Method for dispatching generator set

Abstract

The invention relates to a method for dispatching a generator set, which comprises the following steps of: acquiring historical load data and external influence factors of the generator set, inputting the data into a set dispatching algorithm model, and on the basis of a distribution network power flow dispatching optimization model considering demand response and network frame optimization, obtaining an optimal on-off state combination of the generator set; and therefore, the electrical load can be better adapted. Meanwhile, the peak regulation scheme of the generator set is further determined, the power grid structure is optimized through non-coherence, and the power transmission capacity of the grid frame is further released. And calling a Cplex solver by adopting MATLAB software to solve the constructed model so as to meet the output constraint of the generator set. And through a cross iteration mode, a preset iteration frequency is reached, and finally a scheduling scheme with the lowest total cost is selected. The implementation of the invention not only improves the economical efficiency of the power flow operation of the distribution network, but also remarkably improves the safety of the operation of the distribution network.

Description

Technical Field

[0001] The present invention and the field of electric power dispatching particularly relate to a method for dispatching a generator set. Background Art

[0002] In the power system, the dispatch of generators has always been a core and complex issue. Traditional methods of dispatching generators are often based on fixed rules and simple prediction models, which are difficult to fully adapt to the ever-changing power load and external influencing factors. This leads to the inability to effectively meet the power load in some cases, and the operating efficiency, safety and economy of the power grid are also affected to a certain extent.

[0003] In order to solve the above problems, researchers have begun to explore more advanced methods for dispatching power generators in recent years. Among them, the introduction of advanced algorithm models and optimization techniques to achieve more accurate and efficient dispatching of power generators has become an important research direction. However, although some progress has been made, how to balance the accuracy of dispatching and the complexity of calculation in practical applications is still a problem that needs to be solved.

[0004] The present invention is proposed in this context. By obtaining the historical load data and external influencing factors of the generator set, inputting them into the unit scheduling algorithm model, and considering the demand response and grid-optimized distribution network flow scheduling optimization model, the present invention aims to obtain the optimal start-stop state combination of the generator set to better adapt to the changes in power load. At the same time, the present invention further determines the peak load regulation scheme of the generator set, and optimizes the grid structure using the non-coherent principle, thereby releasing the power transmission capacity of the grid and improving the economy and safety economy of the distribution network flow operation. Summary of the invention

[0005] The purpose of the present invention is to provide a method for dispatching a generator set, aiming to solve the problem that the traditional dispatching method is difficult to adapt to the changing power load and external influencing factors, so as to improve the operating efficiency, economy and safety economy of the power system. Specifically, the present invention obtains the historical load data and external influencing factors of the generator set and inputs them into the unit dispatching algorithm model to obtain the optimal start-stop state combination of the generator set, so as to better adapt to the changes in power load.

[0006] In order to achieve the above object, the present invention provides a method for scheduling a generator set, comprising the following steps:

[0007] First, the historical load data and external influencing factors of the generator set are obtained. These data can reflect the actual operation of the generator set and the changes in the external environment. Then, these data are input into the unit scheduling algorithm model, which can comprehensively consider multiple factors and optimize the start and stop state of the generator set. By considering the distribution network flow scheduling optimization model of demand response and grid optimization, the optimal start and stop state combination of the generator set is obtained, so that the power load can be better adapted. In addition, the present invention further determines the peak load regulation scheme of the generator set, and optimizes the grid structure using the non-coherent principle, thereby releasing the power transmission capacity of the grid. Finally, the constructed model is solved by calling the Cplex solver using MATLAB software to meet the output constraints of the generator set, and through cross-iteration, until the preset number of iterations is reached, the scheduling scheme with the lowest total cost is finally selected.

[0008] Through the above technical solution, the present invention can achieve the following technical effects:

[0009] First, by acquiring and processing the historical load data and external influencing factors of the generator sets, the present invention can more accurately predict and adapt to changes in power load, thereby improving the operating efficiency of the power system. Secondly, by optimizing the start and stop status and peak-shaving scheme of the generator sets, the present invention can improve the economy of the distribution network flow operation and reduce the operating cost of the power system. Finally, by optimizing the grid structure and improving the power transmission capacity, the present invention can improve the safety and economy of the distribution network operation and reduce the risk of failure of the power system. In summary, the present invention provides an effective and practical method for dispatching generator sets, which has significant economic and social benefits. DETAILED DESCRIPTION

[0010] The present invention will be further explained below in conjunction with specific embodiments.

