Alternating current and direct current power distribution network bearing capacity assessment method, device, equipment and medium

Abstract

The invention relates to an AC / DC power distribution network bearing capacity evaluation method, device, equipment and medium, and the method comprises the steps: taking the bearing capacity of a distributed power supply of an AC / DC power distribution network as a target, taking the energy storage, power flow and the output of the distributed power supply as constraints, and constructing an AC / DC power distribution network bearing capacity optimization model; optimizing the bearing capacity optimization model of the AC / DC power distribution network by adopting a KKT condition in combination with the calculation results of the AC optimal power flow and the DC optimal power flow; and solving the optimized AC / DC power distribution network bearing capacity optimization model to obtain the AC / DC power distribution network bearing capacity. According to the method, the new energy bearing capacity of the power system can be evaluated more scientifically.

Description

Technical Field

[0001] The present invention relates to the technical field of AC / DC power distribution networks, and in particular to an AC / DC power distribution network carrying capacity assessment method, device, equipment and medium. Background Art

[0002] As global attention to environmental issues increases, the transformation of energy structure has become one of the key tasks for countries to pursue sustainable development. In particular, the proportion of new energy, such as wind and solar energy, in the power system is growing rapidly, and it is expected that the installed capacity of wind power and photovoltaic power will exceed 1.2 billion kilowatts. It is crucial to reasonably evaluate the carrying capacity of new energy in the power system, which is of guiding significance for the planning and operation of new energy power systems.

[0003] Existing research mainly evaluates the carrying capacity of renewable energy based on mathematical programming models. These methods simulate the power consumption after renewable energy access or use probability distribution to deal with the randomness of renewable energy output, thereby evaluating the consumption and power restriction of renewable energy. However, these methods often ignore the power flow distribution state and constraints of the power grid, making it difficult to accurately reflect the limitations of line power, node voltage, etc. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for evaluating the carrying capacity of an AC / DC distribution network, which can more scientifically evaluate the carrying capacity of an electric power system for new energy.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide an AC / DC distribution network carrying capacity evaluation method, comprising the following steps:

[0006] Taking the distributed power generation capacity of AC / DC distribution network as the target, and energy storage, power flow and distributed power generation output as constraints, an optimization model for the AC / DC distribution network capacity is constructed.

[0007] Combining the calculation results of the AC optimal power flow and the DC optimal power flow, the KKT condition is used to optimize the AC / DC distribution network carrying capacity optimization model;

[0008] The optimized AC / DC distribution network carrying capacity optimization model is solved to obtain the AC / DC distribution network carrying capacity. The objective function f of the AC / DC distribution network carrying capacity optimization model is expressed as: Among them, S j is the soft switch capacity connected to node j, is the capacity of the distributed power group connected to node i in the AC / DC distribution network at time t; N is the number of nodes in the AC / DC distribution network, T is the total operation period, and N SOP is the number of soft switches in the AC / DC distribution network.

[0009] The constraints of the AC / DC distribution network carrying capacity optimization model include soft switch access capacity limitation constraints, distributed power access capacity limitation constraints, AC power flow constraints, DC power flow constraints, node output constraints and system power balance constraints.

[0010] The soft switch access capacity limitation constraint is expressed as: Among them, S j is the soft switch capacity connected to node j, P j is the active power of node j, Q j is the reactive power of node j, S max is the maximum capacity of the soft switch that can be connected to node j; the capacity limit constraint of the distributed power supply access is expressed as: in, is the capacity of the distributed generation group connected to node i in the AC / DC distribution network at time t, is the active power of the distributed generation connected to node i in the AC / DC distribution network at time t, is the reactive power of the distributed generation connected to node i in the AC / DC distribution network at time t.

