Power grid operation adjustment method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power grid operation adjustment method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a power grid operation optimization model is acquired, and the power grid operation optimization model is determined based on a power grid operation objective function and constraint conditions; the constraint conditions comprise a first constraint condition and a second constraint condition, and the second constraint condition comprises a budget parameter of photovoltaic adjustable uncertainty in the micro-grid and tie line transmission power between the active power distribution network and the micro-grid; and solving the power grid operation optimization model to obtain a target operation parameter, and adjusting the operation of the power grid based on the target operation parameter. According to the power grid operation optimization model, the data privacy problem of the active power distribution network and the micro-grid is considered at the same time, the uncertainty of new energy in the micro-grid is considered, data can be protected against leakage and tampering during collaborative optimization of the active power distribution network and the micro-grid, and safe operation of the active power distribution network and the micro-grid can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for adjusting power grid operation. Background Art

[0002] With the accelerated construction of new power systems, high-proportion distributed renewable energy (such as distributed generation (DG) and distributed photovoltaic power generation) with high flexibility, low investment, cleanness and efficiency is widely used. However, these resources are random and uncontrollable, and large-scale application will bring great challenges to the operation of traditional power grids. In order to solve these problems, the concept of microgrids was born. At the same time, the application of microgrids solves the problem of grid connection of distributed power sources with flexibility, large quantity and diversity. The development of microgrids in my country has gone through several stages, from initially providing microgrid power services to local users, to now achieving improved power quality and safety, to the increase in the scale of microgrid industry, standardization and improvement of standards, and large-scale microgrids provide auxiliary services for regional power grids. At present, microgrids have achieved the ability of self-healing and autonomous governance.

[0003] Microgrids can operate in an isolated manner or in a grid-connected manner to provide power and auxiliary services for regional distribution networks. Microgrids and active distribution networks belong to different interest entities, and the coordinated optimization of microgrids and active distribution networks is closer. At the same time, there is also a competitive relationship between the two and the value of power data is becoming increasingly prominent. In the process of coordinated optimization of microgrids and distribution networks, effective measures need to be taken to protect these data from being illegally obtained, leaked or abused. In addition, the new energy within the microgrid has strong uncertainty. Considering the uncertainty within the microgrid helps to improve the safety of microgrid and distribution network operation. Therefore, it is of great significance to study the distributed robust optimization method of active distribution networks considering data privacy.

[0004] The model of distributed robust optimization of active distribution networks considering data privacy is a widely studied issue by scholars around the world. There are usually two cases of distributed optimization models of active distribution networks considering data privacy. The first is a deterministic model, that is, the uncertainty of renewable energy within the microgrid is not considered. This model usually only considers the data privacy problem between the microgrid and the distribution network when they are coordinated and optimized. The model is constructed as a multi-region model for coordinated optimization. Only the corresponding tie line power variables are exchanged between multiple regions, and the parameters between regions are not exchanged. However, this type of model does not consider the uncertainty of renewable energy within the microgrid. For this type of model, the alternating direction method of multipliers (ADMM) is generally used to solve it; the second type is a centralized uncertainty model, which is usually described by a two-stage robust optimization model. In the second stage, random optimization methods and robust optimization methods are used to deal with the uncertainty of renewable energy such as wind power and photovoltaics. For random optimization algorithms, typical scenarios are usually used to characterize the uncertainty of renewable energy. However, the increase in the number of scenarios will significantly reduce the efficiency of algorithm solution. The solution of the centralized method assumes that the information of the microgrid and the active distribution network can be exchanged, and this method does not consider the data privacy protection issue between the two.

[0005] In the first type of deterministic model of the distributed optimization model of the active distribution network considering data privacy, the focus is on the internal data privacy issues between multi-regional models, and the uncertainty of the new energy output of the microgrid is not considered; in the second type of uncertainty model, the random optimization algorithm may require more iterations and computing resources when searching for the optimal solution due to its randomness, resulting in a relatively slow search process, and the quality of its solution may be unstable, sometimes falling into the local optimal solution and difficult to find the global optimal solution, and the algorithm solution efficiency is significantly reduced as the number of scenarios increases. However, since the uncertainty of new energy is considered in the construction of the model of distributed optimization of the active distribution network considering data privacy, the difficulty of solving the model becomes greater. This type of uncertainty model generally does not consider the data privacy issues of the microgrid and the active distribution network.

[0006] The current grid operation adjustment methods only consider the data privacy of microgrids and active distribution networks or only consider the uncertainty of renewable energy output within microgrids. Summary of the invention

[0007] Based on this, it is necessary to provide a grid operation adjustment method, device, computer equipment, computer-readable storage medium and computer program product that can simultaneously consider the data privacy of the microgrid and the active distribution network and the uncertainty of the new energy output within the microgrid to address the above-mentioned technical problems.

[0008] In a first aspect, the present application provides a method for adjusting power grid operation, the method comprising:

[0009] Obtaining a power grid operation optimization model, wherein the power grid operation optimization model is determined based on an objective function and constraint conditions of power grid operation; the objective function is determined based on operating parameters of the active distribution network and operating parameters of the microgrid; the constraint conditions include a first constraint condition and a second constraint condition, and the second constraint condition includes a budget parameter of photovoltaic adjustable uncertainty in the microgrid and a transmission power of a tie line between the active distribution network and the microgrid;

[0010] The power grid operation optimization model is solved to obtain target operation parameters, and the operation of the power grid is adjusted based on the target operation parameters.

[0011] In one embodiment, the objective function of the power grid operation is determined by:

[0012] Obtaining the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operation loss value of the distributed power source in the microgrid, and the abandonment penalty value of photovoltaic power generation;

[0013] The objective function of the grid operation is determined based on the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of the distributed generation in the microgrid and the abandonment penalty value of the photovoltaic power generation.

[0014] In one embodiment, the first constraint condition is determined by:

[0015] Obtaining a linear AC power flow equation, a distribution network node voltage constraint, a static VAR generator reactive power constraint, and a distribution network line current constraint in the active distribution network;

[0016] Determining the first constraint condition according to the linear AC power flow equation, the distribution network node voltage constraint, the static VAR generator reactive power constraint and the distribution network line current constraint;

[0017] The second constraint condition is generated in a manner including:

[0018] Obtain the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, distributed power source active output, distributed power source reactive output, distributed photovoltaic output uncertainty set, microgrid power balance equation and system coupling constraints in the microgrid; wherein the distributed photovoltaic output uncertainty set includes the budget parameters of photovoltaic adjustable uncertainty; the microgrid power balance equation includes the transmission power of the tie line between the active distribution network and the microgrid;

[0019] The second constraint condition is determined based on the upper and lower limit constraints of the distributed photovoltaic active output in the microgrid, the upper and lower limit constraints of the distributed photovoltaic reactive output, the upper and lower limit constraints of the distributed power source active output, the upper and lower limit constraints of the distributed power source reactive output, the distributed photovoltaic output uncertainty set, the microgrid power balance equation and the system coupling constraints.

