Power system robust unit combination acceleration optimization method based on representative scene

Abstract

The invention belongs to the technical field of power systems, and particularly discloses a representative scene-based power system robust unit combination acceleration optimization method, which comprises the following steps of: constructing a robust uncertainty set according to historical predicted power generation data and historical actual power generation data; constructing a unit commitment optimization model; calculating the system net load and the system net load rate of the power system in each time period; respectively constructing a first auxiliary mixed integer linear programming model and a second auxiliary mixed integer linear programming model, and determining a minimum net load representative scene and a maximum net load representative scene; determining a necessary unit set; determining a must-stop unit set; and constructing a robust unit commitment simplified model by taking the set of units needing to be started and the set of units needing to be stopped as optimization schemes so as to realize accelerated optimization of the robust unit commitment problem of the power system. According to the method, the calculation efficiency of the robust unit combination problem can be improved. The problem of low calculation efficiency of the robust unit combination problem in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method for accelerating optimization of robust unit combinations in power systems based on representative scenarios. Background Art

[0002] The safety constrained unit commitment (SCUC) problem is a typical non-convex, large-scale mixed integer optimization problem, which contains a large number of binary variables and continuous variables and is subject to a series of equality and inequality constraints. The commonly used solution method for this problem is the branch-and-bound method, which gradually constructs a solution space tree and prunes it to find the optimal solution. However, since the worst computational complexity of the SCUC problem is exponentially related to the number of binary variables, the branch-and-bound method still faces great computational pressure in terms of computational efficiency in large-scale power systems. Therefore, recent research on accelerated optimization methods for safety constrained unit commitment has focused on how to make the branch-and-bound method more efficient in searching for feasible solutions or even optimal solutions. The first type of method is to identify infeasible solutions in the solution space of the branch-and-bound method through methods such as neighborhood search, internal information induction of the solver, and reinforcement learning, so as to avoid unnecessary searches and improve the search efficiency. The second type of method is to reduce the model size through methods such as unit aggregation and time period aggregation, reduce the decision space to be searched, and reduce the computational burden of the branch-and-bound method. The third type of method directly generates feasible solutions through data-driven or heuristic methods, or uses them as initial solutions to improve the search efficiency of the branch-and-bound method. The fourth type of method is to improve the search efficiency of the branch and bound method by solving multiple small optimization problems in parallel and then repairing the feasible solution or using it to identify the units that must be started and the units that must be shut down.

[0003] However, as more and more renewable energy sources are connected to the power system, the safe operation of the power system requires effective uncertainty management methods to ensure it. Robust optimization provides a promising solution for uncertainty management of renewable energy sources, but the two-stage robust unit commitment problem is more complex and time-consuming than the safety-constrained unit commitment problem mentioned above, and often cannot meet the daily production needs, thus limiting its application in large-scale systems. The safety-constrained unit commitment acceleration method mentioned above is not a targeted acceleration method for the two-stage robust unit commitment problem. Therefore, there is still much room for improvement in the two-stage robust unit commitment acceleration optimization method. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an accelerated optimization method for robust unit combination of a power system based on a representative scenario, which solves the problem of low computational efficiency of the robust unit combination problem in the prior art.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for accelerating optimization of robust unit commitment of power system based on representative scenarios, comprising the following steps: S1. Obtain the historical predicted power generation data and historical actual power generation data of new energy stations, and construct a robust uncertainty set based on the historical predicted power generation data and historical actual power generation data; S2. Obtain the day-ahead optimal dispatching data of the power system and build a unit combination optimization model; S3, setting the new energy forecast deviation to 0, and calculating the system net load and system net load rate of the power system in each period according to the day-ahead forecast output data of the new energy station and the day-ahead load forecast data of each load node in the power system; S4. Considering the new energy prediction deviation within the limited range of the robust uncertainty set, using the objective function to respectively construct the first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, and substituting the calculation results of the system net load and the system net load rate into the first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, respectively, to determine the minimum net load representative scenario and the maximum net load representative scenario; The first auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model for minimizing the sum of the product of the inverse of the system net load rate and the system net load, and the second auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model for maximizing the sum of the product of the system net load rate and the system net load; S5. Substitute the minimum net load representative scenario into the unit combination optimization model for solution, and set the threshold for determining the must-start units according to the minimum and maximum technical output parameters of the units participating in the optimization, and determine the set of must-start units; S6. Substitute the representative scenario of the maximum net load and the set of units that must be turned on into the unit commitment optimization model for solution, and determine the set of units that must be turned off according to the rules for distinguishing the units that must be turned off, combined with the solution results and the set of units that must be turned on; S7. Take the set of units that must be turned on and the set of units that must be turned off as the optimization scheme, and combine the robust uncertainty set and the unit combination optimization model to build a streamlined model of robust unit combination to achieve accelerated optimization of the robust unit combination problem of the power system.

