KR100743124B1 - System And Method for Forecasting Economical Utility of Power Supply by Using Method for Minimizing Generation Cost - Google Patents

Abstract

The present invention relates to an economic dispatch prediction system and method using a generation cost minimization technique.

The present invention provides an economic power supply prediction system using a generation cost minimization method, which automatically monitors and controls a power system, analyzes the power system, generates power flow data, and transmits generated power flow data in real time. An energy management system (EMS: hereinafter referred to as "EMS"); Receives the systemic tidal current data from the EMS and stores the systemic tidal current data for each variable included in the systemic tidal current data from the system data receiving unit and the manager to determine the equation constraints and inequality constraints required for system stability. When the input is received, the optimal output power for each generator is calculated by performing the calculation of the optimum current (OPF: Optimal Power Flow (OPF)) using the sum of the generator secondary cost functions for each generator under the equation constraint and the inequality constraint. A system analysis unit including an OPF calculation unit for calculating a minimum generation cost (hereinafter, referred to as "optimal solution") for each generator; A database storing the branch tidal current data received by the branch data receiver for each variable and storing a result calculated by the branch analyzer; And an operation / management unit for displaying the system tidal current data and the optimal solution and inputting a command to operate the system analysis unit.

According to the present invention, as described above, according to the present invention, it is possible to receive and analyze the flow current data of the system from the EMS in real time, so that it is possible to grasp the power system situation and the power supply and demand situation of the continuously changing power system in real time, The OPF calculation results can be used to determine the optimal power distribution for each generator that can run the generator at minimal cost while ensuring system stability.

Economic feed, optimal tide, OPF,

Description

System and Method for Forecasting Economical Utility of Power Supply by Using Method for Minimizing Generation Cost}

1 is a view briefly showing the configuration of a conventional power supply system;

2 is a view showing the configuration of a system for predicting economic dispatch according to a preferred embodiment of the present invention;

3 is a flowchart showing a method of predicting economic dispatch according to a preferred embodiment of the present invention;

4 is a flowchart illustrating an OPF calculation method according to a preferred embodiment of the present invention;

5 is a flowchart illustrating an IGP calculation method according to a preferred embodiment of the present invention.

<Description of Symbols for Main Parts of Drawings>

210: EMS 220: database

230: system analysis unit 232: system data receiver

234: OPF calculator 236: IGP calculator

240: operation / management

The present invention relates to an economic dispatch prediction system and method using a generation cost minimization technique. More specifically, by analyzing the system power flow data received in real time from the Energy Management System (EMS) and setting the equation constraints and inequality constraints, the generator is operated under the set equation constraints and inequality constraints. By calculating the OPF (Optimal Power Flow) as the objective function of the secondary cost function, the optimal output distribution for each generator that minimizes the generation cost is calculated. The present invention relates to an economic power supply prediction system and method for calculating an interim generating unit price (IGP).

The global power industry is changing from a vertically integrated monopoly system based on economies of scale to a market competition system based on functional division or regional division.

In response to the restructuring of the global power industry, the Korean power market is divided into six power generation companies in accordance with the "Power Industry Restructuring Plan" announced by the government in January 1999. CBP: Cost Based Pool, or competitive power generation market. From April 2004 to 2009, two-way bidding market (TWBP: Two-Bid) bids sales and purchase prices in both the power generation (supply) and power distribution (demand) sectors. Way Based Pool or wholesale power market, and from 2009 or 2010, it will operate as a complete retail competition where consumers can choose their own generation or sales company.

As the environment of the power market changes, so does the environment facing power generation companies.

First, the market price in the power generation competitive market is determined only by the variable costs and power demands submitted by the power generation companies, and the power generation companies tend to bid only the available capacity, so the market price is less volatile. Since the market price is determined by price and sales plan amount and power demand (or demand side bidding price and purchase plan amount), the market price fluctuates greatly according to various variables such as power supply and demand situation and power system situation.

In addition, in the competitive power generation market, capacity payment (CP) is paid to the power generation company, so even in the reliability-oriented management of power generation facilities, the profitability of the company is structurally determined by the composition and capacity of the power generation facilities, but wholesale Since capacity fees are not paid in the strategic market, efforts to reduce power generation costs are urgently needed because price competitiveness by generators determines the company's profits.

In addition, in the competitive power generation market, the start / stop of the generator is decided by the power exchange based on the variable cost of each generator and the available capacity submitted as bid data, and the change bidding is possible only when the necessity for the operation of the facility is certified. Strategic bidding is not possible systematically, but in the wholesale power market, the generators can autonomously determine the start / stop of the generators.The bidding power and price can be strategically bidded according to the needs of the generators and changed bidding whenever necessary. As a result, the bidding strategy of the power generation company became very important.

