Power grid multi-energy collaborative optimization scheduling method and system

Abstract

The invention discloses a multi-energy collaborative optimization scheduling method and system for a power grid, and the method and system effectively cope with the uncertainty challenge of the power grid after the power grid suffers from a disaster fault through the combination of a conditional value-at-risk (CVaR) and an information gap theory (IGDT). According to the method, a multi-energy collaborative optimization scheduling model based on IGDT-CVaR is constructed, power failure loss expectation and a CVaR risk coefficient under multiple scenes are taken as objective functions, and constraint conditions such as node voltage, line capacity, distributed power supply output and an energy storage system are comprehensively considered. Through data-driven model solution, the optimal scheduling plan can be generated, economy and toughness are both considered, and the reliability and stability of the power grid in a complex operation environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a power grid multi-energy collaborative optimization dispatching method and system based on conditional value at risk (CVaR) and information gap theory (IGDT). Background Art

[0002] With the popularization of renewable energy and the rapid development of electric vehicles and energy storage technology, the power grid is facing an increasingly complex operating environment and uncertain factors. Traditional power grid fault recovery models are unable to effectively cope with the challenges brought by these uncertainties, resulting in the stability and resilience of the power grid being affected. Therefore, a system and method is needed that can comprehensively consider multiple uncertain factors and achieve multi-energy collaborative optimization scheduling.

[0003] The power grid is a complex and dynamic power system, which is constantly influenced and integrated by flexible resources such as high-density distributed generation (DG), energy storage systems and controllable loads. The introduction of these resources gives the power grid greater flexibility and adaptability, making it more responsive and intelligent. The introduction of high-density distributed generation (DG) not only enriches the energy source, but also improves the reliability and stability of the power supply system; the application of energy storage systems provides a more convenient means for power regulation, effectively alleviating the fluctuations between supply and demand; and the flexible use of controllable loads further enhances the regulation capacity of the power grid, laying a solid foundation for the efficient use of energy and the intelligent management of the system. The integration and coordinated operation of these flexible resources not only improves the operating efficiency and economy of the power grid, but also provides strong support for the large-scale application of sustainable energy and the construction of smart grids. Summary of the invention

[0004] In view of the defects of the prior art, the present invention provides a method and system for the coordinated optimization and dispatching of multi-energy in power grids. The conditional value at risk (CVaR) theory is used to measure the operating risk of the power grid under various uncertainties, and the information gap theory (IGDT), a non-probabilistic and non-fuzzy mathematical optimization method, is introduced to deal with the decision-making process with incomplete information and more uncertainties. The present invention organically combines CVaR with IGDT to form a multi-energy coordinated optimization and dispatching technology for power grids based on the conditional value at risk-information gap theory, providing dispatchers with a reference for optimized dispatching plans.

[0005] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0006] A method for optimizing the coordinated dispatching of multiple energy sources in a power grid comprises the following steps:

[0007] S1: Based on the conditional value at risk (CVaR), the uncertainty of distributed power sources and loads in the power grid after a disaster is quantitatively analyzed, and the information gap theory (IGDT) is used to participate in the decision-making process to provide support for handling uncertainty under incomplete information conditions.

[0008] S2: Based on the uncertainty quantification analysis, a joint uncertainty model including IGDT and CVaR is constructed. The IGDT-CVaR uncertainty model is used to describe the risk avoidance and opportunity seeking strategies of the power grid under different scenarios.

[0009] S3: Combined with the IGDT-CVaR uncertainty model, a multi-energy coordinated optimization dispatch model for power grids based on the IGDT-CVaR theory is constructed. The objective function of this model is a weighted combination of the expected power outage loss and the CVaR risk coefficient of the power grid in multiple scenarios. The constraints include grid node voltage constraints, line capacity constraints, distributed generation DG output constraints, DG output constraints, network flow constraints, energy storage system charging and discharging power and capacity constraints, network topology constraints, and operation safety constraints.

[0010] S4: Substitute the network structure parameters, load level, distributed power output, and energy storage related data of the power grid into the established power grid multi-energy collaborative optimization dispatch model. By solving the model, the optimal dispatch plan of the power grid after the disaster failure is obtained, which takes into account the economy and resilience of the system.

[0011] S5: According to the model solution results, the multi-energy collaborative optimization dispatching plan of the power grid based on the IGDT-CVaR theory is output to guide the actual operation and dispatching decision of the power grid.

[0012] Furthermore, the sub-steps of step S1 are as follows:

[0013] S1.1: Collect grid operation data, including historical load data, distributed generation (DG) output, energy storage-related data, meteorological data, and fault records, in order to analyze uncertainty factors.

[0014] S1.2: Analyze the sources of uncertainty that affect grid operation, such as load fluctuations, DG output uncertainty, changes in meteorological conditions, equipment failures, etc., and determine the uncertainty factors that need to be modeled.

[0015] S1.3: Based on historical data and expert experience, a set of uncertainty scenarios is constructed to simulate the operation status of the power grid under different conditions. Each scenario represents a possible operation condition.

[0016] S1.4: For each scenario, calculate the conditional value at risk (CVaR) of the grid at different risk levels to quantify its possible maximum loss under extreme conditions.

[0017] S1.5: Evaluate the robustness of decision making under incomplete information conditions through information gap theory (IGDT). Calculate the maximum tolerable information gap (IG) of the system under different uncertainty levels as an input parameter for decision making.

[0018] Furthermore, S1.4 includes a conditional risk value function as follows:

[0019] minC=C EX +μC CVaR (1)

[0020] Where: C is the comprehensive cost of the ideal power grid; C EX is the expected cost of an ideal resilient power grid; C CVaR is the CVaR of the system economic dispatch; μ is C CVaR The weight coefficient of

[0021] Furthermore, the basic model of IGDT decision in S1.5 is as follows:

[0022]

[0023] Where: X is an uncertain parameter. B(X,d) is the objective function. H(X,d) and G(X,d) are equality and inequality constraints respectively. d is the decision variable.

