Intelligent dynamic power dispatching optimization system

Abstract

The invention provides an intelligent dynamic power dispatching optimization system, which comprises a data acquisition module used for acquiring various data information of operation of a power system; the objective function construction module is used for constructing a power generation cost objective function, a power transmission loss objective function, a system stability objective function and an electric energy quality objective function; the optimization calculation module is used for carrying out iterative calculation in combination with each objective function and a preset constraint condition, and determining an optimal generation power distribution scheme; the scheduling execution module is used for performing power scheduling control on the power generation units based on the optimal power generation power distribution scheme; according to the method, the multi-objective optimization function is constructed, the accurate objective function model is adopted, and the efficient optimization algorithm and the dynamic adaptation mechanism are combined, so that the intelligent dynamic optimization scheduling of the power generation power of the power system is realized, and the comprehensive operation performance of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to an intelligent dynamic power dispatching optimization system. Background Art

[0002] Power system dispatching has existed since the early days of the development of the power industry. Its development has been accompanied by the continuous expansion of the scale of power systems, the increasing complexity of their structures, and the continuous improvement of users' requirements for power quality and power supply reliability. In the early days, the scale of power systems was small, and dispatching work mainly relied on manual experience. Information on each power generation unit and load node was collected through communication means such as telephones, and then the dispatcher issued power generation instructions based on experience.

[0003] With the introduction of the concept of smart grid, power system dispatching is developing towards intelligence; smart grid emphasizes the use of advanced information technology, communication technology, control technology, etc. to achieve high automation, informatization and interactivity of the power system; at this stage, distributed energy resources (such as solar energy, wind power generation, etc.) are connected to the power grid in large quantities, and the power market is gradually opening up, which brings new challenges and opportunities to power system dispatching;

[0004] However, many existing power dispatch optimization methods often focus on optimizing a single goal, such as focusing only on the lowest power generation cost or the highest system stability. However, in the actual operation of the power system, multiple goals such as power generation cost, transmission loss, system stability and power quality are interrelated and influence each other. For example, excessive pursuit of reducing power generation costs may cause power generation units to operate in an inefficient range, affecting system stability. Simply emphasizing system stability may increase unnecessary operating costs and ignore the optimization of power quality.

[0005] In terms of power generation cost calculation, the existing technology is not accurate enough in constructing cost functions for different types of power generation units; the cost model for some new power generation technologies, such as energy storage participating in power generation, is not fully considered; in system stability analysis, the modeling of the dynamic characteristics of complex power systems is not accurate enough, especially in the face of large-scale distributed energy access and the widespread application of power electronic equipment, traditional stability analysis models are difficult to accurately reflect the actual dynamic behavior of the system;

[0006] At the same time, the operating state of the power system is changing dynamically. Factors such as load demand, power generation unit availability, and renewable energy output are constantly changing. Most existing dispatch optimization systems lack the ability to quickly adapt to such dynamic changes and cannot adjust the power distribution plan in time according to changes in the system state, resulting in reduced system operating efficiency and even affecting system stability and reliability.

[0007] Therefore, there is an urgent need in the art for an intelligent dynamic power dispatching optimization system to solve the above problems. Summary of the invention

[0008] The present invention provides an intelligent dynamic power dispatching optimization system, which aims to solve the problems existing in the above-mentioned prior art. By constructing a multi-objective optimization function, adopting an accurate objective function model, and combining an efficient optimization algorithm and a dynamic adaptation mechanism, the intelligent dynamic optimization dispatching of the power generation of the power system is realized, thereby improving the comprehensive operation performance of the power system.

[0009] The present invention provides an intelligent dynamic power dispatch optimization system, comprising:

[0010] Data acquisition module, which is used to collect various data information of power system operation, including operating parameters of power generation units, power quality information, and transmission line parameter information;

[0011] An objective function construction module, which is connected to the data acquisition module and is used to construct a power generation cost objective function, a transmission loss objective function, a system stability objective function and a power quality objective function according to the received data information;

[0012] An optimization calculation module, which is connected to the objective function construction module and is used to perform iterative calculations in combination with each objective function and preset constraints to determine an optimal power distribution scheme;

[0013] The scheduling execution module is connected to the optimization calculation module and performs power scheduling control on the power generation unit based on the optimal power distribution plan.

[0014] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the data acquisition module includes:

[0015] The power generation collection unit is used to obtain the number of power generation units, the power generation of each power generation unit, fuel consumption data, operation and maintenance data, and start and stop status data;

[0016] The quality acquisition unit is used to pre-set a number of measurement points and obtain the voltage amplitude, frequency, and current amplitude parameters collected by each measurement in real time;

[0017] An operation status acquisition unit, which is used to obtain the rotor angle, angular velocity, voltage amplitude, and reactive power parameters of each power generation unit;

[0018] The power transmission collection unit is used to obtain the number of power transmission lines, the transmission power of each power transmission line and the line impedance data.

