Unit commitment scheme hot start point selection method and device, electronic equipment and storage medium

Abstract

The invention discloses a unit commitment scheme hot start point selection method and device, electronic equipment and a storage medium, and the method comprises the steps: generating a corresponding historical typical day undirected graph and a corresponding power grid operation day undirected graph according to historical load curve data of a plurality of typical days of a power grid, load curve data of a power grid operation day and a power grid topological structure; a graph similarity evaluation network model is used, similarity scores between the power grid operation day undirected graph and all historical typical day undirected graphs are calculated, each training sample of the model is composed of two historical typical day undirected graphs and similarity labels thereof, and the similarity labels of the two historical typical day undirected graphs are obtained; and selecting an optimal unit commitment scheme corresponding to the historical typical day undirected graph with the highest similarity score as a target unit commitment scheme of the power grid operation day, and taking the optimal unit commitment scheme as a hot start point for solving the total operation cost minimization model. By implementing the method, the solving efficiency can be improved, and the stability and reliability of a power grid are maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching automation, and in particular to a method, device, electronic equipment and storage medium for selecting a hot start point of a unit combination scheme. Background Art

[0002] The unit combination scheme plays a vital role in the dispatch of power systems. Its core goal is to arrange the start and stop and output of generators in advance according to load demand and new energy forecast results to achieve the economic and safety goals of the power system. A reasonable unit combination scheme can not only optimize resource allocation and reduce power generation costs, but also ensure the stability and reliability of power supply.

[0003] However, existing technologies often face the problem of slow solution speed when solving unit combination scheme problems. Although traditional solution methods can provide high-quality optimal solutions, their time complexity is usually high when dealing with large power grids or complex operating conditions. This situation is particularly prominent during peak power demand periods, and the long solution process may lead to delays in power dispatch decisions, thereby affecting the stability and reliability of the power grid. Summary of the invention

[0004] The embodiment of the present invention provides a method, device, electronic device and storage medium for selecting a hot start point of a unit combination scheme. By implementing the present invention, the historical unit combination scheme that is most similar to the power grid operation day can be selected as the hot start point of the power grid operation day, thereby improving the solution speed of the total operating cost minimization model, thereby improving the stability and reliability of the power grid.

[0005] An embodiment of the present invention provides a method for selecting a hot start point of a unit combination scheme, comprising:

[0006] Obtain the load curve data of several historical typical days of the power grid, the load curve data of the power grid operation day and the topological structure of the power grid;

[0007] According to the topological structure of the power grid and the load curve data of several historical typical days of the power grid, an undirected graph of several historical typical days is generated; according to the topological structure of the power grid and the load curve data of the power grid operation day, an undirected graph of the power grid operation day is generated;

[0008] The undirected graphs of each historical typical day and the undirected graphs of the power grid operation day are sequentially input into the graph similarity evaluation network model to generate similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost;

[0009] The undirected graph of the historical typical day with the highest similarity score is selected as the target undirected graph; the optimal unit combination plan of the typical day corresponding to the target undirected graph is used as the target unit combination plan of the power grid operation day;

[0010] The target unit combination scheme is used as the hot start point for solving the total operating cost minimization model.

[0011] Further, the generation method of undirected graph includes:

[0012] Obtaining the load curve data of the power grid on the corresponding date and the topological structure of the power grid; wherein the load curve data includes the load value of the load node at the scheduling time;

[0013] According to the topological structure of the power grid, the vertices and edges of the undirected graph of the corresponding date are generated; wherein the vertices of the undirected graph are the load nodes in the topological structure; and the edges in the undirected graph are the transmission lines in the topological structure;

[0014] According to the connection relationship of load nodes in the topological structure of the power grid, the adjacency matrix of the undirected graph is calculated and generated;

[0015] According to the load curve data of the power grid on the corresponding date, the characteristic value of each vertex in the undirected graph is generated.

[0016] Furthermore, the training process of the graph similarity evaluation network model includes:

[0017] Randomly select a preset number of training samples and repeat the training operation until the graph similarity evaluation network model converges;

[0018] The training operation includes:

[0019] Input the current training sample into the graph similarity evaluation network model to obtain the similarity score of the current training sample;

[0020] According to the mean square error function, the error between the similarity score of the current training sample and the actual similarity is calculated to generate the mean square error;

[0021] According to the mean square error, the graph similarity evaluation network model is judged to see whether it has converged;

[0022] If the model has not converged, use the Adam optimizer to optimize the model parameters according to the current mean square error, and select the next batch of training samples to update them to the current training samples.

