KR101489533B1 - Method for selecting an available transfer capability - Google Patents

Abstract

The present invention introduces an available transmission capacity selection method for calculating the available transmission capacity using a multipurpose particle cluster optimization algorithm in order to take into consideration both the economic efficiency and stability of the power system as well as the environmental aspect of carbon dioxide emissions. The usable transmission capacity selection method includes a maximum transmission capacity determination step, an arithmetic element setting step, and an available transmission capacity calculation step. The maximum transmission capacity determination step determines the maximum transmission capacity by applying a multipurpose particle cluster optimization algorithm to the optimal algae calculation. The operation element setting step sets an operation element. The available transmission capacity calculation step calculates an available transmission capacity defined as a value excluding the calculation element from the maximum transmission capacity.

Description

Method for Selecting Available Transmission Capacity [

The present invention relates to a method of selecting an available transmission capacity. In particular, in order to consider not only the economic efficiency and stability of a power system but also the environmental aspects such as carbon dioxide emission, a multipurpose particle cluster optimization algorithm is used to additionally use the transmission capacity The present invention relates to an available transmission capacity selection method for calculating an available transmission capacity, which means a transmission capacity capable of being used.

It is clear that the limitation of CO2 emissions in the electric power market and the emission trading system will serve as a new variable. Therefore, due to the high levy on CO2 emissions and the high price of CO2 emission rights beyond the price of electric power, There is a need for change.

Under these circumstances, it is important to secure safety margin in order to balance the three objectives of safety, economics and CO2 emission reduction in the power system. In order to operate the power system safely, it is important not only that the power transmission should exceed the power transmission capacity, but also the available power transmission capacity, which determines the power transmission capacity limit of the connecting line connecting the various areas within the power system.

Calculation of the available transmission capacity considering the carbon dioxide emissions is very useful information to the power system operator as a review of the safe and efficient operation of the power system.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and an object of the present invention is to provide an available transmission capacity selection method for calculating an available transmission capacity by using a multipurpose particle cluster optimization algorithm in order to consider not only the economic efficiency and stability of a power system, .

According to an aspect of the present invention, there is provided an available transmission capacity selection method including a maximum transmission capacity determination step, an arithmetic operation element setting step, and an available transmission capacity calculation step. The maximum transmission capacity determination step determines the maximum transmission capacity by applying a multipurpose particle cluster optimization algorithm to the optimal algae calculation. The operation element setting step sets an operation element. The available transmission capacity calculation step calculates an available transmission capacity defined as a value excluding the calculation element from the maximum transmission capacity. Here, the maximum transmission capacity means the overall transmission capacity capable of safely transmitting from one area to another area, and the available transmission capacity means transmission capacity that can be additionally used in addition to the transmission capacity already in use.

Since the present invention calculates the available transmission capacity (ATC) considering the amount of carbon dioxide emission, it is advantageous to provide the operator of the power system with information on the economy and safety of the operation of the power system as well as the environmental aspects such as reduction of greenhouse gas emissions .

1 is a signal flow diagram showing a method of selecting an available transmission capacity according to the present invention.
FIG. 2 is a signal flow diagram illustrating an internal execution step of the optimum operation point calculation step shown in FIG.
Figure 3 shows an example of a non-dominant alignment.
FIG. 4 illustrates a process of assigning a dense distance.
Figure 5 shows the Pareto front line of the optimal algae calculation when the multipurpose particle cluster optimization algorithm is applied to the IEEE 30 bus system.
6 shows an IEEE 30 bus system diagram.

In order to fully understand the present invention and the operational advantages of the present invention and the objects achieved by the practice of the present invention, reference should be made to the accompanying drawings, which are provided for explaining exemplary embodiments of the present invention, and the contents of the accompanying drawings.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the preferred embodiments of the present invention with reference to the accompanying drawings. Like reference symbols in the drawings denote like elements.

Available Transfer Capability (ATC) is defined as transmission capability that can be used for additional commercial activities other than the existing transmission capacity, and can be expressed as Equation (1).