[0019] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the objective function building module includes:

[0020] A power generation cost function construction unit is connected to the power generation collection unit and establishes a power generation cost function model based on the collected power generation data; the power generation cost function model is:

[0021]

[0022] Among them, C total is the power generation cost; n is the total number of power generation units; P g,i is the power generation of the i-th power generation unit; C f,i (P g,i ) is the fuel cost of the i-th power generation unit; C om,i (P g,i ) is the operation and maintenance cost of the i-th power generation unit; C su,i is the startup cost of the i-th power generation unit, C sd,i is the shutdown cost of the i-th power generation unit; δ i Represents the start and stop state of the i-th power generation unit. When δ i =1 indicates start, δ i =0 means shutdown;

[0023] A power transmission loss function building unit is connected to the power transmission acquisition unit and establishes a power transmission loss function model based on the collected power transmission line parameter information; the power transmission loss function model is:

[0024]

[0025] Among them, P loss is the transmission loss; m is the total number of transmission lines; R j and X j are the resistance and impedance of the jth transmission line respectively; P j and Q j are the active power and reactive power flows on the jth transmission line respectively; V j is the voltage amplitude of the jth transmission line;

[0026] A system stability function building unit is connected to the operating status acquisition unit and builds a system stability function model based on the acquired operating status data; the system stability function model is:

[0027] λ min =min k [R e (λ k )]

[0028] Among them, λ min is the system stability coefficient; k is the kth eigenvalue of the power system state matrix, R e Represents its real part; mink Represents minimization calculation; by minimizing the minimum real part eigenvalue, it ensures that the power system has sufficient small signal stability margin to resist small disturbances and maintain stable operation;

[0029] The method for obtaining the power system state matrix and determining the eigenvalue is as follows:

[0030] The rotor angle, angular velocity, voltage amplitude, and reactive power parameters are defined as state variables. After linear analysis, the state matrix is ​​obtained. The eigenvalue solving function in the numerical calculation software is used to solve the state matrix and determine the eigenvalues ​​λ fk ; Take the real part of the eigenvalue with the smallest real part as the system stability coefficient λ min ;

[0031] A power quality function building unit is connected to the quality acquisition unit and establishes a power quality function model based on the collected power quality information; the power quality function model is:

[0032]

[0033] Among them, Q qua is the power quality; p is the total number of measurement points; V l and V rated,l are the actual voltage amplitude and rated voltage amplitude of the lth measuring point respectively; f l and f rated are the actual frequency and rated frequency of the lth measuring point respectively; I h,l is the hth harmonic current amplitude of the lth measuring point; α l , β l , γ h,l The weight coefficients corresponding to voltage deviation, frequency deviation and harmonic content are preset according to the power quality standard.

[0034] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the fuel cost C of the i-th power generation unit f,i (P g,i ) is expressed as:

[0035] C f,i (P g,i )=a i (P g,i ) 2 +b i P g,i +c i

[0036] Among them, the coefficient a i Depends on the quadratic coefficient of the thermal cycle efficiency curve of the steam turbine. The lower the thermal efficiency, the higher the coefficient a. iThe larger the coefficient b i It is related to the linear relationship between fuel consumption rate and power generation, and is used to reflect the additional fuel cost required for each additional unit of power generation within a certain power generation range; coefficient c i Represents fixed cost items including basic fuel handling costs.

[0037] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the operation and maintenance cost C of the i-th power generation unit is om,i (P g,i ) is expressed as:

[0038] C om,i (P g,i ) = d i P g,i +e i

[0039] Among them, the coefficient d i It is related to the degree of equipment wear and power generation. The higher the power generation, the greater the equipment wear and the higher the maintenance cost required. It is used to reflect this linear relationship. The coefficient e i Fixed operation and maintenance costs, including regular inspection and maintenance costs of equipment, do not change with changes in power generation.

[0040] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the optimization calculation module includes:

[0041] The target optimization function construction unit is used to integrate the power generation cost, transmission loss, system stability factor, and power quality to construct a target optimization function model. The target optimization function model is:

[0042] F=ω1C total +ω2P loss +ω3λ min +ω4Q qua

[0043] Wherein, F is the objective function value, ω1, ω2, ω3, ω4 represent the weight of power generation cost, transmission loss, system stability, and power quality, respectively, which are pre-set based on the emphasis, and ω1+ω2+ω3+ω4=1;

[0044] The particle optimization unit searches for the minimum objective function value based on the particle swarm optimization algorithm, defines the minimum objective function value as the ideal target, and defines the power generation allocation scheme corresponding to the global optimal position of the value as the optimal power generation allocation scheme.