Therefore, the power generation company needs to analyze the electricity supply and demand situation or the electricity system situation in real time, and if there is a demand for electricity, it is possible to operate the generator at an optimal cost to supply power and to establish an optimal bidding strategy. This became necessary.

1 is a view showing a brief configuration of a conventional power supply system.

As shown in FIG. 1, the conventional power supply and demand system includes a means for executing a program for predicting demand by inputting previous year's performance data management and combining a prediction model and demand prediction data to predict power supply and demand 110. Means for executing a program for establishing a hydraulic operation plan by inputting management plan data and calculation data management (120), means for executing a program for inputting common input data and annual power generation plan registration data (130), and annual power generation output data Means 140 for executing a program for grasping the generator prevention and maintenance plan through a; and means 150 for synthesizing the information obtained from these means to output the annual power generation plan.

Through this configuration, the annual power generation plan is established by using input data such as electric power demand forecasting, preventive maintenance by generator, hydropower operation plan, and annual power generation plan registration data.

However, the conventional power supply and demand system uses the previous performance data to establish the annual power generation plan, so it is impossible to grasp the supply and demand status of the power system continuously changing in real time, and also has a method of economically determining the output distribution of the generator. There is a problem that power generation companies can not provide a way to maximize their profits under the wholesale strategy market.

In order to solve this problem, the present invention is to analyze the system current data received in real time from the energy management system (EMS) to set the equation constraints and inequality constraints, the generator under the set equation constraints and inequality constraints Calculate OPF (Optimal Power Flow) as the objective function of the second cost function, and calculate the optimal power distribution for each generator that minimizes the generation cost. The purpose of the present invention is to provide an economic power forecasting system and method for calculating interim generating unit price (IGP).

For the first object of the present invention, the present invention, in the economic power supply prediction system using the power generation cost minimization method, automatically monitors and controls the power system and analyzes the power system to generate system power flow (Power Flow) data An energy management system (EMS) (EMS), which transmits the generated systemic tidal flow data in real time; Equation constraints and inequality constraints required for system stability from a system data receiver and an administrator for receiving the system tidal flow data from the EMS and storing the system tidal flow data for each variable included in the system tidal flow data. If the input is received, the optimal output power for each generator is performed by calculating the optimum current (OPF: Optimal Power Flow, or "OPF") by using the sum of the generator secondary cost functions for each generator under the equation constraint and the inequality constraint. And an OPF calculator configured to calculate a minimum power generation cost (hereinafter, referred to as “optimal solution”) for each generator. A database storing the branch tidal current data received by the branch data receiver for each variable and storing a result calculated by the branch analyzer; And an operation / management unit for displaying the system tidal current data and the optimal solution and inputting a command to operate the system analysis unit.

In accordance with a second aspect of the present invention, the present invention provides a method for predicting economic power consumption using a method of minimizing generation costs, comprising: (a) receiving system power flow data in real time from an energy management system (EMS); Making; (b) When an administrator inputs the equation constraints and inequality constraints required for system stability by analyzing the system tidal flow data, the sum of the generator secondary cost functions for each generator under the input equation constraints and inequality constraints is input. Performing an optimal algae (OPF) calculation as an objective function; And (c) outputting an optimum output amount for each generator and a minimum generation cost for each generator (hereinafter, referred to as an "optimal solution") calculated from the OPF calculation. to provide.

For the third object of the present invention, the present invention, the optimal current flow (OPF: Optimal Power Flow, as the objective function of the sum of the generator secondary cost function for each generator under the equality and inequality constraints required for the stability of the system In the recording medium recording a program for performing the calculation (hereinafter referred to as "OPF"), (a) the equation constraint and the inequality constraint and the equation constraint and the inequality constraint required by the administrator for the stability of the system; Receiving an initial value for a state variable, a control variable, and a Lagrangian variable forming a condition (called a "z vector" including all of the above variables); (b) determining whether each variable input to the z vector satisfies the inequality constraint, selecting an unsatisfactory variable as an active set, and a penalty term for the variable selected as the active set. Setting up; (c) a matrix value at a value input into the z vector for the matrix g that first-differentiates the extended Lagrangian function reflecting the equality constraint and the penalty term in the objective function and the matrix W that is second-order partial differentiation. Calculating; (d) performing an operation of multiplying the inverse matrix of W by the -g matrix to calculate a change amount? z of each variable input to the z vector, and update the z vector by adding the? z to the z vector. Making; (e) determining whether or not the size of? z is smaller than a sufficiently small number [epsilon], and proceeding to step (c) if it is large; (f) when the magnitude of Δz is smaller than ε, it is determined whether the values of the variables input to the z vector satisfy the inequality constraint, and select unsatisfactory variables as the new active set, Proceeding to step (c) after setting or updating the petalty term and the penalty coefficient included in the penalty term for the variable selected as the new active set; And (g) when all of the values of the variables satisfy the inequality constraint, the minimum generation cost for each generator calculated using the optimal output amount for each generator and the optimal output amount for each generator included in the z vector finally stored. It provides a recording medium recording the optimum algal calculation program comprising the step of outputting.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. First of all, in adding reference numerals to the components of each drawing, it should be noted that the same reference numerals are used as much as possible even if displayed on different drawings. In addition, in describing the present invention, when it is determined that the detailed description of the related well-known configuration or function is obvious to those skilled in the art or may obscure the gist of the present invention, the detailed description thereof will be omitted.

