CN108879675A - A kind of electric network composition adaptability teaching algorithm - Google Patents

Abstract

The invention discloses an evaluation algorithm for grid structure adaptability, which comprises the steps of: taking the sum of the minimized operation cost and power transmission investment cost as the objective function, establishing a reference grid model; applying Kirchhoff's law, the operating limit of line elements and the power generation Load balance is used as the constraint condition of the objective function to determine the optimal capacity of the transmission line; the line capacity of the actual grid is compared with the optimal line capacity obtained by the reference grid to obtain the suitability information of the grid line. Based on the transmission value theory, this method determines the optimal capacity of the transmission line by establishing a reference grid mathematical model, thereby obtaining the corresponding optimal investment cost and congestion cost, making the transmission investment reach the optimal level, ensuring the safe operation of the grid, and The economy of system construction and operation is improved.

Description

A Adaptive Evaluation Algorithm for Power Network Structure

technical field

The invention relates to the technical field of electric power systems, in particular to an evaluation algorithm for grid structure adaptability.

Background technique

As the foundation of the national economy, the power industry must be able to provide stable and reliable power supply in order to ensure stable and rapid economic development, and power grid planning is the premise to ensure reliable operation of the power system. With the interconnection of large power grids and the reform of China's electric power market, the planning of power networks is facing severe challenges. At the same time, due to the characteristics of diversified and large-scale access to the power grid, active loads such as microgrids, distributed renewable energy sources, and electric vehicles have caused essential changes in the planning and operation of power systems in time domain and space. The balance between generation and demand becomes complex. Therefore, how to timely and accurately determine the planning direction, improve resource utilization, and at the same time avoid resource waste caused by over-construction of the power grid has become a new problem to be solved urgently in power grid planning.

Under the new development situation, power grid planning pays more attention to the construction of transmission lines under the given power generation and power consumption. First, there should be sufficient transmission capacity to ensure power supply under various normal operating conditions and to meet the demand for short-term load growth. Therefore, grid planning should configure transmission components based on future load forecasts and power generation technical indicators. Secondly, the impact of unit outage and line failure under various circumstances should be fully considered, so there should be a certain capacity margin. A reasonable transmission capacity can not only ensure the safe operation of the power grid, but also improve the economy of system construction and operation.

There are many deficiencies in traditional grid planning strategies. First of all, there is no clear standard for the overall evaluation of the transmission network. This standard should be able to find weak links in the system and formulate improvement strategies. Secondly, the planning schedule is fixed. If there is a situation where the line capacity is insufficient due to line power flow fluctuations, it cannot be discovered in time and a capacity expansion decision cannot be made, and the volatility of renewable energy generation cannot be flexibly dealt with. Finally, the selection of new construction and expansion efforts is unreasonable, and a large capacity margin is usually reserved to ensure safety, resulting in waste of resources. Therefore, it is necessary to establish a real-time and reasonable evaluation of the current situation of the power grid, and according to the evaluation indicators, the deficiencies can be found in time, the planning direction and progress can be determined, and the planning that can flexibly deal with the volatility brought about by the access of renewable energy and handle a large amount of data The program has important practical significance.

Contents of the invention

The purpose of the embodiments of the present invention is to provide a power grid structure adaptability evaluation algorithm, which is based on the transmission value theory, determines the optimal transmission capacity based on the balance of supply and demand, and then determines a reasonable power grid planning strategy.

To achieve the above object, the present invention adopts the following technical solutions:

The present invention provides a network structure adaptability evaluation algorithm, the algorithm includes the following steps:

Firstly, the reference grid model is established by minimizing the sum of operation cost and transmission investment cost as the objective function;

Then Kirchhoff's voltage and current law, the operating limit of line components and the balance of power generation load are used as the constraints of the objective function to determine the optimal capacity of the transmission line;

Next, the line capacity of the actual power grid is compared with the optimal line capacity obtained by the reference power grid to obtain the suitability information of the power grid lines.