[0023] Furthermore, the actual similarity is calculated by the following formula:

[0024]

[0025] Among them, η ij is the actual similarity between the undirected graphs of the i-th day and the j-th day in the historical typical day; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t on day j; is the maximum active power of unit g, i.e. the rated capacity of unit g; N g N is the unit set; t A set of scheduling periods.

[0026] Furthermore, the optimal unit combination scheme is determined by the following methods:

[0027] Obtain a solver for solving the total operating cost minimization model, hardware parameters of the unit, cost parameters of the unit, hardware parameters of the transmission line, and load curve data of the corresponding date; wherein the hardware parameters of the unit include: the maximum active power of the unit, the upper limit of the unit's ramp-up power increment, the upper limit of the unit's ramp-down power increment, the power limit when the unit is started, the power limit when the unit is shut down, the proportion of the spinning reserve capacity, and the minimum operating time of the unit; the cost parameters of the unit include the fuel cost coefficient of the unit; the hardware parameters of the transmission line include: the active power limit of the line, the proportion of the spinning reserve capacity, the power transfer factor of the line relative to the unit, and the power transfer factor of the line relative to the load node; the load curve data include: the load value of the load node at the scheduling time;

[0028] The decision variables of the total operating cost minimization model are defined in the solver; the decision variables include the start and stop status of the unit and the active power generated by the unit;

[0029] Convert the objective function and constraints into expressions and import them into the solver;

[0030] The hardware parameters of the unit, the cost parameters of the unit, the hardware parameters of the transmission line and the load curve data of the corresponding date are set as the parameters of the solver, and the solver is called to solve, so that the solver calculates and generates the start and stop status and the active power of power generation for each scheduling period of the corresponding date as the optimal unit combination plan for the current date.

[0031] Furthermore, the total operating cost minimization model includes an objective function and constraints;

[0032] The objective function includes:

[0033]

[0034] Among them, F G represents the total operating cost of the unit combination; is the start / stop status of unit g at time t on the i-th day; is the active power generated by unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; S UD The startup and shutdown costs of the unit; is the fuel cost of unit g on day i at time t;

[0035] The fuel cost is calculated by the following formula

[0036]

[0037] Among them, a g 、b g and c g is the fuel cost coefficient of unit g;

[0038] The constraints include: system power balance constraints, generator set output constraints, generator set ramp constraints, spinning reserve capacity constraints, unit minimum downtime constraints, unit minimum operating time constraints and branch power flow constraints.

[0039] Furthermore, the system power balance constraint is:

[0040]

[0041] in, is the active power generated by unit g at time t on the i-th day; N g Assemble for the crew; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods;

[0042] The generator set output constraint is:

[0043]

[0044] in, is the active power generated by unit g at time t on the i-th day; is the minimum active power of unit g; is the maximum active power of unit g, i.e. the rated capacity of unit g;

[0045] The generator set climbing constraint is:

[0046]

[0047]

[0048] in, is the active power generated by unit g at time t on the i-th day; is the active power generated by unit g on the i-th day at time t-1; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; It is the upper limit of the unit's climbing power increment, that is, the maximum power that can be increased within a specific time period; It is the upper limit of the unit's g-down ramp power increment, that is, the maximum power that can be reduced in a specific time period; is the power limit when unit g is started; is the power limit when unit g is shut down;

[0049] The spinning reserve capacity constraint is:

[0050]

[0051] in, is the start / stop status of unit g at time t on the i-th day; is the maximum active power of unit g, that is, the rated capacity of unit g; Q s is the proportion of spinning reserve capacity, is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods;

[0052] The minimum operating time constraint of the unit is:

[0053]

[0054]

[0055] in, is the minimum operating time of unit g; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; is the start / stop status of unit g at time k on the i-th day;

[0056] The minimum downtime constraint of the unit is:

[0057]

[0058]

[0059] in, is the minimum downtime of unit g; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day;

[0060] The branch power flow constraint is:

[0061]

[0062] in, is the active power limit of line l, LF lg is the power transfer factor of line l relative to unit g, LF ln is the power transfer factor of line l relative to load node n; is the active power generated by unit g at time t on the i-th day; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N l is the set of lines; N t A set of scheduling periods.