[0024] In the IGDT theory, for the predicted value Assume an uncertain parameter X, which is around The fluctuation of is described as follows:

[0025]

[0026] Where: Indicates that the range of difference between the uncertain parameter X and the predicted value does not exceed α represents the fluctuation range of the uncertain parameter, and is restricted to α≥0.

[0027] The mathematical models corresponding to the risk avoidance strategy and opportunity seeking strategy are as follows:

[0028]

[0029]

[0030] Where: β c , β o is the deviation factor of the function, which represents the degree of deviation of the expected cost above or below B0. B0 is the value of X under the determined model. The objective function value when . For a given d, maxB(X,d) and minB(X,d) are functions of X.

[0031] Furthermore, a CVaR-IGDT uncertainty model is constructed in S2, including:

[0032] By mathematical methods, the conditional value at risk CVaR formula is expanded and the expression of CVaR is obtained by summing and integrating, as follows:

[0033]

[0034] Where [f(x,y)-β] + =max{0,f(x,y)-β}. Then the value of CVaR is Ω θ =minF θ (x,β),N s is the total number of scenarios. s is the probability of the sth scenario occurring.

[0035] The joint model of IGDT-CVaR is constructed as follows:

[0036] Risk avoidance strategy:

[0037]

[0038] Opportunity Seeking Strategy:

[0039]

[0040] In the above formula, ε s is an auxiliary variable used to calculate the value of CVaR, is the predicted output of the power grid in unit j in time period t, is the actual output of the power grid in unit j in time period t. C0 is the loss function of the power grid after a disaster in the deterministic model. W Describes the percentage of the required distributed energy and load fluctuations. C is the loss function after the power grid is damaged and failed after considering the conditional risk value. Δ1 is the profit deviation coefficient set by the decision maker. μ is the weight coefficient of CVaR, which describes the decision maker's risk preference.

[0041] Furthermore, in S3, a multi-energy coordinated optimization dispatching model of the power grid based on the IGDT-CVaR theory is constructed, including:

[0042] Objective function construction:

[0043]

[0044] in: represents the expected power outage loss during fault recovery in multiple scenarios; w i represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; Li,t represents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in the scenario s during the period t; β represents the risk coefficient, which reflects the risk preference level of the decision maker; βCVaR represents the product of the conditional risk value CVaR and the risk coefficient; t represents the total fault recovery time period; Ω represents the node set;

[0045] The constraints include: node voltage constraints of the power grid model, line capacity constraints, DG output constraints, DG output constraints, network flow constraints, energy storage charging and discharging power and capacity constraints, topology constraints and operation safety constraints.

[0046] Node voltage constraints:

[0047]

[0048] in, is the minimum value of the line node voltage in the power grid multi-energy fault recovery model, U i is the average value of the line node voltage of the power grid multi-energy fault recovery model, It is the maximum value of the line node voltage in the power grid multi-energy fault recovery model.

[0049] Line capacity constraints:

[0050]

[0051] Among them, P ij Power transmitted by the line for the multi-energy fault recovery model of the power grid; It is the upper limit of line transmission power in the multi-energy fault recovery model of the power grid.

[0052] DG output constraints:

[0053]

[0054] in, Represents the minimum output power of the generator of the power grid model, P DGi represents the output power of the generator of the power grid model, represents the maximum output power of the generator of the power grid model, represents the minimum reactive power of the generator in the power grid model, Q DGi represents the reactive power of the generator in the power grid model, Indicates the maximum reactive power of the generator in the power grid model.

[0055] For the uncontrollable DG, assuming it is wind power output and photovoltaic output, the observed value at each moment is set as the upper limit of the uncontrollable DG output active power, and a certain proportion of the output power lower limit is set, and the maximum power factor constraint is used for the output reactive power.

[0056] DG output constraints:

[0057]

[0058] In the formula, m∈S nd , where S nd The set of nodes that connect uncontrollable DG to the grid. m is the maximum power factor angle of the uncontrollable DG at node m, and α is the minimum output coefficient. ndDG,pre,m,t is the predicted value of the uncontrollable DG output active power at node m during period t.

[0059] Network flow constraints:

[0060]

[0061] Where ∑P is the total active power of the multi-energy coordinated optimization control model of the power grid, P i is the power of the i-th node in the multi-energy coordinated optimization control model of the power grid, r i is the impedance of the i-th node in the multi-energy coordinated optimization control model of the power grid, It is the square of the voltage of the i-th node in the multi-energy coordinated optimization control model of the power grid.

[0062] Energy storage charging and discharging power and capacity constraints:

[0063]

[0064] Where: They represent the energy storage charging and discharging power in period t respectively; Respectively represent the maximum charging and discharging power of energy storage; s ch,t 、s dis,t are 0-1 variables, representing the energy storage charge / discharge flag; They represent the current, minimum, and maximum states of charge of the energy storage respectively; η represents the charge / discharge efficiency.

[0065] Power grid model topology constraints:

[0066] The network topology constraints are as follows:

[0067]

[0068]

[0069]

[0070]

[0071] In the formula, S represents the set of grid islands, E represents the set of grid branches, and N represents the total number of grid nodes; c is Represents the node island partition variable, 1 means node i belongs to island s, Represents the branch island partition variable, 1 means branch ij belongs to island s, α ij Indicates the open and closed state of branch ij, 1 indicates that the branch is closed, 0 indicates that the branch is disconnected, and |S| indicates the number of grid islands. Formula (16) indicates that each node of the grid belongs to only one isolated island; Formula (17) indicates that when a line ij belongs to a certain isolated island, then the nodes i and j at both ends of the line must also belong to this isolated island at the same time; Formula (18) indicates that when line ij does not belong to any isolated island, line ij is in a disconnected state; Formula (19) indicates that the relationship between the number of grid nodes and the number of branches meets the radial requirement; There are bilinear terms in Formula (17), and its linear transformation is shown in Formula (20):

[0072]

[0073] Power grid model operation security constraints:

[0074]

[0075] Where: U max , U min are the upper and lower limits of node voltage in the multi-energy coordinated optimization control model of the power grid; P ij,max , Q ij,max They represent the upper limits of active and reactive power of branch ij in the multi-energy coordinated optimization control model of the power grid, and r i,t is the node voltage relay protection coefficient of the multi-energy coordinated optimization control model of the power grid, α ij Node power relay protection coefficient of multi-energy coordinated optimization control model in power grid.

