JP2021033625A - Operation plan creating apparatus, and program therefor - Google Patents

Abstract

To provide an operation plan creating apparatus and a program therefor which create a plan including output suppression of a renewable energy with considering parameters having uncertainty.SOLUTION: An operation plan creating apparatus 1 according to the present invention has an acquiring unit and first and second uncertainty group generating units. The acquiring unit acquires first data that is data relating to a target electric power system and indicating a power demand past record, second data indicating a power generation result of renewable energy, third data indicating a specification of a power generator, and forth data indicating a state of the electric power system. The first uncertainty group generating unit, based on the first and second data, generates a predictive data indicating a range of statistically possible values. The second uncertainty group generating unit resolves a problem that both of the range indicated by the predictive data and restriction equation including functional restrictions indicated by the third and forth data are satisfied and a value of an objective function using at lease suppression of the renewable energy as costs is made closer to a desired value, thereby generating an operation plan for the power generator to store it in a storage unit.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to an operation planning device and a program.

In recent years, the introduction of renewable energies such as solar power and wind power has been progressing. The amount of renewable energy generated is difficult to predict because it depends on the weather. In addition, there are cases where the amount of power generated by renewable energy exceeds demand, and renewable energy operators are requested to curb output. Conventionally, a generator operation plan that meets the power demand for each time zone has been generated in advance by solving optimization calculations using stable input data and by the experience of the operator, but renewable energy It is difficult to calculate and predict due to the mass introduction of.

It is expected that the power generation operator and the transmission and distribution business operator (TSO) will be separate at home and abroad. The ability of TSO to control frequency and supply and demand in the supply area is called adjustment power. The exchange of charges between each business operator regarding the adjustment power will be described later. The TSO is responsible for matching supply and demand in consideration of the stability of the power system, and if the supply and demand balance is not maintained, the frequency of the power system will fluctuate, which may lead to a power outage in the worst case. is there. If the TSO makes adjustments to increase the generator output in order to match supply and demand, the amount equivalent to the fuel cost will be paid to the power generation company. If the TSO makes adjustments to reduce the generator output, the power generation operator pays the TSO an amount equivalent to the fuel costs that are no longer needed. In addition, if the TSO suppresses renewable energy, it will impair the power generation opportunities of the power generation operator, so it is expected that the TSO will pay a considerable amount of consideration to the power generation operator. Under these conditions, TSO should make a plan to minimize costs.

However, in the conventional technology, a plan including the output suppression of renewable energy is created and instructed while considering the parameters with uncertainty (power fluctuation of renewable energy and power demand with uncertainty). Was not fully considered.

For example, Non-Patent Document 1 describes a power generation planning method using an optimization method that does not assume uncertainty. Non-Patent Document 2 describes a method of generating a generator operation plan with only demand as a parameter having uncertainty in the generator start / stop planning problem, but relates to renewable energy and suppression of renewable energy. There is no description. Further, Patent Document 1 describes that the demand is treated as uncertain and the operation schedule of the generator is generated in consideration of the state and transition of the factor decomposition type Markov determination process (fMDP). Renewable energy curtailment is not envisioned.

It should be noted that Non-Patent Document 3 states that research on a robust optimization method that can find the best solution for the strictest case by using a parameter with uncertainty as a variable has been remarkably developed since the latter half of the 1990s. Has been done.

Japanese Patent No. 5465344

Jun Hasegawa, et al. "Institute of Electrical Engineers of Japan Course: Power System Engineering", Institute of Electrical Engineers of Japan, pp92-103 (2002) "Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem", IEEE Trans. Power syst. Vol.28, no.1, pp52-63, Feb. 2013 Akiko Takeda "Robust Optimization Method and Its Trends", IEEJ Journal C, Vol.134, No.6, pp.760-764

An object to be solved by the present invention is to provide an operation planning device and a program capable of creating a plan including suppression of the output of renewable energy while considering parameters having uncertainty.

The operation plan creating device of the embodiment has an acquisition unit, a first generation unit, and a second generation unit. The acquisition unit is data related to the target power system, that is, the first data showing the actual demand for electric power, the second data showing the actual power generation by renewable energy, the third data showing the specifications of the generator, and the electric power. The fourth data indicating the state of the system is acquired respectively. The first generation unit generates prediction data indicating a statistically possible range based on each of the first data and the second data. The second generator satisfies the range indicated by the prediction data and the constraint equation including the functional constraints indicated by the third data and the fourth data, and at least the value of the objective function with the suppression of renewable energy as the cost. By solving the problem of bringing the value closer to the desired value, an operation plan of the generator is created and stored in the storage unit.