[0006] The beneficial effect of the above scheme is: without significantly reducing the robustness of the optimization results, by quickly solving the unit combination model corresponding to the representative scenario, the set of units that must be started and the set of units that must be shut down are accurately determined, thereby effectively reducing the number of binary variables to be decided, thereby accelerating the solution process of the robust unit combination problem.

[0007] Furthermore, in S1, a robust uncertainty set is constructed based on historical predicted power generation data and historical actual power generation data, specifically including: S11. Calculate the output forecast deviation based on the historical forecast power generation data and the historical actual power generation data; The formula used to calculate the output prediction deviation is:

[0008] in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time The actual power generation data, Represents new energy station No. Day time Forecasted power generation data, Represents a collection of new energy stations. Represents a collection of historical data days. represents a set of scheduling periods; S12. Construct a robust uncertainty set based on the mean and standard deviation of the output forecast deviation; Constructing Robust Uncertain Sets The formula used is:

[0009]

[0010]

[0011] in, Represents the new energy station in the robust uncertainty set Time The output prediction deviation ranges from , express Tiantian New Energy Station Time The mean of the output forecast deviation, express Tiantian New Energy Station Time The standard deviation of the output forecast deviation, Indicates the conservative control coefficient, the default value is 1.5.

[0012] The beneficial effect of the above further scheme is: by defining the output forecast deviation range of the renewable energy stations in each time period within the robust uncertainty set, it is ensured that the subsequent robust unit combination model can not only ensure the safe operation of the system but also avoid excessive conservatism when dealing with the uncertainty of renewable energy power generation, thereby achieving a balance between robustness and economy.

[0013] Furthermore, in S2, the day-ahead optimization dispatching data includes the grid topology data, the cost data of the units participating in the optimization, the operation characteristic data of the units participating in the optimization, the day-ahead load forecast data, and the day-ahead output forecast data of the new energy station; Construct a unit commitment optimization model, including: Construct the unit commitment optimization model and the constraints corresponding to the unit commitment optimization model; The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop status constraints, minimum continuous start and stop constraints, unit climbing constraints, system supply and demand balance constraints, line safety constraints and new energy station power abandonment constraints; The unit commitment optimization model is:

[0014] in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of represents the no-load cost of the units participating in the optimization, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost of the startup event, Indicates the unit In the period Whether a downtime event occurred, represents the downtime cost of the downtime event, Indicates time period unit The output level, Indicates the unit The variable generation cost function is It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted power, Represents the set of units participating in optimization; The upper and lower limits of the unit output are:

[0015] in, and Respectively represent the minimum technical output and maximum technical output of the unit; The start and stop state constraints of the unit are:

[0016]

[0017] in, Indicates the units involved in optimization In the period The operating status of The minimum continuous start and stop constraints of the unit are:

[0018]

[0019] in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred , Indicates the unit Minimum continuous power-on time, Indicates the unit Minimum continuous shutdown time; The unit climbing constraint is:

[0020] in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level; The system supply and demand balance constraint is:

[0021] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output is Representation Node In the period The load forecast value of the day ahead is represents a set of load nodes; The line safety constraints are:

[0022]

[0023] in, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, Indicates line The steady-state power flow limit value is represents a collection of transmission lines; The constraints on power abandonment at new energy stations are:

[0024] in, Represents new energy station In the period of wasted electricity.

[0025] The beneficial effect of the above further scheme is: by introducing a unit combination model containing renewable energy prediction deviations and power curtailment as the basis for the subsequent construction of a robust unit combination model, it helps to optimize the unit start and shutdown plan and power generation plan of the power grid while taking into account the uncertainty of renewable energy power generation.

