A water-wind complementary dispatching scheme optimization method, system and device

Abstract

The application discloses a water and wind light complementary scheduling scheme optimization method, system and device, and the method comprises the steps of defining a multi-dimensional index system reflecting the economy and safety of a scheduling scheme; simulating the propagation process and influence degree of a disturbance based on a logical correlation network; calculating the size of each index of the scheduling scheme; inputting a disturbance scenario to be simulated into the logical correlation network; simulating the propagation effect of the disturbance in the logical correlation network based on the topological structure of the logical correlation network; selecting key indexes; determining the system disturbance risk of the disturbance scenario based on the size and change degree of each key index after the disturbance propagation; selecting a disturbance scenario; calculating the system disturbance risk of each disturbance scenario; calculating the comprehensive risk index of each scheduling scheme; sorting the scheduling schemes; and selecting an optimal scheduling scheme. The logical correlation network can reflect the correlation between indexes, evaluate the cascading influence of a disturbance on multiple indexes, and minimize the risk brought by the disturbance.

Description

Technical Field

[0001] This application relates to the field of hydro-wind-solar hybrid scheduling technology, and in particular to a method, system and equipment for optimizing a hydro-wind-solar hybrid scheduling scheme. Background Technology

[0002] The intermittent and fluctuating nature of renewable energy output poses challenges to power system dispatching and operation. To ensure the safe and economical operation of the power system, scientifically sound dispatching schemes are necessary. Hydropower-wind-solar hybrid dispatching involves multi-objective coordination problems; optimization algorithms typically generate a Pareto solution set (scheme set), forming multiple potential dispatching schemes. A comprehensive evaluation and optimization of this scheme set is required to determine the optimal dispatching strategy. Traditional dispatching scheme evaluation methods typically employ a linear evaluation model of "selecting indicators - determining weights - calculating indicators - scoring," which has certain limitations. First, traditional methods treat each evaluation indicator as independent, ignoring the correlation between indicators. Second, traditional methods cannot simulate the propagation process of disturbances in the system and struggle to assess the cascading effects of disturbances on multiple indicators. This limitation is particularly pronounced in scenarios with high renewable energy volatility, making it difficult to cope with complex multi-scenario and multi-disturbance environments. Summary of the Invention

[0003] In view of this, this application provides a method, system and equipment for optimizing a water-wind-solar hybrid scheduling scheme. By constructing a logically related network and combining the correlation between indicators, the strength and direction of the effects between indicators are scientifically quantified. This overcomes the limitations of traditional scheme evaluation and selection, such as strong subjectivity of weights and neglect of disturbance propagation effects. From the perspective of disturbance, it provides a new technical path and reference basis for screening water-wind-solar hybrid scheduling schemes.

[0004] This application discloses a method for optimizing a hydro-wind-solar hybrid scheduling scheme, which includes:

[0005] Step 1: Define a multi-dimensional index system for the economy and safety of the reaction scheduling scheme;

[0006] Step 2: Construct a logical relationship network of various indicators driven by expert knowledge;

[0007] Step 3: Based on the logical association network, simulate the propagation process and impact of the disturbance; calculate the magnitude of each index of the scheduling scheme, input the disturbance scenario to be simulated into the logical association network, and simulate the propagation effect of the disturbance in the logical association network based on the topology of the logical association network;

[0008] Step 4: Select key indicators. Based on the magnitude and degree of change of each key indicator after the disturbance propagation in Step 3, determine the system disturbance risk of the disturbance scenario. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity.

[0009] Step 5: Select a disturbance scenario, calculate the system disturbance risk for each disturbance scenario according to the process of Steps 1-4, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes and select the optimal scheduling scheme.

[0010] Further, step 1 includes:

[0011] The indicators in the multi-dimensional indicator system include dispatching and operation costs, renewable energy absorption rate, hydropower curtailment rate, system frequency deviation, bus voltage deviation, number of times hydropower units cross the vibration zone, spinning reserve capacity, and renewable energy output change rate. The multi-dimensional system includes economic, safety, and reliability dimensions. The economic dimension includes dispatching and operation costs, renewable energy absorption rate, and hydropower curtailment rate. The safety dimension includes system frequency deviation, bus voltage deviation, and number of times hydropower units cross the vibration zone. The reliability dimension includes spinning reserve capacity insufficiency rate and renewable energy output change rate.