Here, Total Transfer Capability (TTC) refers to the total transmission capacity that can safely transmit from one area to another, and Transmission Reliability Margin (TRM) means the safe operation of the power system. And Existing Transmission Commitments (ETC) means the amount of power currently being transmitted to the power system.

Optimal power flow (OPF) method is one of the general methods to select available transmission capacity (ATC). In the past, using particle swarm optimization (PSO) algorithm, which is one of probability optimization algorithms, Algae calculations were performed. Particle cluster optimization algorithms can only meet a single objective function, so they can not balance the power system economics and the conflicting goals of reducing CO2 emissions.

In the present invention, it is proposed to calculate the usable transmission capacity considering the change of the power system operation according to the carbon dioxide emission by applying the multipurpose particle cluster optimization algorithm.

1 is a signal flow diagram showing a method of selecting an available transmission capacity according to the present invention.

Referring to FIG. 1, the available transmission capacity selection method 100 includes a maximum transmission capacity determination step 110, an arithmetic element setting step 130, and an available transmission capacity calculation step 150.

The maximum transmission capacity determination step 110 determines a maximum transmission capacity TTC by an optimal algorism calculation (OPF) method using a multipurpose particle group optimization (MOPSO) algorithm. The maximum transmission capacity determination step 110 includes an optimum operation point calculation step 111, Setting steps 112 and 113, and a maximum transmission capacity selection step 114. [

The optimum operation point calculation step 111 applies a multipurpose particle unit cost optimization algorithm to the optimal tidal stage to obtain a plurality of optimum operation points. The specific operation performed in the optimum operation point calculation step 111 will be described later with reference to FIG.

The maximum transmission capacity candidate group setting step 112, 113 sets a candidate group of the maximum transmission capacity for a plurality of optimum operating points, and the first maximum transmission capacity candidate setting step 112 and the second maximum transmission capacity candidate setting step 113 ).

In the first maximum transmission capacity candidate setting step 112, it is assumed that assumption accidents are not set, and all the generators in the regions other than the transmission region are fixed, and the load of the whole area is gradually increased until the constraint condition is violated. And sets the transmission capacity when the constraint condition is violated to the maximum transmission capacity candidate.

The second maximum transmission capacity candidate setting step 113 sets the plurality of assumed events and performs the algae calculation while gradually increasing the load on the entire area until the restriction condition is violated on the basis of the set assumption, A plurality of transmission capacities when the constraint condition is violated is set as a candidate of the maximum transmission capacity.

The maximum transmission capacity selection step 114 selects the candidate having the smallest value among the candidate groups of all the maximum transmission capacities as the maximum transmission capacity.

The computation element setting step 130 includes a transmission reliability margin setting step 131 and a current transmission power setting step 132. Transmission reliability margin setting step 131 sets transmission margin margin TRM, and current transmission power setting step 132 sets the current transmission power amount ETC. Here, the transmission reliability margin means a margin for ensuring safe operation of the power system, and the current transmission power (Existing Transmission Commitments) means an amount of power being transmitted to the current power system.

The available transmission capacity calculation step 150 calculates an available transmission capacity (ATC) defined as a value excluding the transmission margin of reliability (TRM) and the current transmission power (ETC) from the maximum transmission capacity (TTC).

FIG. 2 is a signal flow diagram illustrating an internal execution step of the optimum operation point calculation step shown in FIG.

Referring to FIG. 2, the optimal operating point calculation step 111 includes an initialization step 210, a bird flow calculation step 220, a particle fitness calculation step 230, an alignment allocation step 240, A candidate population generation step 260, and a maximum number of households arrival determination step 270. [

In the initialization step 210, the dimension of the multipurpose particle community optimization is set as the number of generators, the active power generation amount of the generator is set as the position vector of the particle, the positions and velocities of the individual particles are randomly generated, ) Parent group with N particles and a candidate group with N particles. In the algae calculation execution step 220, algae calculation is performed on the individual particles. In the fitness calculation step 230, the fitness of the particles is calculated using at least two objective functions. Here, at least two objective functions mean the minimum cost of effective generation and the minimum emission of carbon dioxide, which will be described later.