The block diagram of the operation plan making apparatus 1 which concerns on 1st Embodiment. The figure which shows the list of the definition of each term in the formula (1). The figure which shows the image of the solution when two generators which do not use renewable energy, two generators which use renewable energy, and the number of time frames is 1 to 24 (No. 1). FIG. 2 is a diagram showing an image of a solution when two generators do not use renewable energy, two generators use renewable energy, and the number of time frames is 1 to 24 (No. 2).

Hereinafter, the operation plan creating device and the program of the embodiment will be described with reference to the drawings.

(First Embodiment)
FIG. 1 is a configuration diagram of an operation plan creating device 1 according to the first embodiment. The operation plan creation device 1 includes, for example, an acquisition unit 10, a first uncertainty set generation unit 20, a second uncertainty set generation unit 22, a problem generation unit 30, a preprocessing unit 40, and a plan creation. A unit 50, an output unit 60, and a storage unit 100 are included. The components other than the storage unit 100 are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components are hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing Unit), etc. It may be realized by (including circuits), or it may be realized by the cooperation of software and hardware. The program may be stored in advance in a storage device (a storage device including a non-transient storage medium) such as an HDD (Hard Disk Drive) or a flash memory, or a removable storage device such as a DVD or a CD-ROM. It is stored in a medium (non-transient storage medium) and may be installed by mounting the storage medium in a drive device. The combination of the first uncertainty set generation unit 20 and the second uncertainty set generation unit 22 is an example of the "first generation unit", which is a problem generation unit 30, a preprocessing unit 40, and a planning unit. The combination of 50 and 50 is an example of the "second generation unit".

The storage unit 100 includes, for example, a RAM (Random Access Memory), an HDD, a flash memory, and the like. The storage unit 100 stores, for example, data such as the first actual data 110, the second actual data 112, the generator data 114, the system data 116, the problem data 120, and the planning data 130. Further, the above-mentioned program may be stored in the storage unit 100.

The acquisition unit 10 acquires the first actual data 110, the second actual data 112, the generator data 114, and the system data 116, and stores them in the storage unit 100. The acquisition unit 10 acquires these data from another device via, for example, a network. The network includes WAN (Wide Area Network), LAN (Local Area Network), the Internet, a cellular network, and the like. In this case, the acquisition unit 10 may include a communication interface such as a network card. Further, the acquisition unit 10 may acquire the above data via a keyboard, a mouse, a touch panel, a portable storage device, a drive device, or the like.

The first actual data (demand actual) 110 is actual data of power consumption by a consumer connected to the electric power system to be processed by the operation plan creating device 1. The first actual data 110 integrates the power consumption measured by a power measuring instrument or the like installed in the customer. The first actual data 110 may be the sum of the power consumptions of individual consumers or the sum of the power consumptions of a plurality of consumers.

The first uncertainty set generation unit 20 generates demand forecast data based on the first actual data 110. The demand forecast data sets, for example, the expected value (forecast value, median value) of demand, the fluctuation range, and the like with respect to the future power consumption of the consumer including uncertainty. Regarding the fluctuation range, for example, when the future power consumption of the consumer has a probability distribution, select a fluctuation range such that the range is ± 1σ (68.27 [%]) of the predicted value. Alternatively, it may be defined based on the predicted value of ± 2σ or ± 3σ.

The second actual data (renewable energy power generation record) 112 is actual data of the amount of power generated by the generator using renewable energy (renewable energy) in the power system to be processed by the operation plan creation device 1. Renewable energy includes solar power, wind power, geothermal power, and hydropower. Regarding hydropower, it may be classified as power generation that is not renewable energy. The second actual data 112 is data measured by a power measuring device or the like installed in a generator that generates electricity using renewable energy. The second actual data 112 may be data measured for an aggregate of a large number of generators such as a mega solar or a wind farm.