2 is a diagram showing the configuration of an economic power supply prediction system according to a preferred embodiment of the present invention.

Economic forecasting system according to a preferred embodiment of the present invention is an energy management system (EMS: Energy Management System (hereinafter referred to as "EMS") 210, database 220, system analysis unit 230 and operation / management unit ( 240).

EMS according to a preferred embodiment of the present invention is a facility that automatically monitors and controls the entire power system in Korea. It is a total power supply automation system that performs remote monitoring and control of power system, automatic power generation control and economic power supply function, power system analysis function, data recording and storage function, and power supply simulation training function.

In more detail, the basic software of the EMS is used international standard software widely used in the industry. The operating system (OS) adopts Unix and Windows NT, and the application language uses C, C ++, Fortran, Visual Basic, and the database reports the Habitat for real-time reports. Oracle employs Oracle. In the case of hardware, general-purpose devices are employed to facilitate scalability, maintenance and performance.

EMS consists of main / hobby system, training system and development system.

Here, the main / hobby system is a host server that manages the entire server and monitors the operation status, a communication server for acquiring the data of the field head and substation, a historical data server that stores the acquired data, and generates daily and monthly reports. It consists of a feed console and a power grid that provide data to feeders in real time.

The system analyzer 230 according to a preferred embodiment of the present invention includes a system data receiver 232, an OPF calculator 234, and an IGP calculator 236.

The grid data receiver 232 receives the real-time grid current data transmitted from the EMS 210 and stores the data in the database 220 for each list of bus voltage, generator active power, generator reactive power, and thermal capacity.

The OPF calculator 234 analyzes real-time tidal current data received from the EMS 210 to determine and enter constraints such as generator active power, generator reactive power, bus voltage, and the like. Under the condition of the generator second cost function as the objective function, the calculation of the optimum current (OPF: Optimal Power Flow, or "OPF") to calculate the optimal power distribution for each generator and calculate the minimum generation cost according to the optimum output / Means output to management. A detailed OPF calculation algorithm performed by the OPF calculator 234 will be described later.

System analysis unit 230 according to a preferred embodiment of the present invention may further include an IGP operation unit 236, the IGP operation unit 236 is a temporary generation price (IGP: Interim Generating Unit that is the basis of the bid price determination Price, hereinafter referred to as "IGP", performs a function of calculating an IGP for each generator by receiving a calculation result output from the OPF calculator 232 from an administrator. A detailed description of the IGP calculation method will be given later.

The operation / management unit 240 according to a preferred embodiment of the present invention is a means for interfacing with the manager, and displays the data stored in the database 220, the data output from the system analysis unit 230, or the system administrator. It is a means for inputting data necessary for OPF operation or IGP operation to the analysis unit 230, and input a command for operating the system analysis unit.

Database 220 according to a preferred embodiment of the present invention is a means for storing the real-time system tidal current data received from the EMS for each list, and sorts and stores the calculation result calculated by the OPF operation unit and the result calculated by the IGP operation unit for each generator . The database has a general data structure implemented in a storage system (hard disk or memory) of a computer system using a database management system (DBMS), and can freely create, retrieve, update, add, edit, and delete data. It has a data storage form. DBMSs include relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, DB2, and object-oriented database management systems such as Gemston, Orion, and O2. OODBMS) can be used and data is managed using data manipulation words such as Structured Query Language (SQL).

3 is a flowchart illustrating a method for predicting economic power dispatch according to a preferred embodiment of the present invention.

First, real-time system tidal current data is received from the EMS, and the received real-time system tidal current data is stored in a database (S310). The manager analyzes the bus voltage per bus, the active and reactive power per generator, the transformer tap ratio and phase displacement, and the regional power supply and demand status by using real-time grid current data stored in the database. And timely changes in real-time system current data.