[0020] In one embodiment, solving the power grid operation optimization model to obtain target operation parameters includes:

[0021] Decomposing the power grid operation optimization model into a first power grid operation optimization model and a second power grid operation optimization model by an alternating direction multiplier method;

[0022] The first power grid operation optimization model is converted into a first target model and the second power grid operation optimization model is converted into a second target model by using a column-row generation algorithm;

[0023] Solving the first target model and the second target model, and if the solution results meet a first preset accuracy, using the solution results as the first target results; wherein the first preset accuracy is determined based on the column-row generation algorithm;

[0024] Based on the first target result, determining a second target result of the power grid operation optimization model;

[0025] In a case where the second target result satisfies a second preset accuracy, a target operating parameter is determined based on the second target result, wherein the second preset accuracy is determined based on the alternating direction multiplier method.

[0026] In one embodiment, the method further comprises:

[0027] When the second target result does not meet the second preset accuracy, updating the tie line power of the power grid operation optimization model and the Lagrange multiplier of the power grid operation optimization model;

[0028] Determining a second target result of the power grid operation optimization model based on the first target result includes:

[0029] Based on the first target result, the updated tie line power and the updated Lagrange multiplier, a second target result of the power grid operation optimization model is determined.

[0030] In one embodiment, solving the first target model and the second target model, and taking the solution result as the first target result when the solution result satisfies a first preset accuracy, includes:

[0031] Solving the first target model to obtain a first target solution, and updating a lower limit value of an output result of an objective function in the first target model by using the first target solution;

[0032] Solving the second objective model to obtain a second objective solution, and updating an upper limit value of an output result of an objective function in the second objective model by using the second objective solution;

[0033] If the difference between the upper limit value and the lower limit value of the target value is greater than the first preset accuracy, the target constraint condition in the second target model is added to the first target model to form a new first target model; continue to solve the new first target model to obtain the first target solution, and update the lower limit of the target value of the first target model through the first target solution; solve the second target model to obtain the second target solution, and update the upper limit value of the target value of the second target model through the second target solution; until the difference between the upper limit value and the lower limit value is less than or equal to the first preset accuracy, wherein the target constraint condition is determined based on the cutting plane constraint.

[0034] In a second aspect, the present application further provides a power grid operation adjustment device, the device comprising:

[0035] An acquisition module is used to acquire a power grid operation optimization model, wherein the power grid operation optimization model is determined based on an objective function and constraint conditions of power grid operation; the objective function is determined based on operating parameters of an active distribution network and operating parameters of a microgrid; the constraint conditions include a first constraint condition and a second constraint condition, wherein the second constraint condition is determined based on an uncertain parameter of a distributed photovoltaic power generation output in the microgrid and a transmission power of a tie line between the main distribution network and the microgrid;

[0036] The solution module is used to solve the power grid operation optimization model, obtain target operation parameters, and adjust the operation of the power grid based on the target operation parameters.

[0037] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0039] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0040] The above-mentioned grid operation adjustment method, device, computer equipment, computer readable storage medium and computer program product first obtain a grid operation optimization model, wherein the grid operation optimization model is determined based on the objective function and constraint conditions of grid operation; the objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid; the constraint conditions include the first constraint condition and the second constraint condition, the second constraint condition includes the budget parameter of the photovoltaic adjustable uncertainty in the microgrid and the transmission power of the tie line between the active distribution network and the microgrid; the uncertainty of the new energy inside the microgrid in the grid operation optimization model is determined by the budget parameter of the photovoltaic adjustable uncertainty in the microgrid; the power information is transmitted between the active distribution network and the microgrid through the tie line between the active distribution network and the microgrid, so as to consider the data privacy of the active distribution network and the microgrid. Secondly, by solving the grid operation optimization model, the target operating parameters are obtained, and the operation of the grid is adjusted based on the target operating parameters, which can protect the data from leakage and tampering when the active distribution network and the microgrid are coordinated and optimized, and can also ensure the safe operation of the active distribution network and the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 An application environment diagram of a power grid operation adjustment method in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a method for adjusting power grid operation in one embodiment;

[0044] Figure 3 A flow chart of a method for determining an objective function of a power grid operation in one embodiment;

[0045] Figure 4 A schematic diagram of the voltage amplitudes of various nodes within a power distribution network in one embodiment;

[0046] Figure 5 is a flow chart of a method for determining a first constraint condition in an embodiment;

[0047] Figure 6 A schematic diagram of a daily load curve of a distribution network in one embodiment;

[0048] Figure 7 A schematic diagram of a microgrid load curve and a photovoltaic power generation output curve in an embodiment;

[0049] Figure 8a A schematic diagram of the maximum available active power and planned active output of photovoltaic power generation in a microgrid when the budget parameter of photovoltaic adjustable uncertainty is 0 in an embodiment;

[0050] Figure 8b A schematic diagram of the maximum available active power and planned active output of photovoltaic power generation in a microgrid when the budget parameter of photovoltaic adjustable uncertainty is 4 in an embodiment;

[0051] Figure 8c A schematic diagram of the maximum available active power and planned active output of photovoltaic power generation in a microgrid when the budget parameter of photovoltaic adjustable uncertainty is 8 in an embodiment;

[0052] Fig. 9 A schematic diagram of a process for solving a power grid operation optimization model in one embodiment;

[0053] Fig.10 is a convergence diagram of the ADMM algorithm in one embodiment;

[0054] Fig.11 A schematic diagram of a flow chart for solving a power grid operation optimization model in another embodiment;

[0055] Fig.12 A C&CG algorithm convergence curve diagram in one embodiment;

[0056] Fig.13 It is a structural block diagram of a power grid operation adjustment device in one embodiment;

[0057] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The power grid operation adjustment method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1The invention relates to a power grid operation system for an 18-node distribution network including one microgrid. The terminal or server obtains a power grid operation optimization model, wherein the power grid operation optimization model is determined based on the objective function and constraint conditions of the power grid operation; the objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid; the constraint conditions include a first constraint condition and a second constraint condition, and the second constraint condition includes the budget parameter of the photovoltaic adjustable uncertainty in the microgrid and the transmission power of the tie line between the active distribution network and the microgrid; the power grid operation optimization model is solved to obtain the target operating parameters, and the operation of the power grid is adjusted based on the target operating parameters.

[0060] In an exemplary embodiment, Figure 2 As shown, a method for adjusting power grid operation is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps S202 to S204. Among them:

[0061] Step S202, obtaining a power grid operation optimization model.

[0062] Among them, the grid operation optimization model is determined based on the objective function and constraints of the grid operation; the grid operation optimization model is a two-stage distributed robust optimization model. Among them, the first stage is to minimize the interactive product loss value of the active distribution network and the voltage deviation of each node inside the active distribution network. The second stage is to minimize the operating loss value of distributed photovoltaic power generation inside the microgrid and the penalty value of photovoltaic power generation abandonment in the worst scenario of photovoltaic power generation output. The objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid; the constraints include the first constraint and the second constraint, and the second constraint includes the budget parameters of the photovoltaic adjustable uncertainty in the microgrid and the transmission power of the interconnection line between the active distribution network and the microgrid.

