Source-network-storage two-stage distributed robust operation method and system considering risk avoidance

Abstract

The invention discloses a source network storage two-stage distributed robust operation method and system considering risk avoidance. The method comprises the following steps: acquiring operation data of a power system source network storage; establishing a source network storage mathematical model comprising a distributed power supply, a flexible multi-state switch and an energy storage system; constructing a source-network-storage two-stage distribution robust operation optimization model containing conditional value-at-risk, optimizing day-ahead operation into a first stage and intra-day real-time optimization into a second stage by the operation optimization model, and taking the sum of the first-stage operation cost, the second-stage adjustment cost and the conditional value-at-risk as a target function to obtain a source-network-storage two-stage distribution robust operation optimization model containing the conditional value-at-risk; constructing a probability distribution fuzzy set of the output prediction error of the distributed power supply based on the norm distance, and determining the boundary of the fuzzy set by using a data driving mode; and solving the two-stage distribution robust operation optimization model by using a column and constraint generation algorithm to obtain an operation scheduling scheme of the source network storage. The method can effectively improve the consumption rate of the distributed power supply, and has higher anti-risk capability.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation optimization in power systems, and specifically relates to a two-stage distributed blue-robust operation method and system for source-grid-storage considering risk avoidance. Background Art

[0002] The energy crisis and greenhouse effect caused by traditional power generation are becoming more and more serious. How to effectively utilize renewable energy and reduce carbon emissions from the power system has become an urgent problem to be solved. The combination of "source side" - distributed generators (DGs), "grid side" - flexible multi-state switches (Soft Open Points, SOP), and "storage side" energy storage systems (Energy System Storage, ESS) can not only achieve peak load shaving and valley filling, but also reduce the demand for fossil fuel power generation and increase the absorption rate of new energy. In addition, the output of distributed power sources in the power system has extremely high uncertainty. In order not to be disturbed by the uncertainty of wind and solar output, conditional value-at-risk (Conditional Value-at-Risk, CVaR) and distributed robust optimization (Distributionally Robust Optimization, DRO) are widely used in power system uncertainty optimization.

[0003] Most existing technical routes use DRO and CVaR methods to solve the uncertainty of distributed power output. However, DRO takes into account the uncertainty of the probability distribution of wind and solar forecast errors, and focuses on the average operating cost of the scene under the worst probability distribution, ignoring the differences between scene outputs, which will lead to the risk of high operating costs in some extreme operating scenarios; the CVaR method requires the pre-assumption that the wind and solar output or output error obeys a certain probability distribution. The pre-assumption of probability distribution will have the disadvantage of being too subjective, resulting in certain limitations of CVaR. Summary of the invention

[0004] In view of the problem that the existing power system is overly conservative / risky in handling the uncertainty of distributed power sources, the present invention provides a two-stage distributed blue-power operation method and system of source, grid and storage that takes risk aversion into consideration, which optimizes the operation and scheduling of the source, grid and storage in the power system to effectively improve the absorption rate of distributed power sources and possess stronger risk resistance.

[0005] To this end, the present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a two-stage source-grid-storage distributed blue rod operation method considering risk avoidance, which comprises:

[0007] Obtain the operation data of power system source, grid and storage;

[0008] Establish a source-grid-storage mathematical model that includes distributed power sources, flexible multi-state switches, and energy storage systems;

[0009] A two-stage distributed blue-robust operation optimization model with conditional risk value is constructed. The day-ahead operation optimization is the first stage, and the intraday real-time optimization is the second stage. The sum of the operation cost of the first stage, the adjustment cost of the second stage and its conditional risk value is used as the objective function. A probability distribution fuzzy set of the output forecast error of distributed power generation is constructed based on the norm distance, and the boundary of the fuzzy set is determined by data-driven method.

[0010] The column and constraint generation algorithm is used to solve the two-stage distributed robust operation optimization model to obtain the operation scheduling plan of the source, network and storage.

[0011] Furthermore, the operating data includes wind and solar load day forecast data, line network parameters of the power system, time-of-use electricity price parameters, operating parameters of the flexible multi-state switch and operating parameters of the energy storage system.

[0012] Furthermore, the source-grid-storage mathematical model includes active power constraints and reactive power constraints of the source-grid-storage under long-term scheduling.