Based on the above scheme, the present invention optimizes as follows:

The objective function of the reference grid is expressed as:

In the formula: NT is the number of load periods; NG is the number of generators; NL is the number of lines; C _i is the operating cost of generator i; is the output of generator i in the load period t; is the annual investment cost of line l; T _l is the capacity of line l.

Further, the constraints of the objective function (1) under normal conditions and anticipated accidents are:

In the formula: is the sensitivity matrix of generator nodes and branches; is the sensitivity matrix of load nodes and branches; P _{l, t} is the power flow of line l at load period t; is the power generation limit of generator i, is the lower limit of power generation of generator i; formula (2) is the node power balance equation obtained according to Kirchhoff’s current law, and requires that the power injected into a node is equal to all the power flowing out of the node; formula (3) is The balance equation of branch power flow on node power injection under each event, in which the sensitivity matrix is used to reflect the network structure of the entire system and line reactance and other information; Equation (4) expresses the constraints on the active power transmission capacity of transmission components under each event; Equation (5) expresses the constraints on the active power output of generator sets under each event.

As mentioned above, a grid structure adaptability assessment algorithm, the value range of the variable T _l is:

0≤T _l ≤∞ (6)

The branch node sensitivity matrix H reflects the relationship between node injection and branch power flow, specifically expressed as:

where: Y _d is the diagonal matrix of the branch admittance matrix; Y _rbus is the non-singular matrix obtained by removing the rows and columns of the buffer nodes from the system admittance matrix.

Further, before establishing the reference grid model, the following steps are also included:

Construct the demand function and supply function of the transmission line;

Calculate the optimal transmission capacity when the transmission demand and transmission supply are in balance;

Comparing the optimal transmission capacity determined by the supply and demand function with the optimal transmission capacity determined based on the reference grid model, it is determined that the optimal transmission capacity obtained by the two methods is consistent.

The technical solution provided by the application includes the following beneficial effects:

An evaluation algorithm for the grid structure adaptability provided by the embodiment of the present application includes the steps of: taking the sum of the minimized operation cost and the transmission investment cost as the objective function, establishing a reference grid model; using Kirchhoff's law, the operating limit of the line element And the generation load balance is used as the constraint condition of the objective function to determine the optimal capacity of the transmission line; the line capacity of the actual grid is compared with the optimal line capacity obtained by the reference grid to obtain the suitability information of the grid line. The evaluation algorithm of the embodiment of this application is based on the transmission value theory, and determines the optimal capacity of the transmission line by establishing a reference grid mathematical model, thereby obtaining the corresponding optimal investment cost and congestion cost, so that the transmission investment reaches the optimal level, ensuring Safe operation of the power grid, and improve the economy of system construction and operation.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description serve to explain the principles of the application.

Fig. 1 is a schematic flow chart of a grid structure adaptability evaluation algorithm according to an embodiment of the present application;

FIG. 2 is a two-node B-S interconnection model in the embodiment of the present application.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

Figure 1 is a grid structure adaptability assessment algorithm provided by the embodiment of the present application. As can be seen from Figure 1, the algorithm of this embodiment includes the following steps:

S1. Construct the demand function and supply function of the transmission line, and determine the optimal transmission quantity;

S2. Comparing the optimal transmission quantity determined by using the supply and demand function and the optimal transmission quantity determined based on the reference grid model, and confirming that the optimal transmission quantity obtained by the two methods is consistent;

S3. Establishing a reference grid model with the objective function of minimizing the sum of operation cost and transmission investment cost;

S4. Taking Kirchhoff's voltage and current laws, operating limits of line components and power generation load balance as constraints of the objective function to determine the optimal capacity of the transmission line;

S5. Comparing the line capacity of the actual power grid with the optimal line capacity obtained through the reference power grid, to obtain suitability information of the power grid lines.

