Method, device and equipment for collaborative optimization of network reconfiguration and real-time scheduling

Abstract

The application discloses a kind of fusion network reconstruction and real-time scheduling collaborative optimization method, device and equipment, it is related to power system dispatching control technical field, and the section flow prediction result is obtained by sensitivity matrix, and section flow prediction result is determined to exceed limit state, and can quickly determine limit section;Then in the case where section exceeds limit, with the target of eliminating overload and maximizing new energy consumption, network reconstruction optimization model is established and power supply network is reconstructed, obtains the power supply network after reconstruction, can quickly remove the hard constraint of line overload and section limit, finally constructs dispatching optimization model, and real-time AGC dispatching optimization is carried out to the power supply network after reconstruction using dispatching optimization model, can realize economic operation and consumption maximization, optimize resource allocation within the safe topological boundary, guide dispatching system to preferentially reduce traditional unit output, through the collaborative optimization of network reconstruction and real-time scheduling, effectively improve the adjustment speed.

Description

Technical Field

[0001] This application relates to the field of power system dispatch and control technology, specifically to a collaborative optimization method, apparatus, and equipment that integrates network reconfiguration and real-time dispatch. Background Technology

[0002] As the penetration rate of renewable energy sources such as wind and solar power in the power system continues to increase, the randomness and volatility of their output pose significant challenges to grid operation. Under the background of high-proportion renewable energy integration, existing power systems often experience problems such as line overload, cross-sectional congestion, and difficulties in renewable energy absorption, especially during faults or emergencies. Traditional methods often employ network reconfiguration or power reschedule alone, failing to effectively coordinate both, resulting in high regulation costs, slow response speeds, and poor economic efficiency. Furthermore, when dealing with sharp increases / decreases in renewable energy, they still suffer from insufficient regulation capacity, severe wind and solar curtailment, and poor economic performance. Summary of the Invention

[0003] The purpose of this application is to provide a collaborative optimization method, apparatus, and equipment that integrates network reconfiguration and real-time scheduling, which solves the problems of low adjustment capability, slow response speed, and / or poor economy.

[0004] This application is achieved through the following technical solution:

[0005] Firstly, this application provides a collaborative optimization method integrating network reconfiguration and real-time scheduling, comprising:

[0006] Obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and obtain the cross section power flow prediction result based on the sensitivity matrix;

[0007] The cross-section over-limit status is determined based on the cross-section power flow prediction results; wherein, the cross-section over-limit status is either the cross-section over-limit or the cross-section not over-limit;

[0008] When the cross-section exceeds the limit, a network reconfiguration optimization model is established with the goal of eliminating overload and maximizing the absorption of new energy. Based on the network reconfiguration optimization model, the power supply network is reconfigured to obtain the reconfigured power supply network.

[0009] A scheduling optimization model is constructed, and the scheduling optimization model is used to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling.

[0010] In one possible implementation, obtaining the sensitivity matrix between the AGC unit and its corresponding cross-section includes:

[0011] Obtain the power increment of the AGC unit, and obtain the generator allocation matrix based on the power increment of the AGC unit;

[0012] Based on the generator set allocation matrix, the bus injected power increment is obtained, and the bus injected power increment is combined with the node admittance matrix to obtain the phase angle change vector;

[0013] Based on the phase angle change vector, the branch active power flow increment is obtained, and the branch active power flow increment is combined with the section-branch aggregation matrix to obtain the section total power increment.

[0014] The sensitivity matrix between the AGC unit and its corresponding cross-section is determined based on the total power increment of the cross-section.

[0015] In one possible implementation, obtaining the cross-sectional power flow prediction result based on the sensitivity matrix includes: using Kalman filtering to predict the cross-sectional power flow based on the sensitivity matrix, and obtaining the cross-sectional power flow prediction result.

[0016] In one possible implementation, determining the cross-sectional over-limit state based on the cross-sectional power flow prediction results includes:

[0017] Determine whether the predicted cross-sectional power flow exceeds a preset cross-sectional limit threshold. If so, determine the cross-sectional limit status as cross-sectional limit exceeded; otherwise, determine the cross-sectional limit status as cross-sectional not exceeded.