[0076] Based on the above constructed model, the objective function is substituted into the risk aversion strategy function and opportunity seeking strategy function of IGDT-CVaR to form the final optimization model:

[0077] Risk avoidance strategy:

[0078]

[0079] Opportunity Seeking Strategy:

[0080]

[0081] Where, L i,trepresents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; ε s It is an auxiliary variable used to calculate the value of CVaR; represents the expected power outage loss during fault recovery in multiple scenarios; w i It represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in scenario s during period t. C0 is the power grid disaster loss in the deterministic model. α W Describes the percentage of distributed energy power fluctuation and load fluctuation. C is the loss of the power grid after the disaster considering the conditional risk value. Δ1 is the profit deviation coefficient set by the decision maker. μ is the weight coefficient of CVaR, which describes the decision maker's risk preference.

[0082] The present invention also discloses a multi-energy coordinated optimization dispatching system for a power grid, which can be used to implement the multi-energy coordinated optimization dispatching method for a power grid, specifically comprising:

[0083] Data acquisition and preprocessing module: collects and preprocesses data such as the grid structure parameters, load level, distributed power output, and energy storage system status to ensure data quality and provide support for subsequent analysis and modeling.

[0084] Uncertainty analysis module: Based on the conditional value at risk (CVaR) and information gap theory (IGDT), the uncertainty of distributed power sources and loads in the power grid after a disaster is quantitatively analyzed to provide uncertainty input for subsequent scheduling models.

[0085] Joint uncertainty modeling module: Constructs a joint uncertainty model including IGDT and CVaR, describes the risk avoidance and opportunity seeking strategies of the power grid under different scenarios, and calculates the corresponding optimization objective functions and constraints.

[0086] Multi-energy collaborative optimization scheduling module: Combined with the IGDT-CVaR uncertainty model, a multi-energy collaborative optimization scheduling model for the power grid is constructed, which comprehensively considers the expected power outage losses and CVaR risk factors under multiple scenarios to determine the optimal scheduling plan.

[0087] Model solving and optimization module: Based on the input data and models, solve the multi-energy collaborative optimization scheduling model, calculate the optimal scheduling strategy of the power grid under different scenarios, and ensure the economy and resilience of the system.

[0088] Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

[0089] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned multi-energy collaborative optimization scheduling method for a power grid when executing the program.

[0090] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned power grid multi-energy collaborative optimization scheduling method is implemented.

[0091] Compared with the prior art, the advantages of the present invention are:

[0092] 1. Strong ability to cope with uncertainty: By combining conditional value at risk (CVaR) and information gap theory (IGDT), it can effectively cope with the uncertainty factors of the power grid after a disaster and ensure the robustness and reliability of scheduling decisions.

[0093] 2. Optimize system economy and resilience: Based on the consideration of power outage losses and CVaR risk factors in multiple scenarios, the economy and resilience of the power grid are balanced, and a more economical and effective scheduling solution can be provided in an uncertain environment.

[0094] 3. Multi-energy collaborative optimization: Integrates the coordinated scheduling of multiple energy resources (such as distributed power sources, energy storage systems, etc.), fully utilizes the advantages of various resources, and improves the efficiency and reliability of the overall operation of the system.

[0095] 4. Flexible adaptation to multiple scenarios: It can adapt to different operating scenarios, find the optimal dispatching strategy under various possible fault and disaster scenarios by solving the optimization model, and improve the adaptability and flexibility of the power grid.

[0096] 5. Comprehensive constraint management: The dispatch model takes into account multiple constraints such as the node voltage of the power grid, line capacity, network flow, and energy storage system capacity to ensure the feasibility and safety of the optimized dispatch plan in actual operation.

[0097] 6. Data-driven decision support: Relying on data collection and preprocessing, modeling and analysis based on real-time and historical data provide powerful decision support capabilities, and can adjust scheduling plans in time to cope with dynamically changing operating environments.

[0098] 7. Improve the overall reliability of the system: By comprehensively considering the characteristics and interrelationships of different energy resources, this method can significantly improve the overall reliability and operational stability of the power grid in complex environments.

[0099] 8. Application of conditional value at risk theory: Using conditional value at risk (CVaR) theory to measure the risk of power grid operation overcomes the problems of VaR's inability to perform sub-additive output, inability to measure consistency risk, and inability to describe tail risk when the risk value exceeds the set value, providing a more accurate and comprehensive risk assessment.

[0100] 9. Application of Information Gap Theory: Using non-probabilistic and non-fuzzy mathematical optimization methods based on information gap theory (IGDT) to deal with uncertainties in power grid operation, it is possible to optimize the error of uncertainties while meeting pre-set goals without determining the probability distribution of uncertainties, thereby determining the possible impact of uncertainties on the system. This makes the dispatching scheme highly reliable and robust even when information is incomplete. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a flowchart of a method for multi-energy coordinated optimization and dispatching of a power grid according to an embodiment of the present invention;

[0102] Figure 2 is a topological diagram of a power grid structure according to an embodiment of the present invention;

[0103] Figure 3 It is a schematic diagram of a deterministic model island partitioning scheme according to an embodiment of the present invention;

[0104] Figure 4 Schematic diagram of an island partitioning scheme of an opportunity seeking model according to an embodiment of the present invention;

[0105] Figure 5 Schematic diagram of the risk aversion model island partitioning scheme according to an embodiment of the present invention;

[0106] Figure 6 1 is a graph showing the output power of the system under three models of the embodiment of the present invention;