The administrator analyzes real-time grid current data to set the power balance equation required for stabilization of the power system as an equality constraint, limits the active power output of the generator, limits the generator reactive power, limits the maximum amount of power that can flow on the line, If the input of the voltage limit, the tap ratio of the transformer, and the limitation of the phase shift are set as inequality constraints, an OPF operation is performed to optimize the generator secondary cost function (objective function) under the input constraints (S320). ).

When the power generation cost when the generator is operated at the optimum output amount for each generator and the optimum output amount for each generator to minimize the generation cost by the OPF calculation is calculated (S330). Managers can make economic dispatch plans based on the output.

Furthermore, when the generation company establishes a bidding strategy by calculating the IGP by using the output result, it may be used as an important index of bid price determination (S340).

Hereinafter, the OPF calculation method will be described.

The OPF calculation aims to minimize the objective function and satisfy various constraints caused by power system operation and physical characteristics.

In general, the OPF problem is formulated as follows.

Minimize f (x)

Subject to h (x) = 0

              g (x) ≤0

Where x is a vector of state and control variables, f (x) is the objective function, h (x) is an equality constraint, and g (x) is an inequality constraint.

The variables in the OPF problem can be divided into three types: control variables, state variables, and constraint variables. The control variables are variables that can be adjusted to minimize the objective function, such as the active power of the generator, the tap ratio and phase angle of the transformer, and the state variables are variables that change according to the state of the power system. There is this. In addition, the constraint variables represent the operating limits of the elements constituting the power system, and include the voltage magnitude of the load busbar, the active power and the reactive power of the generator busbar, the active power and the reactive power of the transmission line, and the like. Variables also belong to this.

First, referring to the objective function, the objective function according to the preferred embodiment of the present invention uses the secondary cost function of the generator for power generation, and the secondary cost function of the generator is expressed by Equation 1 below.

Where α _i , β _i and γ _i are the fuel cost coefficients for each generator provided by the power exchange, and P _Gi is the amount of power generated by the generator i.

The objective function according to the preferred embodiment of the present invention is represented by the sum of the cost functions of the generator required for power generation in the power system and is represented by Equation 2.

Next, look at the equation constraints, the power of each bus in the steady state of the power system should be balanced, so the sum of the power entering and leaving the bus should be zero.

Therefore, the equation constraint equation is expressed as Equation 3.

Where P _k and Q _k are the active and reactive power supplied to the line connected to bus k, respectively, P _Gk And Q is _Gk, P _Dk, the active power and reactive power of the generator to be supplied to each bus bar k Wow Q _Dk means active power and reactive power supplied to the load of bus k, respectively.

The inequality constraint is expressed as Equation 4 as a condition indicating inequality such as physical limitations that allow the equipment installed in the power system to operate normally and a restriction to secure the safety of the system.

Equation (1) is the active power limitation condition for each generator, (2) is the reactive power limit condition for each generator, (3) is the maximum line capacity limit condition, (4) is the bus voltage limit condition, and (5) is the tap ratio limit of transformer The condition (6) represents the phase displacement limit condition of the transformer.

In order to minimize the objective function of Equation 2 under such equation constraints and inequality constraints, the present invention uses the extended Lagrangian function and the Newton-Raphson method.

The extended Lagrangian function reflecting the equality and inequality constraints is expressed as in Equation 5.

Here, the penalty function of the inequality constraint is a function used to move the variable within the allowable range when the variable for the inequality constraint is out of the allowable range. The case is not included in Equation 5.

First, considering only the equation constraints, the extended Lagrangian function is expressed as in Equation 6.

According to the optimization theory, Kuhn-Tucker's requirement for the optimal solution should be zero when the equation 6 is differentiated into each variable. Therefore, the derivative of the variable x (the vector of the control variable and the state variable) and the lambda (the Lagrangian vector of the equality constraint) is expressed by Equation 7.

In order to find the solution of Equation 7 using the Newton-Rabson method, Equation 7 is expanded to the second term of the Taylor series and approximated as Equation 8, where Equation 9 is expressed as a matrix. do.

The matrix W represents Equation 6 as a quadratic partial differential matrix for x and λ, where H is a Hessian matrix and J is a Jacobian matrix. The matrix W is a symmetric matrix and the quadratic partial derivatives for λ are zero, so the bottom right matrix element of the matrix W is zero. G is the first-order partial differential matrix of Equation 6, and Lagrange's coefficients λp and λ _q are the incremental costs of active and reactive power, respectively, and P _G , V and θ of the variable x are the active power and Voltage magnitude, phase angle of bus voltage.

Finally, after setting the appropriate initial value for the variable z, calculating the matrix W and g at the initial value, calculate the change amount of z by Equation (9), and change the z value until the change amount of z is below a certain level. Repeating this process with an update yields an optimal solution that minimizes the extended Lagrangian function.

