Partition standby-based provincial network maximum power supply capability optimization scheduling method and system

Abstract

The invention discloses a provincial network maximum power supply capacity optimization scheduling method and system based on partition reserve, and the method comprises the steps: carrying out the preprocessing of collected output, load, reserve, section margin and section-section sensitivity data according to the levels of a unit, a power plant, a district power grid and a provincial power grid; taking a district as a unit, establishing a provincial network maximum power supply capacity optimization scheduling model, and ensuring power supply through spinning reserve scheduling of different types of power supplies in the provincial network; and solving the model by adopting an optimization algorithm to obtain a plurality of scheduling schemes, selecting an optimal scheme according to actual operation requirements, outputting a result and quantitatively evaluating the maximum power supply capacity of the power grid, thereby realizing effective management of the standby capacity of the provincial network partition power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and particularly relates to an optimal dispatching method and system for the maximum power supply capacity of a provincial power grid based on zonal reserve. Background Art

[0002] There are mainly two traditional methods for evaluating the traditional supply capacity (TSC): the analytical method and the optimization method. The analytical method represents the power supply capacity of the power grid as a function of network parameters, which can more accurately reflect the actual characteristics of the power grid, and the TSC can be obtained through simple and rapid analytical calculations. With the increase in the scale of the power grid, since the actual power grid is simplified to a great extent when using the analytical method to model the actual power grid, the solution result of the model is not accurate enough, and the calculation result is generally only used for preliminary qualitative analysis.

[0003] The optimization method mainly converts the TSC calculation into an optimization problem with constraint conditions, establishes a mathematical optimization model and then solves it with a suitable algorithm, and finally converts the result into the solution of the actual problem. At present, many studies have modeled the TSC optimization model, and the main solution methods used are traditional optimization algorithms and intelligent optimization algorithms, and most of them focus on the distribution network and a single area based on zonal operation (such as a single 220 kV area power grid). The optimization objective of the constructed model is mostly the maximum sum of the active power loads carried by nodes or each 220 kV substation in a single area. However, for today's power grid interconnection pattern, the number of UHV DC lines and new energy transmission lines is increasing continuously, but few studies have studied the maximum power supply capacity problem from the perspective of a large power grid based on zonal operation. And how to accurately evaluate the TSC of the area power grid must be based on the optimization dispatching model of the large power grid, but this will bring problems such as large computational complexity and difficult convergence. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an optimal dispatching method and system for the maximum power supply capacity of a provincial power grid based on zonal reserve to solve the problem of evaluating the maximum power supply capacity of a large power grid.

[0005] In order to achieve the above invention objectives, the technical solutions adopted by the present invention are as follows:

[0006] An optimal dispatching method for the maximum power supply capacity of a provincial power grid based on zonal reserve, comprising the following steps:

[0007] S1: Preprocess the collected output, load, reserve, section margin and section - area sensitivity data according to the levels of units, power plants, area power grids, and provincial power grids;

[0008] S2: Establish an optimal dispatching model for the maximum power supply capacity of the provincial power grid with areas as units, and realize power supply guarantee through the rotational reserve dispatching of various power sources (thermal power, traditional hydropower, pumped - storage) in the zones;

[0009] S3: Solve the model using different types of optimization algorithms to obtain numerous scheduling plans, and select the optimal scheduling plan from them according to specific requirements in actual operation, output the results, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the sub-grid of a provincial power grid.

[0010] Preferably, the data preprocessing of output, load, reserve, section margin, and area-section sensitivity in step S1 is specifically as follows:

[0011] S11: Output data processing method:

[0012] 1) Power output of power plant = sum of the power outputs of all units in the power plant;

[0013] 2) Total output within the area = sum of the power generations of all power plants (hydropower, thermal power, pumped storage) within the area;

[0014] 3) Provincial power grid output data = sum of the output data of each area within the provincial power grid

[0015] S12: Load data processing method:

[0016] 1) Equivalent the area load data to the output data of the area plus the received power of the tie line;

[0017] 2) Provincial power grid load data = sum of the load data of each area within the provincial power grid.

[0018] S13: Processing method of reserve data:

[0019] 1) Thermal power spinning reserve: Considering the actual situation of a certain power grid, the upper and lower limits of the unit power output can be set as the rated power and 50% of the rated power respectively, that is, thermal power spinning positive reserve = unit rated power - unit real-time power generation; thermal power spinning negative reserve = unit real-time power generation - 50% of unit rated power;

[0020] 2) Hydropower spinning reserve: The calculation methods of positive and negative spinning reserves of hydropower plants are similar to those of thermal power plants. The upper limit of the hydropower unit also takes the rated power, and the lower limit is taken as 0, that is, hydropower spinning positive reserve = unit rated power - unit real-time power generation; hydropower spinning negative reserve = unit real-time power generation;

[0021] 3) Pumped storage spinning reserve: Since pumped storage power stations generally have two working modes of pumping and generating electricity, therefore, when calculating the positive spinning reserve of pumped storage, it is necessary to consider which stage the pumped storage power station is working in, pay attention to the positive and negative of the real-time power generation P, and the negative spinning reserve is related to the lower limit of the water storage power, that is, pumped storage spinning positive reserve = maximum power generation of pumped storage power plant - real-time power generation (when generating electricity, power P > 0, when storing water, power P < 0); pumped storage spinning negative reserve = real-time power generation - lower limit of water storage power.