[0107] Figure 7 It is a bar graph of energy storage output of a deterministic model according to an embodiment of the present invention;

[0108] Figure 8 is a bar chart of energy storage output of the opportunity model of an embodiment of the present invention;

[0109] Fig. 9 It is a histogram of energy storage output of the risk model of an embodiment of the present invention;

[0110] Fig.10 is a curve showing the variation of α with the return deviation factor Δ1 in the embodiment of the present invention;

[0111] Fig.11 3 is a curve diagram showing the change of α with the risk preference coefficient μ in the embodiment of the present invention. DETAILED DESCRIPTION

[0112] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0113] The implementation steps of this embodiment are as follows: Figure 1 This embodiment uses the IEEE 33-node power grid system as a test example. The specific structure is as follows Figure 2 As shown. The topology consists of 33 nodes and branch lines between them, including 32 section switches and 5 tie switches. Each node represents an electrical device or node in the power system. DG is a generator, distributed at nodes 5, 15, 23, and 30. The power sources at nodes 5 and 15 are composed of wind power equipment and energy storage devices, and the power sources at nodes 23 and 30 are composed of photovoltaic power generation equipment and energy storage devices. Figure 2 The fault area is marked in the figure, and the fault area is at branches 1-2, 11-12, and 28-29. For this fault scenario, the multi-energy collaborative optimization dispatching technology of the power grid based on the conditional risk value-information gap theory of the present invention is used for optimization dispatching. The specific steps are as follows:

[0114] S1: Based on CVaR, quantitative analysis of the dual uncertainties of power grid damage and distributed generation output is carried out, and IGDT is involved in the decision-making process to provide support for handling uncertainties under incomplete information conditions.

[0115] S11: The quantitative analysis model of dual uncertainty of power grid damage and distributed generation output based on CVaR includes:

[0116] Conditional Value at Risk Function:

[0117] minC=C EX +μC CVaR (1)

[0118] Where: C is the comprehensive loss of the power grid after considering the conditional risk value; C EX is the expected cost of an ideal resilient power grid; C CVaR is the CVaR of the system economic dispatch; μ is the risk value coefficient;

[0119] S12: Based on the IGDT participation decision-making process, the resulting risk avoidance strategies and opportunity seeking strategies include:

[0120] IGDT decision basic model:

[0121]

[0122] Where: X is an uncertain parameter. B(X,d) is the objective function. H(X,d) and G(X,d) are equality and inequality constraints respectively. d is the decision variable.

[0123] In the IGDT theory, for the predicted value Assume an uncertain parameter X, which is around The fluctuation of can be described as follows:

[0124]

[0125] Where: Indicates that the range of the uncertainty parameter X and the predicted value does not exceed α represents the fluctuation range of the uncertain parameter, and is restricted to α≥0.

[0126] The mathematical models corresponding to the risk avoidance strategy and opportunity seeking strategy are as follows:

[0127]

[0128]

[0129] Where: β c , β o is the deviation factor of the function, which represents the degree of deviation of the expected cost above or below B0. B0 is the value of X under the determined model. The objective function value when . For a given d, maxB(X,d) and minB(X,d)≤B o is a function of X.

[0130] S2: Constructing the IGDT-CVaR uncertainty model includes:

[0131] First, use mathematical methods to sum and integrate the CvaR formula, then use auxiliary functions to solve the value of CVaR and obtain the following formula:

[0132]

[0133] Where [f(x,y)-β] + =max{0,f(x,y)-β}. Then the conditional risk value is Ω θ =minF θ (x,β).

[0134] The combined formula of IGDT-CVaR is as follows:

[0135]

[0136] Substituting the objective function into the opportunity seeking strategy function yields the following formula:

[0137]

[0138] In the above formula, ε sis an auxiliary variable used to calculate the value of CVaR, is the predicted output of the power grid in unit j in time period t, is the actual output of the power grid in unit j in time period t. C0 is the power outage loss of the power grid in the deterministic model. W Used to describe the situation of distributed energy and load fluctuation. C is the power outage loss of the model after considering the conditional risk value. Δ1 is the profit deviation coefficient set by the decision maker. μ is the weight coefficient of CVaR, which describes the decision maker's inclination towards unknown situations.

[0139] S3: Constructing a multi-energy collaborative optimization dispatching model for a power grid based on the IGDT-CVaR theory includes: determining the model objective function, various model constraints, combining the aforementioned risk avoidance and opportunity seeking strategy functions with the model objective function and combining them with the model constraints.

[0140] The objective function of the model described in S31 is:

[0141]

[0142] in: represents the expected power outage loss during fault recovery in multiple scenarios; w i represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; L i,t represents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in the scenario s during the period t; β represents the risk coefficient, which reflects the risk preference level of the decision maker; βCVaR represents the product of the conditional risk value CVaR and the risk coefficient; t represents the total fault recovery time period; Ω represents the node set;

[0143] S32: The model constraints include grid model node voltage constraints, grid model line capacity constraints, DG output constraints, DG output constraints, grid model network flow constraints, grid model energy storage charging and discharging power and capacity constraints, grid model topology constraints, and grid model operation safety constraints.

[0144] Power grid model node voltage constraints:

[0145]

[0146] in, is the minimum value of the line node voltage in the power grid multi-energy fault recovery model, U i is the average value of the line node voltage of the power grid multi-energy fault recovery model, It is the maximum value of the line node voltage in the power grid multi-energy fault recovery model.

[0147] Power grid model line capacity constraints:

[0148]

[0149] Among them, P ij Power transmitted by the line for the multi-energy fault recovery model of the power grid; It is the upper limit of line transmission power in the multi-energy fault recovery model of the power grid.

[0150] DG output constraints:

[0151]

[0152] in, Represents the minimum output power of the generator of the power grid model, P DGi represents the output power of the generator of the power grid model, represents the maximum output power of the generator of the power grid model, represents the minimum reactive power of the generator in the power grid model, Q DGi represents the reactive power of the generator in the power grid model, Indicates the maximum reactive power of the generator in the power grid model.