[0063] Optionally, before the server obtains the power grid operation optimization model, it generates a power grid operation optimization model, wherein the power grid operation optimization model is determined based on the objective function and constraints of the power grid operation. The objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid. The constraints include a first constraint and a second constraint. The first constraint includes a linear AC power flow equation in the active distribution network, a voltage constraint of the distribution network node, a reactive output constraint of the static VAR generator, and a current constraint of the distribution network line. The second constraint includes the budget parameter of the photovoltaic adjustable uncertainty in the microgrid and the transmission power of the tie line between the active distribution network and the microgrid, and may also include distributed photovoltaic active upper and lower limits, reactive upper and lower limits, and distributed power source active output upper and lower limits constraints, distributed power source reactive output upper and lower limits constraints; microgrid power balance equation constraints. The server determines the power grid operation optimization model based on the first constraint, the second constraint, and the objective function. Among them, the first constraint and the second constraint take into account the linear AC power flow equation of the distribution network, the node voltage constraint of the distribution network and the reactive output constraint of SVG, the line current constraint of the distribution network, and take into account the upper and lower limit constraints of the active and reactive output of distributed photovoltaic power generation in the microgrid, the upper and lower limit constraints of the active and reactive output of DG, the uncertainty set of distributed photovoltaic output, the microgrid power balance equation and the system coupling constraint.

[0064] Optionally, the terminal may generate the power grid operation optimization model and send it to the server, and the server may obtain the power grid operation optimization model.

[0065] Step S204, solving the power grid operation optimization model, obtaining target operation parameters, and adjusting the operation of the power grid based on the target operation parameters.

[0066] Optionally, the server decomposes the established two-stage distributed robust optimization model, that is, the power grid operation optimization model, into two sub-models through the ADMM algorithm: the first power grid operation optimization model is an active distribution network operation optimization problem; the second power grid operation optimization model is a microgrid operation optimization problem considering the uncertainty of new energy, wherein the second power grid operation optimization model is also a two-stage robust optimization problem). Afterwards, the column and constraint generation (C&CG) algorithm is used to generate the second power grid operation optimization model, that is, the microgrid operation optimization problem considering the uncertainty of new energy, until the C&CG algorithm iteratively converges. Finally, the distribution network operation optimization model is solved, and the Lagrange multipliers and the interconnection line interaction power are updated until the ADMM algorithm meets the convergence conditions.

[0067] In the above-mentioned grid operation adjustment method, firstly, a grid operation optimization model is obtained, wherein the grid operation optimization model is determined based on the objective function and constraint conditions of grid operation; the objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid; the constraint conditions include the first constraint condition and the second constraint condition, and the second constraint condition includes the budget parameter of the photovoltaic adjustable uncertainty in the microgrid and the transmission power of the interconnection line between the active distribution network and the microgrid; the uncertainty of the new energy inside the microgrid in the grid operation optimization model is determined by the budget parameter of the photovoltaic adjustable uncertainty in the microgrid; the power information is transmitted between the active distribution network and the microgrid through the interconnection line between the active distribution network and the microgrid, so as to consider the data privacy of the active distribution network and the microgrid. Secondly, by solving the grid operation optimization model, the target operating parameters are obtained, and the operation of the grid is adjusted based on the target operating parameters, which can not only protect the data from leakage and tampering when the active distribution network and the microgrid are coordinated and optimized, but also ensure the safe operation of the active distribution network and the microgrid.

[0068] In an exemplary embodiment, Figure 3 As shown, the method for determining the objective function of the power grid operation includes steps S302 to S304. Among them:

[0069] Step S302, obtaining the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operation loss value of the distributed power source in the microgrid, and the abandonment penalty value of photovoltaic power generation.

[0070] Among them, the interactive product loss value of the active distribution network is calculated by the interactive product loss value coefficient of the active distribution network. and interactive product quantity determination The node voltage deviation of the active distribution network is calculated by the voltage amplitude at the node j of the active distribution network. Determine, where active distribution network node j belongs to the set of active distribution network nodes The operating loss value of distributed power in the microgrid is calculated by the distributed power generation loss value coefficient. and the active output of distributed power sources The penalty value of photovoltaic power generation abandonment is calculated by the penalty cost coefficient of photovoltaic power generation abandonment. , the maximum available active power output of photovoltaic power generation at node j and the planned active power output of photovoltaic power generation at node j .

[0071] Optionally, the server interacts with the product loss value coefficient through the active distribution network and the number of interactive products Determine the interactive product loss value of the active distribution network, recorded as item (1). The voltage amplitude at node j of the active distribution network Determine the node voltage deviation of the active distribution network, recorded as item (2); where the active distribution network node j belongs to the set of active distribution network nodes . The value coefficient of the power generation loss through distributed power generation and the active output of distributed power sources Determine the operating loss value of the distributed power generation in the microgrid, recorded as item (3). , the maximum available active power output of photovoltaic power generation at node j and the planned active power output of photovoltaic power generation at node j Determine the penalty value for abandoned photovoltaic power generation, recorded as item (4).

[0072] The server obtains the interactive product loss value (1) of the active distribution network, the node voltage deviation (2) of the active distribution network, the operating loss value (3) of the distributed power source in the microgrid, and the penalty value (4) of photovoltaic power generation, adds (1)-(4), and takes the minimum value after addition as the objective function.

[0073] In practical applications, taking an 18-node distribution network containing a microgrid as an example, the voltage amplitude of each node in the active distribution network is as follows: Figure 4 As shown in the figure, the upper limit of the voltage amplitude is 1.040, and the lower limit of the voltage amplitude is 0.960. The distribution of voltages at each node in the distribution network is within the upper and lower limits.

[0074] Step S304, determining the objective function of the grid operation based on the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of the distributed generation in the microgrid and the penalty value of the photovoltaic power generation.

[0075] Optionally, the server forms an objective function based on the acquired interactive product loss value (1) of the active distribution network, the node voltage deviation (2) of the active distribution network, the operating loss value (3) of the distributed power source in the microgrid, and the abandonment penalty value (4) of photovoltaic power generation, as shown in formula (1).

[0076] Formula (1)

[0077] In the formula, It represents the loss value coefficient of the active distribution network interaction product; Indicates the number of interactive products; represents the voltage amplitude at node j of the active distribution network; Indicates the value coefficient of distributed generation loss; Indicates the active output of distributed power sources; It represents the penalty cost coefficient for photovoltaic power generation abandonment; represents the maximum available active power output of photovoltaic power generation at node j; The planned active power output of photovoltaic power generation at node j; subscript t represents the time period index; subscript j represents the node index of the active distribution network; T represents the set of time periods; NDG represents the set of DGs in the microgrid; NPV represents the set of photovoltaic power generation in the distribution network.

[0078] In this embodiment, a two-stage distributed robust optimization model that considers both data privacy and the uncertainty of photovoltaic power generation output is established. The first stage is to minimize the loss value of the interactive products of the active distribution network and the voltage deviation of each node within the active distribution network. The second stage is to minimize the operating loss value of distributed photovoltaic power generation within the microgrid and the penalty value of photovoltaic power generation abandonment in the worst photovoltaic power generation output scenario.