[0012] Further, step 2 includes:

[0013] Based on the fundamental principles of water-wind-solar complementarity and expert experience, this paper systematically analyzes the correlation between any two indicators in a multi-dimensional indicator system; constructs a directed weighted graph as a logical correlation network; the directed weighted graph contains nodes and edges, where nodes are indicators in the multi-dimensional indicator system; each edge is a line from a "cause" indicator node to an "effect" indicator node, and each edge has a weight and an attribute; the weight represents the strength of the correlation, and the attribute represents the directionality of the correlation; the directionality includes positive correlation and negative correlation; there is a causal relationship between the "cause" indicator node and the "effect" indicator node.

[0014] Furthermore, the correlation includes directionality, intensity of influence, and type of correlation; wherein, directionality clarifies the direction of the influence of one indicator on the change of another indicator; intensity of influence is used to qualitatively or semi-quantitatively define the strength level of the influence relationship; type of correlation is used to clarify the nature of the correlation between one indicator and another indicator, including direct influence and indirect influence; direct influence means that the change of indicator A directly causes the change of indicator B, that is, direct physical causal influence; indirect influence means that the change of indicator A indirectly causes the change of indicator B by triggering changes in other indicators, that is, influence propagated through the correlation network.

[0015] Furthermore, the topology based on the logically associated network, simulating the propagation effect of disturbances in the logically associated network, includes:

[0016] Step 31: Select a node from the logical association network as the impact node. The impact node is used to send disturbances to other nodes in the logical association network. Starting from the current impact node, traverse all outgoing edges of the impact node, that is, the edges from the impact node as the disturbance node to other disturbed nodes.

[0017] Step 32: For each edge from the disturbing node to the disturbed node, calculate the impact along the edge on the disturbed node under the current disturbance change of the disturbing node. The calculation formula is as follows:

[0018]

[0019] Where A is the disturbing node and B is the disturbed node. For the disturbance quantity, Let be the change in disturbance at node A. This represents the strength of the effect of the perturbing node A on the perturbed node B. This indicates the direction of the effect of the disturbing node A on the disturbed node B;

[0020] Step 33: Superimpose the disturbance effects, and add the disturbance amount The amount of disturbance change accumulated to the disturbed node B superior;

[0021] Step 34: Add the disturbed node B to the queue to be processed, use the breadth-first search algorithm (BFS) to traverse the nodes in the queue, and calculate the next disturbance propagation effect according to the formula in step 32.

[0022] Step 35: Remove the disturbed node from the queue and perform iterative propagation calculation. Calculate its impact on the disturbed node and accumulate it. Add the disturbed node to the queue and return to step 32 until the queue is empty or the preset maximum propagation layer is reached.

[0023] Step 36: Output the size and change of all nodes in the disturbance scenario, which represent the degree of deviation of the system's predicted dimensions after the disturbance propagates through the logical association network.

[0024] Further, step 4 includes:

[0025] Step 41: Based on the system operation status, select key indicators and set risk thresholds and risk sensitivities accordingly; risk thresholds refer to the safety boundary values ​​set for key system performance or security risks, and risk sensitivity clarifies the risk direction of relevant indicators; risk sensitivity is divided into positive sensitivity and negative sensitivity. Positive sensitivity means that the larger the indicator value, the greater the risk, and negative sensitivity means that the smaller the indicator value, the greater the risk.

[0026] Step 42: Based on the results of the disturbance propagation calculation in Step 3, determine the magnitude and degree of change of the key indicators after the disturbance propagation; calculate the risk value of each key indicator according to the risk sensitivity and risk threshold of the key indicators.

[0027] Step 43: Considering the importance of different indicators, introduce weighting factors based on expert opinions, and calculate the risk values ​​of each key indicator by weighting to obtain the system disturbance risk.

[0028] Furthermore, the formula for calculating the risk value of the indicator is as follows:

[0029]

[0030] In the formula, Indicates the first The risk value of each indicator It is a function with maximum value. Indicates the first The value of each indicator, Indicates the first Risk thresholds for each indicator Indicates the first Risk sensitivity of each indicator; This indicates positive sensitivity, meaning the higher the indicator value, the greater the risk. This indicates negative sensitivity, meaning the smaller the indicator value, the greater the risk.