[0153] For the uncontrollable DG, assuming it is wind power processing and photovoltaic output, the observed value at each moment is set as the upper limit of the uncontrollable DG output active power, and the maximum power factor constraint is used for the output reactive power.

[0154] DG output constraints:

[0155]

[0156] In the formula, m∈S nd , where S nd The set of nodes that connect uncontrollable DG to the grid. m is the maximum power factor angle of the uncontrollable DG at node m, and α is the minimum output coefficient. ndDG,pre,m,t is the predicted value of the uncontrollable DG output active power at node m during period t.

[0157] Power grid model network flow constraints:

[0158]

[0159] Where ∑P is the total active power of the multi-energy coordinated optimization control model of the power grid, P i is the power of the i-th node in the multi-energy coordinated optimization control model of the power grid, r i is the impedance of the i-th node in the multi-energy coordinated optimization control model of the power grid, It is the square of the voltage of the i-th node in the multi-energy coordinated optimization control model of the power grid.

[0160] Grid model energy storage charging and discharging power and capacity constraints:

[0161]

[0162] Where: They represent the energy storage charging and discharging power in period t respectively; Respectively represent the maximum charging and discharging power of energy storage; s ch,t 、s dis,t are 0-1 variables, representing the energy storage charge / discharge flag; They represent the current, minimum, and maximum states of charge of the energy storage respectively; η represents the charge / discharge efficiency.

[0163] Power grid model topology constraints:

[0164] The network topology constraints are as follows:

[0165]

[0166]

[0167]

[0168]

[0169] In the formula, S represents the set of grid islands, E represents the set of grid branches, and N represents the total number of grid nodes; c is Represents the node island partition variable, 1 means node i belongs to island s, Represents the branch island partition variable, 1 means branch ij belongs to island s, α ij Indicates the open and closed state of branch ij, 1 indicates that the branch is closed, 0 indicates that the branch is disconnected, and |S| indicates the number of grid islands. Formula (16) indicates that each node of the grid belongs to only one isolated island; Formula (17) indicates that when a line ij belongs to a certain isolated island, then the nodes i and j at both ends of the line must also belong to this isolated island at the same time; Formula (18) indicates that when line ij does not belong to any isolated island, line ij is in a disconnected state; Formula (19) indicates that the relationship between the number of grid nodes and the number of branches meets the radial requirement; There are bilinear terms in Formula (17), and its linear transformation is shown in Formula (20):

[0170]

[0171] Power grid model operation security constraints:

[0172]

[0173] Where: U max , Umin are the upper and lower limits of node voltage in the multi-energy coordinated optimization control model of the power grid; P ij,max , Q ij,max They represent the upper limits of active and reactive power of branch ij in the multi-energy coordinated optimization control model of the power grid, and r i,t is the node voltage relay protection coefficient of the multi-energy coordinated optimization control model of the power grid, α ij Node power relay protection coefficient of multi-energy coordinated optimization control model in power grid.

[0174] S33: The multi-energy collaborative optimization and control model of the power grid based on CVaR-IGDT is: the risk avoidance and opportunity seeking strategy functions substituted into the above constraints and objective functions are combined.

[0175] Substituting the objective function into the risk aversion strategy function yields the following formula:

[0176]

[0177] Substituting the objective function into the opportunity seeking strategy function yields the following formula:

[0178]

[0179] In the above formula, L i,t represents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; ε s It is an auxiliary variable used to calculate the value of CVaR; represents the expected power outage loss during fault recovery in multiple scenarios; w i It represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in the scenario s during the period t. C0 is the power outage loss of the power grid in the deterministic model. α W Describes the percentage of the required source load fluctuation. C is the power outage loss of the model after considering the conditional risk value. Δ1 is the return deviation coefficient set by the decision maker. μ is the weight coefficient of CVaR.

[0180] S4: Substitute the data of grid network structure parameters, load level, distributed power output, etc. into the multi-energy collaborative optimization scheduling model of the power grid based on IGDT-CVaR theory to obtain the optimized scheduling plan.

[0181] like Figure 2 As shown, the electrical network structure parameters include grid data and fault branch data.

[0182] There are distributed power sources at nodes 5, 15, 23, and 30. The power sources at nodes 5 and 15 are wind-storage combined systems, and the power sources at nodes 23 and 30 are photovoltaic-storage combined systems. Each power source has a certain voltage and frequency regulation capability and can serve as the main power source of the island. The parameters of distributed power sources and energy storage in the power grid are shown in Table 1.

[0183] Table 1 Wind and solar power generation parameters

[0184] Power Type Maximum active power / kW Maximum reactive power / kvar Minimum power factor Wind power 500 / 0.9 Photovoltaic 600 / 0.9

[0185] Table 2 Energy storage system parameters

[0186] Maximum charge / discharge power / kW Energy storage capacity / kWh Min. / max. SOC / pu Charge and discharge efficiency 500 1000 0.05 / 0.95 0.95

[0187] The model is solved in MATLAB to find the fault recovery scheme with the minimum power outage loss based on CVaR-IGDT, and the grid island division scheme after the disaster fault under three models is used, such as Figures 3 to 5 shown.

[0188] After the island division is performed on the IEEE-33 node topology with three faults and faulty branches 11, 20, and 28, the system is divided into four independent island areas. Figures 3 to 5 Each island contains a wind+storage device or a solar+storage device, which is designed to ensure the isolation of the fault area while maintaining independent power supply to other non-fault areas through the generators in the island where they are located, thereby reducing the impact of the fault on the entire power grid system.

[0189] First, consider the maximum possible fluctuation and minimum possible fluctuation of the source load of the risk-averse strategy model and the opportunity-seeking strategy model relative to the deterministic model.