[0079] In an exemplary embodiment, Figure 5 As shown, the method for determining the first constraint condition includes steps S502 to S504. Among them:

[0080] Step S502, obtaining a linear AC power flow equation in the active distribution network, a distribution network node voltage constraint, a static VAR generator reactive power constraint, and a distribution network line current constraint.

[0081] Optionally, the server is based on the parent node set of node i in the power grid , represents the sub-node set of node i in the distribution network , the reactance X of the distribution network feeder ij ij , the resistance R of the distribution network feeder ij ij , the net active power injected by node i is P i,t , node i net injected reactive power Q i,t , node i active load P d,i,t , node i reactive load Q d,i,t Determine the linear AC power flow equation. The daily load curve of the distribution network is also the 24-hour load curve; Figure 6 As shown, it includes active power and reactive power.

[0082] The lower limit of the square of the node voltage amplitude The upper limit of the square of the node voltage amplitude Determine the voltage amplitude at distribution network node j, that is, the distribution network node voltage constraint.

[0083] Through the static reactive power generator reactive power lower limit and the upper limit of reactive power output of static VAR generator Determine the reactive power output constraints of the static VAR generator.

[0084] By P ij,t represents the active power of the distribution network feeder ij, Qij,t Represents the reactive power of the distribution network feeder ij, determines the distribution network line current constraint, that is, the maximum value of the square of the line current .

[0085] Step S504, determining a first constraint condition according to a linear AC power flow equation, a distribution network node voltage constraint, a static VAR generator reactive power constraint, and a distribution network line current constraint.

[0086] Optionally, the server determines the first constraint condition according to a linear AC power flow equation, a distribution network node voltage constraint, a static VAR generator reactive power constraint, and a distribution network line current constraint.

[0087] Among them, the linear AC power flow equation, as shown in formula (2a)-formula (2e), includes:

[0088] Formula (2a)

[0089] Formula (2b)

[0090] Formula (2c)

[0091] Formula (2d)

[0092] Formula (2e)

[0093] In the formula, Represents the parent node set of node i in the distribution network; represents the set of child nodes of node i in the distribution network; P ij，t represents the active power of the distribution network feeder ij; P ik,t represents the active power of the distribution network feeder ik; Q ij，t represents the reactive power of the distribution network feeder ij; Q ik,t represents the reactive power of the distribution network feeder ik; X ij represents the reactance of the distribution network feeder ij; R ij represents the resistance of the distribution network feeder ij; P i,t represents the net injected active power of node i; P d,i,t represents the active load of node i; represents the voltage amplitude at node j of the active distribution network; V i,t represents the voltage amplitude at node i of the active distribution network; P dg,i,t represents the active output of distributed generation at node i; P pv,i,t represents the planned active power output of photovoltaic power generation at node i; Q i,t represents the net reactive power injected by node i; Qdg,i,t represents the reactive power output of distributed generation at node i; Q pv,i,t represents the planned reactive power output of photovoltaic power generation at node i; Q cb,i,t represents the reactive output of the capacitor bank at node i; Q d,i,t Represents the reactive load of node i.

[0094] The voltage constraints of the distribution network nodes, as shown in formula (3), include:

[0095] Formula (3)

[0096] In the formula, Represents the lower limit of the square of the node voltage amplitude; Represents the upper limit of the square of the node voltage amplitude; Represents the voltage amplitude at node j of the active distribution network.

[0097] The SVG reactive power output constraint, as shown in formula (4), includes:

[0098] Formula (4)

[0099] In the formula, Indicates the lower limit of reactive power output of static VAR generator SVG; Indicates the upper limit of SVG reactive output; Indicates the reactive power output of SVG.

[0100] The distribution network line current constraint, as shown in formula (5), includes:

[0101] Formula (5)

[0102] In the formula, Indicates the maximum value of the square of the line current. ij,t represents the active power of the distribution network feeder ij, Q ij,t Represents the reactive power of distribution network feeder ij.

[0103] The method for generating the second constraint condition includes steps S506 to S508. Among them:

[0104] Step S506, obtaining the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, distributed power source active output, distributed power source reactive output, distributed photovoltaic output uncertainty set, microgrid power balance equation and system coupling constraints in the microgrid; wherein the distributed photovoltaic output uncertainty set includes the budget parameters of photovoltaic adjustable uncertainty; the microgrid power balance equation includes the transmission power of the interconnection line between the active distribution network and the microgrid.

[0105] Optionally, the server obtains upper and lower limit constraints of distributed photovoltaic active output, upper and lower limit constraints of distributed photovoltaic reactive output, upper and lower limit constraints of distributed power source active output, upper and lower limit constraints of distributed power source reactive output, distributed photovoltaic output uncertainty set, microgrid power balance equation and system coupling constraints in the microgrid; wherein, the distributed photovoltaic output uncertainty set includes the budget parameters of photovoltaic adjustable uncertainty; the microgrid power balance equation includes the transmission power of the interconnection line between the active distribution network and the microgrid.

[0106] In practical applications, the microgrid load curve and photovoltaic power generation output curve within 24 hours are as follows: Figure 7 As shown. Under different budget parameters of the photovoltaic adjustable uncertainty in the microgrid, the maximum available active power of photovoltaic power generation in the microgrid is different from the planned active output, as shown in Figures 8 (a), 8 (b), and 8 (c), which are the maximum available active power of photovoltaic power generation in the microgrid and the planned active output under three uncertain parameters. It can be seen from the figure that the relationship between the maximum available active power of photovoltaic power generation in the microgrid and its planned active output under three uncertain parameters. As can be seen from Figure 8 (a), when the budget parameter of the photovoltaic adjustable uncertainty is When , the maximum available active power of photovoltaic power generation is the same as the planned active power output, which also shows that the photovoltaic power generation prediction is very accurate in this case. As can be seen from Figure 8(b), when the budget parameter of the photovoltaic adjustable uncertainty is When , the maximum available active power of photovoltaic power generation is greater than the planned active power output in the 12th, 13th, 14th and 15th time periods under the worst active power output scenario. As shown in Figure 8(c), when the budget parameter of the photovoltaic adjustable uncertainty is , its maximum available active output is greater than its planned active output in 8 periods. Therefore, it can be seen from the above figure that when the uncertainty parameter is 0, there is no abandonment of photovoltaic power generation inside the microgrid. However, as the uncertainty parameter gradually increases, the photovoltaic abandonment period will increase, and the maximum available active output of photovoltaics will also fluctuate violently, resulting in more photovoltaic abandonment, which will also increase the penalty value of abandonment.

[0107] Step S508, determine the second constraint condition based on the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, upper and lower limit constraints of distributed power source active output, upper and lower limit constraints of distributed power source reactive output, distributed photovoltaic output uncertainty set, microgrid power balance equation and system coupling constraints in the microgrid.

[0108] The second constraint condition includes: the upper and lower limit constraints of the distributed photovoltaic active output in the microgrid, as shown in formula (6), including:

[0109] Formula (6)

[0110] Where P pv,j,t represents the planned active power output of photovoltaic power generation at node j; Represents the maximum available active power output of photovoltaic power generation at node j.