The second uncertainty set generation unit 22 generates the renewable energy power generation amount prediction data based on the second actual data. The renewable energy power generation forecast data sets, for example, the expected value (predicted value, median value) of the future power generation amount of renewable energy, the fluctuation range, etc. with respect to the future power generation amount of renewable energy including uncertainty. Is. Regarding the fluctuation range, for example, when the future power generation amount of renewable energy has a probability distribution, the fluctuation range may be selected so that the range is ± 1σ of the predicted value, or the predicted value. It may be defined based on ± 2σ or ± 3σ. The fluctuation range in the demand forecast data and the fluctuation range in the renewable energy power generation amount forecast data do not have to follow the same probability distribution, and may be set individually.

The generator data 114 is a collection of various information (specification data) related to a generator that does not use renewable energy for each generator (or for each generator group). A generator that does not use renewable energy is, for example, a thermal power generator, a nuclear power generator, a gas generator, a fuel cell, or the like, which can flexibly adjust the amount of power generation to some extent. The generator may include a generator that is not a generator in a narrow sense but has a similar function, such as a storage battery system. The generator data 114 includes, for example, a minimum operating time, a minimum stop time, an output change rate, an output increase rate, an output decrease rate, a maximum output and a minimum output, and a reserve capacity constraint for each generator. The reserve capacity is the reserve capacity of the output of the generator, which is assumed to be immediately output to the system in order to suppress the occurrence of tidal current fluctuation.

The system data 116 includes data such as a reserve capacity securing amount of the power system to be processed by the operation plan creation device 1, an upper limit value of the reserve capacity, and upper and lower limit data of the electric power that can be passed through the transmission line. The reserve capacity reserve amount is the total value of the reserve capacity required for the generator in the entire system. The upper limit of the reserve capacity is the upper limit of the reserve capacity set for each generator, and the reserve capacity of the generator needs to be set smaller than this value.

The problem generation unit 30 generates an objective function and a constraint expression in the optimization calculation using various data included in the demand forecast data, the renewable energy power generation amount forecast data, the generator data 114, and the system data 116 as input values. Note that "generating an expression" is, for example, a process of determining which item of the various data to be assigned to an input value in a predetermined mathematical expression.

The objective function is represented by Eq. (1). The definition of each term in the formula (1) is as shown in FIG. FIG. 2 is a diagram showing a list of definitions of each term in the equation (1). Of these, the activation variable is a signal that rises at the first timing when activation is started and then decreases. A stop variable is a signal that rises at the first timing when a stop is started and then falls.

The constraint equations are represented by equations (2) to (15). Each of the equations (2) and (3) is an equation showing restrictions on starting and stopping the generator.

Each of the equations (4) and (5) is an equation showing restrictions on the minimum operation time and the minimum stop time.

Equation (6) is an equation expressing the balance between supply and demand in the electric power system. Demand is power consumption, and supply is the sum of the amount of electricity generated by generators and renewable energy. These need to match. For example, when the output of renewable energy is suppressed (when r = 1), the total value of generated power is reduced. Equation (7) is an equation in which demand is defined using the _{expected value d (bar) j} ^t and the fluctuation range d (hat) _j ^t. The expected value and the fluctuation range are included in the demand forecast data generated by the first uncertainty set generation unit 20. Equation (8) is an equation that defines the future power generation amount of renewable energy using the expected value z (bar) _j ^t and the fluctuation range z (hat) _j ^t. The expected value and the fluctuation range are included in the renewable energy power generation amount prediction data generated by the second uncertainty set generation unit 22.

Equation (9) is an equation that defines the output change rate of the generator. RU _i ^t is the generator of the ramp-up rate (maximum output increasing rate), RD _i ^t is the generator of the ramp-down rate (maximum output decreasing speed).

Equation (10) is an equation showing the tidal current constraint when the transmission line is sound. Equation (11) is an equation showing a tidal current constraint when a part or all of the transmission line is dropped. f _l ^max is the upper limit of the active power that can be passed through the transmission line, and f _{l and i} ^max are the upper limits of the active power that can be passed through the transmission line when the transmission line i is dropped. a _l' and a _{l, i'are} the sensitivity coefficients of the transmission line. When a transmission line is dropped, the elements of the vector of _{a l and i'are generally smaller than the elements of the vector of a l} ', and as a result, the power that can be passed through the transmission line is limited. The change width of the element itself may be obtained in advance by a tidal current calculation or the like.

Equation (12) is a diagram showing upper and lower limit constraints of the output of the generator. If the generator is _stopped, because it is ^{x i} t = _0, the ^{p i} t = 0. p _i ^min is the lower limit of the output of the generator _i, ^{p i max} is the upper limit of the output of the generator i.