[0026] Furthermore, in S3, the net load and net load rate of the power system in each period are calculated based on the day-ahead forecast output data of the new energy station and the day-ahead load forecast data of each load node in the power system. The formula used is:

[0027]

[0028] in, Indicates the net load of the system. Indicates the net load rate of the system.

[0029] The beneficial effect of the above further scheme is that the calculation result of the system net load rate can be used as the weight in the objective function of the subsequent first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, thereby guiding the two small optimization models to focus on the time periods with small and large system net loads, respectively, thereby effectively screening out representative scenarios.

[0030] Further, in S4, the first auxiliary mixed integer linear programming model is:

[0031]

[0032]

[0033] in, represents the system net load variable, Represents the net load rate of the system. After solving the first auxiliary mixed integer linear programming model, a set of The optimal solution , which is the minimum net load representative scenario; The second auxiliary mixed integer linear programming model is:

[0034]

[0035]

[0036] Among them, after solving the second auxiliary mixed integer linear programming model, a set of The optimal solution , which is the representative scenario of maximum net load.

[0037] The beneficial effect of the above further scheme is that through two small optimization problems, representative scenarios with small and large system net loads are quickly screened out within the range of the robust uncertainty set, providing an effective basis for the analysis of the units that must be started and the units that must be shut down in the subsequent robust unit combination model.

[0038] Furthermore, S5 specifically includes: S51, substituting the minimum net load representative scenario into the unit commitment optimization model to obtain a new unit commitment optimization model; The new unit combination optimization model is:

[0039] in, Indicates the minimum net load representative scenario; S52, solving the new unit combination optimization model to obtain the optimal solution of the first startup mode and the first output level of each unit participating in the optimization unit set; S53: using the threshold value for distinguishing the units that must be turned on, determine the set of units that must be turned on from the optimal solution of the first unit turning-on mode and the first output level.

[0040] The beneficial effect of the above further scheme is: by compressing the search space of the complex robust unit combination model into a single representative scenario, the optimal solution for the first unit startup mode and the first output level is quickly solved, and combined with the judgment threshold set by the minimum / maximum technical output, the necessary units that still need to be started all the time when the system net load is low are accurately identified.

[0041] Furthermore, S6 specifically includes: S61, substituting the maximum net load representative scenario into the unit commitment optimization model to obtain a second unit commitment optimization model; The second unit combination optimization model is:

[0042] in, Indicates the maximum net load representative scenario; S62, substituting the set of units that must be started into the second unit combination optimization model to obtain a third unit combination optimization model; S63, solving the third unit combination optimization model to obtain the optimal solution for the second startup mode and the second output level of each unit participating in the optimization unit set; S64. Determine the set of units that must be shut down according to the rules for distinguishing the units that must be shut down, in combination with the second start-up mode of each unit, the optimal solution of the second output level and the set of units that must be shut down.

[0043] The above further scheme has the beneficial effect that by compressing the search space of the complex robust unit combination model into a single representative scenario and combining it with the determined set of must-start units, the solution time can be effectively reduced, and the optimal solution for the startup mode and output level of the second unit can be quickly obtained. At the same time, through the must-stop unit discrimination rules, the must-stop units that will not be started when the system net load is high can be accurately identified.

[0044] Furthermore, S7 specifically includes: S71. Combining the robust uncertainty set and the unit commitment optimization model, a robust unit commitment model is constructed; The robust unit commitment model is:

[0045]

[0046] in, , , , , ,vector is the first-stage decision variable, vector is the second-stage decision variable, the vector is restricted to the robust uncertainty set The uncertainty parameter, i.e., the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model; S72, taking the set of units that must be started and the set of units that must be stopped as additional constraints, and obtaining a simplified model of robust unit commitment; The simplified model of robust unit commitment is:

[0047]

[0048] in, Represents the vector of decision variables in the first stage of the robust unit commitment model The variables.

[0049] The beneficial effect of the above further scheme is: by taking the set of units that must be turned on and the set of units that must be turned off as additional constraints and directly fixing the determined unit operating states, it is possible to effectively reduce the binary variables to be decided in the robust unit combination model and improve the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Shown is a flow chart of an accelerated optimization method for robust unit commitment of a power system based on a representative scenario. DETAILED DESCRIPTION

[0051] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to explain the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0052] like Figure 1As shown, a method for accelerating optimization of robust unit commitment of a power system based on a representative scenario includes the following steps: S1. Obtain the historical predicted power generation data and historical actual power generation data of new energy sites, and construct a robust uncertainty set based on the historical predicted power generation data and historical actual power generation data.