[0022] S14: Cross-section margin data processing method:

[0023] After selecting the power flow cross-section that requires key attention, the real-time power and the upper and lower limits of the corresponding cross-section power are exported from the dispatching system. The calculation method of the cross-section margin is as follows: When a certain direction is determined as the positive direction, the upper cross-section margin = the upper limit of the cross-section stability limit – the current cross-section power, and the lower cross-section margin = the current cross-section power – the lower limit of the cross-section stability limit.

[0024] S15: Area-cross-section sensitivity data processing method:

[0025] Generally speaking, the electrical distance of the unit connected to the cross-section will have a greater impact on the unit-cross-section sensitivity. Units with the same electrical distance have the same impact on the cross-section. For the vast majority of power station units in the same spatial position, their corresponding electrical connection points are often the same point. Therefore, the sensitivity of a certain unit in the power station to the cross-section can be used to represent the sensitivity of the power station to the cross-section. For different power stations in the same area (especially for this kind of regional power grid with many partitions), the distance between power stations is often not very large. Therefore, the difference between the sensitivity data of different power stations in the area to the same cross-section is very small and can almost be ignored. To sum up, when modeling, the area-cross-section sensitivity data can approximately adopt the cross-section sensitivity data of the unit in a certain backbone power station with the largest capacity in the area.

[0026] Preferably, in step S2, the optimization dispatching model for the maximum power supply capacity of the provincial power grid is specifically determined as:

[0027] Under ideal conditions, the maximum power supply capacity of the regional power grid should be the sum of the positive reserve capacities of the adjustable units in the region. However, due to the influence of actual power grid operation restrictions, it cannot be simply evaluated by superposition. Assume that there are N areas in the provincial power grid. Without considering cross-provincial power support, when no orderly power consumption is adopted in the N areas and the unit starting mode remains unchanged, according to the load level and reserve situation at the evaluation moment, a certain moment is selected under the condition that the safe and stable operation of each area of the provincial power grid can be guaranteed, and the maximum power supply capacity that the provincial power grid can reach by adjusting the output of the adjustable units inside the provincial power grid is evaluated. To simplify the calculation model, it is assumed that the loads of the N areas in a certain provincial power grid increase synchronously according to the load ratio at the evaluation moment.

[0028] S21: The objective function is as follows:

[0029] 1) The total adjustment amount of the output of the conventional power sources in the area is the largest

[0030] When the target provincial power grid does not accept external assistance, the maximum load that the target provincial power grid can bear is the maximum power supply capacity of the target provincial power grid. Its realization mainly depends on increasing the power generation of conventional power sources within the target provincial power grid. Therefore, the maximum increase in the power generation of conventional power sources in each area within the target province is set as the optimization goal, which can be expressed by the following formula:

[0031]

[0032] In the formula, ΔP i represents the total adjustment of the output of conventional power sources in the i-th area within the target provincial power grid, including thermal power, hydropower and pumped storage; N represents the set of provincial power grid areas.

[0033] 2) Maximize the minimum section margin

[0034] A sufficient safety margin is the basic guarantee for the stable operation of the power grid. To make the power flow distribution of the key sections uniform and avoid the situation of excessive heavy load on the sections, a certain space needs to be reserved for the upper and lower margins of the important sections of the provincial power grid after adjustment. Therefore, the objective function is determined to be the maximum of the minimum section margin after the adjustment of the area output.

[0035] There are 9 key power flow sections in a certain provincial power grid. Taking section q as an example, its function expression is listed as follows:

[0036]

[0037] In the formula, M represents the set of sections; q represents the power flow section that needs to be key concerned within the dispatching scope; respectively represent the upper and lower margins after the adjustment of section q; ΔP q represents the power adjustment amount of section q; S i,q represents the sensitivity of area i to power flow section q; Load represents the active power change of the power flow section caused by the load change; ΔL i represents the load power adjustment amount of area i.

[0038] 3) Reserve the most balanced

[0039] After the power adjustment of various types of power sources in each area, it is hoped that the ratio of the positive reserve capacity retained in each area to the load in each area is basically the same, so as to realize the rationality and balance of the positive reserve distribution of the provincial power grid. The function expression is as follows:

[0040]

[0041] In the formula, num(N) represents the number of areas; α i represents the ratio of the positive reserve of the area to the load of the area; P i max represents the maximum power of area i; Pi 0 Denote the power generation before adjustment in area i; L i Denote the load power after adjustment in area i; Denote the actual load power in area i before adjustment; R L.i Denote the proportion of the load in area i before adjustment to the total load of the provincial grid.

