Collaborative optimization scheduling method, system and device for multiple low-voltage transformer areas and storage medium

Abstract

The invention relates to the technical field of low-voltage transformer area collaborative scheduling, and discloses a collaborative optimization scheduling method, system and device for multiple low-voltage transformer areas and a storage medium, and the method comprises the steps: calculating the bus loss of the low-voltage transformer areas according to the power parameters of the transformer areas; establishing a collaborative optimization scheduling model by taking minimization of bus loss of the low-voltage transformer area as an objective function and taking power transmission constraint of the modular multilevel converter, operation constraint of the low-voltage transformer area and coupling constraint as constraint conditions; and solving the collaborative optimization scheduling model by adopting an automatic differential technology to obtain a collaborative optimization scheduling strategy, and performing scheduling control on the low-voltage transformer area according to the collaborative optimization scheduling strategy. According to the method, the internal operation characteristics of the low-voltage transformer area and the dynamic response and adjustment capability of the power electronic equipment are accurately reflected through the collaborative optimization scheduling model, the model solving efficiency is effectively improved through the automatic differential technology, and an effective solution is provided for differential regulation and control strategies and refined energy management of the low-voltage transformer area.

Description

Technical Field

[0001] This invention relates to the field of low-voltage distribution area collaborative scheduling technology, and in particular to a collaborative optimization scheduling method, system, device and storage medium for multiple low-voltage distribution areas. Background Technology

[0002] With the development of new power systems, low-voltage distribution areas, as important carriers of distributed power sources, flexible loads, and energy storage resources, are seeing a continuous increase in both number and connected capacity. Against the backdrop of deep integration of power generation, grid, load, and storage, low-voltage distribution areas are gradually acquiring capabilities such as local autonomy, energy complementarity, and flexible regulation, becoming an important component in building new distribution networks. However, the complex structure, diverse resources, and modeling complexity of power electronic flexible interconnection devices in low-voltage distribution areas cause traditional scheduling models and solution methods to suffer from low modeling precision and low computational efficiency.

[0003] Current dispatching methods for low-voltage distribution areas primarily focus on operational optimization within a single low-voltage distribution area. However, this approach neglects the potential for collaboration and mutual adjustment between different distribution areas. Furthermore, most existing methods employ simplified energy hub modeling, focusing only on the input and output balance of energy flow, ignoring the power flow distribution and line losses within the low-voltage distribution system. Therefore, it is difficult to accurately depict the physical relationships between equipment within a low-voltage distribution area during actual operation, and especially fails to reflect the power losses caused by line distribution, load distribution, and their impact on dispatching strategies.

[0004] On the other hand, existing scheduling methods also employ simplified models for power electronic interconnection devices. While simplified models reduce overall model complexity, they lack sufficient precision in characterizing device operating characteristics, making it difficult to accurately reflect the dynamic response and regulation capabilities of power electronic equipment under actual operating conditions. With the large-scale integration of various power electronic devices in low-voltage distribution areas, system operating characteristics exhibit high nonlinearity and coupling. If traditional modeling methods are still used, it will be difficult to support high-quality scheduling decisions. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a collaborative optimization scheduling method, system, device, and storage medium for multiple low-voltage distribution areas. This addresses the issues of low modeling refinement and insufficient computational efficiency in traditional scheduling methods. By employing refined modeling and automatic differentiation techniques, it achieves highly efficient collaborative optimization scheduling for multiple low-voltage distribution area systems.

[0006] In a first aspect, the present invention provides a collaborative optimization scheduling method for multiple low-voltage distribution areas, the method comprising:

[0007] Obtain the power parameters of each low-voltage distribution area, and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss.

[0008] A collaborative optimization scheduling model is established with minimizing bus losses in the low-voltage distribution area as the objective function and power transmission constraints of modular multilevel converters, operation constraints of the low-voltage distribution area, and coupling constraints as constraints.

[0009] The modular multilevel converter power transmission constraint is constructed based on the medium-voltage AC power transmission mode of the modular multilevel converter. The low-voltage distribution area operation constraint includes low-voltage AC distribution area operation constraint and low-voltage DC distribution area operation constraint. The coupling constraint is constructed based on the power coupling relationship between the modular multilevel converter and the low-voltage distribution area.

[0010] The cooperative optimization scheduling model is solved using automatic differentiation technology to obtain a cooperative optimization scheduling strategy, and the low-voltage distribution area is scheduled and controlled according to the cooperative optimization scheduling strategy.

[0011] Furthermore, the steps for constructing the power transmission constraints of the modular multilevel converter include:

[0012] Based on the power conversion law between the main distribution network, modular multilevel converter and low-voltage DC bus, a power transmission model based on modular multilevel converter is established.

[0013] The power transmission model is used as a power transmission constraint for a modular multilevel converter.

[0014] Furthermore, the power transmission constraints of the modular multilevel converter are expressed by the following formula:

[0015]

[0016] In the formula, This represents the current flowing through the modular multilevel converter at time t. This represents the voltage phasor at the connection point between the modular multilevel converter and the main distribution network at time t. R represents the voltage phasor output by the converter valve in the modular multilevel converter at time t. mmc This represents the equivalent resistance of a modular multilevel converter. This represents the equivalent reactance of a modular multilevel converter, where j represents the imaginary unit.