[0013] Furthermore, the source-grid-storage mathematical model is specifically:

[0014]

[0015]

[0016] Where, t represents the time; for the flexible multi-state switch SOP, i and j represent the node numbers of the distribution network connected to both sides of the flexible multi-state switch SOP; Respectively represent the active power output on both sides of the SOP; Respectively represent the reactive power output on both sides of the SOP; Respectively represent the converter capacities on both sides of the SOP; Respectively represent the active power loss on both sides of the SOP; Respectively represent the loss coefficients on both sides of the SOP; μ i , μ j Respectively represent the reactive power constraint coefficient of the voltage source converter VSC on both sides of the SOP; for the energy storage system, Respectively represent the charging and discharging flags of the energy storage system; and are the charging and discharging power and the maximum charging and discharging power of the energy storage system, respectively. and are the storage energy, minimum and maximum storage energy of the energy storage system respectively. and The initial storage energy and final storage energy of the energy storage system. and are the charging and discharging efficiency of the energy storage system respectively; Respectively represent the active power and reactive power output of wind power; Respectively represent the active power and reactive power of photovoltaic output; S i WT , Represent the capacity of wind power and photovoltaic power respectively.

[0017] Furthermore, the construction of the two-stage source-grid-storage distributed robust operation optimization model includes two-stage objective function setting, constraint condition setting and fuzzy set boundary determination.

[0018] Furthermore, the setting process of the two-stage objective function is as follows:

[0019] The objective function of the first stage is the operating cost, and the objective function of the second stage is the sum of the adjustment cost and its conditional risk value CVaR. The mathematical expression is as follows:

[0020]

[0021] A(x)≤0

[0022]

[0023] B(x,y,ξ)≤0

[0024]

[0025] In the formula, s, p s represents the sth operation scenario and the probability under the sth scenario, m represents the number of scenarios; x represents the first-stage decision variables, including the 0-1 variable of the energy storage operation state; y represents the second-stage decision variables, including the actual output power of the distributed generation, flexible multi-state switch and energy storage system; F ahead (x), F Intraday (y s ) represent the objective functions of the first and second stages respectively; ξ represents the uncertain variable, i.e., the wind and solar load output prediction error; p represents the probability distribution function obeyed by the uncertain variable; represents a fuzzy set; E p (·) represents the expectation, and represents the adjustment cost caused by the uncertainty of wind and solar load output; A(x) represents the first-stage model operation constraints, and B(x, y, ξ) represents the second-stage model operation constraints; C riskrepresents the CVaR caused by the wind and solar load output forecast error; λ∈[0,1] represents the risk preference factor, the larger the λ, the higher the risk; α represents the risk value; β represents the confidence level; G(x,ξ s ) represents the second stage optimization problem.

[0026] Furthermore, the G(x,ξ) represents the second stage optimization problem, and the specific calculation formula is as follows:

[0027]

[0028] m y i s nF Intraday (y s )=c t P s,t,sub

[0029] In the formula, Represents the depreciation cost caused by the change of charging and discharging state of the energy storage system; c t represents the electricity price at time t, P s,t,sub represents the power purchased by the power system from the upper power grid at the tth moment in the sth scenario; X represents the value set of the decision variables in the first stage; Ω ESS Indicates the set of nodes connected to the ESS.

[0030] Furthermore, the two-stage distributed blue-robust operation optimization model of the source-grid-storage system has constraints including the output constraints of distributed power sources, flexible multi-state switches and energy storage systems, and also satisfies the following power flow constraints:

[0031]

[0032] U min ≤U s,t,i ≤U max

[0033] -I ij,max ≤I s,t,ij ≤I ij,max

[0034] P s,t,sub ≥0

[0035] Where: P s,t,ij , Q s,t,ij Respectively represent the active power and reactive power of line ij; P s,t,jk , Q s,t,jk Respectively represent the active power and reactive power of line jk; P s,t,i , Q s,t,i Respectively represent the active power and reactive power flowing into node i; r ij represents the resistance of line ij; xij represents the reactance of line ij; They represent the active power and reactive power of the load of node i at time t respectively; U s,t,i , U s,t,j U represents the voltage of nodes i and j respectively; min , U max ,I ij,max They represent the minimum node voltage, the maximum node voltage, and the maximum line current respectively; I s,t,ij Represents the current of line ij at time t; Ω b Represents a collection of distribution network branches; Respectively represent the active power of wind power, photovoltaic power and energy storage; Represent the reactive power of wind power, photovoltaic power and energy storage respectively.