[0045] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the optimization process of the particle optimization unit includes:

[0046] When n power generation units are defined, the position vector of each particle is represents the power distribution scheme corresponding to the i-th particle, where is the power generation of the jth power generation unit in the ith particle;

[0047] Defines the velocity vector of the particle Used to update the position of particles, the speed update formula is:

[0048]

[0049] Among them, ω is the inertia weight, which is used to balance the global search and local search capabilities of the particle; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers uniformly distributed in the interval 0-1; is the power generated by the jth power generation unit in the best position experienced by the i-th particle; is the power generation of the jth power generation unit in the best position experienced by the entire particle swarm;

[0050] Update the particle's position according to the updated velocity. The formula is: During the update process, it is necessary to check whether the constraints are met, and if not, the particle position is corrected;

[0051] Initialize the particle swarm, including the position and velocity of the particles; then, for each particle, calculate the objective function value F according to its position;

[0052] Update the individual optimal position of each particle and the global optimal position of the entire particle swarm

[0053] Repeat the speed and position update steps until a stop criterion is met, wherein the stop criterion includes reaching a maximum number of iterations or the objective function value F converges;

[0054] Finally, define the global optimal position The corresponding power generation allocation scheme is the optimal power generation allocation scheme.

[0055] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the preset constraint condition is the power generation unit output constraint, that is, When the constraints are not met, they are corrected to the boundary values.

[0056] According to an intelligent dynamic power dispatch optimization system provided by the present invention, the dispatch execution module includes:

[0057] A power distribution unit connected to the particle optimization unit, configured to receive an optimal power distribution plan and generate a corresponding power distribution instruction;

[0058] The execution control unit is used to receive the power distribution instruction and control the power output of each power generation unit.

[0059] Compared with the prior art, the beneficial effects of this application are:

[0060] This application simultaneously considers the four key objectives of power generation cost, transmission loss, system stability and power quality, and constructs a multi-objective optimization function; by reasonably setting the weight coefficient, it is possible to flexibly adjust the priority of each objective in different operating scenarios to achieve an effective balance between the objectives; for example, during the peak load period of the power system, the weight of system stability and power quality can be appropriately increased to ensure reliable power supply of the system; during the low load period, it can focus on reducing power generation cost and transmission loss to improve the economic efficiency of system operation; this multi-objective comprehensive optimization method overcomes the limitation of the single objective of the existing technology and significantly improves the overall performance of the power system;

[0061] In the power generation cost objective function, the fuel cost, operation and maintenance cost, startup cost and shutdown cost of different types of power generation units (such as thermal power, hydropower, wind power, etc.) are considered, and an accurate function model is established based on their physical characteristics; for the transmission loss objective function, the influence of factors such as line resistance, impedance, and active and reactive power flow on the loss is accurately considered; the system stability objective function ensures that the system has sufficient small signal stability margin by accurately analyzing the eigenvalues ​​of the power system state matrix; the power quality objective function comprehensively covers key indicators such as voltage deviation, frequency deviation and harmonic content; these accurate objective function models can more accurately reflect the actual operation of the power system, thereby improving the accuracy of the optimization calculation and providing a more reliable basis for the optimal scheduling of the system, which is a significant improvement compared to the inaccurate model in the existing technology;

[0062] The particle swarm optimization algorithm is used to perform optimal calculations for power generation allocation schemes. The algorithm can effectively search for the optimal solution in a complex solution space by defining the position and velocity vectors of the particles and performing iterative calculations using the velocity update formula and the position update formula. Compared with traditional optimization algorithms, the particle swarm optimization algorithm has better global search capabilities and is not prone to falling into local optimal solutions. At the same time, by reasonably setting parameters such as inertia weights and learning factors, the convergence speed of the algorithm can be accelerated, the calculation efficiency can be improved, and the requirements for real-time dispatching of the power system can be met. This solves the problems of low calculation efficiency and easy falling into local optimality in the optimization algorithm in the prior art, and can find the optimal power generation allocation scheme more quickly and accurately.

[0063] The present application has good dynamic adaptability and can respond to changes in the operating status of the power system in real time; whether it is a sudden fluctuation in load demand, a failure or start-up and shutdown of a power generation unit, or a change in the output of renewable energy, the system can respond quickly to ensure that the power system always operates in the optimal state; this dynamic adaptability overcomes the defect of the existing technology that lacks dynamic adjustment capabilities, effectively ensures the stability, reliability and economy of the power system, and improves the system's ability to adapt to complex and changing operating environments.