[0017]

[0018] In the formula, This represents the active power flowing into the modular multilevel converter from the main distribution network at time t. This represents the reactive power flowing into the modular multilevel converter from the main distribution network at time t. * Indicates conjugate;

[0019]

[0020] In the formula, This represents the active power flowing into the converter bridge in a modular multilevel converter. This indicates the active power flowing into the low-voltage DC bus from the modular multilevel converter.

[0021] Furthermore, the low-voltage AC distribution area operation constraints include the first node active power balance constraint, the first node reactive power balance constraint, the first line power flow constraint, the first node voltage constraint, the first new energy equipment constraint, and the first energy storage operation constraint;

[0022] The low-voltage DC distribution area operation constraints include the second node active power balance constraint, the second line power flow constraint, the second node voltage constraint, the second new energy equipment constraint, and the second energy storage operation constraint.

[0023] Furthermore, the steps for constructing the coupling constraints include:

[0024] Based on the equivalence relationship between the active power flowing into the low-voltage DC bus from the modular multilevel converter and the active power flowing into the root node of the low-voltage distribution area from the low-voltage DC bus, coupling constraints are obtained.

[0025] The active power of the low-voltage distribution area root node includes the active power of the low-voltage AC distribution area root node and the active power of the low-voltage DC distribution area root node.

[0026] Furthermore, the coupling constraint is expressed by the following formula:

[0027]

[0028] In the formula, This represents the active power flowing into the low-voltage DC bus from the modular multilevel converter at time t. This represents the active power flowing into the root node of the low-voltage AC distribution area from the low-voltage DC bus at time t. This represents the active power flowing into the root node of the low-voltage DC distribution area from the low-voltage DC bus at time t.

[0029] Furthermore, the step of solving the cooperative optimization scheduling model using automatic differentiation techniques to obtain the cooperative optimization scheduling strategy includes:

[0030] Establish a compact nonlinear optimization model corresponding to the aforementioned collaborative optimization scheduling model;

[0031] Based on the automatic differentiation technique, the variables to be solved in the compact nonlinear optimization model are declared as active variables, and the variables to be solved are initialized.

[0032] The equality constraints of the compact nonlinear optimization model are explicitly expressed using the active variables, and the Jacobian matrix of the equality constraints is generated based on the Jacobian function.

[0033] A linear equation is constructed based on the Jacobian matrix, and the linear equation is solved using the trigonometric method to obtain the updated variables to be solved.

[0034] Based on the updated variables to be solved, determine whether the objective function has converged. If it has, take the updated variables to be solved as the optimal solution and generate a collaborative optimization scheduling strategy based on the optimal solution. Otherwise, continue iterative calculation according to the above steps until the objective function converges.

[0035] Secondly, the present invention provides a collaborative optimization scheduling system for multiple low-voltage distribution areas, the system comprising:

[0036] The data processing module is used to acquire the power parameters of each low-voltage distribution area, and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss.

[0037] The model building module is used to establish a collaborative optimization scheduling model with the objective function of minimizing the bus loss of the low-voltage distribution area and the constraints of modular multilevel converter power transmission, low-voltage distribution area operation, and coupling.

[0038] The modular multilevel converter power transmission constraint is constructed based on the medium-voltage AC power transmission mode of the modular multilevel converter. The low-voltage distribution area operation constraint includes low-voltage AC distribution area operation constraint and low-voltage DC distribution area operation constraint. The coupling constraint is constructed based on the power coupling relationship between the modular multilevel converter and the low-voltage distribution area.

[0039] The optimization scheduling module is used to solve the collaborative optimization scheduling model using automatic differentiation technology to obtain the collaborative optimization scheduling strategy, and to perform scheduling control on the low-voltage distribution area according to the collaborative optimization scheduling strategy.

[0040] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0041] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0042] This invention provides a collaborative optimization scheduling method, system, device, and storage medium for multiple low-voltage distribution areas. By constructing a collaborative optimization scheduling model for multiple low-voltage distribution areas through refined modeling, this invention accurately reflects the internal operating characteristics of the low-voltage distribution areas and the dynamic response and regulation capabilities of power electronic equipment, achieving high-quality scheduling decisions. Furthermore, based on the model characteristics and solution requirements, automatic differentiation technology is used to solve the model, effectively improving solution efficiency and reducing the contradiction between modeling and solving, thus providing an effective solution for the collaborative scheduling of low-voltage distribution areas. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the collaborative optimization scheduling method for multiple low-voltage distribution areas in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the DC power distribution center control architecture with multiple low-voltage distribution areas connected in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the structure of the collaborative optimization scheduling system for multiple low-voltage distribution areas in an embodiment of the present invention;

[0046] Figure 4 This is an internal structural diagram of the computer device in an embodiment of the present invention;

[0047] Figure label:

[0048] 10. Data processing module; 20. Model building module; 30. Optimization and scheduling module. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 The first embodiment of the present invention proposes a collaborative optimization scheduling method for multiple low-voltage distribution areas, including steps S10 to S30:

[0051] Step S10: Obtain the power parameters of each low-voltage distribution area, and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss.

