Adjustment of terminal equipment of active distribution network and optimization method of active distribution network

Abstract

The present disclosure describes a terminal device for adjusting an active distribution network and an active distribution network optimization method, wherein the terminal device is configured to execute the active distribution network optimization method, and the active distribution network optimization method includes: constructing an objective function that considers network side costs and user side costs, wherein the network side costs include at least one of the distribution network loss costs, the intelligent soft switch loss costs, and the switch action costs, and the user side costs include the temperature control load costs; constructing multiple mathematical models based on the objective function, wherein the multiple mathematical models include at least one of the intelligent soft switch mathematical model and the distribution network optimal power flow model, and the multiple mathematical models also include the temperature control load mathematical model; solving the objective function under multiple constraints so that the objective function obtains the minimum value and obtains the optimization result; adjusting the transmission power of the intelligent soft switch and the switch state of the temperature control load based on the optimization result. Thus, the load and operation cost of the active distribution network can be well reduced.

Description

[0001] This application is a divisional application of a patent application with an application date of March 17, 2023, application number 202310282145.4, and invention name: Active distribution network optimization method based on intelligent soft switches and temperature-controlled loads. Technical Field

[0002] The present disclosure generally relates to the field of power systems, and more particularly to a terminal device for adjusting an active power distribution network and an active power distribution network optimization method. Background Art

[0003] With the rapid economic development, the scale of active distribution network is increasing, and the losses are also increasing. The introduction of intelligent soft switch has completely changed the closed-loop design and open-loop operation of traditional distribution network, greatly improved the real-time and rapidity of distribution network control, and brought many benefits to the operation of distribution network.

[0004] In the application number 201610029970.3, the invention name is "A voltage and reactive power control method for an active distribution network based on intelligent soft switches", which takes the minimum sum of voltage deviations of nodes in the distribution system as the objective function and applies intelligent soft switches to solve the voltage and reactive power control problem of the active distribution network.

[0005] However, in the above-mentioned technologies, the reconstruction of the distribution network is only based on intelligent soft switches, and the impact of the user side (that is, the temperature control load) on the optimized operation of the distribution network is often ignored. In existing research, the temperature control load is considered to be one of the most important user-side management resources, which helps to maintain the balance of power supply and demand in the future smart grid. As the proportion of temperature control loads such as water heaters, air conditioners and some large industrial users in the total power load continues to increase, the distribution network optimization solution based on intelligent soft switches is not suitable for some specific scenarios. For example, for the user side with electric water heaters as the main load, the optimization effect of the current active distribution network optimization solution is often difficult to achieve the expected effect, and often cannot effectively reduce the load and operating cost of the active distribution network. Summary of the invention

[0006] The present disclosure is proposed in view of the above situation, and its purpose is to provide an active distribution network optimization method based on intelligent soft switches and temperature-controlled loads, which can better reduce the load and operating cost of the active distribution network.

[0007] To this end, the present disclosure provides an active distribution network optimization method based on an intelligent soft switch and a temperature-controlled load, the active distribution network optimization method is a method for optimizing a network side having the intelligent soft switch and a user side having the temperature-controlled load, comprising: constructing an objective function that considers network side costs and user side costs, the network side costs comprising at least one of distribution network loss costs, intelligent soft switch loss costs and switch action costs, and the user side costs comprising temperature-controlled load costs; constructing a plurality of mathematical models based on the objective function, the plurality of mathematical models comprising an intelligent soft switch mathematical model for calculating the intelligent soft switch loss costs, and at least one of distribution network optimal power flow models for calculating the distribution network loss costs and the switch action costs, the plurality of mathematical models also comprising a temperature-controlled load mathematical model for calculating the temperature-controlled load costs, and the temperature-controlled load is an electric water heater; processing the plurality of mathematical models and a plurality of constraints based on a second-order cone programming relaxation method to obtain a plurality of target constraints; solving the objective function under the plurality of constraints so that the objective function obtains a minimum value and obtains an optimization result; adjusting the transmission power of the intelligent soft switch and the switch state of the temperature-controlled load based on the optimization result.