[0063] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0064] An embodiment of the present invention provides a device for selecting a hot start point of a unit combination scheme, comprising: a data acquisition module, an undirected graph generation module, a graph similarity evaluation module and a hot start point selection module;

[0065] The data acquisition module is used to acquire the load curve data of several historical typical days of the power grid, the load curve data of the power grid operation day and the topological structure of the power grid;

[0066] The undirected graph generation module is used to generate undirected graphs of several historical typical days according to the topological structure of the power grid and the load curve data of several historical typical days of the power grid; and to generate undirected graphs of power grid operation days according to the topological structure of the power grid and the load curve data of power grid operation days;

[0067] The graph similarity evaluation module is used to sequentially input the undirected graphs of each historical typical day and the undirected graphs of the power grid operation day into the graph similarity evaluation network model to generate similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost;

[0068] The hot start point selection module is used to select the undirected graph of the historical typical day with the highest similarity score as the target undirected graph; use the optimal unit combination plan of the typical day corresponding to the target undirected graph as the target unit combination plan for the power grid operation day; and use the target unit combination plan as the hot start point for solving the total operating cost minimization model.

[0069] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0070] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for selecting a hot start point of a unit combination scheme described in any one of the above method embodiments can be implemented.

[0071] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0072] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for selecting a hot start point of a unit combination scheme described in any one of the above method embodiments can be implemented.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The embodiment of the present invention provides a method, device, electronic device and storage medium for selecting a hot start point of a unit combination scheme. This scheme constructs an undirected graph of a power grid operation day and several historical typical days, and inputs these undirected graphs into a graph similarity evaluation network model, so as to calculate the similarity score between the power grid operation day and each historical typical day. Furthermore, the unit combination scheme corresponding to the historical typical day with the highest similarity score is selected as the hot start point of the power grid operation day, which effectively avoids the process of solving the total operating cost minimization model from scratch. By utilizing the previous effective solution, the solution algorithm can be quickly guided to converge to a high-quality solution faster, thereby greatly shortening the calculation time. When dealing with complex power system scheduling, the solution efficiency can be significantly improved, ensuring that timely scheduling decisions can be made during peak power demand periods to maintain the stability and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of a method for selecting a hot start point of a unit combination scheme provided by an embodiment of the present invention.

[0076] Figure 2 It is a structural schematic diagram of a device for selecting a hot start point of a unit combination scheme provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] like Figure 1 As shown, an embodiment of the present invention provides a method for selecting a hot start point of a unit combination scheme, which at least includes the following steps:

[0079] Step S1, obtaining load curve data of several historical typical days of the power grid, load curve data of the power grid operation day and the topological structure of the power grid;

[0080] Specifically, a historical typical day refers to a representative date selected from historical data in the power system. The load curves of these dates can reflect the operation of the power grid under specific conditions, and can represent the operation characteristics of the power grid under similar conditions, which is helpful to optimize the unit combination plan and improve the operation efficiency of the power grid; a power grid operation day refers to a day when the system needs to perform actual operation and load scheduling, that is, the date that is currently being analyzed, scheduled or predicted. The load curve data and power grid topology structure of this day are known and are used to optimize the unit combination plan of the power grid on this day; the load curve data is the load value of the load node at the scheduling time; the topology of the power grid refers to the network layout formed by each node and transmission line in the power grid; among them, the nodes include: power generation nodes, substation nodes and load nodes.

[0081] Step S2: generating an undirected graph of several typical historical days according to the topological structure of the power grid and the load curve data of several typical historical days of the power grid; generating an undirected graph of the power grid operation day according to the topological structure of the power grid and the load curve data of the power grid operation day;

[0082] Specifically, in a preferred embodiment, the method for generating an undirected graph includes:

[0083] Obtaining the load curve data of the power grid on the corresponding date and the topological structure of the power grid; wherein the load curve data includes the load value of the load node at the scheduling time;

[0084] According to the topological structure of the power grid, the vertices and edges of the undirected graph of the corresponding date are generated; wherein the vertices of the undirected graph are the load nodes in the topological structure; and the edges in the undirected graph are the transmission lines in the topological structure;

[0085] According to the connection relationship of load nodes in the topological structure of the power grid, the adjacency matrix of the undirected graph is calculated and generated;

[0086] According to the load curve data of the power grid on the corresponding date, the characteristic value of each vertex in the undirected graph is generated.

