fluctuation pattern occurs, the value will not deteriorate from this theoretical worst value. Therefore, by solving this power system operation planning problem, it is possible to obtain an operation plan x that is robust against any fluctuation pattern. In addition, since the most severe scenario u is generated while solving the power system operation planning problem, it is not necessary to prepare the scenario s as a candidate in advance.

Also, the system operation KPI (φ ^KPI ), which is the objective function, is defined by the following equation (5) as an example.

In this equation (5), φ ^cost , φ ^loss , and φ ^penalty represent operation cost, transmission loss, and constraint violation penalty, respectively. W ^c , W ^l , and W ^p represent weighting factors for operating cost, transmission loss, and constraint violation penalty, respectively. The weighting factor ratio is generally set as shown in equation (6). Items other than these may be added to the system operation KPI.

Also, the power transmission loss φ ^loss is represented by the following equation (7) as an example. However, in the equation (7), it is assumed that the lowercase time t and the uppercase time T, which is a set of times t included in the planning period, have a relationship of tεT.

In this equation (7), j is a bus number, N _tap is a set of bus numbers with tap adjusting devices such as SVRs, and N _der is a set of bus numbers with customer-owned DERs. C _j ^tap represents the cost per tap adjustment in the tap adjustment equipment. The value of C _j ^tap is obtained, for example, by dividing the sum of the tap adjustment equipment price and the installation cost by the number of tap adjustments that reach the end of the equipment life. x _j,t ^tap represents the number of tap adjustments in the tap adjustment device.
Also, cj _{,tder_r} ^and _{cj,tder_c} ^represent the incentive unit price for the adjustment margin reservation of the customer-owned DER and the incentive unit price for the same-day control. x _{j, t} ^der and y _{j, t} ^der represent the reservation amount of the adjustment margin of the customer-owned DER and the activation command amount for the current day.

As described above, the first term on the right side of the equation (7) corresponds to the adjustment cost of the tap adjusting device, the second term corresponds to the incentive cost for the reserve adjustment capacity, and the third term corresponds to the incentive cost for the actual control command. Note that x _{j, t} ^tap , y _{j, t} ^der are included in the vector x of the operation plan variables, and x _{j, t} ^der is included in the vector y of the current day control variables.

Also, the power transmission loss φ ^loss in the formula (5) is represented by the following formula (8), for example. However, in the equation (8), it is assumed that the lowercase time t and the uppercase time T, which is a set of times t included in the planning period, have a relationship of tεT.

Note that B in equation (8) represents a set of lines. r _ij represents the resistance value in the line from bus (bus) i to bus j, and lij, s, t represents the absolute value of the amount of current in scenario s, time t in the line from bus i to bus j.

Also, the constraint violation penalty φ ^penalty of the expression (5) is expressed by the following expression (9) as an example. However, in the equation (9), it is assumed that the lowercase time t and the uppercase time T, which is a set of times t included in the planning period, have a relationship of tεT.

In the equation (9), ζ _ij,t represents the excess amount of the line constraint on the line connecting the bus i to the bus j, and η _j,t represents the deviation amount of the voltage on the bus j from the allowable range. B represents a set of buses.

The operation plan generation unit 20 in the power system operation plan generation device 10 shown in FIG. 1 solves the power system operation plan problems shown in formulas (1) to (9) above. However, since the power system operation planning problem has a three-layer structure as described above, mathematical programming solvers such as CPLEX and Gurobi do not support multi-structured mathematical programming problems and cannot be solved as they are.

Therefore, below, we will explain an example of how to solve using Bender's decomposition, which is a typical decomposition method. In the Benders decomposition, a mathematical programming problem is decomposed into two parts, a main problem and a subordinate problem. First, the main problem is solved, and then the subordinate problems are solved using the solution as a constraint. The dependent problem passes a constraint equation called the vendor's cut to the primal problem. Add this constraint to the primal problem, limit the solution search region, and solve. By repeating the above, the global optimum solution is obtained.