[0052] Step S20: With minimizing the bus loss in the low-voltage distribution area as the objective function, and with the power transmission constraints of the modular multilevel converter, the operation constraints of the low-voltage distribution area, and the coupling constraints as the constraints, a collaborative optimization scheduling model is established.

[0053] Step S30: The cooperative optimization scheduling model is solved using automatic differentiation technology to obtain the cooperative optimization scheduling strategy, and the low-voltage distribution area is scheduled and controlled according to the cooperative optimization scheduling strategy.

[0054] This invention proposes a collaborative optimization scheduling method for multi-low-voltage distribution area systems. This method is applied to a DC distribution center control architecture based on the joint operation of multiple low-voltage distribution areas, as shown in the schematic diagram below. Figure 2 As shown, the control architecture can be divided into upper and lower layers. The upper layer is the power transmission layer, which includes the main distribution network, modular multilevel converters (MMCs), and low-voltage DC buses. The main distribution network and the MMC are connected through a point of common coupling (PCC). The other end of the MMC is connected to the low-voltage DC bus. In the power transmission layer, the MMC is used to transmit medium-voltage AC power to the low-voltage DC bus. The lower layer is the low-voltage distribution area operation layer, which includes low-voltage DC distribution areas and low-voltage AC distribution areas. Each low-voltage distribution area is connected to the upper-layer low-voltage DC bus to receive DC power from the upper-layer MMC. The low-voltage distribution area includes power nodes, which include power equipment such as gas turbines, energy storage, and photovoltaics, as well as load equipment such as DC residential loads. These nodes are connected to the upper-layer low-voltage DC bus through their respective low-voltage DC / AC buses and DC / AC converters, thus forming a DC distribution center control architecture based on the joint operation of multiple low-voltage distribution areas.

[0055] Based on the above control framework, this embodiment aims to minimize the bus loss in the low-voltage distribution area as the goal of coordinated scheduling. It optimizes the power flow and equipment output of the low-voltage distribution area. First, based on the power parameters of the distribution area, the bus loss in the low-voltage distribution area is calculated. Since the low-voltage distribution area is divided into low-voltage DC and low-voltage AC distribution areas, the bus loss in the low-voltage AC distribution area and the low-voltage DC distribution area need to be calculated separately. The bus loss in the low-voltage DC distribution area can be calculated based on the active power, resistance, and voltage of each power node in the low-voltage DC line. The calculation formula is as follows:

[0056]

[0057] In the formula, This indicates the bus loss in the low-voltage DC distribution area. This represents the active power of the DC line between power node i and power node j at time t. This represents the node voltage of power node i at time t. The resistance of the DC line between power node i and power node j is represented by T, where T represents the total time period.

[0058] The total line loss in the low-voltage AC distribution area is calculated based on the active power, reactive power, resistance, and voltage of each power node in the low-voltage AC line. The calculation formula is as follows:

[0059]

[0060] In the formula, This indicates the bus loss in the low-voltage AC distribution area. This represents the active power of the AC line between power node m and power node n at time t. This represents the reactive power of the AC line between power node m and power node n at time t. This represents the node voltage of power node m at time t. The resistance of the AC line between power node m and power node n is represented by T, where T represents the total time period.

[0061] Based on the above-mentioned low-voltage DC distribution area bus losses and low-voltage AC distribution area bus losses, the low-voltage distribution area bus loss f can be obtained. loss for:

[0062]

[0063] In this embodiment, the objective function is to minimize the bus loss in the low-voltage distribution area, and the objective function is expressed as follows:

[0064] f = min f loss

[0065] Since the low-voltage distribution area operates within the aforementioned control architecture, the operation of the collaborative optimization scheduling model should also be constrained by this control architecture. That is, the operation of the scheduling model will be constrained by the upper power transmission layer and the lower low-voltage distribution area operation layer. The constraints of the power transmission layer will be explained below.

[0066] The power transmission layer in the control architecture uses a modular multilevel converter as the core device to realize power transmission from the main distribution network to the low-voltage DC bus. Therefore, the constraints of the power transmission layer can be understood as the power transmission constraints of the modular multilevel converter. These constraints are determined by the medium-voltage AC power transmission mode of the modular multilevel converter. The specific construction steps include:

[0067] Based on the power conversion law between the main distribution network, modular multilevel converter and low-voltage DC bus, a power transmission model based on modular multilevel converter is established.

[0068] The power transmission model is used as a power transmission constraint for a modular multilevel converter.

[0069] In this embodiment, the main distribution network inputs AC power to the MMC through the PCC. The AC power is converted into DC power by the MMC and flows into the low-voltage DC bus. The parameters involved in this power transmission process mainly include the active and reactive power flowing from the main distribution network into the MMC, the active and reactive power flowing into the converter bridge in the MMC, and the current and DC power flowing from the MMC into the DC bus. For the power transmission process, this embodiment establishes an upper-level power transmission model based on the modular multilevel converter. This model characterizes the power conversion law between the AC and DC systems of the MMC, including current transmission law, complex power transmission law, and power transmission law. Specifically, the current flowing through the MMC is calculated from the voltage phasor at the connection point between the MMC and the main distribution network, the voltage phasor output by the converter valve in the MMC, and the equivalent resistance and equivalent reactance of the MMC.