[0011] The AC power flow constraint is expressed as: in, and are the active power and reactive power of the distributed generation connected to the AC distribution network node i at time t, P d_AC and Q d_AC are respectively the active load and reactive load in the AC distribution network, V i_AC is the voltage of node i in the AC distribution network, G ij_AC and B ij_AC are the conductance and susceptance of the line between node i and node j in the AC distribution network, θ ij_AC is the difference in power factor angle between node i and node j in the AC distribution network, N′ is the number of nodes in the AC distribution network; the DC power flow constraint is expressed as: in, is the active power of the distributed generation connected to the DC distribution network node i at time t, P d_DC is the active load in the DC distribution network, V i_DC is the voltage of node i in the DC distribution network, G ij_DC is the conductance of the line between node i and node j in the DC distribution network, and N″ is the number of nodes in the DC distribution network.

[0012] The node output constraint is expressed as: Where η is the efficiency of the node AC to DC conversion, V i is the voltage of node i in the AC / DC distribution network, V i min and V i max are the voltage lower limit and voltage upper limit of node i in the AC and DC distribution networks, respectively. and are the lower limit and upper limit of active power of distributed generation connected to node i in AC / DC distribution network at time t, respectively. and are the reactive power lower limit and reactive power upper limit of the distributed power source connected to the AC distribution network node i at time t respectively; the system power balance constraint is expressed as: Among them, E battery is the total capacity of distributed power sources connected to the AC / DC distribution network, E i,t is the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, N is the number of nodes in the AC / DC distribution network, T is the total operation period, and are the lower and upper limits of the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the charging and discharging amounts of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the upper limit of charging and discharging of the distributed generation connected to node i in the AC / DC distribution network at time t, respectively, and η charge and η discharge are the charging coefficient and the discharging coefficient respectively.

[0013] The KKT condition is used to optimize the AC / DC distribution network carrying capacity optimization model, specifically:

[0014] Based on the KKT condition, the stability condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0015] Based on the KKT condition, the original feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0016] Based on the KKT condition, the dual feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0017] Based on the KKT condition, the complementary relaxation condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0018] Optimizing the AC / DC distribution network carrying capacity optimization model according to the obtained stability condition, original feasibility condition, dual feasibility condition and complementary slackness condition of the AC / DC distribution network carrying capacity optimization model;

[0019] in, represents the gradient, x * is the calculation result of AC optimal power flow and DC optimal power flow, λ m is the Lagrange multiplier associated with the equality constraint, μ l is the Lagrange multiplier associated with the inequality constraint; f(x * ) is the objective function of the AC / DC distribution network carrying capacity optimization model under the calculation results of AC optimal power flow and DC optimal power flow, h m (x) is the mth equality constraint, h m (x * ) is the mth equality constraint condition under the calculation results of AC optimal power flow and DC optimal power flow, g l (x) is the lth inequality constraint, g l (x * ) is the lth inequality constraint under the calculation results of AC optimal power flow and DC optimal power flow; M is the number of equality constraints, and L is the number of inequality constraints.

[0020] The technical solution adopted by the present invention to solve the technical problem is: to provide an AC / DC distribution network carrying capacity assessment device, comprising:

[0021] A construction module is used to construct an optimization model for AC / DC distribution network carrying capacity, taking the distributed power supply carrying capacity of the AC / DC distribution network as the target and taking energy storage, power flow and distributed power supply output as constraints;

[0022] An optimization module, used to optimize the AC / DC distribution network carrying capacity optimization model by combining the calculation results of the AC optimal power flow and the DC optimal power flow using the KKT condition;

[0023] The solution module is used to solve the optimized AC / DC distribution network carrying capacity optimization model to obtain the AC / DC distribution network carrying capacity.

[0024] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned AC / DC distribution network carrying capacity assessment method when executing the computer program.

[0025] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned AC / DC distribution network carrying capacity assessment method are implemented.