[0053] In this embodiment, a robust uncertainty set is constructed based on historical predicted power generation data and historical actual power generation data, specifically including: S11. Calculate the output forecast deviation based on the historical forecast power generation data and the historical actual power generation data; The formula used to calculate the output prediction deviation is:

[0054] in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time The actual power generation data, Represents new energy station No. Day time Forecasted power generation data, Represents a collection of new energy stations. Represents a collection of historical data days. represents a set of scheduling periods; S12. Construct a robust uncertainty set based on the mean and standard deviation of the output forecast deviation; Constructing Robust Uncertain Sets The formula used is:

[0055]

[0056]

[0057] in, Represents the new energy station in the robust uncertainty set Time The output prediction deviation ranges from , express Tiantian New Energy Station Time The mean of the output forecast deviation, express Tiantian New Energy Station Time The standard deviation of the output forecast deviation, Indicates the conservative control coefficient, the default value is 1.5.

[0058] Exemplarily, the new energy stations may include photovoltaic stations and wind power stations, etc.

[0059] Optionally, the data interval between the historical predicted power generation data and the historical actual power generation data is 1 hour or 15 minutes. In the case of missing historical data, ultra-short-term predicted data can be used as a substitute.

[0060] S2. Obtain the day-ahead optimal dispatching data of the power system and build a unit combination optimization model.

[0061] In this embodiment, the day-ahead optimization dispatching data includes power grid topology data, cost data of the units participating in the optimization, operation characteristic data of the units participating in the optimization, day-ahead load forecast data, and day-ahead output forecast data of new energy stations; Construct a unit commitment optimization model, including: Construct the unit commitment optimization model and the constraints corresponding to the unit commitment optimization model; The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop status constraints, minimum continuous start and stop constraints, unit climbing constraints, system supply and demand balance constraints, line safety constraints and new energy station power abandonment constraints; The unit commitment optimization model is:

[0062] in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of represents the no-load cost of the units participating in the optimization, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost of the startup event, Indicates the unit In the period Whether a downtime event occurred, represents the downtime cost of the downtime event, Indicates time period unit The output level, Indicates the unit The variable generation cost function is It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted power, Represents the set of units participating in optimization; The upper and lower limits of the unit output are:

[0063] in, and Respectively represent the minimum technical output and maximum technical output of the unit; The start and stop state constraints of the unit are:

[0064]

[0065] in, Indicates the units involved in optimization In the period The operating status of The minimum continuous start and stop constraints of the unit are:

[0066]

[0067] in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred , Indicates the unit Minimum continuous power-on time, Indicates the unit Minimum continuous shutdown time; The unit climbing constraint is:

[0068] in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level; The system supply and demand balance constraint is:

[0069] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output is Representation Node In the period The load forecast value of the day ahead is represents a set of load nodes; The line safety constraints are:

[0070]

[0071] in, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, Indicates line The steady-state power flow limit value is represents a collection of transmission lines; The constraints on power abandonment at new energy stations are:

[0072] in, Represents new energy station In the period of wasted electricity.

[0073] Exemplarily, the power grid topology data may include the connection relationship between power network nodes and transmission lines, and the output power transfer distribution factor matrix of each type of unit and node load to each transmission line; The cost data of the units involved in the optimization may include the startup cost, shutdown cost, no-load cost, and variable power generation cost function of the units in each period; The operating characteristic data of the units involved in the optimization may include the minimum / maximum technical output of the units, the climbing capacity of the units in different periods, and the minimum continuous start-up and shutdown time of the units; The load day-ahead forecast data may include the load day-ahead forecast data of each node, with a time resolution of 1 hour or 15 minutes; The day-ahead predicted output data of new energy stations may include the day-ahead predicted output data of new energy stations, with a time resolution of 1 hour or 15 minutes.

[0074] S3. Set the new energy forecast deviation to 0, and calculate the system net load and system net load rate of the power system in each time period based on the day-ahead forecast output data of the new energy station and the day-ahead load forecast data of each load node in the power system.

[0075] In this embodiment, the system net load and system net load rate of the power system in each period are calculated based on the day-ahead predicted output data of the new energy station and the day-ahead load forecast data of each load node in the power system. The formula used is:

[0076]

[0077] in, Indicates the net load of the system. Indicates the net load rate of the system.