[0070]

[0071] In the formula, This represents the current flowing through the modular multilevel converter at time t. This represents the voltage phasor at the connection point between the modular multilevel converter and the main distribution network at time t. R represents the voltage phasor output by the converter valve in the modular multilevel converter at time t. mmc This represents the equivalent resistance of a modular multilevel converter. This represents the equivalent reactance of a modular multilevel converter, where j represents the imaginary unit.

[0072] The complex power flowing into the MMC from the main distribution network is calculated based on the active and reactive power flowing into the modular multilevel converter from the main distribution network. It can be converted into voltage phasors at the connection point between the MMC and the main distribution network, and current flowing through the MMC. Therefore, based on the complex power, we can obtain:

[0073]

[0074] In the formula, This represents the complex power flowing into the modular multilevel converter from the main distribution network. This represents the active power flowing into the modular multilevel converter from the main distribution network at time t. This represents the reactive power flowing into the modular multilevel converter from the main distribution network at time t. This represents the voltage phasor at the connection point between the modular multilevel converter and the main distribution network at time t. This represents the current flowing through the modular multilevel converter at time t. * Indicates conjugate;

[0075] The electrical energy converted by the MMC should be equivalent to the electrical energy flowing into the low-voltage DC bus from the MMC. Therefore, the active power flowing into the converter bridge of the MMC should be equal to the active power flowing into the low-voltage DC bus from the MMC.

[0076]

[0077] In the formula, This represents the active power flowing into the converter bridge of the modular multilevel converter. This indicates the active power flowing into the low-voltage DC bus from the modular multilevel converter.

[0078] exist In the expression, the voltage phasor at the connection point between the MMC and the main distribution network. The voltage phasor output from the converter valve in the MMC Calculated based on the following formula:

[0079]

[0080] In the formula, This represents the voltage amplitude at the connection point between the modular multilevel converter and the main distribution network at time t. This represents the voltage phase angle at the connection point between the modular multilevel converter and the main distribution network at time t. The voltage amplitude output by the converter valve in the modular multilevel converter at time t represents the voltage value. The phase angle of the voltage output from the converter valve in the modular multilevel converter at time t is represented.

[0081] The active power flowing into the MMC from the main distribution network in the complex power calculation formula Reactive power flowing into the MMC from the main distribution network The following formulas are used for calculation:

[0082]

[0083] Where, θ t mmc α tmmc Y mmc As auxiliary variables, they are represented as follows:

[0084]

[0085] As can be seen, the active power flowing into the MMC from the main distribution network and reactive power In fact, it is calculated based on the equivalent resistance and equivalent reactance of the MMC, the voltage phasor and voltage phase angle at the connection point between the MMC and the main distribution network, and the voltage phasor and voltage phase angle output by the converter valve in the MMC.

[0086] Active power flowing into the low-voltage DC bus from the MMC It can be represented by the voltage amplitude at the connection between the low-voltage DC bus and the MMC and the current flowing from the MMC into the low-voltage DC bus:

[0087]

[0088] In the formula, This represents the voltage amplitude at the connection point between the low-voltage DC bus and the modular multilevel converter at time t. The current flowing into the low-voltage DC bus from the modular multilevel converter at time t represents the current.

[0089] The voltage amplitude output by the converter valve in a modular multilevel converter is determined by the DC voltage utilization rate of the MMC, the modulation index of the MMC, and the voltage amplitude at the connection between the low-voltage DC bus and the MMC.

[0090]

[0091] In the formula, μ mmc M represents the DC voltage utilization rate of the MMC. mmc This indicates the modulation scheme of MMC.

[0092] During power transmission, the main distribution network transmits AC power to the MMC (Multi-Channel Network). The MMC converts the AC power through the switching of the converter bridge and inputs the converted DC power to the low-voltage DC target. Therefore, the electrical energy flowing into the converter bridge in the MMC for conversion can be expressed as:

[0093]

[0094] In this embodiment, the power conversion law of MMC in the AC-DC conversion process is mathematically characterized by the above formulas (1) to (11), thereby constructing a refined power transmission model. In the collaborative optimization model, its parameters need to meet the power constraints of the power transmission model. Therefore, the above mathematical expressions of the power transmission model are the modular multilevel converter power transmission constraints in the constraints of the collaborative optimization model. It should be noted that the low-voltage DC bus in the above formulas refers to the low-voltage DC bus in the power transmission layer.

[0095] In addition to the power transmission constraints of the modular multilevel converter, the constraints in the collaborative optimization model also need to satisfy low-voltage distribution area operation constraints when solving for its variables. These low-voltage distribution area operation constraints include low-voltage DC distribution area operation constraints and low-voltage AC distribution area operation constraints. Specifically, the low-voltage AC distribution area operation constraints include the first node active power balance constraint, the first node reactive power balance constraint, the first line power flow constraint, the first node voltage constraint, the first new energy equipment constraint, and the first energy storage operation constraint. Their specific expressions are shown below:

[0096] The active power balance constraint at the first node is expressed as:

[0097]

[0098] In the formula, Let t be the active power output of the distributed power source on the low-voltage AC transformer substation n at time t. The net output of the energy storage device at the low-voltage AC power node n at time t; Let be the resistance of the AC line between power node m and power node n; It is the voltage amplitude at power node m at time t; This represents the active power of the AC line between power node m and power node n at time t; This represents the reactive power of the AC line between power node m and power node n at time t; It is the active power of the AC line between power node n and power node k at time t; It is the normal load of power node n at time t; It is the active power flowing from the low-voltage DC bus into the root node of the low-voltage AC distribution area; It is the electrical power flowing from the root node to the child node in the low-voltage AC distribution area; ψ loss It is the loss factor of active power transmission.