Since the power system operation planning problem consists of a three-layer structure, we apply this Vendor's decomposition twice.
First, the outermost min part is divided into a main problem (hereinafter referred to as an operation plan decision problem), and the other parts are divided into subordinate problems. Since this dependent problem has a double structure of max and min, Bender's decomposition is applied again, and the problem is again divided into a main problem and a dependent problem (hereinafter referred to as the most severe scenario determination problem and today's control amount determination problem, respectively). As a result, the power system operation planning problem with a triple structure can be decomposed into the following three mathematical programming problems.

These are an operation plan decision problem, a most severe scenario decision problem, and a control amount decision problem for the day. The current day control amount determination problem is processed in the current day control amount generation part 40 .

Also, in this relationship, the operation plan decision problem is the main problem, and its subordinate problem corresponds to the most severe scenario decision problem. At the same time, the most severe scenario determination problem is the main problem for the today's controlled variable determination problem, and the today's controlled variable determination problem corresponds to its subordinate problem.

The operation plan decision problem processed by the operation plan generation unit 20 is represented by the following equations (10) and (11).

In these formulas, θ of the objective function of the operation plan determination problem represents the estimated value of the objective function value ηp of the most severe scenario determination problem (=estimated value of system operation KPI). The decision variables are θ and x. The proposed operation plan xp obtained by solving this is determined so as to minimize the estimated value of the system operation KPI. A variable with the symbol p indicates a constant value obtained as a calculation result.

　Bender's cut is set as the constraint condition of formula (11) and added for each iteration of calculation. b1 is sensitivity information defined as the amount of change in ηp when the operation plan xp is slightly changed, and is also called shadow price. Sensitivity information is obtained as a solution of the dual problem of the dependent problem. The above Bender's cut formula can be regarded as a linear approximation formula of η with respect to x. By sequentially adding these, the estimation accuracy of θ is improved, and the operation plan xp is updated.

The most severe scenario determination problem to be processed by the most severe scenario generation unit 30 is represented by the following equation (9).

In these equations, θ of the objective function of the most severe scenario determination problem represents the estimated value of the objective function value φp ^KPI (=system operation KPI) of the most severe scenario determination problem. The decision variables are θ and x. The proposed operation plan xp obtained by solving this is determined so as to minimize the estimated value of the system operation KPI. (13) A vendor's cut is set as the constraint condition of the formula and added for each iteration. b2 is sensitivity information defined as the amount of change in φ ^KPI when the scenario up is slightly changed. By sequentially adding these, the estimation accuracy of is improved and the most severe scenario up is updated.

The today's controlled variable decision problem processed by the today's controlled variable generator 40 is represented by the following equations (14) and (15). The decision variable is the current day control y, which is solved to minimize the estimated value of the system operation KPI.

In these formulas, the φ ^KPI of the objective function for the today's controlled variable determination problem represents the system operation KPI. A system constraint is set as the constraint condition of the equation (15). Therefore, in the today's control amount determination problem, the system operation KPI is directly calculated based on system constraints.

The three decomposed mathematical programming problems are solved alternately by repeated calculations as described above. The condition for judging the end of repeated calculation is represented by the following equation (16).

In this formula, ε is the allowable error rate and is set to 0.0001, for example. UB and LB represent the upper and lower bounds of the objective function value, respectively. In the case of iterative calculations between the operation plan determination problem and the most severe scenario determination problem, UB and LB are calculated by the following equation (17).

In this formula, θp is the value of θ determined by the operation plan decision problem. That is, it can be interpreted that the calculation is repeated until θ, which is the estimated value of η, becomes sufficiently close to the true value of η. In the case of repeated calculation between the most severe scenario decision problem and the control plan for the day, UB and LB are calculated by the following equation (18).

where ηp is the value of η determined by the uncertain parameter determination problem. This can also be interpreted as repeating the calculation until η, which is the estimated value of φ ^KPI , becomes sufficiently close to the true value of φ ^KPI .

In the third embodiment, the power system operation plan generation method will be explained using the processing flow of FIG.