[0036] Furthermore, the process of determining the boundary of the fuzzy set is as follows:

[0037] In the s clustering scenario, a probability distribution fuzzy set is constructed based on the norm distance, and the fuzzy set boundary is determined in a data-driven way. Its mathematical expression is as follows:

[0038]

[0039] In the formula, β1 and β ∞ Indicates the confidence level of the confidence interval; d1 and d ∞ Represents the 1-norm distance and ∞-norm distance of the confidence interval; R + represents a positive real number; represents the reference probability distribution.

[0040] In a second aspect, the present invention provides a source-grid-storage two-stage distributed blue stick operation system considering risk avoidance, which includes:

[0041] An operation data acquisition module is used to obtain the operation data of the power system source, network and storage;

[0042] The source-grid-storage mathematical model building module is used to build a source-grid-storage mathematical model that includes distributed power sources, flexible multi-state switches and energy storage systems;

[0043] The operation optimization model construction module is used to construct a two-stage source-grid-storage distributed blue-rod operation optimization model with conditional risk value;

[0044] The operation scheduling scheme acquisition module uses the column and constraint generation algorithm to solve the two-stage distributed robust operation optimization model to obtain the operation scheduling scheme of the source network storage.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1) The present invention is based on a two-stage distributed blue-rod operation method of a distributed power source, a flexible multi-state switch and an energy storage system, which optimizes the operation of the source, grid and storage in the power system and can effectively improve the absorption rate of distributed power sources.

[0047] 2) The source-grid-storage two-stage distributed robust operation optimization model with two-stage risk avoidance including CVaR established in the present invention has stronger risk resistance ability compared with the traditional TSDRO model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a flow chart of a two-stage source-grid-storage distributed blue rod operation method considering risk avoidance of the present invention;

[0050] Figure 2 It is a schematic diagram of the grid-connected structure of a flexible multi-state switch in a specific implementation manner of the present invention;

[0051] Figure 3 It is a schematic diagram of the grid-connected structure of the energy storage system in a specific implementation manner of the present invention;

[0052] Figure 4 This is an architecture diagram of a source-grid-storage two-stage distributed blue stick operation system that takes risk avoidance into consideration in the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The present invention provides a two-stage source-grid-storage distributed blue rod operation method considering risk avoidance, such as Figure 1 As shown, the steps are as follows:

[0055] Step 1: Obtaining the operation data of the power system source, grid and storage;

[0056] Step 2: Establish a source-grid-storage mathematical model including distributed power sources, flexible multi-state switches and energy storage systems;

[0057] Step 3: Construct a two-stage distributed blue-robust operation optimization model with conditional risk value for source, grid and storage. This operation optimization model optimizes the day-ahead operation as the first stage and the intraday real-time optimization as the second stage. The sum of the operation cost of the first stage, the adjustment cost of the second stage and its conditional risk value is used as the objective function. A probability distribution fuzzy set of DGs output forecast error is constructed based on the norm distance, and the fuzzy set boundary is determined using a data-driven approach.

[0058] Step 4: Use the column and constraint generation algorithm to solve the two-stage distributed robust operation optimization model to obtain the source-grid-storage operation scheduling plan.

[0059] In step one, the operating data includes wind and solar load day forecast data, line network parameters of the power system, time-of-use electricity price parameters, operating parameters of the flexible multi-state switch and operating parameters of the energy storage system.

[0060] In step 2, the source-grid-storage mathematical model includes active power constraints and reactive power constraints of the source-grid-storage under long-term scheduling. Figure 2 This is a schematic diagram of the flexible multi-state switch grid-connected structure. Figure 3 It is a schematic diagram of the grid-connected structure of the energy storage system. The distributed power sources include wind power and photovoltaic power.