[0099] The reactive power balance constraint at the first node can be expressed as:

[0100]

[0101] In the formula, Let t be the reactive power output of the distributed power source on the low-voltage AC transformer substation n at time t. Let be the reactance of the AC line between power node m and power node n at time t; It is the conventional reactive load of power node n; This represents the active power of the AC line between power node m and power node n at time t; This represents the node voltage of power node m at time t; This represents the reactive power of the AC line between power node m and power node n at time t; This represents the reactive power of the AC line between power node n and power node k at time t.

[0102] The first-line power flow constraint is represented as:

[0103]

[0104] In the formula, This represents the upper limit of AC line capacity between power node m and power node n.

[0105] The voltage constraint at the first node is expressed as follows:

[0106]

[0107] In the formula, It is the voltage amplitude at power node m at time t. It is the voltage amplitude at power node n at time t. V a and These are the lower and upper limits of the node voltage, respectively.

[0108] The first constraint on new energy equipment is expressed as follows:

[0109]

[0110] In the formula, The capacity of the distributed power source on power node n in the low-voltage AC distribution area; and These represent the lower and upper limits of the active power output of distributed power sources on power node n in the low-voltage AC distribution area, respectively.

[0111] The first energy storage operation constraint is expressed as:

[0112]

[0113] In the formula, Let t be the net output of the energy storage device at the low-voltage AC power node n in the distribution area. and These represent the discharge power and charging power of the energy storage device at the low-voltage AC power node n at time t, respectively. and These represent the upper limits of the discharge ratio and the charging ratio of energy storage devices, respectively. and S represents the discharge and charging states of the energy storage device at power node n in the low-voltage AC distribution area at time t, respectively; es,n E represents the installed capacity of energy storage at power node n in the low-voltage AC distribution area; es,n,t ξ represents the real-time capacity of the energy storage device at power node n in the low-voltage AC distribution area at time t, σ represents the self-discharge rate of the energy storage device, and ξ represents the energy storage device's self-discharge rate. ch and ξ dis These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. Υ es and These represent the real-time lower and upper capacity limits of energy storage devices, respectively.

[0114] Similarly, the operational constraints of low-voltage DC distribution areas include the active power balance constraint of the second node, the power flow constraint of the second line, the voltage constraint of the second node, the constraint of the second new energy equipment, and the operational constraints of the second energy storage. Specifically, the active power balance constraint of the second node is expressed as follows:

[0115]

[0116] In the formula, The active power output of the distributed power source at the low-voltage DC power node j at time t; The net output of the energy storage device at the low-voltage DC power node j at time t; Let be the active power on the DC line between power node i and power node j at time t. Let be the active power on the DC line between power node j and power node w at time t; Let be the resistance of the DC line between power node i and power node j; It is the voltage amplitude of power node i at time t; It is the normal load of power node j at time t; It is the active power flowing into the root node of the low-voltage DC distribution area from the low-voltage DC bus at time t; It is the electrical power flowing from the root node to the child node of the low-voltage DC distribution area at time t.

[0117] The power flow constraint for the second route is expressed as follows:

[0118]

[0119] In the formula, This represents the upper limit of the current carrying capacity of the DC line between power node i and power node j.

[0120] The voltage constraint at the second node is expressed as follows:

[0121]

[0122] In the formula, V d and These are the lower and upper limits of the node voltage, respectively.

[0123] The constraints on the second new energy equipment are expressed as follows:

[0124]

[0125] In the formula, and These represent the lower limit and upper limit of active power output of distributed power sources on power node j in the low-voltage DC distribution area, respectively.

[0126] The second energy storage operation constraint is expressed as follows:

[0127]

[0128]

[0129] In the formula, and These represent the discharge power and charging power of the energy storage device at low-voltage DC power node j at time t, respectively. and These represent the upper limit of the energy storage discharge ratio and the upper limit of the charging ratio, respectively. and These represent the discharge and charging states of the energy storage device at low-voltage DC power node j at time t, respectively. This represents the energy storage capacity of power node j; The real-time capacity of the energy storage device at low-voltage DC power node j at time t is represented. This represents the self-discharge rate of the energy storage device at power node j; Υ d,es and These represent the lower and upper limits of real-time energy storage capacity, respectively. and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively.