[0111] The upper and lower limits of the active power output of distributed generation are shown in formula (7a), and the upper and lower limits of the reactive power output of distributed generation are shown in formula (7b):

[0112] Formula (7a)

[0113] Formula (7b)

[0114] In the formula, and They represent the lower and upper limits of the reactive power output of distributed generation at node j respectively; Represents the reactive power output of distributed power sources; and They represent the lower and upper limits of the active output of distributed generation at node j respectively; Represents the active output of distributed power sources.

[0115] The uncertainty set of distributed photovoltaic output is shown in formula (8):

[0116] Model the uncertainty in photovoltaics using a box uncertainty set:

[0117] Formula (8)

[0118] In the formula, It is the predicted active output of photovoltaic power generation; is the maximum prediction error of photovoltaic power generation; and is the auxiliary variable of the PV output at the interval boundary; is the budget parameter of the PV adjustable uncertainty, and u represents the uncertainty set.

[0119] The microgrid power balance equation is shown in formula (9a) and formula (9b):

[0120] Formula (9a)

[0121] Formula (9a)

[0122] In the formula, and Represent the active and reactive power of the tie line respectively; and They represent the active and reactive loads of the microgrid respectively.

[0123] The power transmission between the distribution network and the microgrid is carried out through the tie line. The power transmission of the tie line must meet the following constraints, that is, the system coupling constraints, as shown in formulas (10a) and (10b):

[0124] Formula (10a)

[0125] Formula (10b)

[0126] Where P line,j,t represents the active power of the tie line at node j; Q line,j,t Represents the tie line reactive power at node j.

[0127] In this embodiment, the second constraint condition related to the microgrid is determined. The uncertainty of the new energy in the microgrid in the grid operation optimization model is determined by the budget parameter of the photovoltaic adjustable uncertainty in the microgrid; the power information is transmitted between the active distribution network and the microgrid through the interconnection line between the active distribution network and the microgrid to consider the data privacy of the active distribution network and the microgrid.

[0128] In an exemplary embodiment, Fig. 9 As shown, solving the power grid operation optimization model to obtain the target operation parameters includes steps S902 to S910. Among them:

[0129] Step S902 : decomposing the power grid operation optimization model into a first power grid operation optimization model and a second power grid operation optimization model by using an alternating direction multiplier method.

[0130] Optionally, the server decomposes the power grid operation optimization model into a first power grid operation optimization model and a second power grid operation optimization model through an augmented Lagrangian function.

[0131] Among them, the first power grid operation optimization model is obtained based on the active distribution network operation parameters, the objective function is shown in formula (11a), and the constraint conditions are shown in formula (11b).

[0132] Formula (11a)

[0133] st Eq.(2)–Eq.(5) Formula (11b)

[0134] Eq.(2)–Eq.(5) are as follows:

[0135] Formula (2a)

[0136] Formula (2b)

[0137] Formula (2c)

[0138] Formula (2d)

[0139] Formula (2e)

[0140] Formula (3)

[0141] Formula (4)

[0142] Formula (5)

[0143] In the formula, and is the corresponding Lagrange multiplier; and is the penalty factor; and is a penalty term. The loss value of active distribution network interaction products includes: represents the loss value coefficient of interactive products; Represents the number of interactive products; and Represent the active and reactive power of the tie line respectively.

[0144] The second grid operation optimization model is obtained based on the microgrid operation parameters. The objective function is shown in formula (11a), and the constraints are shown in formula (11b).

[0145] Formula (12a)

[0146] st Eq.(6)–Eq.(10) Formula (12b)

[0147] Eq. (6)–Eq. (10) are as follows:

[0148] Formula (6)

[0149] Formula (7a)

[0150] Formula (7b)

[0151] Formula (8)

[0152] Formula (9a)

[0153] Formula (9a)

[0154] Formula (10a)

[0155] Formula (10b)

[0156] Step S904: transforming the first power grid operation optimization model into a first target model and transforming the second power grid operation optimization model into a second target model through a column-row generation algorithm.

[0157] Optionally, the server converts the first power grid operation optimization model into a first target model, that is, a linear programming problem, through the column and row generation algorithm C&CG, and uses the first target model as the main linear programming problem. The server converts the second power grid operation optimization model into a second target model, and uses the second target model as a linear programming sub-problem.

[0158] Among them, the first target model is shown in formula (13a)-formula (13e):

[0159] Formula (13a)

[0160] Formula (13b)

[0161] Formula (13c)

[0162] Formula (13d)

[0163] Formula (13e)

[0164] In the formula, C T represents the first-stage variable coefficient; y represents the first-stage variable; S y represents the set of first-stage variables y; A represents the first-stage variable coefficient; d represents the constant term; E represents the first-stage variable coefficient; h represents the constant term; M represents the uncertain variable coefficient u1 * indicates an uncertain variable; R indicates a real number set; S x represents the set of variables x; G represents the coefficient x of the x variable l represents the lth x variable; η is the objective function value of the second objective model; Formula (13c) represents b T x l is the objective function value of the second objective model corresponding to the lth scenario identified by the subproblem, l∈(1...r); therefore η must be greater than or equal to b T x l , and since the optimization direction of the main problem is to minimize the objective function, b T x lis the lower bound or lower limit of η, then η must be taken for all b T x l , so it is an equivalent transformation.

[0165] The second target model is shown in formula (14a)-formula (14e):

[0166] Formula (14a)

[0167] Formula (14b)

[0168] Formula (14c)

[0169] Formula (14d)

[0170] Formula (14e)

[0171] Where, (14d) is the complementary relaxation constraint; b T represents the coefficient of the x variable; G represents the coefficient of the x variable; x represents the second-stage variable; M represents the coefficient of u; u represents an uncertain variable; represents the Lagrange multiplier corresponding to inequality (14b).

[0172] The method for determining the second target model includes:

[0173] The server converts the second power grid operation optimization model into a second initial model. The second initial model, as shown in formula (15a) to formula (15c), converts the second initial model into a second target model.

[0174] Formula (15a)

[0175] Formula (15b)

[0176] Formula (15c)

[0177] In the formula, represents the Lagrange multiplier corresponding to inequality (15b); F(y,u) represents the objective function of the first stage.

[0178] For the two-stage robust optimization model, the second-stage distribution network security verification problem is a convex problem, and its inner-layer minimization problem is a linear programming model. Therefore, the second-stage max-min optimization problem can be transformed into a single-layer optimization problem, that is, a set of necessary conditions are used to convert the inner-layer minimization problem into the constraints of the outer-layer max problem, thereby converting the second-stage max-min problem into a single-layer max optimization problem. Then the subproblem SP can be converted into the following single-layer max problem, as shown in Formula (14a)-Formula (14e).

[0179] Step S906, solving the first target model and the second target model, and when the solution results meet the first preset accuracy, using the solution results as the first target results; wherein the first preset accuracy is determined based on the column-row generation algorithm.