[0026] Beneficial Effects

[0027] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention proposes an optimization model for the carrying capacity of an AC / DC distribution network integrating an AC / DC system, which fully considers the integrity and complex and changeable characteristics of the distribution network, and also improves the AC / DC distribution network's ability to absorb distributed power sources by introducing an energy storage system. This optimization model can better meet the actual operation needs of future distribution networks, especially in an environment with an increasing number of distributed power sources, and can effectively improve the flexibility and stability of distribution networks. The optimization model of the present invention adopts AC / DC flow constraints, which enables the calculation results to simultaneously meet the needs of AC and DC distribution networks, giving full play to the advantages of DC systems in improving transmission efficiency and reducing power losses. In this way, the carrying capacity of the distribution network can be more accurately evaluated and improved, while ensuring the efficient and economical operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of a method for evaluating the carrying capacity of an AC / DC distribution network according to a first embodiment of the present invention;

[0029] Figure 2 It is a structural topological diagram of the AC / DC power distribution network in the first embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0031] The first embodiment of the present invention relates to a method for evaluating the carrying capacity of an AC / DC distribution network. Figure 1 As shown, the following steps are included:

[0032] Step 1: Taking the distributed power generation capacity of the AC / DC distribution network as the target and energy storage, power flow and distributed power generation output as constraints, an AC / DC distribution network carrying capacity optimization model is constructed. Figure 2is a structural topological diagram of the AC / DC distribution network. The purpose of this implementation is to evaluate the total capacity of the distributed power sources that can be connected to the AC / DC distribution network. Therefore, the objective function f of the AC / DC distribution network carrying capacity optimization model constructed is expressed as:

[0033]

[0034] Among them, S j is the soft switch capacity connected to node j, is the capacity of the distributed power group connected to node i in the AC / DC distribution network at time t; N is the number of nodes in the AC / DC distribution network, T is the total operation period, and N SOP is the number of soft switches in the AC / DC distribution network.

[0035] In this implementation, the total capacity of the soft switch access is expressed as:

[0036]

[0037] Among them, S SOP It is the total capacity of soft switches connected in the AC and DC power distribution system.

[0038] In this implementation, the soft switch access capacity limit is expressed as:

[0039]

[0040] Among them, P j is the active power of node j, Q j is the reactive power of node j, S max is the maximum capacity of the soft switch that node j can access.

[0041] In this implementation, the total capacity of distributed power access is expressed as:

[0042]

[0043] Among them, S is the total capacity of distributed power supply access.

[0044] In this implementation, the capacity limit of distributed power access is expressed as:

[0045]

[0046] in, is the active power of the distributed generation connected to node i in the AC / DC distribution network at time t, is the reactive power of the distributed generation connected to node i in the AC / DC distribution network at time t.

[0047] In this embodiment, the AC power flow equation is expressed as:

[0048]

[0049] in, and are the active power and reactive power of the distributed generation connected to the AC distribution network node i at time t, P d_AC and Q d_AC are respectively the active load and reactive load in the AC distribution network, V i_AC is the voltage of node i in the AC distribution network, G ij_AC and B ij_AC are the conductance and susceptance of the line between node i and node j in the AC distribution network, θ ij_AC is the difference in power factor angle between node i and node j in the AC distribution network, and N′ is the number of nodes in the AC distribution network.

[0050] In this embodiment, the DC power flow equation is expressed as:

[0051]

[0052] in, is the active power of the distributed generation connected to the DC distribution network node i at time t, P d_DC is the active load in the DC distribution network, V i_DC is the voltage of node i in the DC distribution network, G ij_DC is the conductance of the line between node i and node j in the DC distribution network, and N″ is the number of nodes in the DC distribution network.

[0053] In this implementation, the node output constraint is expressed as:

[0054]

[0055] Where η is the efficiency of the node AC to DC conversion, V i is the voltage of node i in the AC / DC distribution network, V i min and V i max are the voltage lower limit and voltage upper limit of node i in the AC and DC distribution networks, respectively. and are the lower limit and upper limit of active power of distributed generation connected to node i in AC / DC distribution network at time t, respectively. and They are the reactive power lower limit and reactive power upper limit of the distributed generation connected to the AC distribution network node i at time t.