[0064] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0065] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0067] Figure 1 It is a structural diagram of an intelligent dynamic power dispatching optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] Embodiment 1:

[0070] The embodiment of the present invention provides an intelligent dynamic power dispatch optimization system, please refer to Figure 1 ,include:

[0071] Data acquisition module, which is used to collect various data information of power system operation, including operating parameters of power generation units, power quality information, and transmission line parameter information;

[0072] An objective function construction module, which is connected to the data acquisition module and is used to construct a power generation cost objective function, a transmission loss objective function, a system stability objective function and a power quality objective function based on the received data information;

[0073] The optimization calculation module is connected to the objective function construction module and is used to perform iterative calculations in combination with various objective functions and preset constraints to determine the optimal power distribution scheme;

[0074] The scheduling execution module is connected to the optimization calculation module and performs power scheduling control on the power generation unit based on the optimal power distribution plan.

[0075] The principles and beneficial effects of this embodiment are as follows: through the data acquisition module, the system can obtain data information of each key point in the power system in real time; the objective function construction module takes into account multiple aspects such as power generation cost, transmission loss, system stability and power quality; this multi-objective optimization method aims to find a balance point, while meeting power demand, minimizing costs and losses, and maintaining system stability and power quality; the optimization calculation module uses an iterative calculation method to determine the optimal power generation distribution plan; this method can handle complex nonlinear problems and find the best one or a group of solutions among many possible solutions; the scheduling execution module automatically adjusts the output power of the power generation unit according to the calculated optimal solution; this not only improves efficiency and reduces the possibility of human error, but also can quickly respond to changes in the power system and enhance the flexibility and adaptability of the system.

[0076] In order to further optimize the above embodiment, the data acquisition module includes:

[0077] The power generation collection unit is used to obtain the number of power generation units, the power generation of each power generation unit, fuel consumption data, operation and maintenance data, and start and stop status data;

[0078] The quality acquisition unit is used to pre-set a number of measurement points and obtain the voltage amplitude, frequency, and current amplitude parameters collected by each measurement in real time;

[0079] An operation status acquisition unit, which is used to obtain the rotor angle, angular velocity, voltage amplitude, and reactive power parameters of each power generation unit;

[0080] The power transmission collection unit is used to obtain the number of power transmission lines, the transmission power of each power transmission line and the line impedance data.

[0081] It should be noted that the total number of registered power generation units is obtained through the communication interface with the power generation unit management system in the power system. Or when the system is initialized, the number of power generation units is manually entered. For traditional power generation units (such as thermal power and hydropower), a power sensor can be installed at the generator outlet. The sensor converts the real-time measured power generation data into an electrical signal and transmits it to the data acquisition module through wired (such as RS-485, Ethernet, etc.) or wireless (such as Wi-Fi, ZigBee, etc.) communication methods. For distributed power generation units (such as solar photovoltaic panels and small wind turbines), if they are equipped with smart inverters, the power generation data can be obtained through the communication interface of the inverter; if there is no smart inverter, additional power monitoring equipment needs to be installed to collect the power generation information and transmit it to the power generation acquisition unit. A flow meter is installed on the fuel supply pipeline to measure the flow data of the fuel, and the fuel consumption is calculated in combination with parameters such as the calorific value of the fuel. At the same time, a liquid level sensor can also be installed at the fuel storage tank to assist in calculating the fuel consumption through the change in liquid level. These data transmit the fuel consumption data to the power generation acquisition unit through the corresponding signal conversion and transmission device.

[0082] The operation and maintenance data of the power generation unit includes equipment operating time, maintenance records, fault alarm information, etc. These data can be obtained through the monitoring system of the power generation unit itself. The monitoring system stores the operation and maintenance data in the local database. The power generation acquisition unit obtains the operation and maintenance data by regularly querying the database or receiving database push. In addition, for the operating status of some key equipment (such as temperature, pressure, vibration, etc.), corresponding sensors can be installed for real-time monitoring. The sensor data is transmitted to the power generation acquisition unit for analyzing the degree of wear and operation and maintenance requirements of the equipment. A status monitoring relay is connected to the control circuit of the power generation unit. When the power generation unit is started or shut down, the relay state changes, and its signal is transmitted to the power generation acquisition unit through the digital input interface, thereby obtaining the start and stop status information of the power generation unit.

[0083] According to the scale and structural characteristics of the power system, several measuring points are pre-set at key nodes of the power grid (such as substation busbars, important load access points, etc.). The selection of measuring points should be able to fully reflect the power quality status, such as setting measuring points at substation busbars of different voltage levels, large industrial load access points, residential distribution rooms, etc.

[0084] A multifunctional power quality monitor is installed at each measuring point. The instrument can measure parameters such as voltage amplitude, frequency, and current amplitude in real time. The voltage transformer and current transformer inside the monitor convert high voltage and high current signals into small signals suitable for measurement. After analog-to-digital conversion, the voltage amplitude, frequency, and current amplitude are calculated using a microprocessor. These data are transmitted to the quality acquisition unit via wired (such as Ethernet, optical fiber, etc.) or wireless (such as 4G, 5G communication network) communication methods.