[0018] In one possible implementation, with the goals of eliminating overload and maximizing the absorption of new energy sources, a network reconfiguration optimization model is established as follows:

[0019]

[0020] Where min represents minimization. This represents the first weighting coefficient. This represents the second weighting coefficient. This represents the third weighting coefficient. Indicates the first The power reduction of each renewable energy power plant, where N represents the total number of renewable energy power plants. Indicates the first The on / off status of each load branch, where Z represents the total number of load branches. This represents the real-time scheduling cost of the AGC unit, and , This represents the marginal cost of the m-th AGC unit. This represents the output of the m-th AGC unit.

[0021] In one possible implementation, the power supply network is reconstructed based on the network reconstruction optimization model to obtain the reconstructed power supply network, including:

[0022] Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined.

[0023] With the goal of solving the network reconfiguration optimization model, the output of the target AGC unit, the actual output of the new energy power station, and the on / off state of the load branch are adjusted to obtain the reconfigured power supply network.

[0024] In one possible implementation, the scheduling optimization model is constructed as follows:

[0025]

[0026] Where min represents minimization. This indicates an overall adjustment of weights. This indicates that the individual adjusts the weight. Indicates the first Power increment of a real-time scheduled AGC unit This indicates the system's real-time power demand. Let represent the average power regulation value of the m-th AGC unit, and , This represents the reference output of the m-th AGC unit, where M represents the total number of AGC units. This represents the sum of the baseline outputs of all AGC units.

[0027] In one possible implementation, the scheduling optimization model is used to perform real-time AGC scheduling optimization on the reconstructed power supply network, including:

[0028] Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined.

[0029] With the goal of solving the scheduling optimization model, the power increment of the target AGC unit is adjusted to achieve real-time AGC scheduling optimization of the reconstructed power supply network.

[0030] Secondly, this application provides a collaborative optimization device that integrates network reconfiguration and real-time scheduling, comprising:

[0031] The cross-sectional power flow prediction module is used to obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and to obtain the cross-sectional power flow prediction result based on the sensitivity matrix.

[0032] The power flow limit judgment module is used to determine the limit-crossing state of the cross section based on the power flow prediction results of the cross section; wherein, the limit-crossing state of the cross section is either the cross section exceeds the limit or the cross section does not exceed the limit;

[0033] The network reconfiguration optimization module is used to establish a network reconfiguration optimization model with the goal of eliminating overload and maximizing the absorption of new energy when the cross-section exceeds the limit. Based on the network reconfiguration optimization model, the power supply network is reconfigured to obtain the reconfigured power supply network.

[0034] The real-time scheduling optimization module is used to construct a scheduling optimization model and use the scheduling optimization model to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling.

[0035] Thirdly, this application provides an electronic device, including a processor and a memory;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory, causing the processor to perform the collaborative optimization method of converged network reconstruction and real-time scheduling as described in any possible implementation of the first aspect.

[0038] Compared with the prior art, this application has the following advantages and beneficial effects:

[0039] This application provides a collaborative optimization method, apparatus, and device that integrates network reconfiguration and real-time scheduling. It obtains cross-sectional power flow prediction results through a sensitivity matrix and determines the cross-sectional over-limit status based on these predictions, enabling rapid identification of over-limit sections. Then, under the condition of cross-sectional over-limit, with the goals of eliminating overload and maximizing renewable energy absorption, a network reconfiguration optimization model is established and the power supply network is reconfigured. The reconfigured power supply network quickly removes the hard constraints of line overload and cross-sectional over-limit. Finally, a scheduling optimization model is constructed, and real-time AGC scheduling optimization is performed on the reconfigured power supply network using this model. This achieves economical operation and maximizes absorption, optimizing resource allocation within a safe topological boundary and guiding the scheduling system to prioritize reducing the output of traditional generating units. Through the collaborative optimization of network reconfiguration and real-time scheduling, the regulation speed is effectively improved. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0041] Figure 1A flowchart illustrating a collaborative optimization method integrating network reconstruction and real-time scheduling, provided in an embodiment of this application;

[0042] Figure 2 A schematic diagram of the structure of a collaborative optimization device that integrates network reconstruction and real-time scheduling, provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram of the structure of a collaborative optimization device integrating network reconstruction and real-time scheduling provided in an embodiment of this application;

[0044] Figure 4 The following is a schematic diagram of the optimization results provided in the embodiments of this application; wherein (a) is a schematic diagram of cross-sectional power change, (b) is a schematic diagram of the adjustment amount of new energy power station, (c) is a schematic diagram of the adjustment amount of real-time dispatched unit, (d) is a schematic diagram of the adjustment amount allocation ratio, (e) is a schematic diagram of cross-sectional power change, and (f) is a schematic diagram of cross-sectional power and limit comparison.