[0008] In this case, the objective function can cover most of the operating costs (that is, network side costs and user side costs), so that the objective function can further fit the use scenarios of the active distribution network related to the electric water heater, and can improve the accuracy of the objective function results, thereby reducing the operating costs of the active distribution network. In addition, based on multiple mathematical models, the values ​​of various parameters (for example, transmission power) in the active distribution network can be obtained more comprehensively and the relationship between the various parameters can be reflected, so that the subsequent optimization methods can be adjusted better. In addition, based on the second-order cone programming relaxation method, multiple mathematical models and multiple constraints can be converted into second-order cone models, which can simplify the calculation process of various parameters in the active distribution network and facilitate the acquisition of calculation results. In addition, based on multiple constraints, multiple mathematical models can be further limited, so that the calculation results of the objective function can be more accurate, that is, the accuracy of the optimization results can be improved. At the same time, based on the minimum value of the objective function, the optimization results with lower network side costs and user side costs can be obtained. In addition, since the active distribution network optimization method includes adjusting the transmission power of the intelligent soft switch and the switching state of the temperature-controlled load, the active distribution network optimization method can be better adjusted based on the optimization results, thereby better reducing the load and operation cost of the active distribution network.

[0009] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the objective function is derived based on the daily network side cost and the user side cost. In this case, the optimization interval of the active distribution network optimization method can be kept within a relatively appropriate range, the optimization cost can be reduced relative to a shorter optimization interval, and the calculation error caused by a longer time range can be reduced relative to a longer optimization interval, thereby improving the optimization effect.

[0010] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the objective function is:

[0011]

[0012] Wherein, i is the i-th node, j is the j-th node, ij is the branch between the i-th node and the j-th node; T is time; F is the objective function; C loss is the network loss cost coefficient; C sop is the intelligent soft switch loss cost coefficient; C switch is the switching loss coefficient; C sell is the electricity price at the user side; B is the set of all branches in the active distribution network; N SOP N is a set of nodes connected to the intelligent soft switch; h is the set of nodes where the temperature control load is located; I ij,t is the current flowing through the branch between the i-th node and the j-th node at time t; r ij is the equivalent resistance between the i-th node and the j-th node; The converter loss generated by the converter on the side where the intelligent soft switch is connected to the i-th node at time t; is the total power of the temperature control load used in the i-th node at time t; α ij is the switch action of the branch between the ith node and the jth node after the active power distribution network is reconfigured; α ij,0 is the switch action of the branch between the i-th node and the j-th node before the active distribution network is reconfigured; ΔT is the time interval. In this case, the value of the objective function can be obtained based on various parameters in the active distribution network (for example, converter loss and temperature control load power, etc.), so that the minimum value of the objective function can be obtained by adjusting various parameters. At the same time, the optimization result can be obtained based on the minimum value of the objective function.

[0013] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the intelligent soft switch mathematical model is:

[0014]

[0015]

[0016]

[0017] in, is the active power of the converter connected to the i-th node by the intelligent soft switch at time t, is the active power of the converter connected to the jth node by the intelligent soft switch at time t, is the reactive power of the converter connected to the ith node by the intelligent soft switch at time t, is the reactive power of the converter of the intelligent soft switch connected to the jth node at time t; is the converter loss connected to the i-th node, is the converter loss connected to the jth node; γ SOP is the converter loss coefficient of the intelligent soft switch; i is the i-th node, j is the j-th node, and t is the t-th time. Thus, the active power and reactive power (that is, transmission power) of the node can be adjusted based on the converter of the intelligent soft switch, thereby reducing the loss of the active distribution network, thereby facilitating the optimization of the active distribution network.

[0018] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the temperature control load is an electric water heater, and the temperature control load mathematical model is:

[0019]

[0020] Where t is time t, T is temperature, Δt is time interval, T t d T is the water temperature of the electric water heater at time t; t s is the ambient temperature at time t; R d and C d are the thermal resistance and thermal capacity of the electric water heater respectively; L is the total capacity of the water tank of the electric water heater; l t is the unit water consumption of the user at time t. represents the working state of the electric water heater at time t, wherein, When , it means that the electric water heater is in the insulation mode. , it indicates that the electric water heater is in heating mode; is the heat consumption coefficient of the electric water heater; dis the active power of the electric water heater during heating. In this case, the water temperature of the electric water heater at a certain time interval can be obtained based on the temperature control load mathematical model, which can fit the use scenario of the active distribution network on the user side. In addition, the water temperature of the electric water heater based on a certain time interval can facilitate the adjustment of the working mode (that is, insulation mode and heating mode) and working state (that is, on and off) of the electric water heater, thereby reducing the loss of the active distribution network and the temperature control load cost.