[0085] Photoelectric encoders or rotary transformers are installed on the generator rotor shaft. These sensors can convert the mechanical angle of the rotor into electrical signals, which are converted into digital signals through signal conditioning circuits, and then the rotor angle and angular velocity information is calculated. For large generators, the rotor angle and angular velocity data can also be obtained using the generator's own monitoring system, and the operating status acquisition unit is connected to it through a communication interface to obtain data.

[0086] Voltage transformers and power sensors are installed at the generator output bus and key nodes of the power grid. The voltage transformer converts high voltage into standard voltage signals, and the power sensor measures active power and reactive power, and obtains the reactive power value through calculation. After signal processing and analog-to-digital conversion, these measurement signals are transmitted to the operation status acquisition unit to obtain voltage amplitude and reactive power parameters.

[0087] Communicate with the grid topology management system of the power system to obtain the topology information of the transmission line and extract the number of transmission lines from it. The number of transmission lines and related line information can also be manually input when the system is initialized.

[0088] In the substations at both ends of the transmission line, power measurement devices (such as power meters, power quality monitors, etc.) are used to measure the active power and reactive power transmission data of the line. The measurement data is transmitted to the transmission acquisition unit through the intra-station communication network (such as Ethernet, intra-station bus, etc.).

[0089] For newly built transmission lines, line impedance data can be obtained from line design data, including line resistance, reactance and other parameters, and stored in the system database. The transmission acquisition unit reads from the database when needed. For transmission lines that are already in operation, line parameter test equipment can be used to perform regular measurements and update the impedance data in the database to ensure the accuracy of the data. At the same time, some advanced power system monitoring equipment can also monitor changes in line impedance in real time and directly transmit the data to the transmission acquisition unit.

[0090] In order to further optimize the above embodiment, the objective function building module includes:

[0091] The power generation cost function building unit is connected to the power generation collection unit and establishes a power generation cost function model based on the collected power generation data; the power generation cost function model is:

[0092]

[0093] Among them, C total is the power generation cost; n is the total number of power generation units; P g,i is the power generation of the i-th power generation unit; C f,i (P g,i ) is the fuel cost of the i-th power generation unit; C om,i (P g,i ) is the operation and maintenance cost of the i-th power generation unit; C su,i is the startup cost of the i-th power generation unit, C sd,i is the shutdown cost of the i-th power generation unit; δ i Represents the start and stop state of the i-th power generation unit. When δ i =1 indicates start, δ i =0 means shutdown;

[0094] The transmission loss function building unit is connected to the transmission acquisition unit and establishes a transmission loss function model based on the collected transmission line parameter information; the transmission loss function model is:

[0095]

[0096] Among them, P loss is the transmission loss; m is the total number of transmission lines; R j and X j are the resistance and impedance of the jth transmission line respectively; P j and Q j are the active power and reactive power flows on the jth transmission line respectively; V j is the voltage amplitude of the jth transmission line;

[0097] The system stability function building unit is connected to the operation status acquisition unit and establishes a system stability function model based on the acquired operation status data; the system stability function model is:

[0098] λ min =min k [R e (λ k )]

[0099] Among them, λ min is the system stability coefficient; k is the kth eigenvalue of the power system state matrix, R e Represents its real part; min kRepresents minimization calculation; by minimizing the minimum real part eigenvalue, it ensures that the power system has sufficient small signal stability margin to resist small disturbances and maintain stable operation;

[0100] The method for obtaining the power system state matrix and determining the eigenvalue is as follows:

[0101] The rotor angle, angular velocity, voltage amplitude, and reactive power parameters are defined as state variables. After linear analysis, the state matrix is ​​obtained. The eigenvalue solving function in the numerical calculation software is used to solve the state matrix and determine the eigenvalues ​​λ k ; Take the real part of the eigenvalue with the smallest real part as the system stability coefficient λ min ;

[0102] The power quality function building unit is connected to the quality acquisition unit and establishes a power quality function model based on the collected power quality information; the power quality function model is:

[0103]

[0104] Among them, Q qua is the power quality; p is the total number of measurement points; V l and V rated,l are the actual voltage amplitude and rated voltage amplitude of the lth measuring point respectively; f l and f rated are the actual frequency and rated frequency of the lth measuring point respectively; I h,l is the hth harmonic current amplitude of the lth measuring point; α l , β l , γ h,l The weight coefficients corresponding to voltage deviation, frequency deviation and harmonic content are preset according to the power quality standard.