[0045] The attached diagram shows the markings and corresponding component names:

[0046] 201-Cross-section power flow prediction module, 202-Power flow limit judgment module, 203-Network reconstruction optimization module, 204-Real-time scheduling optimization module, 301-Memory, 302-Processor, 303-Bus. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0048] Traditional methods often employ network reconfiguration or power rescheduling alone, failing to effectively coordinate the two. This results in high regulation costs, slow response speeds, and poor economic efficiency. Furthermore, when dealing with sharp increases / decreases in renewable energy, there are still problems such as insufficient regulation capacity, severe wind and solar curtailment, and poor economic efficiency.

[0049] Therefore, this application provides a collaborative optimization method that integrates network reconfiguration and real-time scheduling. It obtains cross-sectional power flow prediction results through a sensitivity matrix and determines the cross-sectional over-limit status based on these predictions, enabling rapid identification of over-limit sections. Then, under the condition of cross-sectional over-limit, with the goal of eliminating overload and maximizing renewable energy absorption, a network reconfiguration optimization model is established and the power supply network is reconfigured. The reconfigured power supply network can quickly remove the hard constraints of line overload and cross-sectional over-limit. Finally, a scheduling optimization model is constructed, and real-time AGC scheduling optimization is performed on the reconfigured power supply network using this model. This achieves economical operation and maximizes absorption, optimizing resource allocation within a safe topological boundary and guiding the scheduling system to prioritize reducing the output of traditional generating units. Through the collaborative optimization of network reconfiguration and real-time scheduling, the regulation speed is effectively improved.

[0050] like Figure 1 As shown, this application provides a collaborative optimization method that integrates network reconfiguration and real-time scheduling, which may include:

[0051] S101. Obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and obtain the cross section power flow prediction result based on the sensitivity matrix.

[0052] The sensitivity matrix precisely quantifies the impact of output changes of each AGC (Automatic Generation Control) unit on the power flow of the cross-section. By predicting the power flow of the cross-section using the sensitivity matrix, the risk of power flow exceeding limits can be foreseen in advance within a certain period of time (such as 5-15 minutes). This not only provides a decision-making basis for subsequent network reconfiguration and real-time scheduling, but also gains valuable decision-making and execution time for subsequent time-consuming network reconfiguration and real-time scheduling measures, transforming grid control from a passive control mode to an active control mode.

[0053] S102. Determine the cross-section over-limit status based on the cross-section power flow prediction results; wherein, the cross-section over-limit status is either cross-section over-limit or cross-section not over-limit;

[0054] For example, a cross-section limit threshold can be preset. If the cross-section power flow prediction result of a certain cross-section exceeds the cross-section limit threshold, it can be determined that the cross-section has exceeded the limit; otherwise, it can be determined that the cross-section has not exceeded the limit.

[0055] S103. When the cross-section exceeds the limit, with the goal of eliminating overload and maximizing the absorption of new energy, a network reconfiguration optimization model is established, and the power supply network is reconfigured based on the network reconfiguration optimization model to obtain the reconfigured power supply network.

[0056] First, power flow prediction is performed using a sensitivity matrix to anticipate impending cross-section overload issues. Once an overload is predicted, the network reconfiguration optimization model is immediately initiated. Network reconfiguration primarily serves the safety objective by directly removing the rigid constraints of line overload and cross-section overload by altering the power grid structure. This fundamentally changes the physical path of power flow, opening new channels for overloaded power flows and enabling rapid elimination of overloads.

[0057] S104. Construct a scheduling optimization model and use the scheduling optimization model to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling.

[0058] Real-time scheduling focuses more on economic operation and maximizing absorption, optimizing resource allocation within safe topological boundaries. The scheduling optimization model itself is a coupled system, whose decision variables include both discrete variables representing switching states (reconfiguration) and continuous variables representing unit output (scheduling), which are closely related through nonlinear power flow equations.

[0059] For collaborative optimization, a hierarchical interactive solution strategy can be adopted. For example, a metaheuristic algorithm can be used first for reconstruction optimization to search for feasible topologies that can eliminate overload; then, based on a fixed topology, mathematical programming methods can be used to quickly solve for the optimal economic scheduling scheme. The two stages are not isolated but have interactive feedback. If the scheduling stage finds that the economic operating cost under the current topology is too high or the absorption capacity is insufficient, this information can be fed back to the reconstruction stage, and the weights can be adjusted to search for a better topology again until a globally collaborative scheme that is relatively balanced in terms of safety, economy, and absorption capacity is obtained.