In the sorting step 240, the parent group and the candidate group are compared according to the dominant relation, and the non-dominant sorting and the dense distance allocation are performed on the particles. In the new parent group generation step 250, N particles having a large dense distance are set as a new parent group. In the candidate group generation step 260, a candidate group is created by updating the positions and velocities of new parent group particles. The maximum number of households reaching determination step 270 determines whether the maximum number of households has been reached. If the maximum number of households has not been reached, the step of performing algae calculation 220 is performed. If the maximum number of households has been reached, It concludes.

Hereinafter, the at least two objective functions described above will be described with respect to the minimum cost of effective generation and the minimum emission amount of carbon dioxide.

The minimum cost of effective generation (Min F (P _g )) is expressed by Equation (2).

The minimum emission amount of carbon dioxide (Min E (P _g )) is expressed by Equation (3).

)는 i번째 발전기의 연료계수이며, Where P (P _g ) is the total sum of the effective generation costs of all the generators, Pgi is the active power generation amount of the i-th generator, Ng is the number of generators,

) Is the fuel coefficient of the i-th generator,

)는 i번째 발전기의 이산화탄소 배출량 특성계수를 의미한다. E (P _g ) is the sum of the carbon dioxide emissions of all generators, and the three coefficients

) Denotes the carbon dioxide emission characteristic coefficient of the i-th generator.

The constraint conditions used in the candidate group setting steps 112 and 113 are an equality constraint which is a power algebraic equation, a physical limit of a generator, and an inequality constraint related to a grid operation.

The equation of power flow, which is an equation constraint, is shown in Equation (4).

Where Qi is the reactive power generation amount at the i-th bus line, Pdi is the active power load at the i-th bus line, Qdi is the invalid power at the i-th bus line, Vi is the voltage magnitude of the ith bus, Vj is the voltage magnitude of the jth bus, θi is the voltage phase angle of the ith bus, θj is the voltage phase angle of the jth bus, Yij is the i- And Φij denotes the phase angle of the mutual admittance between the i-th bus line and the j-th bus line.

The inequality constraint condition includes a first inequality constraint on the limit of the active power generation amount Pgi of the generator, a second inequality constraint on the limit of the reactive power generation amount Qgi of the generator, a magnitude Vi of the bus voltage, The third inequality constraint on the limit of the line and the fourth inequality constraint on the line heat capacity (Si) limit.

The first to fourth inequality constraints can be expressed by the following equations (5) to (8).

In general, there are three objectives that need to be addressed in order to solve the multipurpose optimization problem.

1. Maximize the number of Palette-optimal solutions to provide various solutions.

2. The distance between the Pareto front and the actual Pareto front created by the algorithm should be minimized.

3. To obtain a smooth and uniform distribution as possible by maximizing the Pareto solutions to the search space.

In order to solve such a multipurpose optimization problem, the present invention uses a multipurpose particle community optimization (MOPSO) algorithm.

In the present invention, an elitism strategy is adopted to converge to the Pareto optimal solution, and a crowding distance assignment method is used to maintain the diversity of solutions. And was supplemented by a constriction factor.

Elitism Strategy is a method of keeping and managing excellent solutions found in the evolution process separately from the current solution group, and has the advantage of preventing the loss of quick performance and good solution. One of the problems in expanding the particle community optimization (PSO) to multipurpose optimization is that the comparison of the dominant relation in the process of updating the optimal position vector of individual particles is not applied to all particles, It is a point. This produces a parent population of N population consisting of N generations and their descendant generational particles N, allowing 2N particles in total to compute the dominant relationships and then survive the best N particles. The elitism strategy based on the comparison of dominance relations is called a conbine-then-compare method.

This results in more non-dominated solids being secured through non-dominated sorting. Each particle in the group is assigned a different non-dominant order, and this classification process is repeated every time the household number is updated, ultimately leading the particles to the Pareto front line.

If the particles are assigned the same rank, the density is calculated and the priority is assigned to the particles with a larger density. In other words, the particles in the less dense region are selected to prevent early convergence to a single channel and to enhance the diversity of the solution.

Figure 3 shows an example of a non-dominant alignment.