[0105] It should be noted that the fuel cost C of the i-th power generation unit is f,i (P g,i ) is expressed as:

[0106] C f,i (P g,i )=a i (P g,i ) 2 +b i P g,i +c i

[0107] Among them, the coefficient a i Depends on the quadratic coefficient of the thermal cycle efficiency curve of the steam turbine. The lower the thermal efficiency, the higher the coefficient a. i The larger the coefficient b iIt is related to the linear relationship between fuel consumption rate and power generation, and is used to reflect the additional fuel cost required for each additional unit of power generation within a certain power generation range; coefficient c i represents fixed cost items, including basic fuel handling costs;

[0108] The operation and maintenance cost C of the i-th power generation unit om,i (P g,i ) is expressed as:

[0109] C om,i (P g,i ) = d i P g,i +e i

[0110] Among them, the coefficient d i It is related to the degree of equipment wear and power generation. The higher the power generation, the greater the equipment wear and the higher the maintenance cost required. It is used to reflect this linear relationship. The coefficient e i Fixed operation and maintenance costs, including regular inspection and maintenance costs of equipment, do not change with changes in power generation.

[0111] In order to further optimize the above embodiment, the optimization calculation module includes:

[0112] The target optimization function construction unit is used to integrate power generation cost, transmission loss, system stability factor, and power quality to construct a target optimization function model. The target optimization function model is:

[0113] F=ω1C total +ω2P loss +ω3λ min +ω4Q qua

[0114] Wherein, F is the objective function value, ω1, ω2, ω3, ω4 represent the weight of power generation cost, transmission loss, system stability, and power quality, respectively, which are pre-set based on the emphasis, and ω1+ω2+ω3+ω4=1;

[0115] The particle optimization unit searches for the minimum objective function value based on the particle swarm optimization algorithm, defines the minimum objective function value as the ideal target, and defines the power generation allocation scheme corresponding to the global optimal position of the value as the optimal power generation allocation scheme.

[0116] It should be noted that the optimization process of the particle optimization unit includes:

[0117] When n power generation units are defined, the position vector of each particle is represents the power distribution scheme corresponding to the i-th particle, where is the power generation of the jth power generation unit in the ith particle;

[0118] Defines the velocity vector of the particle Used to update the position of particles, the speed update formula is:

[0119]

[0120] Among them, ω is the inertia weight, which is used to balance the global search and local search capabilities of the particle; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers uniformly distributed in the interval 0-1; is the power generated by the jth power generation unit in the best position experienced by the i-th particle; is the power generation of the jth power generation unit in the best position experienced by the entire particle swarm;

[0121] Update the particle's position according to the updated velocity. The formula is: During the update process, it is necessary to check whether the constraints are met, and if not, the particle position is corrected;

[0122] Initialize the particle swarm, including the position and velocity of the particles; then, for each particle, calculate the objective function value F according to its position;

[0123] Update the individual optimal position of each particle and the global optimal position of the entire particle swarm

[0124] Repeat the speed and position update steps until the stopping criteria are met, which include reaching the maximum number of iterations or the objective function value F converges;

[0125] Finally, define the global optimal position The corresponding power generation allocation scheme is the optimal power generation allocation scheme;

[0126] The preset constraint condition is the power generation unit output constraint, that is, When the constraints are not met, they are corrected to the boundary values.

[0127] In order to further optimize the above embodiment, the scheduling execution module includes:

[0128] A power distribution unit connected to the particle optimization unit is used to receive the optimal power distribution plan and generate a corresponding power distribution instruction;

[0129] The execution control unit is used to receive the power distribution instruction and control the power output of each power generation unit.

[0130] It should be noted that the power distribution unit is connected to the particle optimization unit through a high-speed communication interface (such as an Ethernet interface) to receive the optimal power distribution plan calculated by the particle optimization unit in real time. The plan is transmitted in a specific data format (such as a data packet containing the number of each power generation unit and its corresponding optimal power value). The power distribution unit has a corresponding data parsing function and can accurately extract and understand this information.

[0131] According to the received optimal power distribution plan, the power distribution unit generates power distribution instructions according to the power system communication protocol standard (such as IEC61850, etc.). The instruction content includes the address identification of the target power generation unit, the set power value and related control parameters (such as power adjustment rate, etc.). For different types of power generation units (such as thermal power, hydropower, wind power, etc.), the instruction format may be different. The power distribution unit has the ability to convert the instruction format according to the type of power generation unit to ensure that the instruction can be correctly identified and executed by each power generation unit.

[0132] After generating the power distribution instruction, the power distribution unit will verify the instruction to check the integrity and accuracy of the instruction and whether it complies with the system operation constraints (such as the output limit of the power generation unit, etc.). If a problem is found in the instruction (such as the power value is out of range, etc.), it will be corrected or optimized accordingly. At the same time, the power distribution unit can also further optimize and adjust the instruction according to the real-time operating status of the power system (such as the current network topology, load demand change trend, etc.) to improve the stability and economy of the system operation. For example, when the system has a small load fluctuation, the power distribution instruction is appropriately adjusted to avoid frequent and large output adjustments of the power generation unit, reduce equipment wear and system fluctuations.