Specifically, the demand function and supply function of the transmission line are constructed in the S1, and the optimal transmission amount is determined. The specific implementation is illustrated by taking the two-node BS interconnection system shown in FIG. 2 as an example. In the two-node system shown in Figure 2, the left node is called a B node, and the right node is called an S node. There is only one line between two nodes. The power output of node B is P _B , the power output of node S is P _S , and the cost function of node B power is The cost function of S node power supply is B node load D _B =500MW, S node load D _S =1500MW. The capacity of the line between nodes B and S is F.

When _{P B} and PS are both greater than 0, since the node price is determined by the marginal price of the unit with partial load at the node, the node prices π _B and π _S of nodes B and S are respectively:

The transmission price π _T (F) of the line can be expressed by the price difference between the nodes at both ends of the line:

For nodes B and S, Kirchhoff's current law can be obtained:

P _B (F) = D _B +F (3)

P _S (F) = D _S -F (4)

Then, formula (2) becomes

Equation (5) is the inverse function of the transmission demand, and the transmission demand function can be the inverse function of (5):

F(π _T )＝933.3-33.3π _T (6)

Next, the supply function of the transmission line is constructed. Assume that the construction cost of the transmission line is simply expressed in terms of fixed cost plus variable cost. In the average annual cost of transmission facility construction, the variable cost component is related to the transmission capacity F of the line, while the fixed cost component has nothing to do with the transmission capacity, which can be specifically expressed as:

C _T (F)＝C _F +C _V (F) (7)

For simplicity, it is assumed that the variable cost is linearly related to the transmission capacity, namely:

C _V (F) = k·l·F (8)

Among them, l is the length of the line (km), and k is the average annual marginal cost of constructing transmission facilities per km ($/(MW-km-year), then the average annual marginal cost of transmission capacity is:

The above amount is called long-term marginal cost, which is related to the cost of power transmission investment. Divide it by the number of hours per year (τ ₀ =8760h) to get the long-term marginal cost per hour, expressed in $/MWh:

Due to the simplifying assumptions made in Equation (8), the marginal cost of transmission is a constant and has nothing to do with the transmission capacity of the line. Suppose its transmission line is 1000km long, and

k＝35$/(MW-km-vear) (11)

Then the LRMC per hour of power transmission is

c _T (F)＝4.00$/MWh (12)

Based on the above-mentioned demand function and supply function of the transmission line, the optimal transmission capacity is the value when the transmission supply and demand reach a balance. At this time, according to the conclusion that the marginal cost of the line is equal to the marginal cost and equal to the marginal revenue, it is the marginal supply and demand value balance:

π _T = C _T (13)

π _T ＝ C _T not only means that the marginal cost of the line is equal to the marginal revenue, but also means that the transmission value of the line is equal to the variable cost of the construction of the transmission line, which realizes the balance between marginal supply and demand value.

Combining Equation (5) and Equation (12), the optimal transmission capacity can be obtained as:

F＝800MW (14)

When the state such as line disconnection is not considered, a reference grid model is constructed for the B-S interconnection system:

Use the simple Lagrange multiplier method to obtain F=800MW under the reference grid model. Next, the above S2 is carried out, and it can be concluded that the optimal power transmission capacity determined by using the supply and demand function is consistent with the optimal power transmission capacity determined based on the reference grid model.

Further, in said S3, the objective function of the reference grid is expressed as:

where NG is the number of generators; NL is the number of lines; C _i is the operating cost of generator i; is the output of generator i in the load period t; is the annual investment cost of line l; T _l is the capacity of line l.

Among the said S4, the constraint conditions under the normal state of the objective function (16) and the expected accident are:

In the formula: is the sensitivity matrix of generator nodes and branches; is the sensitivity matrix of load nodes and branches; P _{l, t} is the power flow of line l at load period t; is the power generation limit of generator i, is the lower limit of power generation of generator i; Equation (17) is the node power balance equation obtained according to Kirchhoff’s current law, which requires that the power injected into a node is equal to all the power flowing out of the node; Equation (18) is The balance equation of branch power flow on node power injection under each event, in which the sensitivity matrix is used to reflect the network structure of the entire system and line reactance and other information; Equation (19) expresses the constraints on the active power transmission capacity of transmission components under each event; Equation (20) expresses the constraints on the active power output of generator sets under each event.