[0042] S22: The constraints are as follows:

[0043] 1) Power balance constraint

[0044] For the safe and stable operation of the power grid, ensure power balance. In this scenario, it should be ensured that the power adjustment amount of the conventional power sources within the region is equal to the total load growth in each area. The power balance constraint is written as follows:

[0045]

[0046] 2) Spinning reserve capacity constraint

[0047] When adjusting the output of conventional power sources, the adjustment space of conventional power sources should be considered. When calculating by area, it should be considered that its adjustment amount does not exceed the limit of the spinning reserve capacity of the area. The spinning reserve capacity constraint is written as follows:

[0048] NRC i ≤ΔP i ≤PRC i (10)

[0049] In the formula, NRC i Denote the negative spinning reserve (positive number) of the conventional power source in area i; PRC i Denote the positive spinning reserve (positive number) of the conventional power source in area i.

[0050] 3) Section stability constraint

[0051] To ensure the safe and stable operation of the transmission section that is highly concerned during the power supply of the provincial grid, it is necessary to strictly limit the power of the transmission section that changes due to the adjustment of the power output of the area power sources, so that it is always within the specified range. Taking section q as an example, the section stability constraint is written as follows:

[0052]

[0053] In the formula, Denote the upper limit of the power of section q; Denote the lower limit of the power of section q. It can be seen that the higher the sensitivity coefficient of the area to the section, the greater the fluctuation of the section power caused by the power adjustment of the area.

[0054] S23: The calculation process is as follows:

[0055] 1) Generate the conventional power supply information matrix

[0056] Determine the target province, screen the areas included in the target province, extract the conventional power supply information of each area in the target province, and generate the following conventional power supply information matrix.

[0057]

[0058] Where a i , b i , c i respectively represent the current power generation of thermal power, hydropower and pumped storage; respectively represent the spinning positive reserve of thermal power, hydropower and pumped storage; respectively represent the spinning negative reserve of thermal power, hydropower and pumped storage.

[0059] 2) Generate the power flow section information matrix

[0060] The generation method is similar to 1), including the real-time power of the key power flow sections and the upper and lower limits of the corresponding section power.

[0061] 3) Generate the section sensitivity matrix

[0062] According to the method given in the preprocessing of step S1, generate the section-area sensitivity matrix and the section-load sensitivity matrix.

[0063] 4) Solve the model using the multi-objective optimization algorithm

[0064] Substitute the above matrices into the provincial power grid maximum power supply capacity optimization dispatch model, and call the multi-objective optimization algorithm to solve it, calculate the total power adjustment of each area in the target province, the minimum power flow section margin after adjustment, and the proportional deviation of the reserve and load of each area, and obtain the provincial power grid maximum power supply capacity, that is

[0065] 5) Obtain the maximum power supply capacity optimization dispatch plan set of the target province

[0066] Export the values of the decision variables after solving to obtain the maximum power supply capacity optimization dispatch plan of the target province.

[0067] Preferably, different types of optimization algorithms are used to solve the model in step S3, specifically:[[]]

[0068] The weighted quadratic programming method (WQP) and the normalized plane constraint algorithm (NNC) in the traditional optimization algorithm, and the third-generation non-dominated sorting genetic algorithm (NSGA-III) and the decomposition-based multi-objective evolutionary algorithm (MOEA / D) in the intelligent optimization algorithm.

[0069] S31: The Normalized Normal Constraint (NNC) algorithm is a new method for generating a uniformly distributed Pareto solution set. For high-dimensional multi-objective optimization problems, this solution algorithm can effectively compress the feasible space of the problem and transform the multi-objective into multiple single objectives, thereby obtaining a uniformly distributed Pareto solution set. The specific solution steps are as follows:

[0070] 1) Normalize the objective functions: Normalize all objective functions to the interval [0, 1];

[0071] 2) Construct the utopia plane: Define an ideal set of objective values (utopia point) and construct a constraint plane;

[0072] 3) Single-objective optimization: Transform the multi-objective problem into a single-objective problem and gradually approximate the Pareto front by adjusting the constraint plane.

[0073] 4) Iterative solution: Repeat the above steps to generate multiple Pareto efficient solutions and form the Pareto front.

[0074] Compared with other multi-objective optimization algorithms, the advantage of the NNC method is that by transforming the multi-objective optimization problem into a problem of solving the optimal points on the Pareto front corresponding to equally spaced division points, only a small number of key points need to be calculated to efficiently describe the complete Pareto front. This method not only significantly reduces the computational amount but also improves the optimization efficiency, especially suitable for complex problems.

[0075] S32: Since the established model is a multi-objective optimization problem, solving it will obtain many non-dominated solution sets, and there is a game relationship among all objectives, with a "one goes up while the other goes down" rule between them. This makes it difficult for dispatchers to select the best solution from numerous dispatching schemes. Therefore, it is very important to scientifically and reasonably evaluate different power system optimal dispatching schemes.