[0060] In summary, the embodiments of this application transform network reconfiguration and real-time scheduling from independent functional modules into a collaborative optimization system that complements and enhances each other, enabling a more intelligent, flexible, and economical response to the operational challenges of high-proportion renewable energy power grids.

[0061] Optionally, a collaborative optimization cycle can be set. After each collaborative optimization cycle, the collaborative optimization method for network reconstruction and real-time scheduling is executed. Alternatively, it can be triggered immediately upon detection of significant power fluctuations. For example, it can be started periodically at any time between 5 and 15 minutes.

[0062] In one possible implementation, obtaining the sensitivity matrix between the AGC unit and its corresponding cross-section includes:

[0063] S101.1 Obtain the power increment of the AGC unit, and obtain the generator set allocation matrix based on the power increment of the AGC unit.

[0064] The power increment for AGC units is:

[0065]

[0066] in, This represents the power increment of the m-th generator unit among the AGC units participating in frequency regulation. This represents the capacity of the m-th generator unit among the AGC units participating in frequency regulation. This represents the capacity of the a-th generator unit among the AGC units participating in frequency regulation. This represents the set of AGC units participating in frequency regulation, where m represents a specific unit. This means adding up the active power of the generator sets of all AGC units. This represents the power increment of the k-th generator unit that caused the change in output;

[0067] use Construct a matrix to obtain the generator set allocation matrix; the generator set allocation matrix represents the injection method of the output change of the k-th generator set on each bus. On the bus where generator set k is located: On the AGC unit bus: .matrix The columns represent the changes in the injection of a busbar caused by a disturbance in the unit output of a generator set; the rows represent the changes in the injection of a busbar under disturbances from different generator sets.

[0068] S101.2. Based on the generator set allocation matrix, obtain the bus injected power increment, and combine the bus injected power increment with the node admittance matrix to obtain the phase angle change vector.

[0069] The increment of the injected power at the bus is:

[0070]

[0071] Where A represents the generator set allocation matrix, Indicates the increment of power injected into the busbar;

[0072] The phase angle change vector is:

[0073]

[0074]

[0075] in, Represents the phase angle change vector. Represents the nodal admittance matrix. This represents the line reactance between the i-th load node and the j-th load node;

[0076] S101.3. Based on the phase angle change vector, obtain the branch active power flow increment, and combine the branch active power flow increment with the section-branch aggregation matrix to obtain the section total power increment.

[0077] The incremental power flow of the branch is:

[0078]

[0079] in, This represents the active power flow increment on the branch formed by the i-th load node and the j-th load node. This represents the bus node phase angle increment of the i-th load node. L represents the bus node phase angle increment of the j-th load node, and L represents the branch-node correlation matrix (the branch-node correlation matrix is ​​generally known and can be obtained from the power system topology). This can be understood as the coefficient matrix composed of the line phase angle change vector divided by the line reactance.

[0080] The total power increment of the cross section is:

[0081]

[0082] in, This represents the total power increment at the b-th cross-section, and K represents the cross-section-branch aggregation matrix (matrix K is a cross-section × branch matrix. Each column corresponds to a branch, and each row corresponds to a cross-section. If branch j belongs to cross-section b and its direction is consistent with the positive direction of the cross-section, the value is set to +1; if branch j belongs to cross-section b but its direction is opposite to the positive direction of the cross-section, the value is set to -1; if branch j does not belong to cross-section b, the value is set to 0). This matrix merges multiple branches into one cross-section.

[0083] S101.4 Determine the sensitivity matrix between the AGC unit and its corresponding cross section based on the total power increment of the cross section.

[0084]

[0085]

[0086] in, This represents the sensitivity matrix.

[0087] In one possible implementation, obtaining the cross-sectional power flow prediction result based on the sensitivity matrix includes:

[0088] S101.5. Based on the aforementioned sensitivity matrix, Kalman filtering is used to predict the cross-sectional power flow, and the predicted cross-sectional power flow result is obtained as follows:

[0089]

[0090] in, This indicates the cross-sectional power value at the next moment, i.e., the cross-sectional power flow prediction result; This represents the predicted state vector, i.e., the predicted cross-sectional power value at the next moment; This represents the Kalman gain matrix at the current time, which determines the fusion weights of the prediction and measurement results. This represents the actual power value measured by the SCADA system at the current moment; Represents the observation matrix; This represents the power increment of the k-th generator unit that causes the change in output at the current moment.