However, since this does not reflect the inequality constraint, the calculated optimal solution may not satisfy the inequality constraint. Therefore, the processing method in the case where the inequality constraint is not satisfied will be described.

In a preferred embodiment of the present invention, a penalty method is used for converting an inequality constraint into an equality constraint by adding a penalty term to an objective function for a variable that does not satisfy the inequality constraint.

Variables that are added to the objective function as a penalty term outside of the allowable range of the inequality constraint are called active sets. In a preferred embodiment of the present invention, the variables selected as active sets are penalty terms of the form Is added to the objective function.

와

는 k 번째 변수의 최대, 최소 한계를 나타내며, μ와 c는 변수 k 번째 변수 g_k가 허용 범위를 벗어나면 목적함수의 비용을 증가시키는 증가율로서 부등식 제약 조건 범위 내로 이동시키기 위해 적용되는 페널티 계수를 나타낸다.Here, k is an identification means used to distinguish the variables selected as the active set means k-th variable,

Wow

Is the maximum and minimum limits of the k th variable, and μ and c are the increments that increase the cost of the objective function if the k th variable g _k is outside the acceptable range, and is the penalty factor applied to move within the inequality constraint. Indicates.

The penalty coefficient is constantly updated to move the variable within the allowable range each time the active set is newly selected, thereby continuously increasing the cost of the objective function. The method of updating the penalty coefficient is shown in Equation (11).

Where c ⁰ And β values are selected by experience.

In other words, in an optimal solution that takes into account only equality constraints, if a variable falls outside the allowable range of the inequality constraint, a penalty term for that variable is added to increase the cost of the objective function and move the value of the variable within the allowable range. do.

The theoretical content of the OPF calculation method described above is summarized with reference to FIG.

4 is a flowchart illustrating an OPF calculation method according to a preferred embodiment of the present invention.

First, an equal value constraint and an inequality constraint and initial values of state variables, control variables, and Lagrange variables (hereinafter, referred to as “z vectors”) are received from an administrator (S410). Constraints and initial values, the administrator analyzes real-time system tidal current data received from EMS and inputs appropriate values arbitrarily.

Next, the active set is selected by determining whether each variable of the z vector satisfies the inequality constraint (S420). The variable selected as the active set is reflected in the objective function as a penalty term. However, since the initial value is generally set to satisfy the inequality constraint, it is rare to select the active set in the first cycle.

After the selection of the active set is completed, the matrix W and g of Equation 9 in the variable z are calculated using the extended Lagrangian function reflecting the penalty terms of the equality and inequality constraints (S430). The change amount Δz is calculated (S440).

Then, z is updated by adding Δz to the existing variable z (S450), and it is determined whether the magnitude of Δz (∥z∥) is smaller than a predetermined small enough number (ε) to confirm convergence ( S460). Here, ε can be set arbitrarily, but usually it is set to 0.001.

If ∥z∥ is greater than ε, repeat step S460 in step S430. If ∥z∥ is less than ε, and convergence is confirmed, it is determined whether each variable in the z vector satisfies the inequality constraint (S470). The result value (optimum solution) of each variable of the obtained z vector is output (S480). However, if the inequality constraint is not satisfied, the process proceeds to step S420. At this time, the penalty terms and the penalty coefficients of the variables selected as the active set in step S420 are set or updated using Equations 10 and 11, respectively.

The generator's effective power output and minimum cost, calculated in this way, will allow the utility to develop an economic dispatch plan.

Hereinafter, a method of calculating an IGP using the optimal solution calculated from the OPF calculation result will be described.

The IGP is calculated by the OPF calculation, the effective power output per generator, the fuel cost per unit (FC _i , which means the minimum generation cost per generator), and the fuel cost factor (α _i ,). β _i , γ _i ) is calculated using the equation (12).

Here, x means transaction time in units of the development plan report period, y means the last transaction time of the development plan report period.

Each variable used in Equation 12 is defined as shown in Table 1.

variable Justice Explanation QPC _i Secondary Price Coefficient 2nd order coefficients of the 2nd price characteristic curve showing the relationship between generator output and fuel cost LPC _i First Linear Price Coefficient Primary coefficient of the second price characteristic curve representing the relationship between generator output and fuel cost NLPC _i No Load Price Coefficient Constant of the second-order price characteristic curve representing the relationship between generator output and fuel cost TPD Trading Period Duration 1 hour of trading time SUC _i Startup cost Cost of starting generator PSE _{i , t} Generator active power output Generator active power output

Where QPC _i , LPC _i and NLPC _i are calculated by multiplying the fuel heat unit cost FC _i by the secondary heat consumption coefficient γ _i , the primary heat consumption coefficient β _i and the heat consumption constant α _i , respectively.