[0061] The source-grid-storage mathematical model is specifically:

[0062]

[0063]

[0064] Where, t represents the time; for the flexible multi-state switch SOP, i and j represent the node numbers of the distribution network connected to both sides of the flexible multi-state switch SOP; Respectively represent the active power output on both sides of the SOP; Respectively represent the reactive power output on both sides of the SOP; Respectively represent the converter capacities on both sides of the SOP; Respectively represent the active power loss on both sides of the SOP; Respectively represent the loss coefficients on both sides of the SOP; μ i , μ j Respectively represent the reactive power constraint coefficient of the voltage source converter VSC on both sides of the SOP; for the energy storage system, Respectively represent the charging and discharging flags of the energy storage system; and are the charging and discharging power and the maximum charging and discharging power of the energy storage system, respectively. and are the storage energy, minimum and maximum storage energy of the energy storage system respectively. and The initial storage energy and final storage energy of the energy storage system. and are the charging and discharging efficiency of the energy storage system respectively; Respectively represent the active power and reactive power output of wind power; Respectively represent the active power and reactive power of photovoltaic output; Represent the capacity of wind power and photovoltaic power respectively.

[0065] In step three, the construction of the two-stage source-grid-storage distributed robust operation optimization model includes setting the two-stage objective function, setting the constraint conditions and determining the fuzzy set boundary.

[0066] Specifically, the setting process of the two-stage objective function is as follows:

[0067] The objective function of the first stage is the operating cost, and the objective function of the second stage is the sum of the adjustment cost and its conditional risk value CVaR. The mathematical expression is as follows:

[0068]

[0069] A(x)≤0

[0070]

[0071] B(x,y,ξ)≤0

[0072]

[0073] In the formula, s, p s represents the sth operation scenario and the probability under the sth scenario, m represents the number of scenarios; x represents the first-stage decision variables, including the 0-1 variable of the energy storage operation state; y represents the second-stage decision variables, including the actual output power of the distributed generation, flexible multi-state switch and energy storage system; F ahead (x), F Intraday (y s ) represent the objective functions of the first and second stages respectively; ξ represents the uncertain variable, i.e., the wind and solar load output prediction error; p represents the probability distribution function obeyed by the uncertain variable; represents a fuzzy set; E p (·) represents the expectation, and represents the adjustment cost caused by the uncertainty of wind and solar load output; A(x) represents the first-stage model operation constraints, and B(x, y, ξ) represents the second-stage model operation constraints; C riskrepresents the CVaR caused by the wind and solar load output forecast error; λ∈[0,1] represents the risk preference factor, the larger the λ, the higher the risk; α represents the risk value; β represents the confidence level; G(x,ξ s ) represents the second stage optimization problem.

[0074] The G(x,ξ) represents the second stage optimization problem, and the specific calculation formula is as follows:

[0075]

[0076] In the formula, Represents the depreciation cost caused by the change of charging and discharging state of the energy storage system; c t represents the electricity price at time t, P s,t,sub represents the power purchased by the power system from the upper power grid at the tth moment in the sth scenario; X represents the value set of the decision variables in the first stage; Ω ESS Indicates the set of nodes connected to the ESS.

[0077] The two-stage distributed blue-rod operation optimization model of source-grid-storage has constraints including the output constraints of distributed generation, flexible multi-state switch and energy storage system, and also satisfies the following power flow constraints:

[0078]

[0079] U min ≤U s,t,i ≤U max

[0080] -I ij,max ≤I s,t,ij ≤I ij,max

[0081] P s,t,sub ≥0

[0082] Where: s, t, i represent the sth scene, tth time period, and ith node respectively; P s,t,ij , Q s,t,ij Respectively represent the active power and reactive power of line ij; P s,t,jk , Q s,t,jk Respectively represent the active power and reactive power of line jk; P s,t,i , Q s,t,i Respectively represent the active power and reactive power flowing into node i; r ij represents the resistance of line ij; x ij represents the reactance of line ij; They represent the active power and reactive power of the load of node i at time t respectively; U s,t,i , U s,t,j U represents the voltage of nodes i and j respectively;min , U max ,I ij,max They represent the minimum node voltage, the maximum node voltage, and the maximum line current respectively; I s,t,ij Represents the current of line ij at time t; Ω b Represents a collection of distribution network branches; Respectively represent the active power of wind power, photovoltaic power and energy storage; Represent the reactive power of wind power, photovoltaic power and energy storage respectively.