[0091] To enable those skilled in the art to understand the technical solutions described in the embodiments of this application, the method for predicting cross-sectional power flow using Kalman filtering may include:

[0092] The power flow at some cross-sections was obtained from actual measurements using SCADA (Supervisory Control and Data Acquisition) systems.

[0093]

[0094] Based on the measured partial cross-sectional power flow, the prediction is as follows:

[0095]

[0096]

[0097]

[0098] The predicted data is corrected as follows:

[0099]

[0100]

[0101] in, Indicates the cross-sectional power value at the next moment; This represents the predicted state vector, i.e., the predicted cross-sectional power value at the next moment; This represents the Kalman gain matrix at the current time, which determines the fusion weights of the prediction and measurement results; This represents the actual value measured by the SCADA system at the current moment; and These represent the system matrix and the observation matrix, respectively, and must be determined before the filter is applied; This represents the power increment of the k-th generator unit that causes the change in output at the current moment; and These represent the noise of the first process and the noise of the first process (unconsidered random disturbances, prediction errors, etc.), respectively. This represents the prediction covariance matrix for the next time step. Represents the covariance matrix at the current time. Represent the covariance matrix at the next time step; This represents the Kalman gain matrix at the next time step; Let T denote the identity matrix, and T denote the transpose.

[0102] In one possible implementation, determining the cross-sectional over-limit state based on the cross-sectional power flow prediction results includes:

[0103] Determine whether the predicted cross-sectional power flow exceeds a preset cross-sectional limit threshold. If so, determine the cross-sectional limit status as cross-sectional limit exceeded; otherwise, determine the cross-sectional limit status as cross-sectional not exceeded.

[0104] In one possible implementation, with the goals of eliminating overload and maximizing the absorption of new energy sources, a network reconfiguration optimization model is established as follows:

[0105]

[0106] Where min represents minimization. This represents the first weighting coefficient. This represents the second weighting coefficient. This represents the third weighting factor, used to balance the importance of power reduction, switching operations, and scheduling costs; typical values ​​are 0.5, 0.3, and 0.2, respectively. The number of new energy power plants participating in the optimization. This reflects the priority given to the consumption of new energy sources. The higher the value, the more the system tends to avoid reducing new energy sources through reconfiguration and scheduling. The lower the threshold, the more the system allows for moderate wind and solar curtailment to reduce scheduling pressure or operating costs. This restricts frequent changes in the topology. The higher the value, the more inclined the network is to reduce the number of reconfigurations and maintain topology stability; The lower the value, the more aggressive the switching state can be adjusted, and the cross-sectional over-limit can be quickly alleviated through power flow transfer. It reflects the importance of economic objectives, namely, pursuing the minimum operating cost while ensuring safety. The higher the value, the more the system will guide the system to minimize output adjustments for high-marginal-cost units and pursue economical operation. The lower the value, the less important economic factors become, and the system will prioritize safety and the absorption of new energy sources. When the system is severely overloaded, safety becomes the primary objective, thus increasing... The weighting; when the system is running smoothly or there is a large amount of new energy generation, the focus is on improving economic efficiency and consumption targets, thus increasing and The weights are determined by these three weight coefficients, which constitute the dynamic balance coefficients and reflect the weight adaptive strategy.

[0107] Indicates the first The power reduction of each new energy power plant (unit: MW), and , Indicates the maximum transmit power. Indicates the first The actual power output (unit: MW) of each new energy power station, where N represents the total number of new energy power stations. Indicates the first The on / off state of each load branch (set as a binary variable, 0 represents off, 1 represents on), Z represents the total number of load branches. This represents the real-time scheduling cost of the AGC unit (unit: currency). , This represents the marginal cost of the m-th AGC unit. This represents the output of the m-th AGC unit.

[0108] This application's embodiments introduce a weighted adaptive strategy: in emergency situations (such as severe overload), the model automatically increases the weight of safety objectives, prioritizing reconfiguration actions; during normal operation or periods of high renewable energy generation, the weight of economic and absorption objectives is correspondingly increased, guiding the scheduling system to prioritize reducing the output of traditional units, thus freeing up space for renewable energy. This dynamic weighting mechanism enables the system to intelligently adjust its optimization direction in different scenarios, achieving a balance between multiple objectives.

[0109] In one possible implementation, the power supply network is reconstructed based on the network reconstruction optimization model to obtain the reconstructed power supply network, including:

[0110] Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined.