[0031] Further, step 5 includes:

[0032] Step 51: Select one or more representative disturbance scenarios that may be triggered, and evaluate the degree of interference of each scheduling scheme from multiple dimensions; the disturbance scenario includes disturbance type, disturbance node, disturbed node, disturbance value, and disturbance occurrence probability;

[0033] Step 52: For each scheduling scheme, calculate its system disturbance risk under each disturbance scenario according to Steps 1 to 4, and calculate the system disturbance risk under each disturbance scenario by weighting according to the probability of the disturbance scenario occurring, so as to obtain the comprehensive risk index of each scheduling scheme.

[0034] Step 53: Based on the comprehensive risk index calculated in Step 52, sort all scheduling schemes in ascending order;

[0035] Step 54: Select the scheduling scheme with the lowest comprehensive risk index from the sorted scheduling schemes as the recommended optimal scheduling scheme.

[0036] This application also discloses a system for optimizing a hydro-wind-solar hybrid scheduling scheme, which implements the above-described method and includes:

[0037] The definition module is used to define a multi-dimensional indicator system for the economy and security of response scheduling schemes;

[0038] The logical association network construction module is used to construct logical association networks for various indicators driven by expert knowledge.

[0039] The simulation module is used to simulate the propagation process and impact of disturbances based on logical association networks; calculate the magnitude of various indicators of the scheduling scheme; input the disturbance scenario to be simulated into the logical association network; and simulate the propagation effect of the disturbance in the logical association network based on the topology of the logical association network.

[0040] The risk determination module is used to select key indicators and determine the system disturbance risk of the disturbance scenario based on the magnitude and degree of change of each key indicator after the disturbance propagation in the simulation module. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity.

[0041] The scheme selection module is used to select disturbance scenarios. It sequentially uses the definition module, logical association network construction module, simulation module, and risk determination module to calculate the system disturbance risk of each disturbance scenario, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes, and select the optimal scheduling scheme.

[0042] This application also discloses an electronic device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method described above.

[0043] By employing the aforementioned technical solution, this application offers the following advantages: By constructing an expert knowledge-driven logical association network, the system systematically analyzes the relationships between various indicators and quantitatively examines the propagation effect of disturbances within the system. Compared to traditional methods, the logical association network can reflect the correlation between indicators, scientifically quantify the strength and directionality of their effects, simulate the propagation process of specific disturbances within the system, assess their cascading impact on multiple indicators, and provide a basis for the dynamic adjustment of scheduling schemes. Based on the risk assessment results after disturbance propagation, the optimal scheduling scheme is selected to minimize the risks caused by the disturbances. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a flowchart illustrating a preferred method for a water-wind-solar hybrid scheduling scheme according to an embodiment of this application. Detailed Implementation

[0046] The present application will be further described in conjunction with the accompanying drawings and embodiments. The described embodiments are only some, not all, of the embodiments of the present application. All other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of the present application.

[0047] See Figure 1 This application provides an optimal method for a hydro-wind-solar hybrid scheduling scheme, which includes:

[0048] Step 1: Define a multi-dimensional index system for the economy and safety of the reaction scheduling scheme;

[0049] Step 2: Construct a logical relationship network of various indicators driven by expert knowledge;

[0050] Step 3: Based on the logical association network, simulate the propagation process and impact of the disturbance; calculate the magnitude of each index of the scheduling scheme, input the disturbance scenario to be simulated into the logical association network, and simulate the propagation effect of the disturbance in the logical association network based on the topology of the logical association network;

[0051] Step 4: Select key indicators. Based on the magnitude and degree of change of each key indicator after the disturbance propagation in Step 3, determine the system disturbance risk of the disturbance scenario. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity.

[0052] Step 5: Select a disturbance scenario, calculate the system disturbance risk for each disturbance scenario according to the process of Steps 1-4, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes and select the optimal scheduling scheme.

[0053] Optionally, step 1 includes:

[0054] The indicators in the multi-dimensional indicator system include dispatching and operation costs, renewable energy absorption rate, hydropower curtailment rate, system frequency deviation, bus voltage deviation, number of times hydropower units cross the vibration zone, spinning reserve capacity, and renewable energy output change rate. The multi-dimensional system includes economic, safety, and reliability dimensions. The economic dimension includes dispatching and operation costs, renewable energy absorption rate, and hydropower curtailment rate. The safety dimension includes system frequency deviation, bus voltage deviation, and number of times hydropower units cross the vibration zone. The reliability dimension includes spinning reserve capacity insufficiency rate and renewable energy output change rate.