[0083] The process of determining the boundary of the fuzzy set is as follows:

[0084] In the s clustering scenario, a probability distribution fuzzy set is constructed based on the norm distance, and the fuzzy set boundary is determined in a data-driven way. Its mathematical expression is as follows:

[0085]

[0086] In the formula, β1 and β ∞ Indicates the confidence level of the confidence interval; d1 and d ∞ Represents the 1-norm distance and ∞-norm distance of the confidence interval; R + represents a positive real number; represents the reference probability distribution.

[0087] Example 2

[0088] This embodiment provides a source-grid-storage two-stage distributed blue stick operation system considering risk avoidance, such as Figure 4 As shown, it consists of an operation data acquisition module, a source-network-storage mathematical model establishment module, an operation optimization model construction module and an operation scheduling plan acquisition module.

[0089] An operation data acquisition module is used to obtain the operation data of the power system source, network and storage;

[0090] The source-grid-storage mathematical model building module is used to build a source-grid-storage mathematical model that includes distributed power sources, flexible multi-state switches and energy storage systems;

[0091] The operation optimization model construction module is used to construct a two-stage source-grid-storage distributed blue-rod operation optimization model with conditional risk value;

[0092] The operation scheduling scheme acquisition module uses the column and constraint generation algorithm to solve the two-stage distributed robust operation optimization model to obtain the operation scheduling scheme of the source network storage.

[0093] It should be noted that each module in the above-mentioned source-grid-storage two-stage distributed blue stick operation system considering risk aversion can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of a source-grid-storage two-stage distributed blue stick operation system considering risk aversion, please refer to the above definition of a source-grid-storage two-stage distributed blue stick operation method considering risk aversion. The two have the same functions and effects, which will not be repeated here.

[0094] It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A two-stage source-grid-storage distributed blue-rod operation method considering risk aversion, characterized in that: include: Obtain the operation data of power system source, grid and storage; Establish a source-grid-storage mathematical model that includes distributed power sources, flexible multi-state switches, and energy storage systems; A two-stage distributed blue-robust operation optimization model with conditional risk value is constructed. The day-ahead operation optimization is the first stage, and the intraday real-time optimization is the second stage. The sum of the operation cost of the first stage, the adjustment cost of the second stage and its conditional risk value is used as the objective function. A probability distribution fuzzy set of the output forecast error of distributed power generation is constructed based on the norm distance, and the boundary of the fuzzy set is determined by data-driven method. The column and constraint generation algorithm is used to solve the two-stage distributed robust operation optimization model to obtain the operation scheduling plan of the source, network and storage.

2. The source-grid-storage two-stage distributed blue rod operation method according to claim 1 is characterized in that: The operating data include wind and solar load day forecast data, power system line network parameters, time-of-use electricity price parameters, flexible multi-state switch operating parameters and energy storage system operating parameters.

3. The source-grid-storage two-stage distributed blue rod operation method according to claim 1 is characterized in that: The source-grid-storage mathematical model includes active power constraints and reactive power constraints of the source-grid-storage under long-term scheduling.

4. The source-grid-storage two-stage distributed blue rod operation method according to claim 3 is characterized in that: The source-grid-storage mathematical model is specifically: Where, t represents the time; for the flexible multi-state switch SOP, i and j represent the node numbers of the distribution network connected to both sides of the flexible multi-state switch SOP; Respectively represent the active power output on both sides of the SOP; Respectively represent the reactive power output on both sides of the SOP; Respectively represent the converter capacity on both sides of SOP; Respectively represent the active power loss on both sides of the SOP; Respectively represent the loss coefficients on both sides of the SOP; μ i , μ j Respectively represent the reactive power constraint coefficient of the voltage source converter VSC on both sides of the SOP; for the energy storage system, Respectively represent the charging and discharging flags of the energy storage system; and are the charging and discharging power and the maximum charging and discharging power of the energy storage system, respectively. and are the storage energy, minimum and maximum storage energy of the energy storage system respectively. and The initial storage energy and final storage energy of the energy storage system. and are the charging and discharging efficiency of the energy storage system respectively; Respectively represent the active power and reactive power output of wind power; Respectively represent the active power and reactive power of photovoltaic output; Represent the capacity of wind power and photovoltaic power respectively.

5. The source-grid-storage two-stage distributed blue rod operation method according to claim 1, characterized in that: The construction of the source-grid-storage two-stage distributed robust operation optimization model includes two-stage objective function setting, constraint condition setting and fuzzy set boundary determination.