[0078] S4. Considering the new energy prediction deviation within the limited range of the robust uncertainty set, using the objective function to respectively construct the first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, and substituting the calculation results of the system net load and the system net load rate into the first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, respectively, to determine the minimum net load representative scenario and the maximum net load representative scenario; The first auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model that minimizes the sum of the product of the inverse of the system net load rate and the system net load, and the second auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model that maximizes the sum of the product of the system net load rate and the system net load.

[0079] In this embodiment, the first auxiliary mixed integer linear programming model is:

[0080]

[0081]

[0082] in, represents the system net load variable, Represents the net load rate of the system. After solving the first auxiliary mixed integer linear programming model, a set of The optimal solution , which is the minimum net load representative scenario; The second auxiliary mixed integer linear programming model is:

[0083]

[0084]

[0085] Among them, after solving the second auxiliary mixed integer linear programming model, a set of The optimal solution , which is the representative scenario of maximum net load.

[0086] For example, by solving the first auxiliary mixed integer linear programming model, a set of representative scenarios reflecting the potential minimum net load of the next day can be obtained, that is, each new energy station In each period The worst estimate of the output forecast deviation .

[0087] Similarly, solving the second auxiliary mixed integer linear programming model can obtain a set of representative scenarios reflecting the potential maximum net load of the next day, that is, each new energy station In each period The worst estimate of the output forecast deviation .

[0088] S5. Substitute the minimum net load representative scenario into the unit combination optimization model for solution, and set the threshold for determining the must-start units based on the minimum and maximum technical output parameters of the units participating in the optimization to determine the set of must-start units.

[0089] In this embodiment, S5 specifically includes: S51, substituting the minimum net load representative scenario into the unit commitment optimization model to obtain a new unit commitment optimization model; The new unit combination optimization model is:

[0090] in, Indicates the minimum net load representative scenario; S52, solving the new unit combination optimization model to obtain the optimal solution of the first startup mode and the first output level of each unit participating in the optimization unit set; S53: using the threshold value for distinguishing the units that must be turned on, determine the set of units that must be turned on from the optimal solution of the first unit turning-on mode and the first output level.

[0091] For example, the minimum net load representative scenario is substituted into the unit combination optimization model, that is, each new energy station In each period The worst estimate of the output forecast deviation Substitute them into the unit combination optimization model one by one to obtain the first unit combination optimization model. Solving the new unit combination optimization model can obtain the optimal solution for the first unit startup mode and the first output level of the participating unit set, that is, The minimum and maximum technical outputs of the units involved in the optimization set the threshold for determining which units must be started, i.e. As a constraint condition, the set of units that must be started is determined from the optimal solution of the first unit startup mode and the first output level, that is, , among which, the units participating in the optimization in the set of must-start units As long as all scheduling time All of them meet the set threshold for judging the must-start units, which is the must-start unit set element.

[0092] S6. Substitute the representative scenario of the maximum net load and the set of units that must be turned on into the unit combination optimization model for solution, and determine the set of units that must be turned off according to the rules for distinguishing the units that must be turned off, combined with the solution results and the set of units that must be turned on.

[0093] In this embodiment, S6 specifically includes: S61, substituting the maximum net load representative scenario into the unit commitment optimization model to obtain a second unit commitment optimization model; The second unit combination optimization model is:

[0094] in, Indicates the maximum net load representative scenario; S62, substituting the set of units that must be started into the second unit combination optimization model to obtain a third unit combination optimization model; S63, solving the third unit combination optimization model to obtain the optimal solution for the second startup mode and the second output level of each unit participating in the optimization unit set; S64. Determine the set of units that must be shut down according to the rules for distinguishing the units that must be shut down, in combination with the second start-up mode of each unit, the optimal solution of the second output level and the set of units that must be shut down.