Referring to FIG. 3, first, the entire group is classified into a non-dominant set and a remaining dominant set. Three particles (1, 2, 3) located in front 1 are assigned to the first order, resulting in particles of non-dominant set, because they are not dominated by any particle in the whole population.

Then removes the particles temporarily assigned to the first rank to assign the next rank, and assigns rank 2 (front 2) to the remaining dominant group's particles. This process is repeated until all the particles in the group are removed and the particles are ranked.

Here, the horizontal axis f1 and the vertical axis f2 represent one of the minimum cost of effective power generation and the minimum emission amount of carbon dioxide, which are two objective functions.

FIG. 4 illustrates a process of assigning a dense distance.

Referring to FIG. 4, the density is calculated between particles assigned to the same front in the non-dominant arrangement shown in FIG. It selects particles in a relatively dense region to prevent early convergence of a single channel and improves the diversity of the solution. The black dots shown in FIG. 4 indicate non-dominant set particles. The density of any of these particles (i) means the average distance between two particles (i-1, i + 1) on both sides.

Particle cluster optimization (PSO) algorithm is a probabilistic search technique that imitates social behavior patterns of biological communities. It has group-based characteristics, so it can find many non-dominant solutions in one run, It is best suited for optimization of conflicting objective functions that can not unify units.

In order to demonstrate the effectiveness of the present invention, a multipurpose particle community optimization method is applied to the IEEE 30 bus system.

Figure 5 shows the Pareto front line of the optimal algae calculation when the multipurpose particle cluster optimization algorithm is applied to the IEEE 30 bus system.

Referring to FIG. 5, the small points are Pareto optimal solutions, which are distributed in inverse proportion to the two objective functions of effective power generation cost (Fuel cost) and CO ₂ emission (CO ₂ emission) . Also, it can be confirmed that the diversity of solutions for the multi-objective particle group optimization algorithm is well secured from the shape of the distribution. The intermediate large point of the plurality of points representing the optimal solution indicates an optimal tradeoff determined or selected according to the preference of the decision maker among the Pareto optimal solutions.

6 shows an IEEE 30 bus system diagram.

Referring to FIG. 6, the IEEE 30 bus system is a system for applying computation of available transmission capacity (ATC) using OPF, and is composed of 6 generators and 41 transmission lines. The total load Is 195 MW (Mega Watts). Three generations are included, and two generators (G) are included in each area, and linkage line failures are used as assumed events.

In the Particle Cluster Optimization (PSO) and Multipurpose Particle Cluster Optimization (MOPSO) algorithms used for multipurpose optimal algae calculation (OPF) for carbon dioxide emissions and effective generation costs, the number of generations and the size of the population are assumed to be 50.

The coefficients of the effective generation cost function and the carbon dioxide emission function are shown in Tables 1 and 2 below.

Bus (bus) a b c One 0.02 2 0 2 0.0175 1.75 0 13 0.025 3 0 22 0.0625 One 0 23 0.025 3 0 27 0.00834 3.25 0

Bus (bus) alpha beta

One 0.006917 -0.6039 12.618 2 0.01098 -0.0549 13.897 13 0.01488 -0.00302 13.890 22 0.01482 -0.00549 14.002 23 0.01592 -0.00220 14.560 27 0.01598 -0.00275 13.670

In order to demonstrate the effectiveness of the present invention, Table 3 compares the carbon dioxide emissions (MPPSO) and the carbon dioxide emissions (POS), as in the present invention.

Generator bus
PSO MOPSO Power generation (MW) Cost ($ / H) Power generation (MW) Cost ($ / H) Carbon Dioxide Emissions (TON / H) One 44.76 129.59 58.15 183.93 0.89 2 59.24 165.08 50.35 132.48 38.97 13 18.27 63.15 19.57 68.28 19.53 22 23.56 58.25 24.19 60.76 22.54 23 17.60 60.54 20.92 73.70 20.48 27 34.45 121.86 24.82 85.80 23.44 total 197.88 598.47 198.00 604.95 125.85

The following scenario is assumed in order to calculate the available transmission capacity (ATC) from the transmission area to the whole area using the optimal trade-off solution shown in FIG. 5 as a base case.

First case: transmission of power from zone 3 to zone 1.