[0130] In the collaborative optimization scheduling process, in addition to satisfying the constraints of upper-level power transmission and the operational constraints of lower-level low-voltage distribution areas, there is also a power-based coupling relationship between the upper-level power transmission layer and the lower-level low-voltage distribution area operation layer. Therefore, this embodiment also constructs coupling constraints based on the equivalence relationship between the active power flowing into the low-voltage DC bus from the modular multilevel converter and the active power flowing into the root node of the low-voltage distribution area from the low-voltage DC bus. The formula is expressed as follows:

[0131]

[0132] In the formula, This represents the active power flowing into the low-voltage DC bus from the modular multilevel converter at time t. This represents the active power flowing into the root node of the low-voltage AC distribution area from the low-voltage DC bus at time t. This represents the active power flowing into the root node of the low-voltage DC distribution area from the low-voltage DC bus at time t.

[0133] After the above steps, a collaborative optimization scheduling model can be constructed. The collaborative optimization scheduling model takes minimizing the bus loss of the low-voltage distribution area as the objective function and the above formulas (1) to (40) as constraints. It can be seen that in the process of modeling power electronic interconnection devices, the collaborative optimization scheduling model adopts refined modeling of modular multilevel converters. In the process of modeling low-voltage distribution areas, line power loss and node voltage drop constraints are considered, so that the model can fully reflect the internal operating characteristics of low-voltage distribution areas and accurately reflect the dynamic response and regulation capabilities of power electronic equipment under actual working conditions.

[0134] While this refined modeling provides a more reliable modeling foundation for differentiated control strategies and refined energy management, thereby enabling high-quality scheduling decisions, it also introduces a large number of nonlinearities and implicit constraints, resulting in a complex optimization model structure. To improve the model's solution efficiency, reduce the contradiction between modeling and solving, and enhance the model's practical application value, this embodiment selects automatic differentiation technology to handle constraints in the model's solution strategy, thereby improving solution efficiency. The specific solution steps include:

[0135] Establish a compact nonlinear optimization model corresponding to the aforementioned collaborative optimization scheduling model;

[0136] Based on the automatic differentiation technique, the variables to be solved in the compact nonlinear optimization model are declared as active variables, and the variables to be solved are initialized.

[0137] The equality constraints of the compact nonlinear optimization model are explicitly expressed using the active variables, and the Jacobian matrix of the equality constraints is generated based on the Jacobian function.

[0138] A linear equation is constructed based on the Jacobian matrix, and the linear equation is solved using the trigonometric method to obtain the updated variables to be solved.

[0139] Based on the updated variables to be solved, determine whether the objective function has converged. If it has, take the updated variables to be solved as the optimal solution and generate a collaborative optimization scheduling strategy based on the optimal solution. Otherwise, continue iterative calculation according to the above steps until the objective function converges.

[0140] In this embodiment, the above-mentioned cooperative optimization scheduling model is first converted into a corresponding compact nonlinear optimization model, the specific expression of which is shown below:

[0141]

[0142] In the formula, f(x) represents the bus loss of the low-voltage distribution area, x is the independent variable, x = [P, Q, θ, V, I, B, E], where P is a column vector composed of active power related variables, Q is a column vector composed of reactive power related variables, θ is a column vector composed of voltage phase angle related variables, V is a column vector composed of voltage amplitude related variables, I is a column vector composed of current amplitude related variables, B is a column vector composed of power equipment binary state variables, and E is a column vector composed of energy storage capacity related variables; G(x) is the equality constraint condition in the collaborative optimization scheduling model, h(x) is the inequality constraint condition in the collaborative optimization scheduling model, and H and These are the lower and upper limits of the inequality constraints, respectively.

[0143] Then, automatic differentiation is used to solve the above compact nonlinear optimization model. During the solution process, the independent variable x is declared as an active variable in the automatic differentiation system, and the constraints are explicitly expressed using active variables. The Jacobian function is then called to generate the Jacobian matrix, which approximates the nonlinear problem as a linear problem. The linear problem is then solved iteratively. After each solution, it is determined whether the function has converged. If it has converged, the optimal power flow is output; otherwise, the next iteration is performed. Based on the above solution strategy, the specific solution steps can be expressed as follows:

[0144] 1. Set an initial value for the independent variable x;

[0145] 2. In the automatic differentiation system, set a set of active variables X and assign the initial value of x to X;

[0146] 3. Set the iteration count s = 1;

[0147] 4. Based on the expression of the equality constraint G(x), replace x with X to obtain the explicit expression of G(x);

[0148] 5. Call the Jacobian function to generate the Jacobian matrix of G(x);

[0149] 6. The triangular decomposition method is used to solve the linear equation based on the Jacobian matrix, and the increment Δx of the variable to be solved at the s-th iteration is obtained. (s) ;

[0150] 7. Determine f(x+Δx) (s) The convergence of the algorithm is checked; if convergence is found, the iteration ends, and the updated solution variable x+Δx is changed. (s) If the solution is not optimal, the model will proceed to the next iteration.