[0076] To reduce the interference of subjective factors in the calculation process, the weight coefficients are divided into subjective and objective parts. The subjective weight coefficients of different indicators are obtained by the Analytic Hierarchy Process, and the objective weight coefficients of different indicators are obtained by the coefficient of variation method. Finally, the combined weighting method is used to reasonably allocate the two weight coefficients to obtain the comprehensive weight coefficients of each indicator.

[0077] Aiming at the defects that the grey relational analysis method (GRA) has low precision of calculation results, and the technique for order preference by similarity to an ideal solution (TOPSIS) has the situation that the distances of a certain evaluation scheme from the optimal ideal solution and the worst ideal solution are very close, making it impossible to accurately evaluate the advantages and disadvantages of the scheme according to the relative closeness degree and having too strong dependence on data, it is proposed to use the grey relational ideal solution method (GRA-TOPSIS) to evaluate the multi-objective scheduling scheme of the power system. By fusing the Euclidean distance and the grey relational degree as the improved distance evaluation criterion, it provides theoretical support for the selection of the optimal scheduling scheme.

[0078] The present invention also provides an optimal scheduling system for the maximum power supply capacity of a provincial power grid based on zonal reserve. This system can be used to implement the above-mentioned optimal scheduling method for the maximum power supply capacity of a provincial power grid based on zonal reserve. Specifically, it includes: a data preprocessing module, a model construction module, and a simulation and solution module;

[0079] Data preprocessing module: Preprocess the collected data of output, load, reserve, section margin, and section - zonal sensitivity according to the levels of units, power plants, zonal power grids, and provincial power grids;

[0080] Model construction module: Taking zones as units, establish an optimal scheduling model for the maximum power supply capacity of a provincial power grid, and realize power supply guarantee through the rotational reserve scheduling of various power sources (thermal power, traditional hydropower, pumped storage) in different zones;

[0081] Simulation and solution module: Use different types of optimization algorithms to solve the model, obtain numerous scheduling schemes, and select the optimal scheduling scheme from them according to the specific requirements in actual operation, output the results, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the zonal power grids of a certain provincial power grid.

[0082] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned optimal scheduling method for the maximum power supply capacity of a provincial power grid based on zonal reserve.

[0083] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned optimal scheduling method for the maximum power supply capacity of a provincial power grid based on zonal reserve.

[0084] Compared with the prior art, the advantages of the present invention are as follows:

[0085] Aiming at the problems existing in the current assessment of the maximum power supply capacity of large power grids operating based on partitions, an optimization dispatching model for the maximum power supply capacity in units of regions is established by combining the spare capacity information of a certain provincial power grid. The traditional optimization algorithm and the intelligent optimization algorithm are used to solve the model respectively, and numerous dispatching schemes are obtained and the optimal dispatching scheme is selected from them, realizing the collaborative optimization of the spare resources of various power sources (thermal power, hydropower, pumped storage), maximizing the backup capacity of the region, ensuring the stability of the power supply of the provincial power grid, and providing certain reference for actual dispatchers. Description of the Drawings

[0086] Figure 1 It is a schematic flow chart of the method for optimizing the dispatching of the maximum power supply capacity of the provincial power grid based on regional reserves in the embodiment of the present invention;

[0087] Figure 2 It is the power adjustment amount of regions 1 to 10 in the optimization dispatching of the maximum power supply capacity of a certain provincial power grid in the embodiment of the present invention;

[0088] Figure 3 It is the power adjustment amount of regions 11 to 19 in the optimization dispatching of the maximum power supply capacity of a certain provincial power grid in the embodiment of the present invention;

[0089] Figure 4 It is the dispatching scheme of various types of conventional power sources in each region of a certain provincial power grid by the NNC method in the embodiment of the present invention. Detailed Embodiment

[0090] To make the purpose, technical solution and advantages of the present invention clearer and more understandable, the following further elaborates on the present invention according to the drawings and by listing embodiments.

[0091] The present invention provides a method for optimizing the dispatching of the maximum power supply capacity of the provincial power grid based on regional reserves, and the specific process is as Figure 1 shown.

[0092] The specific application steps are as follows:

[0093] The research object is selected as a provincial power grid, and the conventional power source types of this power grid include thermal power, hydropower, and pumped storage. Among them, except for Area 2, all areas contain thermal power spinning reserve, and the total capacity of thermal power spinning reserve is 16,081 MW; Area 6 also contains 1,834 MW of hydropower spinning reserve; Areas 9 and 14 each contain 120 MW and 1,200 MW of pumped storage spinning reserve respectively. When using the commercial optimization solver Gurobi to implement NNC solution, each side of the triangle is equally divided into 10 segments, and a total of 28 uniformly distributed points inside the triangle are obtained. When using the commercial optimization solver Cplex to implement the weighted quadratic programming method for solution, the total adjustment amount of the conventional power source output in the area, the minimum section margin, and the ratio deviation weights of the reserve and load in each area in the objective function are taken as 0.637, 0.2583, and 0.1047 respectively by the analytic hierarchy process. When using the PlatEMO open-source library to implement NSGA-III and MOEA / D for solution, the number of initial populations n = 200 and the maximum number of iterations T = 20,000 are set. Using the above solution algorithms, comprehensively evaluate the maximum power supply capacity that can be achieved by 19 areas of a provincial power grid within a certain 15 minutes on a certain day.