Second case: transmission of power from zone 3 to zone 2

Third case: Transmits power from zone 3 to zone 1 and zone 2 simultaneously

In the first case, as shown in Table 4, in order to calculate the available transmission capacity (ATC) from the area 3 to the area 1, it is assumed that a failure occurs in each of the three connecting lines.

Supposed accident
PSO MOPSO Overload track
ATC (MW) ATC (MW) base case 31.5 24.9 1-2


6-10 19.2 14.6 9-10 18.8 12.9 27-28 22.1 20.5

(MOPSO) algorithm considering the available transmission capacity (ATC) and carbon dioxide emissions obtained by applying the Particle Cluster Optimization (PSO) algorithm when the CO2 emission is not taken into consideration, It can be seen that the transmission capacities (ATC) are different from each other. Calculations for available transmission capacity (ATC), which vary with the presence or absence of carbon dioxide emissions, provide crucial information to system operators.

In the first case, as a result of taking into account the assumption of each connected line, the lowest value is shown on the line 9-10, and it is selected as the available transmission capacity (ATC).

The second case is assumed to be the assumed case of failure in each of the three connected lines as in the first case, and the calculation results of the available transmission capacity (ATC) are shown in Table 5.

Supposed accident
PSO MOPSO Overload track
ATC (MW) ATC (MW) base case 63.0 68.6 24-25


6-10 57.4 54.2 9-10 51.7 54.9 27-28 38.2 35.8

In the second case, it shows the smallest value in the connected line 27-28, and it is selected as available transmission capacity (ATC).

It can be seen that Tables 4 and 5 show different results because there is a directionality in the available transmission capacity (ATC), although the connection lines of the second case and the first case are the same.

In the third case, assumption is assumed for six connected lines, and the available transmission capacity (ATC) is calculated as shown in Table 6.

Supposed accident
PSO MOPSO Overload track
ATC (MW) ATC (MW) base case 101.6 101.5 15-18





6-10 93.0 90.6 9-10 82.1 84.3 10-17 87.9 83.3 10-20 38.6 34.7 23-24 96.9 94.6 27-28 97.8 94.4

As shown in Table 6, in the third case, it shows the smallest value in the connected line 10-20 and it is selected as available transmission capacity (ATC). In other words, it can be recognized that the assumptions on lines 10-20 are the worst case assumptions for the six connected lines, and the overload lines are 15-18.

At this time, the available transmission capacity (ATC) is 38.6 MW when the CO2 emission is not considered, and the available transmission capacity (ATC) when considering the CO2 emission is 34.7 MW.

In today's power market environment, the calculation of accurate and flexible available transmission capacity (ATC) is an indispensable element for system operators. In the present invention, by calculating the available transmission capacity (ATC) considering the amount of carbon dioxide emission, the available transmission capacity calculation method considering both the economical efficiency and safety of the power system operation as well as the environmental aspect such as reduction of greenhouse gas emission is considered.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope of the present invention.

110: Maximum transmission capacity calculation step
130: operation element setting step
150: Available transmission capacity calculation step

Claims (11)