6. The source-grid-storage two-stage distributed blue rod operation method according to claim 5, characterized in that: The setting process of the two-stage objective function is as follows: The objective function of the first stage is the operating cost, and the objective function of the second stage is the sum of the adjustment cost and its conditional risk value CVaR. The mathematical expression is as follows: A(x)≤0 B(x,y,ξ)≤0 In the formula, s, p s represents the sth operation scenario and the probability under the sth scenario, m represents the number of scenarios; x represents the first-stage decision variables, including the 0-1 variable of the energy storage operation state; y represents the second-stage decision variables, including the actual output power of the distributed generation, flexible multi-state switch and energy storage system; F ahead (x), F Intraday (y s ) represent the objective functions of the first and second stages respectively; ξ represents the uncertain variable, i.e., the wind and solar load output prediction error; p represents the probability distribution function obeyed by the uncertain variable; represents a fuzzy set; E p (·) represents the expectation, and represents the adjustment cost caused by the uncertainty of wind and solar load output; A(x) represents the first-stage model operation constraints, and B(x, y, ξ) represents the second-stage model operation constraints; C risk represents the CVaR caused by the forecast error of wind and solar load output; λ∈[0,1] represents the risk preference factor, the larger the λ, the higher the risk; α represents the risk value; β represents the confidence level; G(x,ξ s ) represents the second stage optimization problem.

7. The source-grid-storage two-stage distributed blue rod operation method according to claim 6 is characterized in that: The F ahead (x), F Intraday (y s ) represent the first-stage and second-stage optimization problems respectively, and the specific calculation formula is as follows: In the formula, Represents the depreciation cost caused by the change of charging and discharging state of the energy storage system; c t represents the electricity price at time t, P s,t,sub represents the power purchased by the power system from the upper power grid at the tth moment in the sth scenario; X represents the value set of the decision variables in the first stage; Ω ESS Indicates the set of nodes connected to the ESS.

8. The source-grid-storage two-stage distributed blue rod operation method according to claim 7 is characterized in that: The two-stage distributed blue-rod operation optimization model of source-grid-storage has constraints including the output constraints of distributed generation, flexible multi-state switch and energy storage system, and also satisfies the following power flow constraints: IN min ≤U s,t,i ≤U max -I ij,max ≤I s,t,ij ≤I ij,max P s,t,sub ≥0 Where: P s,t,ij , Q s,t,ij Represent the active power and reactive power of line ij respectively; P s,t,jk , Q s,t,jk Respectively represent the active power and reactive power of line jk; P s,t,i , Q s,t,i Respectively represent the active power and reactive power flowing into node i; r ij represents the resistance of line ij; x ij represents the reactance of line ij; They represent the active power and reactive power of the load of node i at time t respectively; U s,t,i , U s,t,j U represents the voltage of nodes i and j respectively; min , U max ,I ij,max They represent the minimum node voltage, the maximum node voltage, and the maximum line current respectively; I s,t,ij Represents the current of line ij at time t; Ω b Represents a collection of distribution network branches; Respectively represent the active power of wind power, photovoltaic power and energy storage; Represent the reactive power of wind power, photovoltaic power and energy storage respectively.

9. The source-grid-storage two-stage distributed blue rod operation method according to claim 8, characterized in that: The process of determining the boundary of the fuzzy set is as follows: In the s clustering scenario, a probability distribution fuzzy set is constructed based on the norm distance, and the fuzzy set boundary is determined in a data-driven way. Its mathematical expression is as follows: In the formula, β1 and β ∞ Indicates the confidence level of the confidence interval; d1 and d ∞ Represents the 1-norm distance and ∞-norm distance of the confidence interval; R + represents a positive real number; represents the reference probability distribution.

10. A two-stage source-grid-storage distributed blue stick operation system considering risk aversion, characterized in that: include: An operation data acquisition module is used to obtain the operation data of the power system source, network and storage; The source-grid-storage mathematical model building module is used to build a source-grid-storage mathematical model that includes distributed power sources, flexible multi-state switches and energy storage systems; The operation optimization model construction module is used to construct a two-stage source-grid-storage distributed blue-rod operation optimization model with conditional risk value; The operation scheduling scheme acquisition module uses the column and constraint generation algorithm to solve the two-stage distributed robust operation optimization model to obtain the operation scheduling scheme of the source network storage.

Images