[0087] Exemplarily, according to the connection relationship of the load nodes in the topological structure of the power grid, the adjacency matrix of the undirected graph is calculated and generated, including:

[0088] Detect the connection relationship between each load node. If there is a transmission line directly connecting two nodes in the power grid, the cross position corresponding to the two nodes in the adjacency matrix is ​​set to 1, indicating that they are connected; if there is no direct connection between the two nodes, the corresponding position of the matrix remains 0. The matrix is ​​symmetrical, so a line will be symmetrically marked as 1 at the positions of the two nodes, indicating their bidirectional connection relationship.

[0089] Exemplarily, according to the load curve data of the power grid corresponding to the date, the characteristic value of each vertex in the undirected graph is generated, including:

[0090] A day is divided into multiple time periods, and the active load value of the load node in each time period is extracted from the load curve data in turn. The characteristic value of each vertex is composed of the active load value set of the load node in these time periods.

[0091] It is understandable that these eigenvalues ​​not only reflect the load level of the load node in different time periods, but also reflect the load change trend over time, providing important basic data for subsequent graph similarity evaluation. At the same time, the integration of these eigenvalues ​​also provides strong support for the dynamic analysis and optimal dispatch of the power grid.

[0092] Step S3, sequentially inputting the undirected graphs of each historical typical day and the undirected graphs of the power grid operation day into the graph similarity evaluation network model, generating similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost;

[0093] In an optional embodiment, the training process of the graph similarity evaluation network model includes:

[0094] Randomly select a preset number of training samples and repeat the training operation until the graph similarity evaluation network model converges;

[0095] The training operation includes:

[0096] Input the current training sample into the graph similarity evaluation network model to obtain the similarity score of the current training sample;

[0097] According to the mean square error function, the error between the similarity score of the current training sample and the actual similarity is calculated to generate the mean square error;

[0098] According to the mean square error, the graph similarity evaluation network model is judged to see whether it has converged;

[0099] If the model has not converged, use the Adam optimizer to optimize the model parameters according to the current mean square error, and select the next batch of training samples to update them to the current training samples.

[0100] It can be understood that judging whether the graph similarity evaluation network model has converged is based on the mean square error, including: judging whether the performance of the model has stabilized within a smaller range based on the calculated mean square error. If the mean square error is lower than the preset threshold, the model is considered to have converged; if not, the model still needs further optimization.

[0101] In an optional embodiment, the structure of the graph similarity evaluation network model includes: an input layer, a graph convolution layer, and a fully connected layer;

[0102] The input layer is used to receive two undirected graphs to be evaluated;

[0103] The graph convolution layer is used to extract features corresponding to undirected graphs. It includes three layers of convolution processing. The first layer converts 24-dimensional input features into 128 dimensions, the second layer converts 128-dimensional features into 96 dimensions, and the third layer converts 96-dimensional features into 48-dimensional features. The outputs of all convolution layers use the tanh activation function to increase the nonlinear expression ability of the model and effectively capture the structural information of the graph.

[0104] The fully connected layer is used to fuse and transform the extracted features. It contains three fully connected layers. The first layer maps the 48-dimensional input features to 144 dimensions, the second layer maps the 144 dimensions to 36 dimensions, and the third layer outputs a 1-dimensional similarity evaluation value. The third layer is processed by the sigmoid activation function to obtain the final similarity score.

[0105] In a preferred embodiment, the actual similarity is calculated by the following formula:

[0106]

[0107] Among them, η ij is the actual similarity between the undirected graphs of the i-th day and the j-th day in the historical typical day; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t on day j; is the maximum active power of unit g, i.e. the rated capacity of unit g; N g N is the unit set; t A set of scheduling periods.