Equation (13) is an equation that defines that the sum of the output of the generator and the reserve capacity falls within the upper and lower limits of the output of the generator. q _i, ^{a t} is a generator reserve force. The argument a belongs to the set A, and A is a set consisting of a combination of TMSR, TMNSR, and TMOR.

Equation (14) is an equation that defines the amount of reserve capacity for the entire power system. q (bar) _k ^t is the reserve power required by the entire power system, Nr is the type of reserve force required. Types of reserve capacity include TMSR, T10, and T30. TMSR is an abbreviation for Ten-Minute Spinning Reserve, and means a reserve capacity (generator for partial load operation) that can be supplied within 10 minutes. Ak _{is, A TMSR = {TMSR},} A T10 = {TMSR, TMNSR}, which is either A T30 = {TMSR, TMNSR, TMOR}. TMNSR is an abbreviation for Ten-Minute Non Spinning Reserve, and means reserve power (hydraulic power / thermal power while waiting for stop) that can be started within 10 minutes. TMOR is an abbreviation for Thirty-Minute Operating Reserve, which means the reserve capacity that can be supplied to the load within 30 minutes.

Equation (15) is an equation that defines the upper limit of the reserve capacity of each generator. q (bar) _{i, K} ^t is the upper limit of the reserve capacity of the generator i. For a generator, the total value of TMSR, TMNSR, and TMOR, which are the components of the reserve capacity of the generator, must be smaller than the upper limit of the reserve capacity.

The mathematical formulas (objective function and constraint formula) created by the problem generation unit 30 according to the above idea are stored in the storage unit 100 as the problem data 120.

The pre-processing unit 40 transforms the formula according to the processing method performed by the planning unit 50. For example, when the plan creation unit 50 uses the probability planning method, a process of creating a plurality of scenarios is performed. The following describes an example of using the robust optimization method.

For example, the preprocessing unit 40 transforms equation (7) into equation (16) and transforms equation (8) into equation (17) using _{the new variables ζ j} ^t and η _j ^t.

Further, the pretreatment unit 40 uses the formula (1) as the formula (18), the formulas (2) to (5) as the formula (19), and the formulas (9) and (13) to (15) as the formula (20). ), The equations (10) to (12) are transformed into the equation (21), and the equation (6) is transformed into the equation (22).

Then, the preprocessing unit 40 transforms the expression of the term including the uncertain parameter. In equation (18), w is a function of x and r, y is a function of d and z, and w is not a function of y. Demand d _j ^t and renewable for energy output z _j ^t the uncertain parameters, including changes, these to meet the solutions be any variation range, first cost is maximized b ^T y Therefore, the pretreatment unit 40 transforms the equation (18) into the equation (23). Further, since y is also a variable of w, ζ, and η, the second term of the equation (24) may be solved in order to obtain the minimum cost when w is fixed. Therefore, the pretreatment unit 40 transforms the equation (23) into the equation (24). The equation (25) is a summary of the constraint equations.

The planning unit 50 creates the planning data 130 by using a method such as a robust optimization method, a stochastic planning method, or a genetic algorithm, and stores the planning data 130 in the storage unit 100. The planning data 130 is a set of parameters that minimizes the objective function.

The output unit 60 directly gives an instruction by using communication or the like, or transmits an operation schedule according to the plan data 130 created by the plan creation unit 50.

According to the first embodiment described above, it is possible to create a plan including suppression of the output of renewable energy while considering parameters having uncertainty.

(Second embodiment)
Hereinafter, the second embodiment will be described. In the second embodiment, the preprocessing unit 40 transforms the formula for the robust optimization method, and the plan creation unit 50 creates the plan data 130 using the robust optimization method.

The optimal solution related to equation (24) is defined by equation (26). Equation (26) represents the economical dispatch cost when w, ζ, and η are fixed. If the amount of power generated by renewable energy is large, the amount of power generated by the generator will be reduced and the power generation cost will be small, and if the demand is low, the power generation cost will be small. Therefore, it can be assumed that the worst case appears when both ζ and η are zero or more. Therefore, the uncertainty set can be simplified as in Eq. (27).

By applying the dual theorem to equation (27), equation (28) is obtained. a (small circle) b is, a, a vector of the same number of elements as each b, for the i-th element of each vector is determined by calculating the a _i · b _i (element product).