[0180] Optionally, the server solves the first target model and the second target model by calling a tool such as MATLAB. The server first obtains the optimal solution of the first-stage decision variable y by solving the first target model. Then, the obtained value of the first-stage decision variable is passed as a parameter to the second target model. When the first-stage variable y is fixed, the second target model is solved to obtain the uncertain variable . Then the uncertain variable The first target model is passed as a parameter to the first target model until the iterative calculation between the first target model and the second target model converges, that is, the first preset accuracy is met.

[0181] Step S908: Determine a second target result of the power grid operation optimization model based on the first target result.

[0182] Optionally, the server obtains a first target result based on a column-row generation algorithm, and determines a second target result of the power grid operation optimization model according to the first target result.

[0183] Step S910, when the second target result satisfies a second preset accuracy, determining the target operating parameter based on the second target result, wherein the second preset accuracy is determined based on an alternating direction multiplier method.

[0184] Optionally, when the second target result satisfies a second preset accuracy, the target operating parameter is determined based on the second target result obtained by the alternating direction multiplication method.

[0185] The conditions for satisfying the second preset accuracy are shown in formula (16a) and formula (16b):

[0186] Formula (16a)

[0187] Formula (16b)

[0188] Among them, formula (16a) indicates that the difference of the interactive power of the coupling variable tie line between the distribution network and the node subject in the kth iteration should meet the accuracy requirement; formula (16b) indicates whether the overall benefit of the active distribution network distributed scheduling model considering the data privacy of the node subject is optimal; and represents the convergence accuracy; f 1 (y k * ) represents the first stage objective function of the kth iteration; f 1 (y k-1 * ) represents the first stage objective function of the k-1th iteration; f2(x k * ,u k * ) represents the second stage objective function of the kth iteration; f2(x k-1 * ,u k-1 * ) represents the second stage objective function of the k-1th iteration; x k * represents the second stage variable of the kth iteration; u k * represents the uncertain variable of the kth iteration; x k-1 * represents the second stage variable of the k-1th iteration; u k-1 * represents the uncertain variable of the k-1th iteration.

[0189] In practical applications, such as Fig.10 As shown, with the increase of the number of iterations, the value of the objective function of the power grid operation optimization model gradually meets the second preset accuracy, so that the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of distributed photovoltaic power generation in the microgrid and the penalty value of photovoltaic power generation are minimized.

[0190] In this embodiment, the ADMM algorithm is used to decompose the power grid optimization model into two sub-models. The first target model is the active distribution network operation optimization problem; the second target model is the microgrid operation optimization problem considering the uncertainty of new energy. Afterwards, since the uncertainty of new energy within the microgrid is considered, the C&CG algorithm is used to solve the second target model until the convergence condition is met. Finally, the Lagrange multiplier and the tie line power in the ADMM algorithm are updated until the power grid optimization model meets the convergence condition.

[0191] Continuing with the above embodiment, the method further includes: when the second target result does not meet the second preset accuracy, updating the tie line power of the power grid operation optimization model and the Lagrange multiplier of the power grid operation optimization model.

[0192] Based on the first target result, determining the second target result of the power grid operation optimization model includes: based on the first target result, the updated tie line power and the updated Lagrange multiplier, determining the second target result of the power grid operation optimization model.

[0193] Optionally, the updating method of the tie line power of the power grid operation optimization model and the Lagrange multiplier of the power grid operation optimization model is as shown in formulas (17a) and (17b).

[0194] If the convergence criteria (16a) and (16b) cannot be satisfied simultaneously, the Lagrange penalty function multiplier is updated according to the formula:

[0195] Formula (17a)

[0196] Formula (17b)

[0197] In the formula, λ k+1 represents the Lagrange multiplier of the k+1th iteration; λ k Meaning represents the Lagrange multiplier of the kth iteration; U k represents the Lagrange multiplier of the kth iteration; u k+1 , the Lagrange multiplier γ of the k+1th iteration represents the iteration step size; represents the penalty function.

[0198] In an exemplary embodiment, Fig.11 As shown, solving the power grid operation optimization model to obtain the target operation parameters includes steps S1102 to S1106. Among them:

[0199] Step S1102, solving the first target model to obtain a first target solution, and updating the lower limit value of the output result of the target function in the first target model through the first target solution.

[0200] Optionally, the server solves the first target model to obtain a first target solution, and updates a lower limit value of an output result of the target function in the first target model through the first target solution, which is recorded as LB.

[0201] Step S1104, solving the second objective model to obtain a second objective solution, and updating the upper limit value of the output result of the objective function in the second objective model through the second objective solution.

[0202] Optionally, the second objective model is solved to obtain a second objective solution, and the upper limit value of the output result of the objective function in the second objective model is updated by the second objective solution, which is recorded as UB.

[0203] Step S1106, if the difference between the upper limit value and the lower limit value of the target value is greater than the first preset accuracy, the target constraint condition in the second target model is added to the first target model to form a new first target model; continue to solve the new first target model to obtain the first target solution, and update the lower limit of the target value of the first target model through the first target solution; solve the second target model to obtain the second target solution, and update the upper limit value of the target value of the second target model through the second target solution; until the difference between the upper limit value and the lower limit value of the target value is less than or equal to the first preset accuracy, wherein the target constraint condition is determined based on the cutting plane constraint.

[0204] Optionally, if UB-LB ≤ first preset accuracy , then the current target value and optimal solution are returned and the loop is exited. The current target value and optimal solution are the target operation parameters.

[0205] Optionally, if UB-LB>first preset accuracy , select the target constraint condition from the constraint conditions in the second target model, the target constraint condition is determined based on the cutting plane constraint, specifically, formula (14c)-(14d) is added to the first target model as the cutting plane constraint to form a new first target model; continue to solve the new first target model to obtain the first target solution, and update the target value lower limit of the first target model through the first target solution; solve the second target model to obtain the second target solution, and update the target value upper limit of the second target model through the second target solution; until the difference between the target value upper limit and the target value lower limit is less than or equal to the first preset accuracy, that is, it ends when convergence, and obtains the target operating parameters.

[0206] like Fig.12 As shown, the C&CG algorithm convergence curve, the difference between the target value upper limit and the target value lower limit is less than or equal to the first preset accuracy, and gradually converges.

[0207] In this embodiment, the target operating parameters can be obtained by solving the first model and the second model through the C&CG algorithm; the power grid is operated according to the target operating parameters; and when the active distribution network and the microgrid are coordinated and optimized, data can be protected from leakage and tampering, and the safe operation of the active distribution network and the microgrid can be ensured.

[0208] In one embodiment, an IEEE 18-node distribution network containing one microgrid is used as a test system to verify the effectiveness of the proposed two-stage distributed robust optimization method. Figure 1The microgrid and distribution network topology is shown, and the microgrid is connected to node 5. There are 2 DGs in the microgrid, and the maximum capacity of each DG is 1.5 MW. The power generation cost is 150 yuan / MW and the power factor is 0.9. There is a photovoltaic power station with a maximum installed capacity of 2MW and a power factor of 0.85. There is an SVG in the distribution network with a maximum reactive power compensation capacity of 1 Mvar. The active power prediction error of the photovoltaic power station is 20%, and the penalty cost coefficient for photovoltaic power generation is 250 yuan / MW. The voltage amplitude range of each node in the distribution network is 0.96–1.04 pu. The convergence accuracy of ADMM and C&CG algorithms is 10 -3 The computer configuration is: Intel(R) Xeon(R) i7-3681 CPU / 3.40-GHz main frequency 16 GB RAM workstation. The proposed algorithm is implemented in the MATLAB environment and the commercial solver GUROBI is called for solution.