5 is a flowchart illustrating an IGP calculation method according to a preferred embodiment of the present invention.

First, the optimum output amount for each generator and the minimum generation cost for each generator calculated by the above-described OPF operation are received (S510).

And α _i , β _i , the γ _i multiplied by the QPC FC _{i i,} LPC _i and NLPC _i are calculated (S520).

Subsequently, when the TPD is input from the manager, the SUC _{i previously} stored for each generator is read, and the IGP for each generator is calculated using Equation 12 (S530).

The IGP for each generator calculated using Equation 12 may be used as an important index for determining a bid price for each power generation company in the bidding market. For example, if there are three generation companies A, B, and C and need 50 [MWh] of power, each generator has OPF calculation according to the method described above. If the IGP calculation is performed using Equation 12 and the minimum generation cost is calculated, the IGP value of each generator is calculated. Assuming that the total IGP value of the generators owned by each power generation company is summed up, assuming that A power generation company is 500,000 won, B power generation company is 600,000 won, and C power generation company is 700,000 won, A power company is at least 600,000 won. A bidding strategy can be established by selecting a minimum bid price.

The above description is merely illustrative of the technical idea of the present invention, and those skilled in the art to which the present invention pertains may make various modifications and changes without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention but to describe the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The protection scope of the present invention should be interpreted by the following claims, and all technical ideas within the equivalent scope should be interpreted as being included in the scope of the present invention.

As described above, according to the present invention, it is possible to receive and analyze the flow current data of the system from the EMS in real time, so that it is possible to grasp the power system situation and the power supply and demand situation of the continuously changing power system in real time, and use the OPF calculation result It is possible to obtain the optimal power distribution for each generator that can operate the generator at the minimum cost while ensuring the stability of the system.

Furthermore, by calculating the IGP for each generator using the OPF calculation result, when the power company participates in the bidding market, the IGP can be used as an important indicator of bid price determination.

In other words, it is meaningful in that power generation companies can make a great contribution to establishing economic dispatch plans and bidding strategies in the wholesale strategic market.

Claims (21)

In the economic forecasting system using the generation cost minimization method, Energy Management System (EMS), which automatically monitors and controls the power system, analyzes the power system, generates grid flow data, and transmits the generated grid flow data in real time. box); Receives the systemic tidal current data from the EMS and stores the systemic tidal current data for each variable included in the systemic tidal current data from the system data receiving unit and the manager to determine the equation constraints and inequality constraints required for system stability. When the input is received, the optimal output power for each generator is calculated by performing the calculation of the optimum current (OPF: Optimal Power Flow (OPF)) using the sum of the generator secondary cost functions for each generator under the equation constraint and the inequality constraint. A system analysis unit including an OPF calculation unit for calculating a minimum generation cost (hereinafter, referred to as "optimal solution") for each generator; A database storing the branch tidal current data received by the branch data receiver for each variable and storing a result calculated by the branch analyzer; And An operation / management unit for displaying the system current data and the optimal solution and inputting a command to operate the system analysis unit Economic forecasting system using the power generation cost minimization method comprising a. The method of claim 1, The system analysis unit further includes an IGP operation unit for calculating a temporary generation price (IGP: Interim Generating Unit Price (IGP)) for each generator using the optimal solution. Feed prediction system. The method of claim 2, The IGP calculation unit stores an IGP calculation program, but the IGP calculation program includes: (a) inputting the optimum output amount for each generator (hereinafter referred to as "PSE") and the minimum generation cost for each generator calculated by the OPF calculation; (b) The power consumption constant, the primary heat consumption coefficient, and the secondary heat consumption coefficient for each generator are multiplied by the minimum power generation cost for each generator (NLPC: No Load Price Coefficient, hereinafter referred to as "NLPC"), primary Calculating an incremental price coefficient (LPC: Linear Price Coefficient, hereinafter referred to as "LPC") and a second incremental price coefficient (QPC: Quadratic Price Coefficient, hereinafter referred to as "QPC"); And (c) Upon receiving a trading period duration (TPD) information from the manager, calculate the generator IGP by using the following equation and output the generator IGP to the operation / management unit. Steps to