[0055] Optionally, step 2 includes:

[0056] Based on the fundamental principles of water-wind-solar complementarity and expert experience, this paper systematically analyzes the correlation between any two indicators in a multi-dimensional indicator system; constructs a directed weighted graph as a logical correlation network; the directed weighted graph contains nodes and edges, where nodes are indicators in the multi-dimensional indicator system; each edge is a line from a "cause" indicator node to an "effect" indicator node, and each edge has a weight and an attribute; the weight represents the strength of the correlation, and the attribute represents the directionality of the correlation; the directionality includes positive correlation and negative correlation; there is a causal relationship between the "cause" indicator node and the "effect" indicator node.

[0057] Optionally, the correlation includes directionality, intensity, and type; wherein, directionality clarifies the direction of the influence of one indicator on the change of another indicator, such as: "an increase in the rate of change of wind and solar power output leads to a decrease in the absorption rate, i.e., a negative impact"; intensity is used to qualitatively or semi-quantitatively define the strength level of the influence relationship, usually represented by a predefined level scale (e.g., 1-5 levels, where 1 represents a weak impact and 5 represents an extremely strong impact); type is used to clarify the nature of the correlation between one indicator and another indicator, including direct and indirect influence; direct influence means that the change of indicator A directly causes the change of indicator B, i.e., a direct physical causal influence; indirect influence means that the change of indicator A indirectly causes the change of indicator B by triggering changes in other indicators, i.e., influence propagated through a correlation network.

[0058] Optionally, the topology based on the logically associated network, simulating the propagation effect of disturbances in the logically associated network, includes:

[0059] Step 31: Select a node from the logical association network as the impact node. The impact node is used to send disturbances to other nodes in the logical association network. Starting from the current impact node, traverse all outgoing edges of the impact node, that is, the edges from the impact node as the disturbance node to other disturbed nodes.

[0060] Step 32: For each edge from the disturbing node to the disturbed node, calculate the impact along the edge on the disturbed node under the current disturbance change of the disturbing node. The calculation formula is as follows:

[0061]

[0062] Where A is the disturbing node and B is the disturbed node. For the disturbance quantity, Let be the change in disturbance at node A. This represents the strength of the effect of the perturbing node A on the perturbed node B. This indicates the direction of the effect of the disturbing node A on the disturbed node B;

[0063] Step 33: Superimpose the disturbance effects, and add the disturbance amount The amount of disturbance change accumulated to the disturbed node B superior;

[0064] Step 34: Add the disturbed node B to the queue to be processed, use the breadth-first search algorithm (BFS) to traverse the nodes in the queue, and calculate the next disturbance propagation effect according to the formula in step 32.

[0065] Step 35: Remove the disturbed node from the queue and perform iterative propagation calculation. Calculate its impact on the disturbed node and accumulate it. Add the disturbed node to the queue and return to step 32 until the queue is empty or the preset maximum propagation layer is reached.

[0066] Step 36: Output the size and change of all nodes in the disturbance scenario, which represent the degree of deviation of the system's predicted dimensions after the disturbance propagates through the logical association network.

[0067] Optionally, step 4 includes:

[0068] Step 41: Based on the system's operational status, select key indicators and set targeted risk thresholds and risk sensitivities. Risk thresholds refer to the safety boundary values ​​set for key system performance or safety risks. Risk sensitivity clarifies the risk direction of relevant indicators. For example, a wind and solar power absorption rate below 90% is a negative threshold, and the number of times the system crosses the vibration zone more than 10 times is a positive threshold. Risk sensitivity is divided into positive sensitivity and negative sensitivity. Positive sensitivity indicates that the larger the indicator value, the greater the risk, while negative sensitivity indicates that the smaller the indicator value, the greater the risk.

[0069] Step 42: Based on the results of the disturbance propagation calculation in Step 3, determine the magnitude and degree of change of the key indicators after the disturbance propagation; calculate the risk value of each key indicator according to the risk sensitivity and risk threshold of the key indicators.

[0070] Step 43: Considering the importance of different indicators, introduce weighting factors based on expert opinions, and calculate the risk values ​​of each key indicator by weighting to obtain the system disturbance risk.