[0190] Risk aversion strategy model: Setting the deviation factor Δ1 to 0.5 and the risk value coefficient to 0.05, the maximum acceptable power outage load loss is 1792.88×(1+0.5)=2689.32. When it is brought into the risk aversion strategy model for solution, the maximum tolerable load fluctuation of the power grid is 4.85%, the maximum tolerable photovoltaic power generation output fluctuation is 13.50%, and the maximum tolerable wind power generation output fluctuation is 16.26%.

[0191] Opportunity-seeking strategy model: Setting the deviation factor Δ1 to 0.5 and the risk value coefficient to 0.15, the maximum acceptable power outage load loss is 1792.88×(1-0.5)=896.44, which is brought into the risk aversion strategy model for solution. That is, when the system source and load change in a favorable direction due to external factors, the minimum load fluctuation is 6.94%, the minimum photovoltaic power generation output fluctuation is 19.34%, and the minimum wind power generation output fluctuation is 23.30%.

[0192] The output power of the power grid system under the three models is Figure 6 , the deterministic model is a case where the parameters are assumed to be constant, and neither the information gap theory nor the conditional risk value participates in the regulation; the opportunity seeking strategy model and the risk aversion strategy model are the power grid multi-energy collaborative optimization dispatching models considering the information gap theory-conditional risk value. Figure 6 It can be seen that under the opportunity-seeking strategy model, the system will seek to generate more power, while after adopting the risk-avoidance strategy, it will seek to be conservative to avoid unknown risks, and the system output will decrease. The output of the deterministic model system is between the two.

[0193] Figures 7 to 9 The energy storage charging and discharging conditions and load recovery conditions under each model. When the load exceeds the specific value of the distributed power output, the missing power will be supplemented by the energy storage power station. When the distributed power output exceeds the specific value of the load, the excess power will be stored in the energy storage power station. Figures 7 to 9 It can be clearly seen that energy storage can help restore the power supply of some important loads and reduce the impact of power outages. This improves the reliability of power supply in the power grid. The output of energy storage varies for different models. The opportunity-seeking strategy model takes into account the possible beneficial effects of source-load fluctuations, and the energy storage is charged and discharged frequently, with a large total amount of charge and discharge. The risk-averse strategy model takes into account the possible adverse effects of source-load fluctuations, and the energy storage has almost no chance to charge, relying on its own reserve power to be in a discharge state most of the time.

[0194] In the embodiment, according to my country's new energy and load forecast error requirements, it is assumed that the actual output fluctuation deviation of wind power and photovoltaic power is 15% of the predicted value, and the load fluctuation range is 5%. The three models are solved in the "worst scenario", and the results of power outage losses and source-load fluctuation range are shown in Table 3.

[0195] Table 3 Results of power outage losses and source load fluctuation range

[0196]

[0197] It can be seen from Table 3 that the grid multi-energy collaborative optimization dispatching method based on the conditional risk value-information gap theory proposed in the present invention can set the risk / opportunity return deviation factor, select the power outage loss within the tolerable range, obtain the response interval of uncertainty variables such as wind, solar, and load, and more accurately characterize the fluctuation range of uncertainty variables, thereby providing a decision-making basis for dispatchers.

[0198] This embodiment takes the risk aversion model as an example to describe the relationship between the return deviation factor Δ1 and the wind and solar power output weighted uncertainty α, where the wind and solar power output weighted uncertainty α is expressed as:

[0199] Specific results such as Fig.10 shown.

[0200] Under the risk aversion strategy of the present invention, when the profit deviation factor Δ1 increases, the decision maker will relax the requirements for power outage losses of the power grid, and the protection level of the power grid against unpredictable disasters will be improved. In the risk aversion model of the present invention, the decision maker believes that the uncertainty of wind and solar power output is harmful, so a more conservative strategy is formulated when considering wind and solar power output, which will cause the loss of the power grid to increase. In addition, as long as the weighted output of wind and solar energy is within If the power grid is changed within a certain range, the power outage loss of the power grid will be lower than the value (1+Δ1)C0 desired by the decision maker.

[0201] The risk preference coefficient μ corresponds to the weight coefficient of CVaR, indicating the decision maker's preference level for risk, μ ≥ 0. Taking the return deviation coefficient as 0.5, μ increases from 0.05 to 0.35 in the opportunity seeking strategy model, with an interval of 0.03; the change curve of the wind and solar power output weighted uncertainty in the IGDT-CVaR model is as follows: Fig.11 As shown. Fig.11 It can be seen that as the risk preference coefficient increases, decision makers are more inclined to avoid the impact of unknown risks, which increases the acceptable range of wind and solar fluctuations.

[0202] In another embodiment of the present invention, a power grid multi-energy collaborative optimization dispatching system is provided, which can be used to implement the above-mentioned power grid multi-energy collaborative optimization dispatching method, specifically including:

[0203] Data acquisition and preprocessing module: collects and preprocesses data such as the grid structure parameters, load level, distributed power output, and energy storage system status to ensure data quality and provide support for subsequent analysis and modeling.

[0204] Uncertainty analysis module: Based on conditional value at risk (CVaR) and information gap theory (IGDT), quantitative analysis is performed on the uncertainty of distributed power sources and loads after the power grid is affected by a disaster, providing uncertainty input for subsequent scheduling models.

[0205] Joint uncertainty modeling module: Constructs a joint uncertainty model including IGDT and CVaR, describes the risk avoidance and opportunity seeking strategies of the power grid under different scenarios, and calculates the corresponding optimization objective functions and constraints.

[0206] Multi-energy collaborative optimization scheduling module: Combined with the IGDT-CVaR uncertainty model, a multi-energy collaborative optimization scheduling model for the power grid is constructed, which comprehensively considers the expected power outage losses and CVaR risk factors under multiple scenarios to determine the optimal scheduling plan.

[0207] Model solving and optimization module: Based on the input data and models, solve the multi-energy collaborative optimization scheduling model, calculate the optimal scheduling strategy of the power grid under different scenarios, and ensure the economy and resilience of the system.

[0208] Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

[0209] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the multi-energy collaborative optimization scheduling method of the power grid.