In the flow of FIG. 5, when the process is started, in processing step S2, as a function of the operation plan generation unit 20, solutions to equations (10) and (11), which are operation plan determination problems, are obtained. However, here, in order to make the operation plan decision problem the main problem and other parts to be subordinate problems, the generation and addition of the vendor's cut are performed by the formulas (10) and (11) in the processing step S1, and on the condition that Processing step S2 is performed. The operation plan variable input (tap adjustment device input 100, customer-owned DER information 110) and the conditions of the most severe scenario (objective function value η, sensitivity information b1) used at this time are as shown in FIG. be.

Next, as the processing of the most severe scenario generation unit 30, in processing step S4, the solutions of equations (12) and (13), which are the most severe scenario determination problem, are obtained. However, in order to make the most severe scenario decision problem the main problem and the day control amount decision problem the subordinate problem, the generation and addition of vendor's cuts are performed according to the formulas (12) and (13) in processing step S3. Processing step S4 is performed on the condition that The uncertain parameter fluctuation information (renewable energy power supply output and electric vehicle charging demand) used at this time, the conditions for the control amount of the day (objective function value η, sensitivity information b2), the operation plan xp, etc. are shown in Fig. 1.

Next, in processing step S5 as processing of the control amount generation unit 40 on the current day, the solution of equations (14) and (15), which is the control amount determination problem for the current day, is obtained. However, the today's control amount determination processing in processing step S5 includes determination of the end of calculation using equations (16) and (18) for the convergence of the most severe scenario determination problem in processing step S6, and the operation plan determination problem in processing step S7. It is repeatedly executed until the calculation end determination using the equations (16) and (17) of convergence is satisfied.
Demand information and system information used at this time are as shown in FIG.

FIG. 6 is a diagram showing the difference in effect when comparing the present invention and the conventional method. The final output of the power system operation plan generating apparatus in FIG. 1 is displayed on a monitor screen or the like, and is shown as an image diagram in that case.

According to this notation, in the conventional case, the system operation KPI for one assumed scenario is obtained as point information, but in the case of the present invention, the most severe scenario is internally generated, A system operation KPI value 270 (theoretical worst value) is calculated for it. Therefore, the system operation KPI value is output in the form of being within the range even if any conceivable scenario occurs. In other words, compared to the conventional method, it is determined as a range rather than as a point.

10: power system operation plan generation device, 20: operation plan generation unit, 30: most severe scenario generation unit, 40: current day control amount generation unit

Claims

A power system operation plan generation device that takes into account uncertainty parameters in the power system, determines operation plan variables that should be determined in advance and on-day control variables that can be determined in real time, and performs an operation plan for the power system,
an operation plan generation unit that obtains information on operation plan variables and information on current day control variables in the power system and creates an operation plan proposal for the objective function value in the most severe scenario assuming the severe state of the power system; a most severe scenario generation unit that obtains the most severe scenario and the objective function value at that time from the variation information of the certainty parameter, the operation plan, and the objective function value of the current day control variable; A power system operation plan generation device, comprising: a current day controlled variable generation unit that determines an operation plan using the most severe scenario.
The power system operation plan generation device according to claim 1,
The power system operation plan generating apparatus, wherein the operation plan variables include one or more of a tap position of a tap adjusting device and an adjustment margin of distributed energy sources owned by the customer.
The power system operation plan generation device according to claim 1,
The power system operation plan generating device, wherein the control variable for the current day includes one or more of a distributed energy source owned by a system operator and an activation command amount for an adjustment margin of a distributed energy source owned by a reserved consumer. .
A power system operation plan generation method that takes into account uncertainty parameters in the power system, determines operation plan variables that should be determined in advance and intraday control variables that can be determined in real time, and performs an operation plan for the power system,
For the power system operation planning problem, obtain the information of the operation plan variables and the information of the control variables of the day in the power system, and create an operation plan proposal for the objective function value in the most severe scenario assuming the severe state of the power system. The power system operation planning problem is divided into an operation plan decision problem that is a main problem and a subordinate problem, and the subordinate problem is further divided into a main problem of the most severe scenario decision problem and a subordinate problem of the day control amount determination problem, An electric power system operation plan generation method, characterized in that an electric power system operation plan is performed as a solution to a three-layer electric power system operation plan problem.