[0071] Optionally, the formula for calculating the risk value of the indicator is:

[0072]

[0073] In the formula, Indicates the first The risk value of each indicator It is a function with maximum value. Indicates the first The value of each indicator, Indicates the first Risk thresholds for each indicator Indicates the first Risk sensitivity of each indicator; This indicates positive sensitivity, meaning the higher the indicator value, the greater the risk. This indicates negative sensitivity, meaning the smaller the indicator value, the greater the risk.

[0074] Optionally, step 5 includes:

[0075] Step 51: Select one or more representative disturbance scenarios that may be triggered, and evaluate the degree of interference of each scheduling scheme from multiple dimensions; the disturbance scenario includes disturbance type (event type), disturbance node (which indicator is affected by the disturbance first), disturbed node, disturbance value (the impact intensity of the disturbance), and disturbance occurrence probability (the possibility of occurrence; this parameter can be omitted if there is no disturbance).

[0076] Step 52: For each scheduling scheme, calculate its system disturbance risk under each disturbance scenario according to Steps 1 to 4, and calculate the system disturbance risk under each disturbance scenario by weighting according to the probability of the disturbance scenario occurring, so as to obtain the comprehensive risk index of each scheduling scheme.

[0077] Step 53: Based on the comprehensive risk index calculated in Step 52, sort all scheduling schemes in ascending order;

[0078] Step 54: Select the scheduling scheme with the lowest comprehensive risk index from the sorted scheduling schemes as the recommended optimal scheduling scheme.

[0079] This application also discloses a system for optimizing a hydro-wind-solar hybrid scheduling scheme, which implements the above-described method and includes:

[0080] The definition module is used to define a multi-dimensional indicator system for the economy and security of response scheduling schemes;

[0081] The logical association network construction module is used to construct logical association networks for various indicators driven by expert knowledge.

[0082] The simulation module is used to simulate the propagation process and impact of disturbances based on logical association networks; calculate the magnitude of various indicators of the scheduling scheme; input the disturbance scenario to be simulated into the logical association network; and simulate the propagation effect of the disturbance in the logical association network based on the topology of the logical association network.

[0083] The risk determination module is used to select key indicators and determine the system disturbance risk of the disturbance scenario based on the magnitude and degree of change of each key indicator after the disturbance propagation in the simulation module. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity.

[0084] The scheme selection module is used to select disturbance scenarios. It sequentially uses the definition module, logical association network construction module, simulation module, and risk determination module to calculate the system disturbance risk of each disturbance scenario, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes, and select the optimal scheduling scheme.

[0085] This application also discloses an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements the method described above.

[0086] The following example, using the selection of a scheduling scheme for a water-wind-solar system, provides a specific implementation case of this application.

[0087] First, eight indicators were selected, including dispatching operation cost, renewable energy absorption rate, hydropower curtailment rate, system frequency deviation, bus voltage deviation, number of times hydropower unit vibration zone is crossed, spinning reserve capacity, and renewable energy output change rate, to reflect the economy and safety of the dispatching scheme and to construct a multi-dimensional indicator system.

[0088] Construct a logical network to abstract the above 8 indicators into indicator nodes:

[0089] Economic nodes: dispatching and operation costs (C1), renewable energy consumption rate (C2), and hydropower abandonment rate (C3).

[0090] Safety nodes: system frequency deviation (S1), bus voltage deviation (S2), number of times the hydropower unit crosses the vibration zone (S3), spinning reserve capacity (S4), and rate of change of new energy output (S5).

[0091] For confidentiality reasons, all parameters in this application's implementation cases are fictitious data, used only to illustrate the patent implementation process, and cannot be used for comparative analysis of actual hydro-wind-solar hybrid systems. Specific details of each indicator node are shown in Table 1.

[0092] Table 1. Details of the selected indicator nodes

[0093]

[0094] Except for the system frequency deviation and the number of times the hydropower unit crosses the vibration zone, all other parameters are per-unit values ​​and not actual values.

[0095] There are currently 6 candidate schemes, and a recommended scheduling scheme needs to be selected from them. The initial values ​​of the indicators for the 6 candidate schemes are shown in Table 2.