[0095] For example, the maximum net load representative scenario is substituted into the unit combination optimization model, that is, each renewable energy station In each period The worst estimate of the output forecast deviation As known quantities, they are substituted into the unit combination optimization model one by one by adding additional constraints to obtain the second unit combination optimization model. Then, the set of must-start units is substituted into the second unit combination optimization model by adding additional constraints to obtain the third unit combination optimization model, that is, the third unit combination optimization model is: Solve the third unit combination optimization model and obtain the set of units participating in the optimization The optimal solution for the second startup mode of each unit and the second output level is According to the rules for distinguishing the units that must be shut down, combined with the second unit startup mode, the second output level optimal solution and the set of units that must be started, the set of units that must be shut down is determined, that is: , in the set of units that must be shut down, the units participating in the optimization As long as the entire scheduling period of the two representative scenes The running status of all the groups is 0, that is, not started, which is the group that must be shut down. element.

[0096] S7. Take the set of units that must be turned on and the set of units that must be turned off as the optimization scheme, and combine the robust uncertainty set and the unit combination optimization model to build a streamlined model of robust unit combination to achieve accelerated optimization of the robust unit combination problem of the power system.

[0097] In this embodiment, S7 specifically includes: S71. Combining the robust uncertainty set and the unit commitment optimization model, a robust unit commitment model is constructed; The robust unit commitment model is:

[0098]

[0099] in, , , , , ,vector is the first-stage decision variable, vector is the second-stage decision variable, the vector is restricted to the robust uncertainty set The uncertainty parameter, i.e., the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model; S72, taking the set of units that must be started and the set of units that must be stopped as additional constraints, and obtaining a simplified model of robust unit commitment; The simplified model of robust unit commitment is:

[0100]

[0101] in, Represents the vector of decision variables in the first stage of the robust unit commitment model The variables.

[0102] Exemplarily, the two equations of the simplified robust unit combination model are to find the specific location of the variables expressing the operating status of the corresponding units in the robust unit combination model and add constraints according to the numbers of the units participating in the optimization, and then use the column and constraint generation algorithm to solve the simplified robust unit combination model.

[0103] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for accelerating optimization of robust unit commitment in power system based on representative scenarios, characterized in that: The method comprises: S1. Obtain historical predicted power generation data and historical actual power generation data of new energy stations, and construct a robust uncertainty set based on the historical predicted power generation data and the historical actual power generation data; S2. Obtain the day-ahead optimal dispatching data of the power system and build a unit combination optimization model; S3, setting the new energy forecast deviation to 0, and calculating the system net load and system net load rate of the power system in each period according to the day-ahead forecast output data of the new energy station and the day-ahead load forecast data of each load node in the power system; S4. Considering the new energy prediction deviation within the limited range of the robust uncertainty set, using the objective function to respectively construct a first auxiliary mixed integer linear programming model and a second auxiliary mixed integer linear programming model, and substituting the calculation results of the system net load and the system net load rate into the first auxiliary mixed integer linear programming model and the second auxiliary mixed integer linear programming model, respectively, to determine the minimum net load representative scenario and the maximum net load representative scenario; The first auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model for minimizing the sum of the product of the inverse of the system net load rate and the system net load, and the second auxiliary mixed integer linear programming model is an auxiliary mixed integer linear programming model for maximizing the sum of the product of the system net load rate and the system net load; S5, substituting the minimum net load representative scenario into the unit combination optimization model for solution, and setting the threshold for determining the must-start units according to the minimum and maximum technical output parameters of the units participating in the optimization, and determining the must-start unit set; S6, substituting the maximum net load representative scenario and the set of units that must be started into the unit commitment optimization model for solution, and determining the set of units that must be stopped according to the rules for distinguishing the units that must be stopped, combining the solution result and the set of units that must be started; S7. Taking the set of units that must be started and the set of units that must be stopped as optimization schemes, and combining the robust uncertainty set and the unit combination optimization model, a simplified robust unit combination model is constructed to achieve accelerated optimization of the robust unit combination problem of the power system.

2. The method according to claim 1, characterized in that In S1, constructing a robust uncertainty set based on the historical predicted power generation data and the historical actual power generation data specifically includes: S11, calculating the output prediction deviation according to the historical predicted power generation data and the historical actual power generation data; The formula used to calculate the output prediction deviation is: in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time The actual power generation data, Represents new energy station No. Day time Forecasted power generation data, Represents a collection of new energy stations. Represents a collection of historical data days, represents a set of scheduling periods; S12, constructing the robust uncertainty set according to the mean and standard deviation of the output prediction deviation; The construction of the robust uncertainty set The formula used is: in, Represents the new energy station in the robust uncertainty set Time The output prediction deviation ranges from , express Tiantian New Energy Station Time The mean of the output forecast deviation, express Tiantian New Energy Station Time The standard deviation of the output forecast deviation, Indicates the conservative control coefficient, the default value is 1.