[0133] The execution control unit is connected to the power distribution unit through a reliable communication link (such as an optical fiber communication network) to receive the power distribution instructions. The communication link has high reliability and real-time performance, ensuring that the instructions can be accurately transmitted to the execution control unit. The execution control unit decodes and verifies the received instructions, confirms the source and validity of the instructions, and prevents illegal instructions or erroneous instructions from causing adverse effects on the power generation unit.

[0134] According to the received power distribution instruction, the execution control unit converts the power setting value in the instruction into a corresponding control signal (such as an analog voltage or current signal, a digital control signal, etc.), and performs signal amplification and conditioning so that it can drive the power regulation equipment of the power generation unit (such as the speed governor of the steam turbine, the guide vane adjustment mechanism of the water turbine, the pitch angle controller of the wind turbine, etc.). For different types of power generation units, the execution control unit has corresponding signal conversion and adaptation functions to meet the control requirements of different power generation equipment.

[0135] The execution control unit sends a control signal to the power regulating device of the power generation unit to adjust the output of the power generation unit. At the same time, the execution control unit monitors the actual power output of the power generation unit in real time and obtains feedback information through the power sensor installed at the output end of the power generation unit. The actual power generation is compared with the power generation required by the instruction to calculate the power deviation. If the deviation exceeds the allowable range, the execution control unit adjusts the control signal according to the pre-set control algorithm (such as PID control algorithm, etc.) to reduce the power deviation so that the actual power generation of the power generation unit can track the power generation required by the instruction as soon as possible. This feedback control mechanism can ensure that the power generation unit accurately adjusts the output according to the power generation allocation instruction and improves the control accuracy and stability of the power system. In addition, the execution control unit also has fault detection and protection functions. When a fault occurs in the power generation unit or the power regulating device, it can detect the fault signal in time and take corresponding protection measures (such as stopping adjustment, issuing an alarm signal, switching to standby equipment, etc.) to prevent the fault from expanding and ensure the safe operation of the power system.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent dynamic power dispatch optimization system, characterized in that: include: Data acquisition module, which is used to collect various data information of power system operation, including operating parameters of power generation units, power quality information, and transmission line parameter information; An objective function construction module, which is connected to the data acquisition module and is used to construct a power generation cost objective function, a transmission loss objective function, a system stability objective function and a power quality objective function according to the received data information; An optimization calculation module, which is connected to the objective function construction module and is used to perform iterative calculations in combination with each objective function and preset constraints to determine an optimal power distribution scheme; The scheduling execution module is connected to the optimization calculation module and performs power scheduling control on the power generation unit based on the optimal power distribution plan.

2. According to claim 1, an intelligent dynamic power dispatch optimization system is characterized in that: The data acquisition module comprises: The power generation collection unit is used to obtain the number of power generation units, the power generation of each power generation unit, fuel consumption data, operation and maintenance data, and start and stop status data; The quality acquisition unit is used to pre-set a number of measurement points and obtain the voltage amplitude, frequency, and current amplitude parameters collected by each measurement in real time; An operation status acquisition unit, which is used to obtain the rotor angle, angular velocity, voltage amplitude, and reactive power parameters of each power generation unit; The power transmission collection unit is used to obtain the number of power transmission lines, the transmission power of each power transmission line and the line impedance data.

3. The intelligent dynamic power dispatch optimization system according to claim 2 is characterized in that: The objective function building module includes: A power generation cost function construction unit is connected to the power generation collection unit and establishes a power generation cost function model based on the collected power generation data; the power generation cost function model is: Among them, C total is the power generation cost; n is the total number of power generation units; P g,i is the power generation of the i-th power generation unit; C j,i (P g,i ) is the fuel cost of the i-th power generation unit; C om,i (P g,i ) is the operation and maintenance cost of the i-th power generation unit; C su,i is the startup cost of the i-th power generation unit, C sd,i is the shutdown cost of the i-th power generation unit; δ i Represents the start and stop state of the i-th power generation unit. When δ i =1 indicates start, δ i =0 means shutdown; A power transmission loss function building unit is connected to the power transmission acquisition unit and establishes a power transmission loss function model based on the collected power transmission line parameter information; the power transmission loss function model is: Among them, P loss is the transmission loss; m is the total number of transmission lines; R j and X j are the resistance and impedance of the jth transmission line respectively; P j and Q j are the active power and reactive power flows on the jth transmission line respectively; V j is the voltage amplitude of the jth transmission line; A system stability function building unit is connected to the operating status acquisition unit and builds a system stability function model based on the acquired operating status data; the system stability function model is: l min =min k [R e (l k )] Among them, λ min is the system stability coefficient; k is the kth eigenvalue of the power system state matrix, R e Represents its real part; min k Represents minimization calculation; by minimizing the minimum real part eigenvalue, it ensures that the power system has sufficient small signal stability margin to resist small disturbances and maintain stable operation; The method for obtaining the power system state matrix and determining the eigenvalue is as follows: The rotor angle, angular velocity, voltage amplitude, and reactive power parameters are defined as state variables. After linear analysis, the state matrix is ​​obtained. The eigenvalue solving function in the numerical calculation software is used to solve the state matrix and determine the eigenvalues ​​λ k ; Take the real part of the eigenvalue with the smallest real part as the system stability coefficient λ min ; A power quality function building unit is connected to the quality acquisition unit and establishes a power quality function model based on the collected power quality information; the power quality function model is: Among them, Q qua is the power quality; p is the total number of measurement points; V l and V rated,l are the actual voltage amplitude and rated voltage amplitude of the lth measuring point respectively; f l and f rated are the actual frequency and rated frequency of the lth measuring point respectively; I h,l is the hth harmonic current amplitude of the lth measuring point; α l , β l , γ h,l The weight coefficients corresponding to voltage deviation, frequency deviation and harmonic content are preset according to the power quality standard.