[0210] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0211] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the power grid multi-energy collaborative optimization scheduling method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0212] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0213] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0214] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0216] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for multi-energy coordinated optimization dispatching of a power grid, characterized in that: The following steps are involved: S1: Based on the conditional value at risk (CVaR), the uncertainty of distributed power sources and loads in the power grid after a disaster is quantitatively analyzed, and the information gap theory (IGDT) is used to participate in the decision-making process to provide support for handling uncertainty under incomplete information conditions; S2: Based on the uncertainty quantitative analysis, a joint uncertainty model including IGDT and CVaR is constructed; the IGDT-CVaR uncertainty model is used to describe the risk avoidance and opportunity seeking strategies of the power grid under different scenarios; S3: Combined with the IGDT-CVaR uncertainty model, a multi-energy coordinated optimization dispatch model for power grids based on the IGDT-CVaR theory is constructed; the objective function of the model is a weighted combination of the expected power outage loss and the CVaR risk coefficient of the power grid under multiple scenarios, and the constraints include grid node voltage constraints, line capacity constraints, distributed generation DG output constraints, DG output constraints, network flow constraints, energy storage system charging and discharging power and capacity constraints, network topology constraints, and operation safety constraints; S4: Substitute the network structure parameters, load level, distributed power output, and energy storage-related data of the power grid into the established power grid multi-energy collaborative optimization dispatch model; by solving the model, the optimal dispatch plan of the power grid after the disaster failure is obtained, which takes into account the economy and resilience of the system; S5: According to the model solution results, the multi-energy collaborative optimization dispatching plan of the power grid based on the IGDT-CVaR theory is output to guide the actual operation and dispatching decision of the power grid.

2. The method for multi-energy coordinated optimization dispatching of a power grid according to claim 1, characterized in that: The sub-steps of step S1 are as follows: S1.1: Collect grid operation data, including historical load data, DG output, energy storage related data, meteorological data and fault records, in order to analyze uncertainty factors; S1.2: Analyze the sources of uncertainty that affect grid operation, such as load fluctuations, DG output uncertainty, changes in meteorological conditions, equipment failures, etc., and determine the uncertainty factors that need to be modeled; S1.3: Based on historical data and expert experience, a set of uncertainty scenarios is constructed to simulate the operation status of the power grid under different conditions; each scenario represents a possible operation condition; S1.4: For each scenario, calculate the conditional value at risk (CVaR) of the power grid at different risk levels to quantify its maximum possible loss under extreme conditions; S1.5: Evaluate the robustness of decision-making under incomplete information conditions through the information gap theory IGDT; calculate the maximum tolerable information gap IG of the system under different uncertainty levels as the input parameter for decision-making.

3. The method for multi-energy coordinated optimization dispatching of a power grid according to claim 2 is characterized in that: S1.4 includes the conditional value-at-risk function as follows: minC=C EX +μC CVaR (1) Where: C is the comprehensive cost of the ideal power grid; C EX is the expected cost of an ideal resilient power grid; C CVaR is the CVaR of the system economic dispatch; μ is C CVaR The weight coefficient of .

4. The method for multi-energy coordinated optimization dispatching of a power grid according to claim 2, characterized in that: The basic IGDT decision model in S1.5 is as follows: Where: X is an uncertain parameter; B(X,d) is the objective function; H(X,d) and G(X,d) are equality and inequality constraints respectively; d is the decision variable; In the IGDT theory, for the predicted value Assume an uncertain parameter X, which is around The fluctuation of is described as follows: Where: Indicates that the range of difference between the uncertain parameter X and the predicted value does not exceed α represents the fluctuation range of the uncertain parameter, with the restriction of α≥0; The mathematical models corresponding to the risk avoidance strategy and opportunity seeking strategy are as follows: Where: β c , β o is the deviation factor of the function, which represents the degree of deviation of the expected cost above or below B0; B0 is the value of X under the determined model. The objective function value when ; for a given d, maxB(X,d) and minB(X,d) are functions of X.

5. The method for multi-energy coordinated optimization dispatching of a power grid according to claim 1, characterized in that: The CVaR-IGDT uncertainty model is constructed in S2, including: By mathematical methods, the conditional value at risk CVaR formula is expanded and the expression of CVaR is obtained by summing and integrating, as follows: Where [f(x,y)-β] + =max{0,f(x,y)-β}; then the value of CVaR is Ω θ =minF θ (x,β),N s is the total number of scenarios; π s is the probability of the sth scenario occurring; The joint model of IGDT-CVaR is constructed as follows: Risk avoidance strategy: Opportunity Seeking Strategy: In the above formula, ε s is an auxiliary variable used to calculate the value of CVaR, is the predicted output of the power grid in unit j in time period t, is the actual output of the power grid in unit j in time period t; C0 is the loss function of the power grid after the disaster in the deterministic model; α W Describes the percentage of the required distributed energy and load fluctuations; C is the loss function after the power grid is damaged and failed after considering the conditional risk value; Δ1 is the profit deviation coefficient set by the decision maker; μ is the weight coefficient of CVaR, which explains the decision maker's tendency to risk.