5.

3. The method according to claim 2, characterized in that In S2, the day-ahead optimization dispatching data includes power grid topology data, cost data of the units participating in the optimization, operation characteristic data of the units participating in the optimization, day-ahead load forecast data, and day-ahead output forecast data of new energy stations; The constructing of the unit commitment optimization model specifically includes: Constructing the unit commitment optimization model and the constraint conditions corresponding to the unit commitment optimization model; The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop status constraints, minimum continuous start and stop constraints, unit climbing constraints, system supply and demand balance constraints, line safety constraints and new energy station power abandonment constraints; The unit commitment optimization model is: in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of represents the no-load cost of the units participating in the optimization, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost of the startup event, Indicates the unit In the period Whether a downtime event occurred, represents the downtime cost of the downtime event, Indicates time period unit The output level, Indicates the unit The variable generation cost function is It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted electricity, Represents the set of units participating in optimization; The upper and lower limits of the unit output are: in, and Respectively represent the minimum technical output and maximum technical output of the unit; The start and stop state constraints of the unit are: in, Indicates the units involved in optimization In the period The operating status of The minimum continuous start and stop constraints of the unit are: in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred , Indicates the unit Minimum continuous power-on time, Indicates the unit Minimum continuous shutdown time; The unit climbing constraint is: in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level; The system supply and demand balance constraint is: in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output is Representation Node In the period The load forecast value of the day ahead is represents a set of load nodes; The line safety constraints are: in, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, Indicates line The steady-state power flow limit value is represents a collection of transmission lines; The constraints on power abandonment at new energy stations are: in, Represents new energy station In the period of wasted electricity.

4. The method according to claim 3, characterized in that In S3, the system net load and system net load rate of the power system in each time period are calculated based on the day-ahead predicted output data of the new energy station and the day-ahead load forecast data of each load node in the power system, and the formula used is: in, Indicates the net load of the system. Indicates the net load rate of the system.

5. The method according to claim 4, characterized in that In S4, the first auxiliary mixed integer linear programming model is: in, represents the system net load variable, Represents the net load rate of the system. After solving the first auxiliary mixed integer linear programming model, a set of The optimal solution , which is the minimum net load representative scenario; The second auxiliary mixed integer linear programming model is: Among them, after solving the second auxiliary mixed integer linear programming model, a set of The optimal solution , which is the representative scenario of maximum net load.

6. The method according to claim 5, characterized in that The S5 specifically includes: S51, substituting the minimum net load representative scenario into the unit commitment optimization model to obtain a new unit commitment optimization model; The new unit combination optimization model is: in, Indicates the minimum net load representative scenario; S52, solving the new unit combination optimization model to obtain the optimal solution of the first startup mode and the first output level of each unit participating in the optimization unit set; S53: using the must-start unit discrimination threshold, determine the must-start unit set from the first unit startup methods and the first output level optimal solution.

7. The method according to claim 6, characterized in that The S6 specifically includes: S61, substituting the maximum net load representative scenario into the unit commitment optimization model to obtain a second unit commitment optimization model; The second unit combination optimization model is: in, Indicates the maximum net load representative scenario; S62, substituting the set of must-start units into the second unit combination optimization model to obtain a third unit combination optimization model; S63, solving the third unit combination optimization model to obtain the optimal solution for the second startup mode and the second output level of each unit participating in the optimization unit set; S64. Determine the set of units that must be shut down according to a rule for distinguishing the units that must be shut down, in combination with the second startup modes of each unit, the second optimal solution for the output level, and the set of units that must be shut down.

8. The method according to claim 7, characterized in that The S7 specifically includes: S71, constructing a robust unit commitment model by combining the robust uncertainty set and the unit commitment optimization model; The robust unit commitment model is: in, , , , , ,vector is the first-stage decision variable, the vector is the second-stage decision variable, the vector is restricted to the robust uncertainty set The uncertainty parameter, i.e., the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model; S72, taking the set of units that must be started and the set of units that must be stopped as additional constraints to obtain the simplified model of the robust unit commitment; The simplified model of robust unit commitment is: in, Represents the vector of decision variables in the first stage of the robust unit commitment model The variables.

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