[0094] Step 1: Preprocess the collected output, load, reserve, section margin, and area-section sensitivity data according to the levels of units, power plants, area power grids, and provincial power grids.

[0095] Step 2: Establish an optimization dispatch model for the maximum power supply capacity of the provincial power grid with areas as units, and realize power supply guarantee through the spinning reserve dispatch of various power sources (thermal power, traditional hydropower, pumped storage) in the sub-areas.

[0096] Step 3: Use different types of optimization algorithms to solve the model, obtain numerous dispatch plans, and select the optimal dispatch plan according to the specific requirements in actual operation, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the area power grids in a provincial power grid.

[0097] Export the required information from the area reserve capacity monitoring system, generate the corresponding information matrix and sensitivity matrix according to Step 1, and the dimensions of each matrix are shown in Table 1.

[0098] Table 1 Dimensions of the information matrix and sensitivity matrix of a provincial power grid

[0099]

[0100]

[0101] Import the information matrix and sensitivity matrix in Table 1 into the provincial power grid's maximum power supply capacity optimization scheduling model established in Step 2. Calculate using the weighted quadratic programming method, NNC, NSGA-III, and MOEA / D algorithms respectively. After obtaining the corresponding non-dominated solution sets, it is necessary to construct a suitable evaluation index system for the optimization scheduling scheme for these three optimization objectives, select the combined weights to calculate the comprehensive weights of each objective, and use the GRA-TOPSIS method to evaluate the non-dominated solution sets of its multi-objective optimization scheduling scheme.

[0102] Next, taking the NNC algorithm as an example, solve the optimization scheduling scheme set for the maximum power supply capacity of a provincial power grid, select the optimal scheduling scheme, and compare it with the optimal scheduling schemes selected by the other three algorithms.

[0103] According to the provincial power grid's maximum power supply capacity optimization scheduling model, 72 non-dominated solutions, that is, 72 schemes, are obtained by the NNC method. The calculation results of each optimization scheduling scheme are shown in Table 2.

[0104] Table 2 Non-dominated solutions of the optimization scheduling scheme for the maximum power supply capacity of a provincial power grid

[0105]

[0106] Through Table 2, a preliminary analysis is carried out on the 72 schemes obtained in the provincial power grid's maximum power supply capacity optimization scheduling model: at the current load level, a provincial power grid can adjust the power output of the regional power sources to ensure the power supply of up to 14,099.08 MW of load at most; the maximum value of the minimum section margin of 9 important sections of concern is 1,338.68 MW, and the minimum value is 814.33 MW; the maximum ratio deviation between the standby and load of each region finally is 2.97, and the minimum is 0.28.

[0107] 72 optimization scheduling schemes involved in the model can be obtained by NNC. Based on the GRA-TOPSIS method, a comprehensive evaluation model for the provincial power grid's maximum power supply capacity optimization scheduling scheme is constructed. The relevant parameter values for the comprehensive evaluation of the maximum power supply capacity optimization scheduling scheme are shown in Table 3. By arranging the 72 obtained optimization scheduling schemes in descending order of relative closeness, it is found that Scheme 35 has the highest relative closeness among all the schemes. Therefore, from the perspective of comprehensive evaluation, Scheme 35 is the optimal scheduling scheme under the scenario of the provincial power grid's maximum power supply capacity.

[0108] Table 3 Relevant parameter values for the comprehensive evaluation of the maximum power supply capacity optimization scheduling scheme

[0109]

[0110] The relatively reasonable selection results of the power system optimization scheduling scheme obtained by the comprehensive evaluation model of the provincial power grid's maximum power supply capacity optimization scheduling scheme constructed by the GRA-TOPISIS method are shown in Table 4.

[0111] Table 4 Selection Results of Optimal Dispatching Schemes for the Maximum Power Supply Capacity of a Provincial Power Grid

[0112]

[0113] Through the selection similar to the above, the optimal dispatching schemes of the four algorithms are obtained respectively. The power adjustment amounts of the conventional power sources in Areas 1 - 19 of a provincial power grid under the optimal dispatching schemes of the four algorithms can be sorted out as shown in Figure 2 、 3 .