Determining a maximum transmission capacity (TTC) by applying a multipurpose particle cluster optimization (MOPSO) algorithm to an optimal algae calculation (OPF);
An operation element setting step of setting an operation element; And
And an available transmission capacity calculation step of calculating an available transmission capacity (ATC) defined by a value excluding an operation element from the maximum transmission capacity (TTC)
The maximum transmission capacity determination step may include:
An optimal operating point calculation step for obtaining a plurality of optimal operating points by applying a multipurpose particle cluster optimization (MOPSO) algorithm to an optimal algae calculation (OPF);
A maximum transmission capacity candidate group setting step of setting a candidate group of the maximum transmission capacity for the plurality of optimum operation points, and setting a maximum transmission capacity candidate in a state in which an assumed event is not set and an assumed event is set; And
And a maximum transmission capacity selection step of selecting a candidate having the smallest value among the candidate groups of all the maximum transmission capacity as the maximum transmission capacity,
The total transfer capability means the total transmission capacity capable of safely transmitting from one area to another area,
The available transfer capability means an available transmission capacity in addition to the transmission capacity already in use. delete 2. The method according to claim 1,
The number of generators for multipurpose particle cluster optimization is set as the number of generators, the active power generation amount of the generator is set as the position vector of the particle, the positions and velocities of the individual particles are randomly generated, An initialization step of generating a candidate group having a population and N particles;
Performing algae calculation for performing algae calculation on the individual particles;
Calculating the fitness of the particles using at least two objective functions;
The parent group and the candidate group are compared after the combination by the dominant relation, and non-dominant alignment and dense distance allocation are performed on the particles, and the dense distance is calculated for the particles assigned to the same non dominant order, A sorting step of assigning a priority to the particles according to a large rank;
A new parent group setting step of setting N particles having a larger densification distance as a new parent group among the particles assigned priority in accordance with the order of high density in the sorting and allocating step;
A candidate group generation step of generating a candidate group by updating the positions and velocities of the new parent group particles; And
A maximum number of households arrival judging step of judging whether or not the maximum number of households has been reached, performing the algae calculation performing step when the maximum number of households has not been reached, and terminating the calculation when reaching the maximum number of households How to select transmission capacity. 4. The method of claim 3, wherein the at least two objective functions comprise:
A minimum cost of effective generation and a minimum emission amount of carbon dioxide. 5. The method of claim 4,
The minimum cost of effective generation (Min F (P _g )

ego,
The minimum emission amount of carbon dioxide (Min E (P _g )

Lt;
Where P (P _g ) is the total sum of the effective generation costs of all the generators, Pgi is the active power generation amount of the i-th generator, Ng is the number of generators,

) Is the fuel coefficient of the i-th generator,
E (P _g ) is the sum of the carbon dioxide emissions of all generators, and the three coefficients

) Represents the carbon dioxide emission characteristic coefficient of the i-th generator. 6. The method of claim 5, wherein the maximum transmission capacity candidate group setting step comprises:
In the absence of an assumed incident, the generators in the areas other than the transmission area are fixed, and the algae calculation is carried out while gradually increasing the loads of all the stations until the restriction condition is violated, and the transmission capacity A first maximum transmission capacity candidate setting step of setting a maximum transmission capacity candidate; And
A plurality of assumption incidents are set and the algae calculation is carried out while gradually increasing the load on all the stations until the constraint condition is violated on the basis of the set assumption incidents and a plurality of transmission capacities And a first maximum transmission capacity candidate setting step of setting the maximum transmission capacity candidate as a candidate of the maximum transmission capacity. 7. The method of claim 6,
Wherein the at least one of the at least two of the at least two of the at least two of the at least two of the at least two of the at least one of the at least two of the at least one of the at least two of the at least one of the plurality of at least one of 8. The method of claim 7, wherein the equation of power equation,

And

Lt;
Where Qi is the reactive power generation amount at the i-th bus line, Pdi is the active power load at the i-th bus line, Qdi is the invalid power at the i-th bus line, Vi is the voltage magnitude of the ith bus, Vj is the voltage magnitude of the jth bus, θi is the voltage phase angle of the ith bus, θj is the voltage phase angle of the jth bus, Yij is the i- And? Ij denotes the phase angle of the mutual admittance between the i-th bus line and the j-th bus line. 8. The method of claim 7,
First Inequality Constraint on Limit of Effective Power Generation Power (Pgi) of Generator;
A second inequality constraint on the limit of the generator's reactive power generation output (Qgi);
A third inequality constraint on the limit of the magnitude (Vi) of the bus voltage; And
And a fourth inequality constraint on the thermal capacity Si limit of the line. 10. The method of claim 9,
The first inequality constraint may include:

The second inequality constraint may include:

The third inequality constraint may include:

The fourth inequality constraint may include:

Wherein the available transmission capacity selection method comprises: 2. The method according to claim 1,
A transmission margin setting step of setting transmission margin reliability margin (TRM); And
Is a current transmission power amount setting step of setting a current transmission power amount (ETC)
The Transmission Reliability Margin means a margin for ensuring safe operation of the power system,
Wherein the current transmission power amount (Existing Transmission Commit) means an amount of power currently being transmitted to the power system.

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