[0111] By analyzing the sensitivity matrix, the "critical units" that have the greatest impact on a specific section can be quickly identified. When executing control, these highly sensitive units can be prioritized for adjustment, achieving the most significant power flow control effect with the smallest adjustment amount, avoiding ineffective or inefficient adjustments, and improving the response speed and efficiency of the entire control system.

[0112] With the goal of solving the network reconfiguration optimization model, the output of the target AGC unit, the actual output of the new energy power station, and the on / off state of the load branch are adjusted to obtain the reconfigured power supply network.

[0113] It is worth noting that, in the process of adjusting the output of the target AGC unit, the actual output of the new energy power station, and the on / off status of the load branch, the following constraints should also be met:

[0114] Line capacity constraints:

[0115]

[0116] in, This represents the active power flow between the i-th load node and the j-th load node. U represents the maximum capacity of the line, and U represents the set of lines.

[0117] Node voltage safety constraints:

[0118]

[0119] in, This represents the voltage amplitude at the i-th load node. This represents the upper voltage limit of the i-th load node. This represents the lower voltage limit of the i-th load node. Represents a set of nodes.

[0120] Power balance constraints:

[0121]

[0122] in, Indicates the output of the target AGC unit. Indicates the first The actual power output of each new energy power station Indicates power load. This indicates network loss.

[0123] Switch operation logic constraints:

[0124]

[0125] This indicates the on / off state of the d-th load branch (set as a binary variable, 0 for off and 1 for on). Represents the set of load branches;

[0126] Renewable energy output constraints:

[0127]

[0128] in, Indicates the upper limit of new energy output. This represents a collection of new energy generating units;

[0129] Unit ramp rate constraint:

[0130]

[0131] in, Indicates AGC unit The rate of ascent was reduced. Indicates AGC unit Increase the climbing speed. This indicates an AGC (Automatic Guided Vehicle) unit combination. This indicates the current ramp rate of the AGC unit. This indicates the ramp rate of the AGC unit at the previous moment.

[0132] In one possible implementation, the scheduling optimization model is constructed as follows:

[0133]

[0134] Where min represents minimization. This indicates an overall adjustment of weights. This represents the individual adjustment weight, with typical values ​​of 100.0 and 1.0, used to balance overall demand satisfaction and individual fairness; M is the number of units participating in the scheduling.

[0135] Indicates the first The power increment of a real-time scheduled AGC generator set (unit: MW), and ; This represents the power of the m-th generator set in the AGC unit at the current moment. This represents the power of the m-th generator unit in the AGC unit at the previous moment. This represents the system's real-time power demand (in MW), and ; This indicates the change in load power; Indicates the first Power reduction of a new energy power plant; Let represent the average power regulation value of the m-th AGC unit, and , This represents the reference output of the m-th AGC unit, where M represents the total number of AGC units. This represents the sum of the baseline outputs of all AGC units.

[0136] In one possible implementation, the scheduling optimization model is used to perform real-time AGC scheduling optimization on the reconstructed power supply network, including:

[0137] Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined.

[0138] With the goal of solving the scheduling optimization model, the power increment of the target AGC unit is adjusted to achieve real-time AGC scheduling optimization of the restructured power supply network. For example, linear programming or quadratic programming can be used to solve the problem, ensuring a fast response.

[0139] It is worth noting that the following constraints should also be met when adjusting the power increment of the target AGC unit:

[0140] AGC spare capacity constraints:

[0141]

[0142] in, This indicates the standby capacity of the generator set in the AGC unit. This indicates the maximum standby capacity of the generator set in the AGC unit. This indicates the minimum standby capacity of the generator set in the AGC unit.

[0143] Cross-sectional power safety constraints:

[0144]

[0145] in, Indicates the cross-sectional power value. Indicates the maximum allowable power of the cross-section. This indicates the minimum allowable power output per section. Unit output upper and lower limit constraints:

[0146]

[0147] in, This represents the active power value of each generator set. This represents the maximum active power of each generator set. This represents the minimum active power of each generator set.

[0148] Power direction constraint (to avoid reverse modulation):

[0149]

[0150] in, This represents the power increment of the m-th generator unit among the AGC units participating in frequency regulation. This indicates the system's real-time power demand.