[0011] The method of the present invention first obtains the historical load data of the generator set and external influencing factors, such as weather, temperature, holidays, etc., which have a significant impact on power demand. Then, these data are input into a pre-built generator scheduling algorithm model, which can consider the demand response and grid optimization of the distribution network flow scheduling optimization, and obtain the optimal start-stop state combination of the generator set through complex calculations and analysis.

[0012] When determining the peak load regulation scheme of the generator set, the present invention uses the non-coherent principle to optimize the grid structure. The so-called non-coherent principle means that in the power system, the response speed and amplitude of different generator sets in response to load changes are not completely consistent. By rationally utilizing this principle, the power flow distribution of the grid can be optimized and the power transmission capacity of the grid can be improved.

[0013] Finally, the present invention uses MATLAB software to call Cplex solver to solve the constructed model. Through cross-iteration, the model parameters are continuously optimized until the preset number of iterations is reached. Among all the iteration results, the scheduling scheme with the lowest total cost is selected as the final scheme. In this way, not only the constraints of the generator set output are met, but also the economy and safety economy of the distribution network tidal operation are improved.

[0014] In the present invention, the term "generator set" refers to a combination of equipment that converts mechanical energy into electrical energy, including but not limited to steam turbines, water turbines, wind turbines, etc. and their supporting generators and control systems. The term "historical load data" refers to the actual load record of the generator set over a period of time in the past. The term "external influencing factors" refers to external factors that have a significant impact on electricity demand in addition to the factors of the generator set itself, such as weather, temperature, holidays, etc. The term "optimal start-stop state combination" refers to the combination of generator set start-stop states that minimizes the total cost under the conditions of meeting electricity demand and generator set output constraints. The term "peak shaving scheme" refers to a scheme that adjusts the operating state of the generator set to meet electricity demand during peak electricity demand periods. The term "non-coherence principle" refers to the phenomenon that in a power system, the response speed and amplitude of different generator sets in response to load changes are not completely consistent.

[0015] The method for dispatching generator sets provided by the present invention can be widely used in various power systems, especially those systems that need to frequently adjust the operating status of generator sets to adapt to changes in power demand. By implementing the method of the present invention, not only can the operating efficiency and economy of the power system be improved, but also the safety and economy of the power system can be significantly improved, providing strong technical support for the development of the power industry.

[0016] Example 1

[0017] An embodiment of the present invention provides a method for scheduling a generator set, which may include the following steps:

[0018] Step 101, in this embodiment of the present invention, historical load data and external influencing factors of the generator set are obtained. This step is the basis of the method for dispatching the generator set, and provides data support for subsequent dispatching decisions by collecting and analyzing the load data of the generator set in the past period of time, as well as various external factors affecting the operation of the generator set, such as weather conditions, market demand, energy prices, etc.

[0019] Step 102: In the embodiment of the present invention, the acquired historical load data and external influencing factors are input into the unit dispatch algorithm model. The model is a distribution network flow dispatch optimization model that takes into account demand response and grid optimization, and can intelligently analyze and predict the operating status of the generator set and the power flow distribution of the power grid based on the input data.

[0020] Step 103, in the embodiment of the present invention, the optimal start-stop state combination of the generator set is obtained through the unit scheduling algorithm model. This step is the core of the method for scheduling the generator set. Through the calculation of the optimization algorithm, the state combination that makes the operation of the generator set most economical and efficient under the premise of meeting the power load demand is obtained. This optimization not only takes into account the operating efficiency of the generator set itself, but also fully considers the overall operating status and demand response of the power grid.

[0021] Step 104: In the embodiment of the present invention, a peak load regulation scheme of the generator set is determined according to the optimal combination of start and stop states. The peak load regulation scheme refers to adjusting the operating state of the generator set to meet the load demand of the power grid during the peak period of power demand. The non-coherent principle is used to optimize the power grid structure and release the power transmission capacity of the grid. This can not only improve the operating efficiency of the power grid, but also enhance the stability and security of the power grid.

[0022] The non-coherence principle refers to the phenomenon that the voltage, frequency and other electrical quantities at different nodes or regions in the power grid do not change in the same pattern and amplitude after being disturbed. The embodiments of the present invention utilize this phenomenon to optimize the power grid structure so that the power grid can transmit power more efficiently during peak power demand periods, thereby meeting load demand.