[0108] The optimal unit combination solution is determined by:

[0109] Preferably, a solver for solving a total operating cost minimization model, hardware parameters of the unit, cost parameters of the unit, hardware parameters of the transmission line, and load curve data of a corresponding date are obtained; wherein the hardware parameters of the unit include: the maximum active power of the unit, the upper limit of the unit's ramp-up power increment, the upper limit of the unit's ramp-down power increment, the power limit when the unit is started, the power limit when the unit is shut down, the proportion of the spinning reserve capacity, and the minimum operating time of the unit; the cost parameters of the unit include the fuel cost coefficient of the unit; the hardware parameters of the transmission line include: the active power limit of the line, the proportion of the spinning reserve capacity, the power transfer factor of the line relative to the unit, and the power transfer factor of the line relative to the load node; the load curve data include: the load value of the load node at the scheduling time;

[0110] The decision variables of the total operating cost minimization model are defined in the solver; the decision variables include the start and stop status of the unit and the active power generated by the unit;

[0111] Convert the objective function and constraints into expressions and import them into the solver;

[0112] The hardware parameters of the unit, the cost parameters of the unit, the hardware parameters of the transmission line and the load curve data of the corresponding date are set as the parameters of the solver, and the solver is called to solve, so that the solver calculates and generates the start and stop status and the active power of power generation for each scheduling period of the corresponding date as the optimal unit combination plan for the current date.

[0113] It should be noted that the solver is an existing commercial solver, which can be selected from Gurobi, CPLEX, MOSEK, GLPK, SCIP, COIN-OR, Pyomo and AMPL for solving.

[0114] Step S4: Select the undirected graph of the historical typical day with the highest similarity score as the target undirected graph; and use the optimal unit combination plan of the typical day corresponding to the target undirected graph as the target unit combination plan for the power grid operation day.

[0115] It is understandable that taking the optimal unit combination scheme of the typical day corresponding to the target undirected graph as the target unit combination scheme of the power grid operation day can ensure that the power grid achieves the best operating efficiency and economy under the current operating conditions. By referring to the optimal scheme of historical typical days, this method can improve the scientificity and accuracy of the unit combination, and also provide a reliable decision-making basis for the power grid operation day, reducing the operating risks caused by insufficient information or wrong decisions. At the same time, with the experience of historical data, the overall operating cost can be optimized while maintaining the safe and stable operation of the power grid, thereby improving the flexibility and response speed of power grid management.

[0116] Step S5: Using the target unit combination scheme as a hot start point for solving the total operating cost minimization model.

[0117] In a preferred embodiment, the total operating cost minimization model includes an objective function and constraints;

[0118] The objective function includes:

[0119]

[0120] Among them, F G represents the total operating cost of the unit combination; is the start / stop status of unit g at time t on the i-th day; is the active power generated by unit g at time t on the i-th day; is the start / stop status of unit g at time i-1 on the i-th day; S UD The startup and shutdown costs of the unit; is the fuel cost of unit g on day i at time t;

[0121] The fuel cost is calculated by the following formula

[0122]

[0123] Among them, a g 、b g and c g is the fuel cost coefficient of unit g;

[0124] The constraints include: system power balance constraints, generator set output constraints, generator set ramp constraints, spinning reserve capacity constraints, unit minimum downtime constraints, unit minimum operating time constraints and branch power flow constraints.

[0125] In a preferred embodiment, the system power balance constraint is:

[0126]

[0127] in, is the active power generated by unit g at time t on the i-th day; N g Assemble for the crew; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods;

[0128] The generator set output constraint is:

[0129]

[0130] in, is the active power generated by unit g at time t on the i-th day; is the minimum active power of unit g; is the maximum active power of unit g, i.e. the rated capacity of unit g;

[0131] The generator set climbing constraint is:

[0132]

[0133]

[0134] in, is the active power generated by unit g at time t on the i-th day; is the active power generated by unit g on the i-th day at time t-1; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; It is the upper limit of the unit's climbing power increment, that is, the maximum power that can be increased within a specific time period; It is the upper limit of the unit's g-down ramp power increment, that is, the maximum power that can be reduced in a specific time period; is the power limit when unit g is started; is the power limit when unit g is shut down;

[0135] The spinning reserve capacity constraint is:

[0136]

[0137] in, is the start / stop status of unit g at time t on the i-th day; is the maximum active power of unit g, that is, the rated capacity of unit g; Q s is the proportion of spinning reserve capacity, is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods;