[0151] Based on the aforementioned collaborative optimization method integrating network reconfiguration and real-time scheduling, this application selects a 118-node power system as an example for analysis. Case 1 considers only network reconfiguration, Case 2 considers only real-time scheduling, and Case 3 considers both. This example uses a MATLAB platform to build an optimization model and employs the Gurobi solver. Cost comparison results for each example are shown in Table 1; the optimization results of the proposed method are shown in... Figure 4 As shown. Figure 4 As shown in (a), the cross-sectional power decreased after the network reconstruction and real-time scheduling were jointly optimized. Lower cross-sectional power means greater safety, and the distance from the power limit of the cross-section is greater; as shown in (a), the cross-sectional power was reduced. Figure 4 As shown in (b), the regulation capacity of new energy power plants has been reduced by approximately 80MW; Figure 4 As shown in (c), the real-time dispatch unit regulation capacity increased by approximately 12MW; Figure 4 As shown in (d), this is a pie chart comparing the decrease in new energy regulation with the increase in real-time dispatch unit regulation; Figure 4 As shown in (e), the power change at key section 1 decreased by nearly 60 MW; Figure 4 As shown in (f), the power of the key section 1 after adjustment is less than the power before adjustment, and is within the upper and lower limits.

[0152] Table 1. Planning Cost Table for the Example (Values ​​in the table are in US dollars)

[0153]

[0154] As can be seen, the collaborative optimization method that integrates network reconfiguration and real-time scheduling provided in this application embodiment can more economically and securely address the threat of uncertainty in new energy sources, making power system scheduling more reliable.

[0155] like Figure 2 As shown, based on the same inventive concept, another embodiment of this application also provides a collaborative optimization device that integrates network reconstruction and real-time scheduling, comprising:

[0156] The cross-sectional power flow prediction module 201 is used to obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and to obtain the cross-sectional power flow prediction result based on the sensitivity matrix.

[0157] The power flow limit judgment module 202 is used to determine the limit-crossing state of the cross section based on the power flow prediction result of the cross section; wherein, the limit-crossing state of the cross section is either the cross section exceeds the limit or the cross section does not exceed the limit;

[0158] The network reconfiguration optimization module 203 is used to establish a network reconfiguration optimization model with the goal of eliminating overload and maximizing the absorption of new energy when the cross-section exceeds the limit. Based on the network reconfiguration optimization model, the power supply network is reconfigured to obtain the reconfigured power supply network.

[0159] The real-time scheduling optimization module 204 is used to construct a scheduling optimization model and use the scheduling optimization model to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling.

[0160] The collaborative optimization device for integrating network reconstruction and real-time scheduling provided in this application embodiment can execute the above-mentioned method and technical solution. Its principle and beneficial effects are similar, and will not be described again here.

[0161] like Figure 3 As shown, based on the same inventive concept, another embodiment of this application also provides an electronic device, including a processor 302 and a memory 301; the memory 301 and the processor 302 are interconnected via a bus 303.

[0162] The memory 301 stores computer-executed instructions;

[0163] The processor 302 executes the computer execution instructions stored in the memory 301, causing the processor 302 to execute a collaborative optimization method for integrating network reconstruction and real-time scheduling as described in any embodiment of this application.

[0164] For specific examples, memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0165] This application provides a computer-readable storage medium storing computer-executable instructions. When these instructions are executed by a processor, they are used to implement the collaborative optimization method for fusion network reconstruction and real-time scheduling described in any of the above embodiments.

[0166] This application embodiment can also provide a computer program product, including a computer program that, when executed by a processor, implements the collaborative optimization method for fusion network reconstruction and real-time scheduling described in any of the above embodiments.

[0167] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0168] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0172] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A collaborative optimization method integrating network reconstruction and real-time scheduling, characterized in that, include: Obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and obtain the cross section power flow prediction result based on the sensitivity matrix; The cross-section over-limit status is determined based on the cross-section power flow prediction results; wherein, the cross-section over-limit status is either the cross-section over-limit or the cross-section not over-limit; When the cross-section exceeds the limit, a network reconfiguration optimization model is established with the goal of eliminating overload and maximizing the absorption of new energy. Based on the network reconfiguration optimization model, the power supply network is reconfigured to obtain the reconfigured power supply network. A scheduling optimization model is constructed, and the scheduling optimization model is used to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling. With the goals of eliminating overload and maximizing the absorption of new energy sources, a network reconfiguration optimization model is established as follows: ； Where min represents minimization. This represents the first weighting coefficient. This represents the second weighting coefficient. This represents the third weighting coefficient. Indicates the first The power reduction of each renewable energy power plant, where N represents the total number of renewable energy power plants. Indicates the first The on / off status of each load branch, where Z represents the total number of load branches. This represents the real-time scheduling cost of the AGC unit, and , This represents the marginal cost of the m-th AGC unit. This represents the output of the m-th AGC unit; The scheduling optimization model is constructed as follows: ； Where min represents minimization. This indicates an overall adjustment of weights. This indicates that the individual adjusts the weight. Indicates the first Power increment of a real-time scheduled AGC unit This indicates the system's real-time power demand. Let represent the average power regulation value of the m-th AGC unit, and , This represents the reference output of the m-th AGC unit, where M represents the total number of AGC units. This represents the sum of the baseline outputs of all AGC units.