4. The intelligent dynamic power dispatch optimization system according to claim 3 is characterized in that: The fuel cost C of the i-th power generation unit f,i (P g,i ) is expressed as: C f,i (P g,i )=a i (P g,i ) 2 +b i P g,i +c i Among them, the coefficient a i Depends on the quadratic coefficient of the thermal cycle efficiency curve of the steam turbine. The lower the thermal efficiency, the higher the coefficient a. i The larger the coefficient b i It is related to the linear relationship between fuel consumption rate and power generation, and is used to reflect the additional fuel cost required for each additional unit of power generation within a certain power generation range; coefficient c i Represents fixed cost items including basic fuel handling costs.

5. The intelligent dynamic power dispatch optimization system according to claim 4, characterized in that: The operation and maintenance cost C of the i-th power generation unit om,i (P g,i ) is expressed as: C om,i (P g,i )=d i P g,i +e i Among them, the coefficient d i It is related to the degree of equipment wear and power generation. The higher the power generation, the greater the equipment wear and the higher the maintenance cost required. It is used to reflect this linear relationship. The coefficient e i Fixed operation and maintenance costs, including regular inspection and maintenance costs of equipment, do not change with changes in power generation.

6. The intelligent dynamic power dispatch optimization system according to claim 5, characterized in that: The optimization calculation module comprises: The target optimization function construction unit is used to integrate the power generation cost, transmission loss, system stability factor, and power quality to construct a target optimization function model. The target optimization function model is: F=ω1C total +ω2P loss +w3m min +ω4Q qua Wherein, F is the objective function value, ω1, ω2, ω3, ω4 represent the weight of power generation cost, transmission loss, system stability, and power quality, respectively, which are pre-set based on the emphasis, and ω1+ω2+ω3+ω4=1; The particle optimization unit searches for the minimum objective function value based on the particle swarm optimization algorithm, defines the minimum objective function value as the ideal target, and defines the power generation allocation scheme corresponding to the global optimal position of the value as the optimal power generation allocation scheme.

7. The intelligent dynamic power dispatch optimization system according to claim 6, characterized in that: The optimization process of the particle optimization unit includes: When n power generation units are defined, the position vector of each particle is represents the power distribution scheme corresponding to the i-th particle, where is the power generation of the jth power generation unit in the ith particle; Defines the velocity vector of the particle Used to update the position of particles, the speed update formula is: Among them, ω is the inertia weight, which is used to balance the global search and local search capabilities of the particle; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers uniformly distributed in the interval 0-1; is the power generated by the jth power generation unit in the best position experienced by the i-th particle; is the power generation of the jth power generation unit in the best position experienced by the entire particle swarm; Update the particle's position according to the updated velocity. The formula is: During the update process, it is necessary to check whether the constraints are met, and if not, the particle position is corrected; Initialize the particle swarm, including the position and velocity of the particles; then, for each particle, calculate the objective function value F according to its position; Update the individual optimal position of each particle and the global optimal position of the entire particle swarm Repeat the speed and position update steps until a stop criterion is met, wherein the stop criterion includes reaching a maximum number of iterations or the objective function value F converges; Finally, define the global optimal position The corresponding power generation allocation scheme is the optimal power generation allocation scheme.

8. The intelligent dynamic power dispatch optimization system according to claim 7, characterized in that: The preset constraint condition is the power generation unit output constraint, that is, When the constraints are not met, they are corrected to the boundary values.

9. The intelligent dynamic power dispatch optimization system according to claim 8, characterized in that: The scheduling execution module includes: A power distribution unit connected to the particle optimization unit, configured to receive an optimal power distribution plan and generate a corresponding power distribution instruction; The execution control unit is used to receive the power distribution instruction and control the power output of each power generation unit.

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