If _{the optimum value of max ζ, η} S (w, ζ, η) is R (w), R (w) is expressed by the equation (29).

Since the equation (29) is a bilinear optimization problem, the preprocessing unit 40 transforms the equation (29) into a linear programming problem in consideration of the following points. Since it can be proved that the optimum solutions ζ * and η * exist on the endpoints of the set D × Z, the optimum solutions ζ * and η * represented by binary vectors exist for each w. In particular, the multilinear term in the objective function R (w) has a part represented by the product of the continuous variable and the binary variable (ζ or η), and is the product of the positive continuous variable κ and the binary variable β. Can be rewritten as κβ. This can be linearized by introducing a new variable ν = κβ that satisfies the linear constraint condition represented by Eq. (30). However, κ _m is the upper limit of κ. By substituting ν = κβ for S in this way, R (w) can be treated as a linear problem.

The plan creation unit 50 finds a solution by repeatedly calculating the main problem (outer min) and the sub problem (inner max) by using a method such as the bender decomposition method, and creates the plan data 130. ..

3 and 4 are diagrams showing, for example, an image of a solution when two generators do not use recycled energy, two generators using recycled energy, and the number of time frames is 1 to 24.

According to the second embodiment described above, the cost is minimized even if the renewable energy suppression is used as a cost function and the demand and the generated power of the renewable energy take any value within the range set as the input condition. It is possible to give a renewable energy suppression command and a generator operation command.

According to at least one embodiment described above, the first data (first actual data 110) showing the actual demand for electric power and the second data indicating the actual power generation by renewable energy are the data related to the target electric power system. Acquisition unit 10 that acquires data (second actual data 112), third data (generator data 114) indicating the specifications of the generator, and fourth data (system data 116) indicating the state of the power system, respectively. A first generation unit (first uncertainty set generation unit 20, second uncertainty set generation unit 20) that generates prediction data indicating a statistically possible range based on each of the first data and the second data. 22), the range indicated by the prediction data, and the constraint equation including the functional constraints indicated by the third data and the fourth data are satisfied, and at least the value of the objective function with the suppression of renewable energy as the cost is set to the desired value. It is uncertain because it has a second generation unit (problem generation unit 30, preprocessing unit 40, plan creation unit 50) that creates an operation plan of the generator and stores it in the storage unit 100 by solving the problem to be approached. It is possible to create a plan that includes suppression of renewable energy output, taking into account the parameters of the nature.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.

1 ... Operation plan creation device, 10 ... Acquisition unit, 20 ... First uncertainty set generation unit, 22 ... Second uncertainty set generation unit, 30 ... Problem generation unit, 40 ... Preprocessing unit, 50 ... Plan creation Unit, 60 ... Output unit, 100 ... Storage unit, 110 ... 1st actual data, 112 ... 2nd actual data, 114 ... Generator data, 116 ... System data, 120 ... Problem data, 130 ... Plan data

Claims (3)

Data related to the target power system, the first data showing the actual demand for electric power, the second data showing the actual power generation by renewable energy, the third data showing the specifications of the generator, and the state of the electric power system. The acquisition unit that acquires the fourth data shown, and
Based on each of the first data and the second data, a first generation unit that generates prediction data indicating a statistically possible range, and a first generation unit.
Satisfy the range indicated by the prediction data and the constraint equation including the functional constraints indicated by the third data and the fourth data, and at least bring the value of the objective function at the cost of suppressing renewable energy closer to the desired value. By solving the problem, the second generator that creates the operation plan of the generator and stores it in the storage unit,
An operation planning device equipped with. The second generation unit
A problem generation unit that generates problem data by applying input values to the objective function and the constraint expression,
A pre-processing unit that transforms the problem data to apply it to the robust optimization method,
A planning unit that creates an operation plan for the generator based on the robust optimization method for the modified formula, and
To prepare
The operation planning device according to claim 1. On the computer
Data related to the target power system, the first data showing the actual demand for electric power, the second data showing the actual power generation by renewable energy, the third data showing the specifications of the generator, and the state of the electric power system. Get each of the 4th data shown,
Based on each of the first data and the second data, prediction data indicating a statistically possible range is generated.
Satisfy the range indicated by the prediction data and the constraint equation including the functional constraints indicated by the third data and the fourth data, and at least bring the value of the objective function at the cost of suppressing renewable energy closer to the desired value. By solving the problem, the operation plan of the generator is created and stored in the storage unit.
program.

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