[0096] Table 2 Initial values ​​of indicators for each candidate scheme

[0097]

[0098] Based on the fundamental principle of water-wind-solar complementarity and expert experience, the correlation between the two indicators is systematically sorted out to construct a logical correlation network of each evaluation indicator. The correlation between each indicator node is shown in Table 3.

[0099] Table 3. Relationships between indicator nodes

[0100]

[0101] Table 3. Relationships between indicator nodes (continued)

[0102]

[0103] After constructing the logical relationship network for each evaluation indicator, this case study sets up four disturbance scenarios to calculate the relevant risks faced by each solution under these scenarios, thereby selecting the appropriate solution. The four disturbance scenarios are as follows:

[0104] Scenario 1: Positive deviation in wind and solar power forecasts, resulting in a 20% surge in the rate of change in renewable energy output (S5).

[0105] Scenario 2: The negative deviation in wind and solar power forecasts leads to a sharp drop of 10% in the rate of change of renewable energy output (S5).

[0106] Scenario 3: Hydropower unit fails and shuts down, causing the spinning reserve capacity S4 to decrease by 30%.

[0107] Scenario 4: Sudden increase in load causes a transient drop of 0.15Hz in system frequency deviation S1.

[0108] The specific details of each disturbance scenario (probability of occurrence, disturbance node, disturbance value, etc.) are shown in Table 4.

[0109] Table 4. Specific details of each disturbance scenario

[0110]

[0111] Based on the topology of the logical network, the propagation effect of disturbances in the logical network is simulated. The propagation process of scenario 1 is shown in Table 5.

[0112] Table 5. Disturbance propagation in Scenario 1

[0113]

[0114] The propagation process of scenario 2 is shown in Table 6.

[0115] Table 6. Disturbance Propagation in Scenario 2

[0116]

[0117] The propagation process of scenario 4 is shown in Table 7.

[0118] Table 7. Disturbance propagation in scenario 4

[0119]

[0120] The propagation process in scenario 3 is relatively simple, and the propagation process for scenario 3 can be obtained similarly. After perturbation propagation calculation, the degree of change of each indicator in scenarios 1-4 is shown in Table 8.

[0121] Table 8. Changes in Indicator Nodes for Each Disturbance Scenario

[0122]

[0123] Based on practical considerations, seven key indicators were selected: system frequency deviation (S1), bus voltage deviation (S2), number of times the hydropower unit passes through the vibration zone (S3), spinning reserve capacity (S4), dispatching and operation cost (C1), renewable energy absorption rate (C2), and hydropower curtailment rate (C3). Risk values ​​for each key indicator were calculated based on their risk sensitivity. When calculating the risk values ​​of the key indicators, the unit value for all indicators was uniformly calculated using only the numerical portion. For example, if the value of indicator S1 is 50Hz and the value of indicator S2 is 40%, then the risk value should be calculated using either 40 or 50.

[0124] When calculating the risk value of key indicators, the risk threshold, risk sensitivity and weighting factor of each indicator are shown in Table 9.

[0125] Table 9 Risk thresholds, sensitivity, and weighting factors for key indicators

[0126]

[0127] For candidate scheme 1, the initial values ​​of each indicator and the magnitude of the indicator after perturbation are shown in Table 10.

[0128] Table 10 Initial values ​​and magnitudes of indicators after perturbation for candidate scheme 1

[0129]

[0130] Based on the changes in the selected key indicators after the disturbance, and combined with the risk threshold and risk sensitivity of the indicators, the risk value of each key indicator is calculated. Combined with the risk weight factor of each key indicator, the system disturbance risk under each scenario is calculated. Based on the probability of occurrence of each scenario, the comprehensive risk index of Scheme 1 is calculated, as shown in Table 11.

[0131] Table 11 Risk Calculation Results of Candidate Solution 1

[0132]

[0133] For candidate scheme 2, the initial values ​​of each indicator and the magnitude of the indicator after perturbation are shown in Table 12.

[0134] Table 12 Initial values ​​and magnitudes of indicators after perturbation for Candidate Scheme 2

[0135]

[0136] Based on the changes in the selected key indicator nodes after the disturbance, and combined with the risk threshold and risk sensitivity of the indicators, the risk value of each key indicator is calculated. Combined with the risk weight factor of each key indicator, the system disturbance risk under each scenario is calculated. Based on the probability of occurrence of each scenario, the comprehensive risk index of Scheme 2 is calculated, as shown in Table 13.