6. The method for multi-energy coordinated optimization dispatching of a power grid according to claim 1, characterized in that: In S3, a multi-energy coordinated optimization dispatching model for power grid based on IGDT-CVaR theory is constructed, including: Objective function construction: in: represents the expected power outage loss during fault recovery in multiple scenarios; w i represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; L i,t represents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in the scenario s during the period t; β represents the risk coefficient, which reflects the risk preference level of the decision maker; βCVaR represents the product of the conditional risk value CVaR and the risk coefficient; t represents the total fault recovery time period; Ω represents the node set; The constraints include: node voltage constraints of the power grid model, line capacity constraints, DG output constraints, DG output constraints, network flow constraints, energy storage charging and discharging power and capacity constraints, topology constraints and operation safety constraints; Node voltage constraints: in, is the minimum value of the line node voltage in the power grid multi-energy fault recovery model, U i is the average value of the line node voltage in the power grid multi-energy fault recovery model, is the maximum value of the line node voltage in the power grid multi-energy fault recovery model; Line capacity constraints: Among them, P ij Power transmitted by the line for the multi-energy fault recovery model of the power grid; The upper limit of the line transmission power of the multi-energy fault recovery model of the power grid; DG output constraints: in, Represents the minimum output power of the generator of the power grid model, P DGi represents the output power of the generator of the power grid model, represents the maximum output power of the generator of the power grid model, represents the minimum reactive power of the generator in the power grid model, Q DGi represents the reactive power of the generator in the power grid model, Indicates the maximum reactive power of the generator in the power grid model; For uncontrollable DG, assuming it is wind power output and photovoltaic output, etc., set the observed value at each moment as the upper limit of the uncontrollable DG output active power, set a certain proportion of the output power lower limit, and use the maximum power factor constraint on the output reactive power; DG output constraints: In the formula, m∈S nd , where S nd is the node set of the uncontrollable DG connected to the grid; γ m is the maximum power factor angle of the uncontrollable DG at node m, α is the minimum output coefficient; P ndDG,pre,m,t is the predicted value of the uncontrollable DG output active power at node m during period t; Network flow constraints: Where ∑P is the total active power of the multi-energy coordinated optimization control model of the power grid, P i is the power of the i-th node in the multi-energy coordinated optimization control model of the power grid, r i is the impedance of the i-th node in the multi-energy coordinated optimization control model of the power grid, is the square of the voltage of the i-th node in the multi-energy coordinated optimization control model of the power grid; Energy storage charging and discharging power and capacity constraints: Where: They represent the energy storage charging and discharging power in period t respectively; Respectively represent the maximum charging and discharging power of energy storage; s ch ,t、s dis,t are 0-1 variables, representing the energy storage charge / discharge flag; They represent the current, minimum, and maximum states of charge of the energy storage respectively; η represents the charge / discharge efficiency; Power grid model topology constraints: The network topology constraints are as follows: In the formula, S represents the set of grid islands, E represents the set of grid branches, and N represents the total number of grid nodes; c is Represents the node island partition variable, 1 means node i belongs to island s, Represents the branch island partition variable, 1 means branch ij belongs to island s, α ij represents the open and closed state of branch ij, 1 represents branch closed, 0 represents branch disconnected, |S| represents the number of grid islands; Formula (16) indicates that each node of the grid belongs to only one isolated island; Formula (17) indicates that when a line ij belongs to a certain isolated island, then the nodes i and j at both ends of the line must also belong to this isolated island; Formula (18) indicates that when line ij does not belong to any isolated island, line ij is in a disconnected state; Formula (19) indicates that the relationship between the number of grid nodes and the number of branches meets the radial requirement; There are bilinear terms in Formula (17), and their linear transformation is shown in Formula (20): Power grid model operation security constraints: Where: U max , U min are the upper and lower limits of node voltage in the multi-energy coordinated optimization control model of the power grid; P ij,max , Q ij,max They represent the upper limits of active and reactive power of branch ij in the multi-energy coordinated optimization control model of the power grid, and r i,t is the node voltage relay protection coefficient of the multi-energy coordinated optimization control model of the power grid, α ij Node power relay protection coefficient of multi-energy coordinated optimization control model of power grid; Based on the above constructed model, the objective function is substituted into the risk aversion strategy function and opportunity seeking strategy function of IGDT-CVaR to form the final optimization model: Risk avoidance strategy: Opportunity Seeking Strategy: Where, L i,t represents the active load demand of node i in the multi-energy coordinated optimization control model of the power grid; ε s It is an auxiliary variable used to calculate the value of CVaR; represents the expected power outage loss during fault recovery in multiple scenarios; w i It represents the load weight of node i in the multi-energy coordinated optimization control model of the power grid, reflecting the importance of the load; represents the actual load power of node i in the multi-energy coordinated optimization control model of the power grid in the scenario s during the period t; C0 is the disaster loss of the power grid in the deterministic model; α W Describes the percentage of distributed energy power fluctuation and load fluctuation; C is the loss after the power grid is damaged by a disaster considering the conditional risk value; Δ1 is the profit deviation coefficient set by the decision maker; μ is the weight coefficient of CVaR, which explains the decision maker's tendency to risk.

7. A multi-energy coordinated optimization dispatching system for a power grid, characterized by: The system can be used to implement the power grid multi-energy collaborative optimization dispatching method according to any one of claims 1 to 6, specifically including: Data acquisition and preprocessing module: collects and preprocesses data such as the grid structure parameters, load level, distributed power output, and energy storage system status to ensure data quality and provide support for subsequent analysis and modeling; Uncertainty analysis module: Based on the conditional value at risk (CVaR) and information gap theory (IGDT), it quantitatively analyzes the uncertainty of distributed power sources and loads after the power grid is damaged by a fault, and provides uncertainty input for subsequent scheduling models; Joint uncertainty modeling module: builds a joint uncertainty model including IGDT and CVaR, describes the risk avoidance and opportunity seeking strategies of the power grid under different scenarios, and calculates the corresponding optimization objective functions and constraints; Multi-energy collaborative optimization scheduling module: Combined with the IGDT-CVaR uncertainty model, a multi-energy collaborative optimization scheduling model for the power grid is constructed, which comprehensively considers the expected power outage loss and CVaR risk coefficient under multiple scenarios to determine the optimal scheduling plan; Model solving and optimization module: solves the multi-energy collaborative optimization dispatch model based on the input data and model, calculates the optimal dispatch strategy of the power grid under different scenarios, and ensures the economy and resilience of the system; Dispatch plan generation module: Based on the model solution results, it generates a specific power grid dispatch plan and outputs optimized dispatch decisions to guide the actual operation and dispatch management of the power grid.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for multi-energy coordinated optimization and dispatching of power grids as described in one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for multi-energy coordinated optimization and scheduling of a power grid as described in one of claims 1 to 6 is implemented.

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