[0023] Specifically, optimizing the grid structure can include adjusting the topology of the grid, optimizing the impedance parameters of the lines, and reasonably configuring reactive power compensation devices. These measures can effectively improve the power flow distribution of the grid, improve the transmission efficiency of the grid, and reduce the loss and failure risk of the grid.

[0024] Step 105: In the embodiment of the present invention, the constructed model is solved by calling the Cplex solver using MATLAB software. Cplex is a powerful mathematical programming solver that can efficiently solve various complex optimization problems. By calling the Cplex solver, the optimal solution that meets the output constraints of the generator set can be quickly obtained.

[0025] Step 106, in the embodiment of the present invention, after cross iteration, until the preset number of iterations is reached, the scheduling scheme with the lowest total cost is selected. This step is the final link of the method for scheduling generator sets. Through multiple iterative calculations, the scheduling scheme is continuously optimized, and finally the scheduling scheme with the lowest total cost and the best economy is selected. This optimization method can not only improve the economy of power grid operation, but also ensure the safe and stable operation of the power grid.

[0026] In summary, this method can not only improve the economy of distribution network flow operation, but also improve the safety and economy of distribution network operation, making important contributions to the stable operation and energy conservation and emission reduction of the power system.

[0027] Example 2

[0028] In the method of the present invention, a key step is to use MATLAB software to call the Cplex solver to solve the constructed model to meet the power constraints of the generator set. The specific implementation method of this step will be described in detail below.

[0029] In the specific implementation, the powerful mathematical software MATLAB is used to call the Cplex solver through the functions in its optimization toolbox. Cplex is an efficient mathematical programming solver that can handle a variety of optimization problems such as linear programming, integer programming, and quadratic programming. Therefore, it is very suitable for solving the complex model constructed.

[0030] Step 1: Build an optimization model

[0031] When constructing the optimization model, it is necessary to comprehensively consider the historical load data of the generator sets, external influencing factors, and the structural characteristics of the power grid. These data are input into the model as the basis for decision variables and constraints.

[0032] Step 2: Set the objective function and constraints

[0033] The objective function is the core of the optimization model, which defines the goal to be optimized, that is, the lowest total cost. In the present invention, the total cost includes the operating cost, start-up and shutdown cost, and possible penalty cost of the generator set. The constraints include the output constraints of the generator set and the power flow constraints of the power grid. These constraints ensure the feasibility and practicality of the solution.

[0034] Step 3: Call the Cplex solver

[0035] In the MATLAB environment, the Cplex solver is called by writing code. First, the optimization model needs to be converted into a format that Cplex can recognize, which usually involves converting the objective function and constraints into linear or quadratic expressions. Then, the model data is passed to the Cplex solver through the interface between MATLAB and Cplex.

[0036] Step 4: Solve and analyze the results

[0037] After receiving the model data, the Cplex solver will use its efficient algorithm to solve it. During the solution process, Cplex will continue to explore the possible solution space until it finds the optimal solution that satisfies all constraints and minimizes the objective function value. After the solution is completed, you can use MATLAB to extract and analyze the solution results.

[0038] Step 5: Cross-iteration and solution selection

[0039] In order to meet the output constraints of the generators and find the scheduling solution with the lowest total cost, multiple cross iterations are required. In each iteration, the model is adjusted according to the previous solution results, and the Cplex solver is called again for solving. This process is repeated until the preset number of iterations is reached or other stop conditions are met. Finally, the scheduling solution with the lowest total cost is selected from all iteration results as the final solution.

[0040] By using MATLAB software to call the Cplex solver to solve the constructed model, the method for dispatching the generator set in this embodiment can efficiently find the optimal dispatching solution that meets the output constraints of the generator set. This not only improves the economy of the distribution network flow operation, but also improves the safety and economy of the distribution network operation. At the same time, the method is flexible and scalable, and can adapt to power grid systems of different scales and complexities.

[0041] Example 3

[0042] This embodiment provides a method for scheduling a generator set, which may include the following steps:

[0043] Step 301: Initialize parameters, including setting the number of iterations, a preset total cost threshold, etc. These parameters can be adjusted according to actual needs to ensure the accuracy and economy of the scheduling solution.