2. The collaborative optimization method for fusion network reconstruction and real-time scheduling according to claim 1, characterized in that, Obtain the sensitivity matrix between the AGC unit and its corresponding cross-section, including: Obtain the power increment of the AGC unit, and obtain the generator allocation matrix based on the power increment of the AGC unit; Based on the generator set allocation matrix, the bus injected power increment is obtained, and the bus injected power increment is combined with the node admittance matrix to obtain the phase angle change vector; Based on the phase angle change vector, the branch active power flow increment is obtained, and the branch active power flow increment is combined with the section-branch aggregation matrix to obtain the section total power increment. The sensitivity matrix between the AGC unit and its corresponding cross-section is determined based on the total power increment of the cross-section.

3. The collaborative optimization method for fusion network reconstruction and real-time scheduling according to claim 1, characterized in that, Obtaining the cross-sectional power flow prediction result based on the sensitivity matrix includes: using Kalman filtering to predict the cross-sectional power flow based on the sensitivity matrix, and obtaining the cross-sectional power flow prediction result.

4. The collaborative optimization method for fusion network reconstruction and real-time scheduling according to claim 1, characterized in that, Determining the cross-sectional over-limit status based on the cross-sectional power flow prediction results includes: Determine whether the predicted cross-sectional power flow exceeds a preset cross-sectional limit threshold. If so, determine the cross-sectional limit status as cross-sectional limit exceeded; otherwise, determine the cross-sectional limit status as cross-sectional not exceeded.

5. The collaborative optimization method for fusion network reconstruction and real-time scheduling according to claim 2, characterized in that, Based on the aforementioned network reconstruction optimization model, the power supply network is reconstructed to obtain the reconstructed power supply network, including: Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined. With the goal of solving the network reconfiguration optimization model, the output of the target AGC unit, the actual output of the new energy power station, and the on / off state of the load branch are adjusted to obtain the reconfigured power supply network.

6. The collaborative optimization method for fusion network reconstruction and real-time scheduling according to claim 1, characterized in that, The scheduling optimization model is used to perform real-time AGC scheduling optimization on the reconstructed power supply network, including: Based on the sensitivity matrix between the AGC unit and its corresponding cross-section, the target AGC unit with a sensitivity greater than the preset sensitivity threshold is determined. With the goal of solving the scheduling optimization model, the power increment of the target AGC unit is adjusted to achieve real-time AGC scheduling optimization of the reconstructed power supply network.

7. A collaborative optimization apparatus integrating network reconstruction and real-time scheduling, wherein the apparatus is capable of executing the collaborative optimization method integrating network reconstruction and real-time scheduling as described in any one of claims 1 to 6, characterized in that, include: The cross-sectional power flow prediction module is used to obtain the sensitivity matrix between the AGC unit and its corresponding cross section, and to obtain the cross-sectional power flow prediction result based on the sensitivity matrix. The power flow limit judgment module is used to determine the limit-crossing state of the cross section based on the power flow prediction results of the cross section; wherein, the limit-crossing state of the cross section is either the cross section exceeds the limit or the cross section does not exceed the limit; The network reconfiguration optimization module is used to establish a network reconfiguration optimization model with the goal of eliminating overload and maximizing the absorption of new energy when the cross-section exceeds the limit. Based on the network reconfiguration optimization model, the power supply network is reconfigured to obtain the reconfigured power supply network. The real-time scheduling optimization module is used to construct a scheduling optimization model and use the scheduling optimization model to perform real-time AGC scheduling optimization on the reconstructed power supply network, thereby completing the collaborative optimization of the integrated network reconstruction and real-time scheduling.

8. An electronic device, characterized in that, Including processor and memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the collaborative optimization method for converged network reconstruction and real-time scheduling as described in any one of claims 1 to 6.

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