[0138] The minimum operating time constraint of the unit is:

[0139]

[0140]

[0141] in, is the minimum operating time of unit g; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; is the start / stop status of unit g at time k on the i-th day;

[0142] The minimum downtime constraint of the unit is:

[0143]

[0144]

[0145] in, is the minimum downtime of unit g; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day;

[0146] The branch power flow constraint is:

[0147]

[0148] in, is the active power limit of line l, LF lg is the power transfer factor of line l relative to unit g, LF ln is the power transfer factor of line l relative to load node n; is the active power generated by unit g at time t on the i-th day; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N l is the set of lines; N t A set of scheduling periods.

[0149] For example, is the start / stop status of unit g at time t on the i-th day, and the value can be 0 or 1, where 0 means shutdown and 1 means startup; N t is the set of scheduling periods, N t ={1, 2, 3, ..., 24}.

[0150] It is understandable that if a direct solution method is used to solve the total operating cost minimization model, it usually starts from a random or default initial state, which may lead to a slower convergence of the algorithm because the initial point is often far away from the optimal solution, which requires more iterations to reach a satisfactory solution, increasing computing time and resource consumption. In addition, the quality of the solution obtained by direct solution may not be ideal because it explores a larger search space and may miss some potential high-quality solutions.

[0151] The target unit combination scheme is used as the hot start point for solving the total operating cost minimization model. The hot start point solution method uses the undirected graph of historical typical days and the corresponding optimal unit combination scheme to select an initial state close to the optimal solution, providing a good starting point for the optimization process. This method ensures that the optimization algorithm can converge faster and significantly reduce the number of required iterations, thereby reducing computing time and resource consumption. At the same time, since the selection of the hot start point is based on historical data and actual operating conditions, it can better guide the search process, improve the quality of the solution, and avoid falling into the local optimal solution.

[0152] Another embodiment of the present invention provides a method for selecting a hot start point of a unit combination scheme, which includes the following steps after step S5 in addition to steps S1 to S5 of the above embodiment:

[0153] Step S6, input the target unit combination scheme into the solver for solving, and determine whether the total operating cost minimization model solution is feasible at this time; if the solution is feasible, the target unit combination scheme at this time is the hot start point; if the solution is not feasible, the undirected graph of the historical typical day with the highest similarity score that has not been selected is used as the target undirected graph, and the optimal unit combination scheme of the typical day corresponding to the target undirected graph is updated to the target unit combination scheme of the power grid operation day, and the target unit combination scheme at this time is input into the solver for re-solving until the total operating cost minimization model solution is feasible.

[0154] Specifically, in a preferred embodiment, three power grid operation days are selected to set the hot start point to solve the total operating cost minimization model, and the solution time is compared with the solution time directly solved by the solver. The comparison results are as follows:

[0155]

[0156]

[0157] Among them, in the grid operation day 1, the unit combination scheme corresponding to the historical typical day 03-01 is not feasible to solve; in the grid operation day 2, the unit combination scheme corresponding to the historical typical day 05-12 is not feasible to solve.

[0158] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0159] like Figure 2 As shown, an embodiment of the present invention provides a device for selecting a hot start point of a unit combination scheme, comprising: a data acquisition module 101, an undirected graph generation module 102, a graph similarity evaluation module 103 and a hot start point selection module 104.

[0160] The data acquisition module 101 is used to acquire the load curve data of several historical typical days of the power grid, the load curve data of the power grid operation day and the topological structure of the power grid;

[0161] The undirected graph generation module 102 is used to generate undirected graphs of several historical typical days according to the topological structure of the power grid and the load curve data of several historical typical days of the power grid; and to generate undirected graphs of power grid operation days according to the topological structure of the power grid and the load curve data of the power grid operation days;

[0162] The graph similarity evaluation module 103 is used to sequentially input the undirected graphs of each historical typical day and the undirected graphs of the power grid operation day into the graph similarity evaluation network model to generate similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost;

[0163] The hot start point selection module 104 is used to select the undirected graph of the historical typical day with the highest similarity score as the target undirected graph; use the optimal unit combination plan of the typical day corresponding to the target undirected graph as the target unit combination plan for the power grid operation day; and use the target unit combination plan as the hot start point for solving the total operating cost minimization model.