[0209] The results of the ADMM algorithm when considering the data privacy of the microgrid and the distribution network and the centralized algorithm when not considering the data privacy are analyzed. When the data privacy of the microgrid and the distribution network is not considered, the centralized algorithm can be used for solution. This algorithm assumes that the information between the microgrid and the distribution network can be fully interactive, so it can be solved centrally. When the data privacy of the microgrid and the distribution network is considered, a distributed solution is required to protect the internal information of the two stakeholders. The information exchanged is only the information of the tie line power. As can be seen from Table 1, when the ADMM algorithm is used for solution, the number of iterations of the algorithm increases with the improvement of convergence accuracy, the calculated objective function value also gradually increases, and the calculation time also increases. Compared with the centralized algorithm, it can be seen that the solution result and solution time of the centralized algorithm are better than the solution result of the distributed algorithm. This is because the centralized algorithm without considering the data privacy of the distribution network and the microgrid can fully interact with information, which can accelerate the solution speed of the algorithm in this case, and the solution result is relatively better under the condition of complete information.

[0210] Table 1 Analysis of the results of the ADMM algorithm when considering data privacy and the centralized algorithm when not considering data privacy

[0211]

[0212] The present invention sets up a comparison of the distribution network operating cost, microgrid operating cost and photovoltaic abandonment percentage under three uncertain parameter conditions. The comparison is made when the uncertain parameters are 0, 4 and 8. The case where the uncertain parameter is 0 is used as the benchmark comparison case. In this case, it means that the photovoltaic prediction error is 0, that is, there is no abandonment of photovoltaic power generation. The comparison of the distribution network operating cost, microgrid operating cost and photovoltaic abandonment percentage under three uncertain parameter conditions is shown in Table 2. It can be seen from Table 2 that when When the operating cost of the distribution network is At the same time, when The operating cost of the distribution network is higher than As can be seen from Table 2, as the uncertainty parameters increase, the distribution network operating cost will decrease, the microgrid operating cost will gradually increase, and the percentage of photovoltaic abandonment will also increase.

[0213] Table 2. Impact of uncertainty budget parameters on distribution network and microgrid operating costs and photovoltaic power curtailment

[0214]

[0215] Figures 8(a), 8(b), and 8(c) are the maximum available active power and planned active output of photovoltaic power generation in the microgrid under three uncertain parameters. The relationship between the maximum available active power of photovoltaic power generation in the microgrid and its planned active output under three uncertain parameters. As can be seen from Figure (8a), when the uncertainty budget parameter When , the maximum available active power of photovoltaic power generation is the same as the planned active power output, which also shows that the photovoltaic power generation prediction is very accurate in this case. As can be seen from Figure (8b), when the uncertain parameters When , the maximum available active power of photovoltaic power generation is greater than the planned active power output in the 12th, 13th, 14th and 15th time periods under the worst active power output scenario. As shown in Figure (8c), when the budget parameter is uncertain When the power plant is in operation, its maximum available active power output is greater than its planned active power output in 8 time periods.

[0216] Therefore, it can be seen from the above figure that when the uncertainty parameter is 0, there is no abandonment of photovoltaic power generation inside the microgrid. However, as the uncertainty parameter gradually increases, the photovoltaic abandonment period will increase, and the maximum available active output of photovoltaics will also fluctuate violently, resulting in more photovoltaic abandonment, which will also increase the penalty value of abandonment.

[0217] Table 3. The number of iterations and computation time of C&CG algorithm under three uncertain parameters

[0218]

[0219] It can be seen from Table 3 that the number of iterations of the C&CG algorithm will increase with the increase of uncertain parameters, but it will stabilize at 9 times, and the calculation time will not exceed 10 seconds. The above results verify the effectiveness and fast convergence of the solution algorithm proposed in this paper in solving the two-stage distributed robust optimization model.

[0220] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0221] Based on the same inventive concept, the embodiment of the present application also provides a power grid operation adjustment device for implementing the power grid operation adjustment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power grid operation adjustment device embodiments provided below can refer to the limitations of the power grid operation adjustment method above, and will not be repeated here.

[0222] In an exemplary embodiment, Fig.13 As shown, a power grid operation adjustment device is provided, including: an acquisition module 1301 and a solution module 1302, wherein:

[0223] The acquisition module 1301 is used to obtain a power grid operation optimization model, wherein the power grid operation optimization model is determined based on the objective function and constraint conditions of the power grid operation; the objective function is determined based on the operating parameters of the active distribution network and the operating parameters of the microgrid; the constraint conditions include a first constraint condition and a second constraint condition, and the second constraint condition is determined based on the uncertain parameters of the distributed photovoltaic power generation output in the microgrid and the transmission power of the interconnection line between the main distribution network and the microgrid.

[0224] The solving module 1302 is used to solve the power grid operation optimization model, obtain target operation parameters, and adjust the operation of the power grid based on the target operation parameters.

[0225] In an exemplary embodiment, a power grid operation adjustment device further includes:

[0226] The objective function determination module is used to obtain the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of the distributed power generation in the microgrid and the penalty value of photovoltaic power generation for abandonment; based on the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of the distributed power generation in the microgrid and the penalty value of photovoltaic power generation for abandonment, the objective function of the grid operation is determined.

[0227] In an exemplary embodiment, a power grid operation adjustment device further includes:

[0228] The first constraint condition determination module is used to obtain the linear AC power flow equation, the distribution network node voltage constraint, the static VAR generator reactive power output constraint and the distribution network line current constraint in the active distribution network; and determine the first constraint condition based on the linear AC power flow equation, the distribution network node voltage constraint, the static VAR generator reactive power output constraint and the distribution network line current constraint.

[0229] The second constraint condition determination module is used to obtain the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, distributed power source active output, distributed power source reactive output, distributed photovoltaic output uncertainty set and microgrid power balance equation in the microgrid; wherein, the distributed photovoltaic output uncertainty set includes the budget parameters of photovoltaic adjustable uncertainty; the microgrid power balance equation includes the transmission power of the interconnection line between the active distribution network and the microgrid; based on the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, distributed power source active output, distributed power source reactive output, distributed photovoltaic output uncertainty set and microgrid power balance equation in the microgrid, the second constraint condition is determined.

[0230] In an exemplary embodiment, the solution module 1302 includes:

[0231] The decomposition unit is used to decompose the power grid operation optimization model into a first power grid operation optimization model and a second power grid operation optimization model by using an alternating direction multiplier method.

[0232] The conversion unit is used to convert the first power grid operation optimization model into the first target model and convert the second power grid operation optimization model into the second target model through a column-row generation algorithm.