[0137] Table 13 Risk Calculation Results for Candidate Solution 2

[0138]

[0139] Similarly, the comprehensive risk indices for schemes 3 to 6 can be calculated. The comprehensive risk indices for the six candidate schemes are shown in Table 14.

[0140] Table 14 Comprehensive Risk Index of Candidate Solutions

[0141]

[0142] Based on the comprehensive risk index analysis of each candidate scheme, Scheme 1 and Scheme 6 have the lowest comprehensive risk and are therefore recommended. Scheme 2 and Scheme 3 are the next lowest and can also be recommended. Scheme 5 has a relatively high risk and can be considered as an alternative. Scheme 4 has a high comprehensive risk and is not recommended.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of the claims of this application.

Claims

1. A method for optimizing a hydro-wind-solar hybrid scheduling scheme, characterized in that, include: Step 1: Define a multi-dimensional index system for the economy and safety of the reaction scheduling scheme; Step 2: Construct a logical relationship network of various indicators driven by expert knowledge; Step 3: Based on the logical association network, simulate the propagation process and impact of the disturbance; calculate the magnitude of each index of the scheduling scheme, input the disturbance scenario to be simulated into the logical association network, and simulate the propagation effect of the disturbance in the logical association network based on the topology of the logical association network; Step 4: Select key indicators. Based on the magnitude and degree of change of each key indicator after the disturbance propagation in Step 3, determine the system disturbance risk of the disturbance scenario. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity. Step 5: Select a disturbance scenario, calculate the system disturbance risk for each disturbance scenario according to the process of Steps 1-4, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes and select the optimal scheduling scheme.

2. The method according to claim 1, characterized in that, Step 1 includes: The indicators in the multi-dimensional indicator system include dispatching and operation costs, renewable energy absorption rate, hydropower curtailment rate, system frequency deviation, bus voltage deviation, number of times hydropower units cross the vibration zone, spinning reserve capacity, and renewable energy output change rate. The multi-dimensional system includes economic, safety, and reliability dimensions. The economic dimension includes dispatching and operation costs, renewable energy absorption rate, and hydropower curtailment rate. The safety dimension includes system frequency deviation, bus voltage deviation, and number of times hydropower units cross the vibration zone. The reliability dimension includes spinning reserve capacity insufficiency rate and renewable energy output change rate.

3. The method according to claim 1, characterized in that, Step 2 includes: Based on the fundamental principles of water-wind-solar complementarity and expert experience, this paper systematically analyzes the correlation between any two indicators in a multi-dimensional indicator system; constructs a directed weighted graph as a logical correlation network; the directed weighted graph contains nodes and edges, where nodes are indicators in the multi-dimensional indicator system; each edge is a line from the "cause" indicator node to the "effect" indicator node, and each edge has a weight and an attribute; the weight represents the strength of the correlation, and the attribute represents the directionality of the correlation; the directionality includes positive correlation and negative correlation; there is a causal relationship between the "cause" indicator node and the "effect" indicator node.

4. The method according to claim 3, characterized in that, The correlation includes directionality, intensity of influence, and correlation type; where directionality clarifies the direction of the influence of one indicator on the change of another indicator; intensity of influence is used to qualitatively or semi-quantitatively define the strength level of the influence relationship; correlation type is used to clarify the nature of the correlation between one indicator and another indicator, including direct influence and indirect influence; direct influence means that the change of indicator A directly causes the change of indicator B, that is, direct physical causal influence; indirect influence means that the change of indicator A indirectly causes the change of indicator B by triggering changes in other indicators, that is, influence propagated through the correlation network.