[0021] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the distribution network optimal power flow model is:

[0022]

[0023]

[0024] Where p is the active power injected into the node, q is the reactive power injected into the node; P is the active power, Q is the reactive power; i is the i-th node, j is the j-th node, k is the k-th node; ij is the branch between the i-th node and the j-th node, jk is the branch between the j-th node and the k-th node; P jk,t is the active power of the branch between the jth node and the kth node at time t, P ij,t is the active power of the branch between the i-th node and the j-th node at time t; Q jk,t is the reactive power of the branch between the jth node and the kth node at time t, Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; I ij,t is the current flowing through the branch between the i-th node and the j-th node at time t, r ij is the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t, V j,t is the voltage of the jth node at time t; P j,t is the active power injected into the jth node at time t, Q j,t is the reactive power injected by the jth node at time t; δ(j) is the parent node set of the jth node in the active distribution network, π(j) is the child node set of the jth node in the active distribution network; g j,t are the equivalent conductance of the load connected to the jth node at time t, b j,t is the equivalent susceptance of the load connected to the jth node at time t; N is the node set. In this case, at least one of the active power, reactive power, current and voltage can be adjusted based on the optimal power flow model of the distribution network, thereby reducing the loss of the active distribution network.

[0025] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the multiple constraints include at least one of the intelligent soft switch capacity constraint, the upper and lower limit constraints of the water temperature of the electric water heater, the active distribution network flow constraint, the active distribution network safety constraint, the photovoltaic power output constraint considering reactive power, the switch action number constraint, and the distribution network radiation operation constraint. In this case, the values ​​of each parameter in the intelligent soft switch mathematical model, the temperature control load mathematical model, and the distribution network optimal power flow model can be further restricted, so that the calculation result of the objective function can be made more accurate, that is, the accuracy of the optimization result can be improved.

[0026] In addition, in the active power distribution network optimization method involved in the present disclosure, optionally, the heat preservation mode and the heating mode are set based on the upper and lower limits of the water temperature of the electric water heater. In this case, the working mode of the electric water heater (that is, the heat preservation mode and the heating mode) can be adjusted based on the water temperature of the electric water heater, so that the water temperature of the electric water heater can be within the range of the upper and lower limits of the water temperature.

[0027] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the active distribution network power flow constraint is processed based on the Big M method, thereby simplifying the calculation process of the active distribution network power flow constraint.

[0028] In addition, in the active distribution network optimization method involved in the present disclosure, optionally, the objective function is solved by a solver so that the objective function obtains a minimum value and obtains the optimization result, and the solver is a YALMIP+CPLEX platform. In this case, since the objective function and multiple objective constraints include multiple parameters, the minimum value of the objective function can be obtained more accurately and conveniently based on the YALMIP+CPLEX platform, thereby obtaining the optimization result.

[0029] According to the present disclosure, an active distribution network optimization method based on intelligent soft switches and temperature-controlled loads can be provided, which can better reduce the load and operating cost of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:

[0031] Figure 1 is an exemplary scenario diagram showing the active distribution network optimization method involved in the examples of the present disclosure.

[0032] Figure 2 Detailed description is given of an exemplary block diagram of an active distribution network in an active distribution network optimization method according to an example of the present disclosure.

[0033] Figure 3Detailed description is given of an exemplary flow chart of an active power distribution network optimization method according to an example of the present disclosure.

[0034] Figure 4 It is a schematic diagram showing the change of the working mode of the electric water heater in the active power distribution network optimization method involved in the example of the present disclosure over time.

[0035] Figure 5 Detailed description is given of an exemplary structural diagram of an active distribution network in an active distribution network optimization method according to the present disclosure.

[0036] Figure 6 1 is an exemplary comparison diagram showing the operating time of the electric water heaters in Embodiment 1, Embodiment 2 and Embodiment 3 involved in the present disclosure.

[0037] Figure 7 1 is an exemplary comparison diagram showing load curves of electric water heaters in Embodiment 1, Embodiment 2 and Embodiment 3 of the present disclosure.

[0038] Figure 8 1 is an exemplary comparison diagram showing the transmission power of the intelligent soft switch in Embodiment 1 and Embodiment 3 of the present disclosure.