[0044] Step 302: Obtain historical load data and external influencing factors of the generator set and input them into the generator set dispatch algorithm model. In this step, the historical load data may include information such as past power generation and load demand, while the external influencing factors may include factors such as weather conditions, electricity price changes, and policy adjustments. These data are input into the generator set dispatch algorithm model for subsequent optimization processing.

[0045] Step 303, considering the demand response and grid optimization of the distribution network flow scheduling optimization model, to obtain the optimal start-stop state combination of the generator set. In this step, demand response refers to adjusting the operating state of the generator set according to the change of power demand, while grid optimization is to improve the power transmission efficiency by optimizing the grid structure. By comprehensively considering these two factors, an optimal start-stop state combination of the generator set can be obtained, so that the power load can be more adaptable.

[0046] Step 304: Determine the peak load regulation plan for the generator set. The peak load regulation plan refers to meeting the load demand by adjusting the operating state of the generator set during the peak period of power demand. In this step, the non-coherent principle can be used to optimize the grid structure and release the power transmission capacity of the grid.

[0047] Step 305: Use MATLAB software to call the Cplex solver to solve the constructed model. Cplex is an efficient mathematical programming solver that can be used to solve complex optimization problems. By calling the Cplex solver, the previously constructed scheduling optimization model can be solved to obtain the optimal solution that meets the output constraints of the generator set.

[0048] Step 306: Determine whether the current number of iterations has reached the preset number of iterations. If not, return to step 302 to continue iterative calculation; if it has reached the preset number of iterations, proceed to the next step.

[0049] Step 307: Select the dispatching scheme with the lowest total cost from all iteration results as the final scheme. In this step, the total cost may include the operating cost, maintenance cost, and possible penalty cost of the generator set. By comparing the total costs in different iteration results, a dispatching scheme with the best economic efficiency can be selected.

[0050] Through the above steps, the method for dispatching generator sets provided by the embodiment of the present invention can continuously optimize the dispatching scheme by cross iteration until the preset number of iterations is reached and the dispatching scheme with the lowest total cost is selected as the final scheme. This method not only takes into account multiple factors such as demand response and grid optimization, but also uses an efficient mathematical programming solver for solving, thereby improving the economy and safety economy of the distribution network flow operation.

[0051] Furthermore, the method of this embodiment can also be flexibly adjusted according to actual conditions. For example, when initializing parameters, different iteration times and total cost thresholds can be set according to actual needs; when selecting a scheduling scheme, other factors such as environmental protection, reliability, etc. can also be comprehensively considered. These flexible adjustments make the method of the embodiment of the present invention more suitable for various application scenarios and needs.

[0052] In addition, the method of this embodiment has good scalability and versatility. With the continuous development of the power system and the improvement of the intelligence level, more influencing factors and optimization objectives may be incorporated into the scheduling problem in the future. The method of the embodiment of the present invention can easily expand and adapt to these new needs and challenges by constructing a flexible optimization framework.

[0053] 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 replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dispatching a generator set, characterized in that: The following steps are involved: The historical load data and external influencing factors of the generator set are obtained, and the historical load data and the external influencing factors are input into the unit scheduling algorithm model; the unit scheduling algorithm model is optimized by considering the demand response and grid-optimized distribution network flow scheduling, and the optimal start-stop state combination of the generator set is calculated.

2. A method for dispatching a generator set according to claim 1, characterized in that: It also includes determining the peak-shaving plan for the generator sets, optimizing the grid structure using the non-coherent principle, and releasing the power transmission capacity of the grid.

3. A method for dispatching a generator set according to claim 1, characterized in that: The Cplex solver is called by MATLAB software to solve the optimized unit scheduling algorithm model to meet the output constraints of the generator sets.

4. A method for dispatching a generator set according to claim 3, characterized in that: The solution results are cross-iterated until the preset number of iterations is reached, and finally the scheduling plan with the lowest total cost is selected.

5. A power system, characterized in that: The power system acquires and processes the historical load data and external influencing factors of the generator sets in real time to dynamically adjust the start and stop status and peak load regulation plan of the generator sets.

6. A power system according to claim 5, characterized in that: By continuously adjusting and optimizing the start and stop status and peak load regulation schemes of the generator sets, the grid structure can be optimized and the power transmission capacity can be improved.

7. Application of the method for dispatching a generator set as claimed in any one of claims 1 to 4 in the field of power dispatching.