5. The method according to claim 3, characterized in that, The topology based on logically related networks simulates the propagation effect of disturbances in logically related networks, including: Step 31: Select a node from the logical association network as the impact node. The impact node is used to send disturbances to other nodes in the logical association network. Starting from the current impact node, traverse all outgoing edges of the impact node, that is, the edges from the impact node as the disturbance node to other disturbed nodes. Step 32: For each edge from the disturbing node to the disturbed node, calculate the impact along the edge on the disturbed node under the current disturbance change of the disturbing node. The calculation formula is as follows: Where A is the disturbing node and B is the disturbed node. For the disturbance quantity, Let be the change in disturbance at node A. This represents the strength of the effect of the perturbing node A on the perturbed node B. This indicates the direction of the effect of the disturbing node A on the disturbed node B; Step 33: Superimpose the disturbance effects, and add the disturbance amount The amount of disturbance change accumulated to the disturbed node B superior; Step 34: Add the disturbed node B to the queue to be processed, use the breadth-first search algorithm (BFS) to traverse the nodes in the queue, and calculate the next disturbance propagation effect according to the formula in step 32. Step 35: Remove the disturbed node from the queue and perform iterative propagation calculation. Calculate its impact on the disturbed node and accumulate it. Add the disturbed node to the queue and return to step 32 until the queue is empty or the preset maximum propagation layer is reached. Step 36: Output the size and change of all nodes in the disturbance scenario, which represent the degree of deviation of the system's predicted dimensions after the disturbance propagates through the logical association network.

6. The method according to claim 1, characterized in that, Step 4 includes: Step 41: Based on the system operation status, select key indicators and set risk thresholds and risk sensitivities accordingly; risk thresholds refer to the safety boundary values ​​set for key system performance or security risks, and risk sensitivity clarifies the risk direction of relevant indicators; risk sensitivity is divided into positive sensitivity and negative sensitivity. Positive sensitivity means that the larger the indicator value, the greater the risk, and negative sensitivity means that the smaller the indicator value, the greater the risk. Step 42: Based on the results of the disturbance propagation calculation in Step 3, determine the magnitude and degree of change of the key indicators after the disturbance propagation; calculate the risk value of each key indicator according to the risk sensitivity and risk threshold of the key indicators. Step 43: Considering the importance of different indicators, introduce weighting factors based on expert opinions, and calculate the risk values ​​of each key indicator by weighting to obtain the system disturbance risk.

7. The method according to claim 6, characterized in that, The formula for calculating the risk value of the indicator is as follows: In the formula, Indicates the first The risk value of each indicator It is a function with maximum value. Indicates the first The value of each indicator, Indicates the first Risk thresholds for each indicator Indicates the first Risk sensitivity of each indicator; This indicates positive sensitivity, meaning the higher the indicator value, the greater the risk. This indicates negative sensitivity, meaning the smaller the indicator value, the greater the risk.

8. The method according to claim 5, characterized in that, Step 5 includes: Step 51: Select one or more representative disturbance scenarios that may be triggered, and evaluate the degree of interference of each scheduling scheme from multiple dimensions; the disturbance scenario includes disturbance type, disturbance node, disturbed node, disturbance value, and disturbance occurrence probability; Step 52: For each scheduling scheme, calculate its system disturbance risk under each disturbance scenario according to Steps 1 to 4, and calculate the system disturbance risk under each disturbance scenario by weighting according to the probability of the disturbance scenario occurring, so as to obtain the comprehensive risk index of each scheduling scheme. Step 53: Based on the comprehensive risk index calculated in Step 52, sort all scheduling schemes in ascending order; Step 54: Select the scheduling scheme with the lowest comprehensive risk index from the sorted scheduling schemes as the recommended optimal scheduling scheme.

9. A system for optimizing a hydro-wind-solar hybrid scheduling scheme, implementing the method described in any one of claims 1-8, characterized in that, include: The definition module is used to define a multi-dimensional indicator system for the economy and safety of response scheduling schemes; The logical association network construction module is used to construct logical association networks for various indicators driven by expert knowledge. The simulation module is used to simulate the propagation process and impact of disturbances based on logically related networks. The magnitudes of various indicators of the scheduling scheme are calculated, the disturbance scenario to be simulated is input into the logical association network, and the propagation effect of the disturbance in the logical association network is simulated based on the topology of the logical association network. The risk determination module is used to select key indicators and determine the system disturbance risk of the disturbance scenario based on the magnitude and degree of change of each key indicator after the disturbance propagation in the simulation module. Among them, the key indicators are the core indicators used to assess system risk in the multi-dimensional indicator system, that is, the indicators used to set risk thresholds and risk sensitivity. The scheme selection module is used to select disturbance scenarios. It sequentially uses the definition module, logical association network construction module, simulation module, and risk determination module to calculate the system disturbance risk of each disturbance scenario, calculate the comprehensive risk index of each scheduling scheme, sort the scheduling schemes, and select the optimal scheduling scheme.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the method of any one of claims 1-8.

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