Let the value range of the variable T _l be:

0≤T _l ≤∞ (21)

The branch node sensitivity matrix H reflects the relationship between node injection and branch power flow, and its definition is as follows:

where: Y _d is the diagonal matrix of the branch admittance matrix; Y _rbus is the non-singular matrix obtained by removing the rows and columns of the buffer nodes from the system admittance matrix. Specifically, formulas (17) to (20) need to consider the power flow of each operating branch under different time periods and various line interruption events.

After the optimal line capacity is obtained, the suitability information of each line can be obtained by comparing the optimal line capacity obtained by the reference grid with the known line capacity in the actual grid. For a certain line, if the actual line capacity is greater than the optimal line capacity, it means that the line is over-invested and the economy is poor. If the actual line capacity is less than the optimal line capacity, it means that the line is underinvested, the line may be blocked and affect the security of the system, and there is an investment demand on this line. The difference in operation and investment between the actual grid and the reference grid can be used as an indicator for evaluating power transmission companies, and planners can use this as a basis to determine grid planning strategies.

The above descriptions are only specific embodiments of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (5)

1. A network structure adaptability evaluation algorithm, is characterized in that, comprises the following steps: The reference grid model is established by minimizing the sum of operation cost and transmission investment cost as the objective function; Using Kirchhoff's voltage and current laws, operating limits of line components and power generation load balance as the constraints of the objective function to determine the optimal capacity of the transmission line; The line capacity of the actual grid is compared with the optimal line capacity obtained by the reference grid to obtain the suitability information of the grid line. 2. A kind of grid structure adaptability evaluation algorithm according to claim 1, is characterized in that, the objective function of described reference grid is expressed as: In the formula: NT is the number of load periods; NG is the number of generators; NL is the number of lines; C _i is the operating cost of generator i; is the output of generator i in the load period t; is the annual investment cost of line l; T _l is the capacity of line l. 3. a kind of grid structure adaptability assessment algorithm according to claim 2, is characterized in that, the constraint condition of described objective function is: In the formula: is the sensitivity matrix of generator nodes and branches; is the sensitivity matrix of load nodes and branches; P _{l, t} is the power flow of line l at load period t; is the power generation limit of generator i, is the lower limit of power generation of generator i; formula (2) is the node power balance equation obtained according to Kirchhoff’s current law, and requires that the power injected into a node is equal to all the power flowing out of the node; formula (3) is The balance equation of branch power flow on node power injection under each event, in which the sensitivity matrix is used to reflect the network structure of the entire system and line reactance and other information; Equation (4) expresses the constraints on the active power transmission capacity of transmission components under each event; Equation (5) expresses the constraints on the active power output of generator sets under each event. 4. a kind of grid structure adaptability assessment algorithm according to claim 3, is characterized in that, the value range of described variable _T1 is: 0≤T _l ≤∞ (6) The branch node sensitivity matrix H reflects the relationship between node injection and branch power flow, specifically expressed as: where: Y _d is the diagonal matrix of the branch admittance matrix; Y _rbus is the non-singular matrix obtained by removing the rows and columns of the buffer nodes from the system admittance matrix. 5. A grid structure adaptability assessment algorithm according to any one of claims 1 to 4, characterized in that, before establishing a reference grid model, the following steps are also included: Construct the demand function and supply function of the transmission line; Calculate the optimal transmission capacity when the transmission demand and transmission supply are in balance; Comparing the optimal transmission capacity determined by the supply and demand function with the optimal transmission capacity determined based on the reference grid model, it is determined that the optimal transmission capacity obtained by the two methods is consistent.