[0151] In this embodiment, the core of iteratively solving the nonlinear optimization problem is to construct a linear approximation subproblem using the derivative information (such as the Jacobian matrix) at the current point, solve for the increment of the variable, update the variable values, and gradually approach the optimal solution. The optimization model of the multi-low-voltage distribution system is a nonlinear constrained optimization problem, and the Jacobian matrix of its equality constraints (reflecting the sensitivity of the constraints to the variables) is key to constructing the linear subproblem. For the equality constraint G(x) = 0, at the independent variable x at the current iteration point, it can be approximated by Taylor expansion as follows:

[0152] G(x+Δx)≈G(x)+J(x)Δx=0

[0153] In the formula, J(x) is the Jacobian matrix at the independent variable x based on the iteration point;

[0154] Based on the Taylor expansion above, the linear equation can be obtained as follows:

[0155] J(x)Δx=-G(x)

[0156] Then, the triangular decomposition method is used to solve the above linear equations to obtain the increment Δx of the variable to be solved. Then, the original variable and the calculated increment are substituted into the objective function f to determine its convergence. Based on the judgment result, the optimal solution of the model is finally obtained.

[0157] After obtaining the optimal solution of the collaborative optimization scheduling model, a corresponding collaborative optimization scheduling strategy is generated based on the optimal solution, and the low-voltage distribution area system is scheduled and controlled according to the collaborative optimization scheduling strategy. The specific steps for generating the scheduling strategy can refer to the conventional generation steps, and will not be described in detail here.

[0158] This embodiment provides a collaborative optimization scheduling method for multiple low-voltage distribution areas. Based on a DC distribution center control architecture with multiple low-voltage distribution areas connected, this invention performs refined modeling of the power transmission of modular multilevel converters and the operation of low-voltage distribution areas. Using this refined model as a constraint, a collaborative optimization scheduling model for multiple low-voltage distribution areas is established. This model accurately reflects the internal operating characteristics of the low-voltage distribution areas and the dynamic response and regulation capabilities of power electronic equipment, providing a reliable modeling foundation for differentiated control strategies and refined energy management. This enables high-quality scheduling decisions. Furthermore, based on the model characteristics and solution requirements, automatic differentiation technology is used to solve the model, effectively improving solution efficiency and reducing the contradiction between modeling and solving, thus providing an effective solution for the collaborative scheduling of low-voltage distribution areas.

[0159] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes a collaborative optimization scheduling system for multiple low-voltage distribution areas, comprising:

[0160] Data processing module 10 is used to acquire the power parameters of each low-voltage distribution area and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss.

[0161] Model building module 20 is used to establish a collaborative optimization scheduling model with the objective function of minimizing the bus loss of the low-voltage distribution area and the constraints of modular multilevel converter power transmission, low-voltage distribution area operation and coupling.

[0162] The modular multilevel converter power transmission constraint is constructed based on the medium-voltage AC power transmission mode of the modular multilevel converter. The low-voltage distribution area operation constraint includes low-voltage AC distribution area operation constraint and low-voltage DC distribution area operation constraint. The coupling constraint is constructed based on the power coupling relationship between the modular multilevel converter and the low-voltage distribution area.

[0163] The optimization scheduling module 30 is used to solve the collaborative optimization scheduling model using automatic differentiation technology to obtain a collaborative optimization scheduling strategy, and to perform scheduling control on the low-voltage distribution area according to the collaborative optimization scheduling strategy.

[0164] The technical features and effects of the collaborative optimization scheduling system for multiple low-voltage distribution areas proposed in this invention are the same as those of the method proposed in this invention, and will not be repeated here. Each module in the above-mentioned collaborative optimization scheduling system for multiple low-voltage distribution areas can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0165] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0166] Please see Figure 3 The diagram illustrates the internal structure of a computer device in one embodiment. This computer device can specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a collaborative optimization scheduling method for multiple low-voltage distribution zones. The display screen of the computer device can be a liquid crystal display (LCD) or an e-ink display. The input devices of the computer device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse, etc.

[0167] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0168] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0169] In summary, the embodiments of this invention propose a collaborative optimization scheduling method, system, device, and storage medium for multiple low-voltage distribution areas. The method acquires the power parameters of each low-voltage distribution area, calculates the low-voltage distribution area bus losses based on these parameters, including line power, line resistance, and node voltage, and the low-voltage distribution area bus losses include both low-voltage AC and low-voltage DC bus losses. A collaborative optimization scheduling model is established with minimizing the low-voltage distribution area bus losses as the objective function, and with constraints such as modular multilevel converter power transmission constraints, low-voltage distribution area operation constraints, and coupling constraints. The collaborative optimization scheduling model is solved using automatic differentiation technology to obtain a collaborative optimization scheduling strategy, and the low-voltage distribution areas are then scheduled and controlled according to this strategy. This invention constructs a collaborative optimization scheduling model for multiple low-voltage distribution areas through refined modeling, accurately reflecting the internal operating characteristics of low-voltage distribution areas and the dynamic response and regulation capabilities of power electronic equipment. By solving the model using automatic differentiation technology, the efficiency of the solution is effectively improved, enabling high-quality scheduling decisions. Thus, it provides an effective solution for differentiated control strategies and refined energy management of low-voltage distribution areas.