[0164] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0165] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0166] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for selecting a hot start point of a unit combination scheme described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0167] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0168] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0169] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0170] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0171] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0172] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for selecting a hot start point of any of the above-mentioned unit combination schemes of the present invention.

[0173] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0175] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for selecting a hot start point of a unit combination scheme, characterized in that: include: Obtain the load curve data of several historical typical days of the power grid, the load curve data of the power grid operation day and the topological structure of the power grid; According to the topological structure of the power grid and the load curve data of several historical typical days of the power grid, an undirected graph of several historical typical days is generated; according to the topological structure of the power grid and the load curve data of the power grid operation day, an undirected graph of the power grid operation day is generated; The undirected graphs of each historical typical day and the undirected graphs of the power grid operation day are sequentially input into the graph similarity evaluation network model to generate similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost; The undirected graph of the historical typical day with the highest similarity score is selected as the target undirected graph; the optimal unit combination plan of the typical day corresponding to the target undirected graph is used as the target unit combination plan of the power grid operation day; The target unit combination scheme is used as the hot start point for solving the total operating cost minimization model.

2. A method for selecting a hot start point of a unit combination scheme as claimed in claim 1, characterized in that: The generation methods of undirected graphs include: Obtaining the load curve data of the power grid on the corresponding date and the topological structure of the power grid; wherein the load curve data includes the load value of the load node at the scheduling time; According to the topological structure of the power grid, the vertices and edges of the undirected graph of the corresponding date are generated; wherein the vertices of the undirected graph are the load nodes in the topological structure; and the edges in the undirected graph are the transmission lines in the topological structure; According to the connection relationship of load nodes in the topological structure of the power grid, the adjacency matrix of the undirected graph is calculated and generated; According to the load curve data of the power grid on the corresponding date, the characteristic value of each vertex in the undirected graph is generated.

3. The method for selecting a hot start point of a unit combination scheme according to claim 1, characterized in that: The training process of the graph similarity evaluation network model includes: Randomly select a preset number of training samples and repeat the training operation until the graph similarity evaluation network model converges; The training operation includes: Input the current training sample into the graph similarity evaluation network model to obtain the similarity score of the current training sample; According to the mean square error function, the error between the similarity score of the current training sample and the actual similarity is calculated to generate the mean square error; According to the mean square error, the graph similarity evaluation network model is judged to see whether it has converged; If the model has not converged, use the Adam optimizer to optimize the model parameters according to the current mean square error, and select the next batch of training samples to update them to the current training samples.

4. The method for selecting a hot start point of a unit combination scheme according to claim 1, characterized in that: The actual similarity is calculated by the following formula: Among them, η ij is the actual similarity between the undirected graphs of the i-th day and the j-th day in the historical typical day; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t on day j; is the maximum active power of unit g, i.e. the rated capacity of unit g; N g N is the unit set; t A set of scheduling periods.

5. The method for selecting a hot start point of a unit combination scheme according to claim 1, characterized in that: The optimal unit combination solution is determined by: Obtain a solver for solving the total operating cost minimization model, hardware parameters of the unit, cost parameters of the unit, hardware parameters of the transmission line, and load curve data of the corresponding date; wherein the hardware parameters of the unit include: the maximum active power of the unit, the upper limit of the unit's ramp-up power increment, the upper limit of the unit's ramp-down power increment, the power limit when the unit is started, the power limit when the unit is shut down, the proportion of the spinning reserve capacity, and the minimum operating time of the unit; the cost parameters of the unit include the fuel cost coefficient of the unit; the hardware parameters of the transmission line include: the active power limit of the line, the proportion of the spinning reserve capacity, the power transfer factor of the line relative to the unit, and the power transfer factor of the line relative to the load node; the load curve data include: the load value of the load node at the scheduling time; The decision variables of the total operating cost minimization model are defined in the solver; the decision variables include the start and stop status of the unit and the active power generated by the unit; Convert the objective function and constraints into expressions and import them into the solver; The hardware parameters of the unit, the cost parameters of the unit, the hardware parameters of the transmission line and the load curve data of the corresponding date are set as the parameters of the solver, and the solver is called to solve, so that the solver calculates and generates the start and stop status and the active power of power generation for each scheduling period of the corresponding date as the optimal unit combination plan for the current date.