[0056] In this implementation, the system power balance constraint is expressed as:

[0057]

[0058] Among them, E battery is the total capacity of distributed power sources connected to the AC / DC distribution network, E i,t is the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, N is the number of nodes in the AC / DC distribution network, T is the total operation period, and are the lower and upper limits of the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the charging and discharging amounts of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the upper limit of charging and discharging of the distributed generation connected to node i in the AC / DC distribution network at time t, respectively, and η charge and η discharge are the charging coefficient and the discharging coefficient respectively, and Δt represents the duration.

[0059] Based on the above, the AC / DC distribution network carrying capacity optimization model constructed in this embodiment is expressed as:

[0060]

[0061] Step 2: Combine the calculation results of AC optimal power flow and DC optimal power flow and use KKT conditions to optimize the AC / DC distribution network carrying capacity optimization model. Specifically:

[0062] Based on the KKT condition, the stability condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0063] Based on the KKT condition, the original feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as:

[0064] Based on the KKT condition, the dual feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: This means that the Lagrange multipliers associated with the inequality constraints must be non-negative.

[0065] Based on the KKT condition, the complementary relaxation condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: This means that for each inequality constraint, either g l (x), or μ l =0, in other words, if an inequality constraint has no practical restrictions on the solution, then its corresponding multiplier must be zero.

[0066] in, represents the gradient, x * is the calculation result of AC optimal power flow and DC optimal power flow, λ m is the Lagrange multiplier associated with the equality constraint, μ l is the Lagrange multiplier associated with the inequality constraint; f(x * ) is the objective function of the AC / DC distribution network carrying capacity optimization model under the calculation results of AC optimal power flow and DC optimal power flow, h m (x) is the mth equality constraint, h m (x * ) is the mth equality constraint condition under the calculation results of AC optimal power flow and DC optimal power flow, g l (x) is the lth inequality constraint, g l (x * ) is the lth inequality constraint under the calculation results of AC optimal power flow and DC optimal power flow; M is the number of equality constraints, and L is the number of inequality constraints.

[0067] Finally, the AC / DC distribution network carrying capacity optimization model is optimized according to the obtained stability condition, original feasibility condition, dual feasibility condition and complementary relaxation condition of the AC / DC distribution network carrying capacity optimization model. The optimized model is expressed as:

[0068]

[0069] Step 3, solving the optimized AC / DC distribution network carrying capacity optimization model to obtain the AC / DC distribution network carrying capacity.

[0070] In this step, the AC / DC distribution network carrying capacity optimization model optimized in step 2 is calculated to obtain P j , Q j , P i , Q i Taking partial derivatives, we get:

[0071]

[0072] h m (P j ,Q j ,P i ,Q i )=0

[0073] g l (P j ,Q j ,P i ,Q i )=0

[0074] It can be seen that the objective function of the AC / DC distribution network carrying capacity optimization model is transformed into a convex function, and the number of equations is greater than the unknown number of equations, so this equation can be solved. By solving the above equation, the carrying capacity of the AC / DC distribution network can be obtained.

[0075] It is not difficult to find that this implementation method uses KKT conditions to transform the optimization model. Compared with the traditional power flow calculation method, KKT condition optimization can more accurately handle the nonlinearity and complex constraints in the power grid, ensuring that the solution reaches the global optimum in a complex power grid environment. It not only improves the adaptability of the power grid to the volatility and dynamic behavior of renewable energy, but also enhances the distribution network's carrying and absorption capacity for distributed power sources. At the same time, it provides a solid theoretical foundation and technical support for the design and optimization of the distribution network. It can deeply analyze and explore the carrying potential of the distribution network for distributed power sources, while adapting to the trend of more efficient and sustainable development of the power system, and has important practical significance and broad application prospects.