[0233] The first solving unit is used to solve the first target model and the second target model, and when the solution result meets the first preset accuracy, the solution result is used as the first target result; wherein the first preset accuracy is determined based on the column-row generation algorithm.

[0234] The second solving unit is used to determine a second target result of the power grid operation optimization model based on the first target result.

[0235] The target operating parameter determination unit is used to determine the target operating parameter based on the second target result when the second target result meets the second preset accuracy, wherein the second preset accuracy is determined based on the alternating direction multiplier method.

[0236] In an exemplary embodiment, the solution module 1302 further includes:

[0237] An updating unit is used to update the tie line power of the power grid operation optimization model and the Lagrange multiplier of the power grid operation optimization model when the second target result does not meet the second preset accuracy.

[0238] The second solving unit is also used to determine the second target result of the power grid operation optimization model based on the first target result, the updated tie line power and the updated Lagrange multiplier.

[0239] In an exemplary embodiment, the first solving unit includes:

[0240] The lower limit value determination subunit is used to solve the first target model, obtain the first target solution, and update the lower limit value of the output result of the target function in the first target model through the first target solution.

[0241] The upper limit value determination subunit is used to solve the second target model, obtain the second target solution, and update the upper limit value of the output result of the target function in the second target model through the second target solution.

[0242] An updating solving subunit is used to add the target constraint conditions in the second target model to the first target model to form a new first target model if the difference between the upper limit value and the lower limit value of the target value is greater than a first preset accuracy; continue to solve the new first target model to obtain the first target solution, and update the lower limit of the target value of the first target model through the first target solution; solve the second target model to obtain the second target solution, and update the upper limit of the target value of the second target model through the second target solution; until the difference between the upper limit value and the lower limit value of the target value is less than or equal to the first preset accuracy, wherein the target constraint conditions are determined based on the cutting plane constraints.

[0243] Each module in the above-mentioned power grid operation adjustment device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0244] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.14As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the operating parameters of the active distribution network and the operating parameters of the microgrid. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adjusting the operation of a power grid is implemented.

[0245] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0246] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0247] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0248] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0249] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0250] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0251] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for adjusting power grid operation, characterized in that: The method comprises: Obtaining a power grid operation optimization model, wherein the power grid operation optimization model is determined based on an objective function and constraint conditions of power grid operation; the objective function is determined based on operating parameters of the active distribution network and operating parameters of the microgrid; the constraint conditions include a first constraint condition and a second constraint condition, and the second constraint condition includes a budget parameter of photovoltaic adjustable uncertainty in the microgrid and a transmission power of a tie line between the active distribution network and the microgrid; The power grid operation optimization model is solved to obtain target operation parameters, and the operation of the power grid is adjusted based on the target operation parameters.

2. The method according to claim 1, characterized in that The method for determining the objective function of the power grid operation includes: Obtaining the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operation loss value of the distributed power source in the microgrid, and the abandonment penalty value of photovoltaic power generation; The objective function of the grid operation is determined based on the interactive product loss value of the active distribution network, the node voltage deviation of the active distribution network, the operating loss value of the distributed generation in the microgrid and the abandonment penalty value of the photovoltaic power generation.

3. The method according to claim 1, characterized in that The method for determining the first constraint condition includes: Obtaining a linear AC power flow equation, a distribution network node voltage constraint, a static VAR generator reactive power constraint, and a distribution network line current constraint in the active distribution network; Determining the first constraint condition according to the linear AC power flow equation, the distribution network node voltage constraint, the static VAR generator reactive power constraint and the distribution network line current constraint; The second constraint condition is generated in a manner including: Obtain the upper and lower limit constraints of distributed photovoltaic active output, distributed photovoltaic reactive output, distributed power source active output, distributed power source reactive output, distributed photovoltaic output uncertainty set, microgrid power balance equation and system coupling constraints in the microgrid; wherein the distributed photovoltaic output uncertainty set includes the budget parameters of photovoltaic adjustable uncertainty; the microgrid power balance equation includes the transmission power of the tie line between the active distribution network and the microgrid; The second constraint condition is determined based on the upper and lower limit constraints of the distributed photovoltaic active output in the microgrid, the upper and lower limit constraints of the distributed photovoltaic reactive output, the upper and lower limit constraints of the distributed power source active output, the upper and lower limit constraints of the distributed power source reactive output, the distributed photovoltaic output uncertainty set, the microgrid power balance equation and the system coupling constraints.

4. The method according to claim 1, characterized in that: The step of solving the power grid operation optimization model to obtain target operation parameters includes: Decomposing the power grid operation optimization model into a first power grid operation optimization model and a second power grid operation optimization model by an alternating direction multiplier method; The first power grid operation optimization model is converted into a first target model and the second power grid operation optimization model is converted into a second target model by using a column-row generation algorithm; Solving the first target model and the second target model, and if the solution results meet a first preset accuracy, using the solution results as the first target results; wherein the first preset accuracy is determined based on the column-row generation algorithm; Determining a second target result of the power grid operation optimization model based on the first target result; In a case where the second target result satisfies a second preset accuracy, a target operating parameter is determined based on the second target result, wherein the second preset accuracy is determined based on the alternating direction multiplier method.

5. The method according to claim 4, characterized in that The method further comprises: When the second target result does not meet the second preset accuracy, updating the tie line power of the power grid operation optimization model and the Lagrange multiplier of the power grid operation optimization model; Determining a second target result of the power grid operation optimization model based on the first target result includes: Based on the first target result, the updated tie line power and the updated Lagrange multiplier, a second target result of the power grid operation optimization model is determined.

6. The method according to claim 4, characterized in that The step of solving the first target model and the second target model, and taking the solution result as the first target result when the solution result satisfies a first preset accuracy, includes: Solving the first target model to obtain a first target solution, and updating a lower limit value of an output result of an objective function in the first target model by using the first target solution; Solving the second objective model to obtain a second objective solution, and updating an upper limit value of an output result of an objective function in the second objective model by using the second objective solution; If the difference between the upper limit value and the lower limit value is greater than the first preset accuracy, the target constraint condition in the second target model is added to the first target model to form a new first target model; continue to solve the new first target model to obtain the first target solution, and update the target value lower limit of the first target model through the first target solution; solve the second target model to obtain the second target solution, and update the target value upper limit of the second target model through the second target solution; until the difference between the target value upper limit and the target value lower limit is less than or equal to the first preset accuracy, wherein the target constraint condition is determined based on the cutting plane constraint.

7. A power grid operation adjustment device, characterized in that: The device comprises: An acquisition module is used to acquire a power grid operation optimization model, wherein the power grid operation optimization model is determined based on an objective function and constraint conditions of power grid operation; the objective function is determined based on operating parameters of the active distribution network and operating parameters of the microgrid; the constraint conditions include a first constraint condition and a second constraint condition, and the second constraint condition is determined based on an uncertain parameter of distributed photovoltaic power generation output in the microgrid and a transmission power of a tie line between the main distribution network and the microgrid; The solution module is used to solve the power grid operation optimization model, obtain target operation parameters, and adjust the operation of the power grid based on the target operation parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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