[0170] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0171] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A collaborative optimization scheduling method for multiple low-voltage distribution areas, characterized in that, include: Obtain the power parameters of each low-voltage distribution area, and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss. A collaborative optimization scheduling model is established with minimizing bus losses in the low-voltage distribution area as the objective function and power transmission constraints of modular multilevel converters, operation constraints of the low-voltage distribution area, and coupling constraints as constraints. The modular multilevel converter power transmission constraint is constructed based on the medium-voltage AC power transmission mode of the modular multilevel converter. The low-voltage distribution area operation constraint includes low-voltage AC distribution area operation constraint and low-voltage DC distribution area operation constraint. The coupling constraint is constructed based on the power coupling relationship between the modular multilevel converter and the low-voltage distribution area. The cooperative optimization scheduling model is solved using automatic differentiation technology to obtain a cooperative optimization scheduling strategy, and the low-voltage distribution area is scheduled and controlled according to the cooperative optimization scheduling strategy.

2. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 1, characterized in that, The steps for constructing the power transmission constraints of the modular multilevel converter include: Based on the power conversion law between the main distribution network, modular multilevel converter and low-voltage DC bus, a power transmission model based on modular multilevel converter is established. The power transmission model is used as a power transmission constraint for a modular multilevel converter.

3. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 2, characterized in that, The power transmission constraints of the modular multilevel converter are expressed by the following formula: In the formula, This represents the current flowing through the modular multilevel converter at time t. This represents the voltage phasor at the connection point between the modular multilevel converter and the main distribution network at time t. R represents the voltage phasor output by the converter valve in the modular multilevel converter at time t. mmc This represents the equivalent resistance of a modular multilevel converter. This represents the equivalent reactance of a modular multilevel converter, where j represents the imaginary unit. In the formula, This represents the active power flowing into the modular multilevel converter from the main distribution network at time t. This represents the reactive power flowing into the modular multilevel converter from the main distribution network at time t. * Indicates conjugate; In the formula, This represents the active power flowing into the converter bridge in a modular multilevel converter. This indicates the active power flowing into the low-voltage DC bus from the modular multilevel converter.

4. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 1, characterized in that, The low-voltage AC distribution area operation constraints include the first node active power balance constraint, the first node reactive power balance constraint, the first line power flow constraint, the first node voltage constraint, the first new energy equipment constraint, and the first energy storage operation constraint. The low-voltage DC distribution area operation constraints include the second node active power balance constraint, the second line power flow constraint, the second node voltage constraint, the second new energy equipment constraint, and the second energy storage operation constraint.

5. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 1, characterized in that, The steps for constructing the coupling constraints include: Based on the equivalence relationship between the active power flowing into the low-voltage DC bus from the modular multilevel converter and the active power flowing into the root node of the low-voltage distribution area from the low-voltage DC bus, coupling constraints are obtained. The active power of the low-voltage distribution area root node includes the active power of the low-voltage AC distribution area root node and the active power of the low-voltage DC distribution area root node.

6. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 5, characterized in that, The coupling constraint is expressed by the following formula: In the formula, This represents the active power flowing into the low-voltage DC bus from the modular multilevel converter at time t. This represents the active power flowing into the root node of the low-voltage AC distribution area from the low-voltage DC bus at time t. This represents the active power flowing into the root node of the low-voltage DC distribution area from the low-voltage DC bus at time t.

7. The collaborative optimization scheduling method for multiple low-voltage distribution areas according to claim 1, characterized in that, The step of solving the cooperative optimization scheduling model using automatic differentiation techniques to obtain the cooperative optimization scheduling strategy includes: Establish a compact nonlinear optimization model corresponding to the aforementioned collaborative optimization scheduling model; Based on the automatic differentiation technique, the variables to be solved in the compact nonlinear optimization model are declared as active variables, and the variables to be solved are initialized. The equality constraints of the compact nonlinear optimization model are explicitly expressed using the active variables, and the Jacobian matrix of the equality constraints is generated based on the Jacobian function. A linear equation is constructed based on the Jacobian matrix, and the linear equation is solved using the trigonometric method to obtain the updated variables to be solved. Based on the updated variables to be solved, determine whether the objective function has converged. If it has, take the updated variables to be solved as the optimal solution and generate a collaborative optimization scheduling strategy based on the optimal solution. Otherwise, continue iterative calculation according to the above steps until the objective function converges.

8. A collaborative optimization scheduling system for multiple low-voltage distribution areas, characterized in that, include: The data processing module is used to acquire the power parameters of each low-voltage distribution area, and calculate the low-voltage distribution area bus loss based on the power parameters. The power parameters include line power, line resistance and node voltage. The low-voltage distribution area bus loss includes low-voltage AC distribution area bus loss and low-voltage DC distribution area bus loss. The model building module is used to establish a collaborative optimization scheduling model with the objective function of minimizing the bus loss of the low-voltage distribution area and the constraints of modular multilevel converter power transmission, low-voltage distribution area operation, and coupling. The modular multilevel converter power transmission constraint is constructed based on the medium-voltage AC power transmission mode of the modular multilevel converter. The low-voltage distribution area operation constraint includes low-voltage AC distribution area operation constraint and low-voltage DC distribution area operation constraint. The coupling constraint is constructed based on the power coupling relationship between the modular multilevel converter and the low-voltage distribution area. The optimization scheduling module is used to solve the collaborative optimization scheduling model using automatic differentiation technology to obtain the collaborative optimization scheduling strategy, and to perform scheduling control on the low-voltage distribution area according to the collaborative optimization scheduling strategy.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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