[0114] According to the formula , the solution results of the maximum power supply capacity of the four schemes can be calculated. The NNC scheme is 14099.08 MW, the weighted quadratic programming scheme is 13019.46 MW, the NSGA-III scheme is 10842.39 MW, and the MOEA / D scheme is 9856.96 MW. It can be seen that the NNC scheme has the largest maximum power supply capacity. The maximum power supply capacity of the weighted quadratic programming scheme is about 92.34% of the NNC scheme, and the maximum power supply capacities of the NSGA-III and MOEA / D schemes are about 76.90% and 69.91% of the NNC scheme respectively.

[0115] Further statistical analysis of the results shows the values of the other two optimization objectives when different algorithms obtain the optimal dispatching schemes, that is, the minimum values of the margins of 9 key sections and the proportional deviations of the reserves and loads in each area are shown in Table 5.

[0116] Table 5 Optimal Dispatching Target Values of the Maximum Power Supply Capacity of the Provincial Power Grid for the Four Algorithms

[0117]

[0118] When achieving the goal of the largest power adjustment amount in the area, that is, the largest power supply, the optimal dispatching schemes selected by the four algorithms are analyzed as follows:

[0119] (1) Using the NNC algorithm can increase the maximum power supply capacity of the provincial power grid to 14099.08 MW. At the same time, the minimum section margin value after adjustment is the largest, which is 1057.52 MW, and the remaining positive reserves in each area are basically proportional to the loads in each area. This method performs optimally in terms of improving power supply capacity, ensuring grid safety, avoiding section over-limit, and balancing the distribution of positive reserves. In contrast, the minimum section margins of the NSGA-III and MOEA / D algorithms are less than 1000 MW, with over-limit risks, and the proportional deviations between reserves and loads are large, resulting in unreasonable utilization of reserves and difficult dispatching.

[0120] (2) Among the maximum power supply capabilities solved by the four optimization algorithms, the NNC is the best, followed by the weighted quadratic programming method. The two intelligent optimization algorithms are relatively worse. The main reason is that their solution results are unstable, prone to falling into local optima, and have a slow convergence speed. Therefore, for the optimization scheduling work of a certain 15 minutes within a day in the power grid, the cost performance is not high. Although the solution result of the weighted quadratic programming method is not the global optimum, it is very close to the NNC scheme and still has certain reference value in the formulation of the scheduling plan.

[0121] In summary, due to its superior solution effect, the optimal scheduling scheme calculated by the NNC method can be used as the calculation result of the optimization scheduling model for the maximum power supply capacity of the provincial power grid.

[0122] Figure 4 Shows the scheduling schemes of various types of conventional power sources in each area when the NNC algorithm solves the optimization scheduling model for the maximum power supply capacity.

[0123] From Figure 4 it can be seen that except for Area 2 where the thermal power plant's spinning positive reserve is 0 and the output is not adjusted, the output of thermal power plants in other areas is increased. The adjustment amounts in each area are different. In addition to being related to the initial solution of the algorithm, it is also related to the characteristics of the area itself. For example, in Area 3, due to the limitation of its remaining thermal power spinning positive reserve, it is unable to supply too much load.

[0124] Areas 9 and 14 with pumped storage respectively call 99.89% and 99.99% of the pumped storage spinning positive reserve. Similarly, Area 6 with hydropower can also almost adjust out all the spinning positive reserve of the hydropower source. At the same time, by comparing with other areas, it can be found that the total output adjustment of conventional power sources in Area 6 is also significantly greater than that in other areas. The reason is that there is more positive reserve capacity (especially hydropower) left in this area, and most of the positive reserve capacity is fully utilized during the optimization calculation.

[0125] The NNC scheduling scheme can, without violating the limits, increase the power generation power of the conventional power sources in Province A, especially the hydropower undertakes relatively more output adjustment, improving the economy while reducing environmental pollution. At the same time, as a large-scale energy storage power source with both peak shaving and valley filling functions for the power grid, the efficient utilization of pumped storage can ensure the maximum power supply of the target province. Therefore, scientifically arranging the power source combination, reasonably using the reserve capacity, and promoting the full output and utilization of clean energy are crucial for ensuring power supply and building a clean and low-carbon power system. In summary, the NNC scheduling scheme can provide important reference information for dispatchers in the scheduling work arrangements for peak summer and peak winter of the power grid under the "dual carbon" goal.

[0126] In another embodiment of the present invention, an optimized dispatching system for the maximum power supply capacity of a provincial power grid based on zonal reserve is provided. This system can be used to implement the above-mentioned optimized dispatching method for the maximum power supply capacity of a provincial power grid based on zonal reserve. Specifically, it includes: a data preprocessing module, a model construction module, and a simulation and solution module;

[0127] Data preprocessing module: Preprocess the collected data of output, load, reserve, section margin, and zonal-section sensitivity according to the levels of units, power plants, zonal power grids, and provincial power grids;

[0128] Model construction module: Establish an optimized dispatching model for the maximum power supply capacity of a provincial power grid with zones as units, and realize power supply guarantee through the rotational reserve dispatching of various power sources (thermal power, traditional hydropower, pumped storage) in different zones;

[0129] Simulation and solution module: Use different types of optimization algorithms to solve the model, obtain numerous dispatching plans, and select the optimal dispatching plan according to the specific requirements in actual operation, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the zonal power grids of a certain provincial power grid.