[0076] A second embodiment of the present invention relates to an AC / DC power distribution network carrying capacity assessment device, comprising:

[0077] A construction module is used to construct an optimization model for AC / DC distribution network carrying capacity, taking the distributed power supply carrying capacity of the AC / DC distribution network as the target and taking energy storage, power flow and distributed power supply output as constraints;

[0078] An optimization module, used to optimize the AC / DC distribution network carrying capacity optimization model by combining the calculation results of the AC optimal power flow and the DC optimal power flow using the KKT condition;

[0079] The solution module is used to solve the optimized AC / DC distribution network carrying capacity optimization model to obtain the AC / DC distribution network carrying capacity.

[0080] The objective function f of the AC / DC distribution network carrying capacity optimization model constructed by the building module is expressed as: Among them, S j is the soft switch capacity connected to node j, is the capacity of the distributed power group connected to node i in the AC / DC distribution network at time t; N is the number of nodes in the AC / DC distribution network, T is the total operation period, and N SOP is the number of soft switches in the AC / DC distribution network.

[0081] The constraints of the AC / DC distribution network carrying capacity optimization model constructed by the construction module include soft switch access capacity limitation constraints, distributed power supply access capacity limitation constraints, AC power flow constraints, DC power flow constraints, node output constraints and system power balance constraints.

[0082] The soft switch access capacity limitation constraint is expressed as: Among them, S j is the soft switch capacity connected to node j, P j is the active power of node j, Q j is the reactive power of node j, S max is the maximum capacity of the soft switch that can be connected to node j; the capacity limit constraint of the distributed power supply access is expressed as: in, is the capacity of the distributed generation group connected to node i in the AC / DC distribution network at time t, is the active power of the distributed generation connected to node i in the AC / DC distribution network at time t, is the reactive power of the distributed generation connected to node i in the AC / DC distribution network at time t.

[0083] The AC power flow constraint is expressed as: in, and are the active power and reactive power of the distributed generation connected to the AC distribution network node i at time t, P d_AC and Q d_AC are respectively the active load and reactive load in the AC distribution network, V i_AC is the voltage of node i in the AC distribution network, G ij_AC and B ij_AC are the conductance and susceptance of the line between node i and node j in the AC distribution network, θ ij_AC is the difference in power factor angle between node i and node j in the AC distribution network, N′ is the number of nodes in the AC distribution network; the DC power flow constraint is expressed as: in, is the active power of the distributed generation connected to the DC distribution network node i at time t, P d_DC is the active load in the DC distribution network, V i_DC is the voltage of node i in the DC distribution network, G ij_DC is the conductance of the line between node i and node j in the DC distribution network, and N″ is the number of nodes in the DC distribution network.

[0084] The node output constraint is expressed as: Where η is the efficiency of the node AC to DC conversion, V i is the voltage of node i in the AC / DC distribution network, V i min and V i max are the voltage lower limit and voltage upper limit of node i in the AC and DC distribution networks, respectively. and are the lower limit and upper limit of active power of distributed generation connected to node i in AC / DC distribution network at time t, respectively. and are the reactive power lower limit and reactive power upper limit of the distributed power source connected to the AC distribution network node i at time t respectively; the system power balance constraint is expressed as: Among them, E battery is the total capacity of distributed power sources connected to the AC / DC distribution network, E i,t is the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, N is the number of nodes in the AC / DC distribution network, T is the total operation period, and are the lower and upper limits of the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the charging and discharging amounts of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the upper limit of charging and discharging of the distributed generation connected to node i in the AC / DC distribution network at time t, respectively, and η charge and η discharge are the charging coefficient and the discharging coefficient respectively.