6. The method for selecting a hot start point of a unit combination scheme according to claim 1, characterized in that: The total operating cost minimization model includes an objective function and constraints; The objective function includes: Among them, F G represents the total operating cost of the unit combination; is the start / stop status of unit g at time t on the i-th day; is the active power generated by unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; S UD The startup and shutdown costs of the unit; is the fuel cost of unit g on day i at time t; The fuel cost is calculated by the following formula Among them, a g 、b g and c g is the fuel cost coefficient of unit g; The constraints include: system power balance constraints, generator set output constraints, generator set ramp constraints, spinning reserve capacity constraints, unit minimum downtime constraints, unit minimum operating time constraints and branch power flow constraints.

7. A method for selecting a hot start point of a unit combination scheme as claimed in claim 6, characterized in that: The system power balance constraint is: in, is the active power generated by unit g at time t on the i-th day; N g Assemble for the crew; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods; The generator set output constraint is: in, is the active power generated by unit g at time t on the i-th day; is the minimum active power of unit g; is the maximum active power of unit g, i.e. the rated capacity of unit g; The generator set climbing constraint is: in, is the active power generated by unit g at time t on the i-th day; is the active power generated by unit g on the i-th day at time t-1; is the start / stop status of unit g at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; It is the upper limit of the unit's climbing power increment, that is, the maximum power that can be increased within a specific time period; It is the upper limit of the unit's g-down ramp power increment, that is, the maximum power that can be reduced in a specific time period; is the power limit when unit g is started; is the power limit when unit g is shut down; The spinning reserve capacity constraint is: in, is the start / stop status of unit g at time t on the i-th day; is the maximum active power of unit g, that is, the rated capacity of unit g; Q s is the proportion of spinning reserve capacity, is the load of load node n at time t on the i-th day; N n is the set of load nodes; N t is a set of scheduling periods; The minimum operating time constraint of the unit is: in, is the minimum operating time of unit g; is the start / stop status of unit h at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; is the start / stop status of unit g at time k on the i-th day; The minimum downtime constraint of the unit is: in, is the minimum downtime of unit g; is the start / stop status of unit h at time t on the i-th day; is the start / stop status of unit g at time t-1 on the i-th day; The branch power flow constraint is: in, is the active power limit of line l, LF lg is the power transfer factor of line l relative to unit g, LF ln is the power transfer factor of line l relative to load node n; is the active power generated by unit g at time t on the i-th day; is the load of load node n at time t on the i-th day; N n is the set of load nodes; N l is the set of lines; N t A set of scheduling periods.

8. A device for selecting a hot start point of a unit combination scheme, characterized in that: include: Data acquisition module, undirected graph generation module, graph similarity evaluation module and hot start point selection module; The data acquisition module is used to acquire the load curve data of several historical typical days of the power grid, the load curve data of the power grid operation day and the topological structure of the power grid; The undirected graph generation module is used to generate undirected graphs of several historical typical days according to the topological structure of the power grid and the load curve data of several historical typical days of the power grid; and to generate undirected graphs of power grid operation days according to the topological structure of the power grid and the load curve data of power grid operation days; The graph similarity evaluation module is used to sequentially input the undirected graphs of each historical typical day and the undirected graphs of the power grid operation day into the graph similarity evaluation network model to generate similarity scores between the undirected graphs of the power grid operation day and the undirected graphs of each historical typical day; wherein each training sample when training the graph similarity evaluation network model includes: undirected graphs of two different historical typical days and actual similarity labels between the two; the actual similarity of the undirected graphs of two different historical typical days is calculated based on the optimal unit combination schemes of the two different historical typical days; the optimal unit combination scheme is the unit combination scheme with the minimum total operating cost; The hot start point selection module is used to select the undirected graph of the historical typical day with the highest similarity score as the target undirected graph; use the optimal unit combination plan of the typical day corresponding to the target undirected graph as the target unit combination plan for the power grid operation day; and use the target unit combination plan as the hot start point for solving the total operating cost minimization model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for selecting a hot start point of a unit combination scheme as described in any one of claims 1 to 7 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the method for selecting a hot start point of a unit combination scheme as described in any one of claims 1 to 7.

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