(Where SUC is the start-up cost per generator, x is the first unit transaction time of the generation plan declaration period, and y is the last transaction time of the generation plan declaration period.) Economic forecasting system using the power generation cost minimization method, characterized in that for performing. The method of claim 1, The OPF operation unit stores an OPF program for performing the OPF calculation, the OPF program, (a) state variables, control variables, and Lagrangian variables forming all of the equation constraints and the inequality constraints and the equation constraints and the inequality constraints required by the administrator to stabilize the system; And receiving an initial value for “z vector”); (b) determining whether each variable input to the z vector satisfies the inequality constraint, selecting an unsatisfactory variable as an active set, and a penalty term for the variable selected as the active set. Setting up; (c) a matrix value at a value input into the z vector for the matrix g that first-differentiates the extended Lagrangian function reflecting the equality constraint and the penalty term in the objective function and the matrix W that is second-order partial differentiation. Calculating; (d) performing a multiplication operation of the inverse matrix of W and the -g matrix to calculate a change amount? Z of each variable input to the z vector, and add the? z to the z vector to add the z vector Updating; (e) determining whether or not the size of? z is smaller than a sufficiently small number [epsilon], and proceeding to step (c) if it is large; (f) when the magnitude of Δz is smaller than ε, it is determined whether the values of the variables input to the z vector satisfy the inequality constraint, and select unsatisfactory variables as the new active set, Proceeding to step (c) after setting or updating the petalty term and the penalty coefficient included in the penalty term for the variable selected as the new active set; And (g) when the values of each variable satisfy the inequality constraint, the minimum generation cost for each generator calculated by using the optimal output amount for each generator and the optimum output amount for each generator included in the z vector finally stored. Step to output Economic forecasting system using the power generation cost minimization method, characterized in that for performing. The method according to claim 1 or 4, The equation constraint condition is a power supply prediction system using a power generation cost minimization method characterized in that it comprises a power balance equation for each bus. The method according to claim 1 or 4, The inequality constraints include active power limitation conditions for each generator, reactive power limitation conditions for each generator, maximum line capacity limitation conditions, bus voltage limitation conditions, transformer tap ratio limitation conditions, and transformer phase displacement limitation conditions. Economic forecasting system using power generation cost minimization technique. The method of claim 4, wherein The penalty term is the economic power supply prediction system using a power generation cost minimization method characterized in that using the following equation.

(Wherein x is the state variable and the control variable, g _k is a variable selected as the active set,

Wow

Is the maximum and minimum limits of g _k , and μ and c are the penalty coefficients applied to move within the range of the inequality constraint as a rate of increase that increases the cost of the objective function if g _k is outside the acceptable range. it means.) The method of claim 4, wherein The penalty coefficient is an economic power supply prediction system using a generation cost minimization method, characterized in that for updating using the following equation.

(Where i is the number of iterations.) In the economic forecasting method using the generation cost minimization method, (a) receiving power flow data in real time from an energy management system (EMS); (b) When an administrator inputs the equation constraints and inequality constraints required for system stability by analyzing the system tidal flow data, the sum of the generator secondary cost functions for each generator under the input equation constraints and inequality constraints is input. Performing an optimal algae (OPF) calculation as an objective function; And (c) outputting the optimum output amount for each generator and the minimum generation cost for each generator (hereinafter referred to as "optimal solution") calculated from the OPF calculation Economic feeding forecasting method using a power generation cost minimization method comprising a. The method of claim 9, After step (c), (d) calculating the generator-specific IGP which is an index of bidding price determination using the optimal solution and outputting the generator-specific IGP; Economic feeding forecasting method using the minimizing the cost of power generation, characterized in that it further comprises. The method of claim 10, In step (d), (a) inputting the optimum output amount for each generator (hereinafter referred to as "PSE") and the minimum generation cost for each generator calculated by the OPF calculation; (b) The power consumption constant, the primary heat consumption coefficient, and the secondary heat consumption coefficient for each generator are multiplied by the minimum power generation cost for each generator (NLPC: No Load Price Coefficient, hereinafter referred to as "NLPC"), primary Calculating an incremental price coefficient (LPC: Linear Price Coefficient, hereinafter referred to as "LPC") and a second incremental price coefficient (QPC: Quadratic Price Coefficient, hereinafter referred to as "QPC"); And (c) Upon receiving a trading period duration (TPD) information from the manager, calculate the generator IGP by using the following equation and output the generator IGP to the operation / management unit. Steps to