[0085] The optimization module includes:

[0086] The stability determination unit is used to obtain the stability condition of the AC / DC distribution network carrying capacity optimization model based on the KKT condition, which is expressed as:

[0087] The original feasibility determination unit is used to obtain the original feasibility condition of the AC / DC distribution network carrying capacity optimization model based on the KKT condition, which is expressed as:

[0088] The dual feasibility determination unit is used to obtain the dual feasibility condition of the AC / DC distribution network carrying capacity optimization model based on the KKT condition, which is expressed as:

[0089] The complementary slackness determination unit is used to obtain the complementary slackness conditions of the AC / DC distribution network carrying capacity optimization model based on the KKT condition, which is expressed as:

[0090] An optimization unit, used for optimizing the AC / DC distribution network carrying capacity optimization model according to the obtained stability condition, original feasibility condition, dual feasibility condition and complementary slackness condition of the AC / DC distribution network carrying capacity optimization model;

[0091] in, represents the gradient, x * is the calculation result of AC optimal power flow and DC optimal power flow, λ m is the Lagrange multiplier associated with the equality constraint, μ l is the Lagrange multiplier associated with the inequality constraint; f(x * ) is the objective function of the AC / DC distribution network carrying capacity optimization model under the calculation results of AC optimal power flow and DC optimal power flow, h m (x) is the mth equality constraint, h m (x * ) is the mth equality constraint condition under the calculation results of AC optimal power flow and DC optimal power flow, g l (x) is the lth inequality constraint, g l (x * ) is the lth inequality constraint under the calculation results of AC optimal power flow and DC optimal power flow; M is the number of equality constraints, and L is the number of inequality constraints.

[0092] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the AC / DC distribution network carrying capacity assessment method of the first embodiment when executing the computer program.

[0093] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for evaluating the carrying capacity of an AC / DC distribution network are implemented.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the carrying capacity of an AC / DC distribution network, characterized in that: The following steps are involved: Taking the distributed power carrying capacity of AC / DC distribution network as the target, and energy storage, power flow and distributed power output as the constraints, Construct an AC / DC distribution network capacity optimization model; Combining the calculation results of the AC optimal power flow and the DC optimal power flow, the KKT condition is used to optimize the AC / DC distribution network carrying capacity optimization model; The optimized AC / DC distribution network carrying capacity optimization model is solved to obtain the AC / DC distribution network carrying capacity.

2. The AC / DC distribution network carrying capacity assessment method according to claim 1, characterized in that: The objective function f of the AC / DC distribution network carrying capacity optimization model is expressed as: Among them, S j is the soft switch capacity connected to node j, is the capacity of the distributed power group connected to node i in the AC / DC distribution network at time t; N is the number of nodes in the AC / DC distribution network, T is the total operation period, and N SOP is the number of soft switches in the AC / DC distribution network.

3. The AC / DC distribution network carrying capacity assessment method according to claim 1, characterized in that: The constraints of the AC / DC distribution network carrying capacity optimization model include soft switch access capacity limitation constraints, distributed power access capacity limitation constraints, AC power flow constraints, DC power flow constraints, node output constraints and system power balance constraints.

4. The AC / DC distribution network carrying capacity assessment method according to claim 3, characterized in that: The soft switch access capacity limitation constraint is expressed as: Among them, S j is the soft switch capacity connected to node j, P j is the active power of node j, Q j is the reactive power of node j, S max is the maximum capacity of the soft switch that can be connected to node j; the capacity limit constraint of the distributed power supply access is expressed as: in, is the capacity of the distributed generation group connected to node i in the AC / DC distribution network at time t, is the active power of the distributed generation connected to node i in the AC / DC distribution network at time t, is the reactive power of the distributed generation connected to node i in the AC / DC distribution network at time t.