[0130] In another embodiment of the present invention, a terminal device is provided. This terminal device includes a processor and a memory. The memory is used to store a computer program. The computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; The processor described in the embodiment of the present invention can be used for the operation of the optimized dispatching method for the maximum power supply capacity of a provincial power grid based on zonal reserve, including the following steps:

[0131] S1: Preprocess the collected data of output, load, reserve, section margin, and zonal-section sensitivity according to the levels of units, power plants, zonal power grids, and provincial power grids;

[0132] S2: Taking the area as a unit, establish an optimization dispatching model for the maximum power supply capacity of the provincial grid, and realize power supply guarantee through the spinning reserve dispatching of various power sources (thermal power, traditional hydropower, pumped storage) in the sub-region;

[0133] S3: Use different types of optimization algorithms to solve the model, obtain numerous dispatching plans, and select the optimal dispatching plan according to the specific requirements in actual operation, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the sub-region power grid of a certain provincial grid.

[0134] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the terminal device, used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device, and of course can also include the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, one or more instructions suitable for being loaded and executed by the processor are also stored. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory.

[0135] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for optimizing the dispatching of the maximum power supply capacity of the provincial grid based on sub-region reserve in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0136] S1: Preprocess the collected output, load, reserve, section margin, and area-section sensitivity data according to the levels of units, power plants, sub-region power grids, and provincial power grids;

[0137] S2: Taking the area as a unit, establish an optimization dispatching model for the maximum power supply capacity of the provincial grid, and realize power supply guarantee through the spinning reserve dispatching of various power sources (thermal power, traditional hydropower, pumped storage) in the sub-region;

[0138] S3: Use different types of optimization algorithms to solve the model, obtain numerous dispatching plans, and select the optimal dispatching plan according to the specific requirements in actual operation, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the reserve capacity of the sub-region power grid of a certain provincial grid.

[0139] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0140] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0141] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0143] Those of ordinary skill in the art will realize that the embodiments described herein are for helping readers understand the implementation methods of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A method for optimizing and dispatching the maximum power supply capacity of a provincial network based on partitioned backup, characterized in that: The following steps are involved: S1: pre-process the collected output, load, reserve, section margin and section-section sensitivity data according to the levels of units, power plants, regional power grids and provincial power grids; S2: Based on the area, establish an optimization dispatching model for the maximum power supply capacity of the provincial grid, and ensure power supply by dispatching the rotating reserve of various power sources including thermal power, traditional hydropower, and pumped storage in the area; S3: Use optimization algorithms to solve the model, obtain the dispatching plan, output the results, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the spare capacity of a provincial grid.

2. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 1 is characterized in that: The data preprocessing in step S1 specifically includes: S11: Output data processing method, including: Power plant output = the sum of the outputs of all units in the power plant; Total output in the area = the sum of power generated by all power plants in the area; Provincial network output data = the sum of the output data of each area in the provincial network; S12: Load data processing method, including: The area load data is equivalent to the area output data plus the interconnection line power; The provincial network load data is the sum of the load data of each area within the provincial network. S13: A method for processing backup data, including: Thermal power spinning reserve: Thermal power spinning positive reserve is the difference between the rated power of the unit and the real-time power generation of the unit; thermal power spinning negative reserve is the difference between the real-time power generation of the unit and 50% of the rated power of the unit; Hydropower spinning reserve: Hydropower spinning positive reserve is the difference between the rated power of the unit and the real-time power generation of the unit; hydropower spinning negative reserve is the real-time power generation of the unit; Pumped storage rotating reserve: The pumped storage rotating positive reserve is the difference between the maximum power generation power of the pumped storage power station and the real-time power generation power, calculated during power generation; the pumped storage rotating negative reserve is the difference between the real-time power generation power and the lower limit of the water storage power, calculated during water storage. S14: Section allowance data processing method: After selecting the flow section of key concern, its real-time power and the corresponding upper and lower limits of the section power are derived from the dispatching system. The calculation method of the section margin is: the upper margin of the section is the difference between the upper limit of the section stability limit and the current power of the section, and the lower margin of the section is the difference between the current power of the section and the lower limit of the section stability limit. S15: Area-section sensitivity data processing method: When modeling, the area-section sensitivity data can be approximated by the section sensitivity data of a backbone power station unit with the largest content in the area.

3. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 1 is characterized in that: The optimization scheduling model for the maximum power supply capacity of the provincial network in step S2 is determined specifically as follows: taking N areas within the provincial network as units, without considering inter-provincial power support, without taking orderly power consumption and without changing the startup mode, according to the load level and standby situation at the evaluation time, a certain time is selected, and the maximum power supply capacity that can be achieved by adjusting the output of the adjustable units within the provincial network is evaluated.

4. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 3 is characterized in that: In step S2, the objective function is established, including: The total amount of conventional power output adjustment in the area is the largest, and the formula is: Where ΔP i It represents the total output adjustment of conventional power sources in the ith area in the target provincial grid, including thermal power, hydropower and pumped storage; N represents the collection of provincial grid areas. The minimum section margin is the largest, and the formula is: In the formula, M represents the collection of sections; q represents the flow section that needs to be focused on within the dispatching range; Respectively represent the upper and lower margins of section q after adjustment; ΔP q Indicates the power adjustment of section q; S i,q It indicates the sensitivity of area i to the power flow section q; Load indicates the change in active power of the power flow section caused by load change; ΔL i Represents the load power adjustment amount of area i. The most balanced reserve is retained, and the formula is: In the formula, num(N) represents the number of patches; α i Indicates the ratio of the area's positive reserve to the area's load; P i max represents the maximum power of area i; P i 0 represents the power generation of area i before adjustment; L i represents the adjusted load power of area i; Represents the actual load power of zone i before adjustment; R L.i It indicates the proportion of the load of area i to the total load of the provincial network before adjustment.

5. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 4 is characterized in that: In step S2, constraints are established, including: Power balance constraint, the formula is: In the formula, ΔL i Represents the load adjustment of zone i. The spinning reserve capacity constraint is: NRC i ≤ΔP i ≤PRC i (10) Where NRC i Indicates the negative spinning reserve of the conventional power supply in area i; PRC i Indicates the positive spinning reserve of the conventional power supply in area i. Section stability constraint, the formula is: In the formula, Indicates the upper limit of section q power; It indicates the lower limit of the power of section q. It can be seen that the higher the sensitivity coefficient of the area to the section, the greater the fluctuation of the section power caused by the power adjustment of the area.

6. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 5 is characterized in that: The calculation process of step S2 includes: Generate a general power information matrix with the formula: In the formula, a i , b i 、c i Respectively represent the current power generation of thermal power, hydropower and pumped storage; They represent the rotating positive reserve of thermal power, hydropower and pumped storage respectively; They represent the rotating negative reserve of thermal power, hydropower and pumped storage respectively. Generate a power flow section information matrix in a similar way to a conventional power source information matrix; Generate a cross-sectional sensitivity matrix by preprocessing the sensitivity data given in step S1; According to the method given in the preprocessing of step S1, a section-slice sensitivity matrix and a section-load sensitivity matrix are generated. The multi-objective optimization algorithm is used to solve the model, calculate the total power adjustment amount of each area in the target province, the minimum power flow section margin after adjustment, and the proportional deviation of the reserve and load in each area, and obtain the maximum power supply capacity of the provincial network, that is: Obtain the optimal dispatching plan set for the maximum power supply capacity of the target province and derive the values ​​of the solved decision variables.

7. The method for optimizing and dispatching the maximum power supply capacity of a provincial network based on zoned standby according to claim 1 is characterized in that: The optimization algorithms in step S3 include: weighted quadratic programming method WQP, normalized normal plane constraint algorithm NNC, third generation non-dominated sorting genetic algorithm NSGA-III and decomposition-based multi-objective evolutionary algorithm MOEA / D; The hierarchical analysis method is used to obtain the subjective weight coefficient of each indicator. The coefficient of variation method is used to obtain the objective weight coefficient of each indicator. Use the combined weighting method to reasonably allocate the subjective and objective weight coefficients. The grey correlation ideal solution method is used to calculate the relative distance between each scheduling scheme and the ideal solution. According to the calculated correlation and distance criteria, the optimal scheduling plan is selected.

8. A provincial network maximum power supply capacity optimization dispatching system based on zoned backup, characterized by: The system can be used to implement the method for optimizing and dispatching the maximum power supply capacity of a provincial network based on partitioned standby according to any one of claims 1 to 7, and specifically comprises: a data preprocessing module, a model building module and a simulation solution module; Data preprocessing module: preprocess the collected output, load, reserve, section margin and section-section sensitivity data according to the levels of units, power plants, regional power grids and provincial power grids; Model building module: Based on the area, establish the optimization dispatching model of the maximum power supply capacity of the provincial grid, and realize the power supply guarantee through the rotating reserve dispatching of the sub-area power supply; Simulation solution module: Use different types of optimization algorithms to solve the model and obtain a number of scheduling schemes. According to the specific requirements in actual operation, select the optimal scheduling scheme and output the results, quantitatively evaluate the maximum power supply capacity of the power grid, and effectively manage the spare capacity of a provincial grid.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing and scheduling the maximum power supply capacity of a provincial network based on partitioned backup as described in one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored on a computer-readable storage medium, and when the program is executed by a processor, the method for optimizing and scheduling the maximum power supply capacity of a provincial network based on partitioned backup as described in one of claims 1 to 7 is implemented.

Images