(Where SUC is the start-up cost per generator, x is the first unit transaction time of the generation plan declaration period, and y is the last transaction time of the generation plan declaration period.) Economic feeding forecasting method using a power generation cost minimization method comprising a. The method of claim 9, In step (b), (b1) state variables, control variables, and Lagrangian variables for forming the equation constraints and the inequality constraints and the equation constraints and the inequality constraints required for the stability of the system from the manager (all the above variables). And receiving an initial value for “z vector”); (b2) determining whether each variable input to the z vector satisfies the inequality constraint, selecting an unsatisfactory variable as an active set, and a penalty term for the variable selected as the active set; Setting up; (b3) A matrix of values input to the z vector for each of the matrix g and the quadratic partial differential matrix W, which firstly differentiate the extended Lagrangian function reflecting the equality constraint and the penalty term in the objective function. Calculating a value; (b4) calculating an amount of change Δz of each variable input to the z vector by multiplying the inverse matrix of W by the -g matrix, and adding the Δz to the z vector to add the z vector Updating; (b5) determining whether or not the size of? z is smaller than a sufficiently small number [epsilon], and proceeding to step (b3) if it is large; (b6) If the magnitude of Δz is smaller than ε, it is determined whether the values of the variables input to the z vector satisfy the inequality constraint, and select the unsatisfactory variable as the new active set. Proceeding to step (b3) after setting or updating the petalty term and the penalty coefficient included in the penalty term for the variable selected as the new active set; And (b7) when all of the values of the variables satisfy the inequality constraint, the minimum generation cost for each generator calculated using the optimal output amount for each generator and the optimum output amount for each generator included in the z vector finally stored; Step to output Economic feeding forecasting method using a power generation cost minimization method comprising a. The method according to claim 9 or 12, The equation constraint is economic power prediction method using a power generation cost minimization method characterized in that it comprises a power balance equation for each bus. The method according to claim 9 or 12, The inequality constraints include active power limitation conditions for each generator, reactive power limitation conditions for each generator, maximum line capacity limitation conditions, bus voltage limitation conditions, transformer tap ratio limitation conditions, and transformer phase displacement limitation conditions. Economic forecasting method using power generation cost minimization technique. The method of claim 12, The penalty term is an economic power supply prediction method using a generation cost minimization method characterized in that using the following equation.

(Wherein x is the state variable and the control variable, g _k is a variable selected as the active set,

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Is the maximum and minimum limits of g _k , and μ and c are the penalty coefficients applied to move within the range of the inequality constraint as a rate of increase that increases the cost of the objective function if g _k is outside the acceptable range. it means.) The method of claim 12, The penalty coefficient is an economic power supply prediction method using a generation cost minimization method, characterized in that for updating using the following equation.

(Where i is the number of iterations.) A program that performs the calculation of Optimal Power Flow (OPF) based on the sum of the generator's secondary cost functions for each generator under equal and inequality constraints required for system stability. In the recording medium recording the (a) a state variable, a control variable and a Lagrangian variable forming all of the equation constraints and the inequality constraints and the equation constraints and the inequality constraints required by the administrator for the stability of the system (including all of the above) Receiving an initial value for “z vector”); (b) determining whether each variable input to the z vector satisfies the inequality constraint, selecting an unsatisfactory variable as an active set, and a penalty term for the variable selected as the active set. Setting up; (c) a matrix value at a value input into the z vector for the matrix g that first-differentiates the extended Lagrangian function reflecting the equality constraint and the penalty term in the objective function and the matrix W that is second-order partial differentiation. Calculating; (d) performing an operation of multiplying the inverse matrix of W by the -g matrix to calculate a change amount? z of each variable input to the z vector, and update the z vector by adding the? z to the z vector. Making; (e) determining whether or not the size of? z is smaller than a sufficiently small number [epsilon], and proceeding to step (c) if it is large; (f) when the magnitude of Δz is smaller than ε, it is determined whether the values of the variables input to the z vector satisfy the inequality constraint, and select unsatisfactory variables as the new active set, Proceeding to step (c) after setting or updating the petalty term and the penalty coefficient included in the penalty term for the variable selected as the new active set; And (g) If all of the values of the variables satisfy the inequality constraints, output the minimum generation cost for each generator calculated using the optimal output amount for each generator and the optimum output amount for each generator included in the z vector finally stored. Steps to Recording medium recording an optimal algal calculation program to perform. The method of claim 17, The equation constraint condition is a recording medium recording an optimal tidal flow calculation program comprising a power balance equation for each bus. The method of claim 17, The inequality constraints include active power limitation conditions for each generator, reactive power limitation conditions for each generator, maximum line capacity limitation conditions, bus voltage limitation conditions, transformer tap ratio limitation conditions, and transformer phase displacement limitation conditions. Recording medium recording an optimal algal calculation program The method of claim 17, The penalty term is a recording medium for recording an optimal bird calculation program, characterized in that using the following equation.

(Wherein x is the state variable and the control variable, g _k is a variable selected as the active set,

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Is the maximum and minimum limits of g _k , and μ and c are the penalty coefficients applied to move within the range of the inequality constraint as an increase rate that increases the cost of the objective function if g _k is outside of an acceptable range. it means.) The method of claim 17, And the penalty coefficient is updated using the following equation.

(Where i is the number of iterations.)

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