5. The AC / DC distribution network carrying capacity assessment method according to claim 3, characterized in that: The AC power flow constraint is expressed as: in, and are the active power and reactive power of the distributed generation connected to the AC distribution network node i at time t, P d_AC and Q d_AC are respectively the active load and reactive load in the AC distribution network, V i_AC is the voltage of node i in the AC distribution network, G ij_AC and B ij_AC are the conductance and susceptance of the line between node i and node j in the AC distribution network, θ ij_AC is the difference in power factor angle between node i and node j in the AC distribution network, N′ is the number of nodes in the AC distribution network; the DC power flow constraint is expressed as: in, is the active power of the distributed generation connected to the DC distribution network node i at time t, P d_DC is the active load in the DC distribution network, V i_DC is the voltage of node i in the DC distribution network, G ij_DC is the conductance of the line between node i and node j in the DC distribution network, and N″ is the number of nodes in the DC distribution network.

6. The AC / DC distribution network carrying capacity assessment method according to claim 3, characterized in that: The node output constraint is expressed as: Where η is the efficiency of the node AC to DC conversion, V i is the voltage of node i in the AC / DC distribution network, V i min and V i max are the voltage lower limit and voltage upper limit of node i in the AC and DC distribution networks, respectively. and are the lower limit and upper limit of active power of distributed generation connected to node i in AC / DC distribution network at time t, respectively. and are the reactive power lower limit and reactive power upper limit of the distributed power source connected to the AC distribution network node i at time t respectively; the system power balance constraint is expressed as: Among them, E battery is the total capacity of distributed power sources connected to the AC / DC distribution network, E i,t is the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, N is the number of nodes in the AC / DC distribution network, T is the total operation period, and are the lower and upper limits of the capacity of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the charging and discharging amounts of the distributed generation connected to node i in the AC / DC distribution network at time t, and are the upper limit of charging and discharging of the distributed generation connected to node i in the AC / DC distribution network at time t, respectively, charge and η discharge are the charging coefficient and the discharging coefficient respectively, and Δt represents the duration.

7. The AC / DC distribution network carrying capacity assessment method according to claim 1, characterized in that: The KKT condition is used to optimize the AC / DC distribution network carrying capacity optimization model, specifically: Based on the KKT condition, the stability condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: Based on the KKT condition, the original feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: Based on the KKT condition, the dual feasibility condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: m l ≥0, Based on the KKT condition, the complementary relaxation condition of the AC / DC distribution network carrying capacity optimization model is obtained, which is expressed as: m l g l (x)=0, Optimizing the AC / DC distribution network carrying capacity optimization model according to the obtained stability condition, original feasibility condition, dual feasibility condition and complementary slackness condition of the AC / DC distribution network carrying capacity optimization model; in, represents the gradient, x * is the calculation result of AC optimal power flow and DC optimal power flow, λ m is the Lagrange multiplier associated with the equality constraint, μ l is the Lagrange multiplier associated with the inequality constraint; f(x * ) is the objective function of the AC / DC distribution network carrying capacity optimization model under the calculation results of AC optimal power flow and DC optimal power flow, h m (x) is the mth equality constraint, h m (x * ) is the mth equality constraint condition under the calculation results of AC optimal power flow and DC optimal power flow, g l (x) is the lth inequality constraint, g l (x * ) is the lth inequality constraint under the calculation results of AC optimal power flow and DC optimal power flow; M is the number of equality constraints, and L is the number of inequality constraints.

8. An AC / DC distribution network carrying capacity assessment device, characterized in that: include: A construction module is used to construct an optimization model for AC / DC distribution network carrying capacity, taking the distributed power supply carrying capacity of the AC / DC distribution network as the target and taking energy storage, power flow and distributed power supply output as constraints; An optimization module, used to optimize the AC / DC distribution network carrying capacity optimization model by combining the calculation results of the AC optimal power flow and the DC optimal power flow using the KKT condition; The solution module is used to solve the optimized AC / DC distribution network carrying capacity optimization model to obtain the AC / DC distribution network carrying capacity.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for evaluating the carrying capacity of an AC / DC distribution network as claimed in any one of claims 1 to 7 are implemented.

10. 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 for evaluating the carrying capacity of an AC / DC distribution network as claimed in any one of claims 1 to 7 are implemented.

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