[0039] Fig. 9 1 is an exemplary comparison diagram showing the voltage values ​​of each node in the active power distribution network in Embodiment 1, Embodiment 2 and Embodiment 3 involved in the present disclosure. DETAILED DESCRIPTION

[0040] Hereinafter, the preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same symbols are assigned to the same components, and repeated descriptions are omitted. In addition, the accompanying drawings are only schematic diagrams, and the ratio of the dimensions of the components or the shapes of the components may be different from the actual ones.

[0041] It should be noted that the terms "including" and "having" and any variations thereof in the present disclosure, such as a process, method, system, product or device including or having a series of steps or units, are not necessarily limited to those steps or units clearly listed, but may include or have other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. All methods described in the present disclosure may be performed in any suitable order, unless otherwise indicated herein or clearly contradicted by the context.

[0042] In some examples, the terms "one", "an" and "the" and similar references used in the context of describing specific examples of the present disclosure (particularly in the context of the claims below) may be interpreted as covering both singular and plural forms. The description of the numerical range here is intended only to be used as a shorthand method for individually referring to each individual value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually listed here. All methods described in the present disclosure may be performed in any suitable order, unless otherwise indicated herein or clearly contradictory to the context. The use of any and all examples or exemplary language (e.g., "such as") provided with respect to certain examples in the present disclosure is intended only to better illustrate the present disclosure, rather than to limit the scope of the present disclosure. Any language in the specification should not be interpreted as indicating any non-claimed element that is essential for the implementation of the present disclosure.

[0043] In some examples, the active distribution network can sometimes be referred to as a distribution network or a distribution system, etc., and the active distribution network optimization method based on intelligent soft switches and temperature-controlled loads can sometimes be referred to as an active distribution network optimization method, a distribution network optimization method, an optimization method or a method, etc.

[0044] In some examples, the active power distribution network optimization method may be a method for optimizing the network side and the user side, wherein the network side may have an intelligent soft switch and the user side may have a temperature-controlled load.

[0045] Figure 1 is an exemplary scenario diagram showing the active distribution network optimization method involved in the examples of the present disclosure.

[0046] In some examples, reference Figure 1 In the scenario, the active distribution network 1 can be adjusted based on a terminal device 2 (eg, a computer), wherein the terminal device 2 can be configured to be loaded with an application that can be used to implement the active distribution network 1 optimization method involved in the present disclosure.

[0047] Figure 2 is an exemplary block diagram showing an active power distribution network 1 in an active power distribution network 1 optimization method according to an example of the present disclosure.

[0048] In some examples, reference Figure 2 The active distribution network 1 may include at least one of an intelligent soft switch 11 , a temperature control load 12 , a switch 13 , an AC power source 14 and / or a photovoltaic power source 15 .

[0049] In some examples, the intelligent soft switch 11 may include a fully controlled device and may be controlled based on a control signal. In some examples, the intelligent soft switch 11 may include at least one of a gate turn-off thyristor, a power field effect transistor, a field effect transistor, and an insulated gate bipolar transistor. In this case, it is possible to control on or off based on a control signal.

[0050] In some examples, the smart soft switch 11 can be connected to two nodes in the active distribution network 1. In some examples, the two nodes can be adjacent or a certain number of nodes can be separated between the two nodes. In this case, the access position of the smart soft switch 11 can be flexibly selected, so that the optimization of the active distribution network 1 can be facilitated.

[0051] In some examples, the smart soft switch 11 may include a converter. In some examples, the converter may be connected to two nodes of the active power distribution network 1 to which the smart soft switch 11 is connected. In this case, the transmission power of the node to which the smart soft switch 11 is connected can be controlled based on the converter, so that the power flow (i.e., at least one of current, voltage, and power) distribution of the power distribution network can be optimized.

[0052] In some examples, the temperature control load 12 may include at least one of an air conditioner, an electric water heater, and a refrigerator.

[0053] Figure 3 Detailed description of the invention is given in conjunction with a flowchart of an exemplary method for optimizing an active power distribution network 1 according to an example of the present disclosure.

[0054] In some examples, reference Figure 3 The active distribution network 1 optimization method may include constructing an objective function that takes into account network-side costs and user-side costs (step S001), constructing multiple mathematical models based on the objective function (step S002), obtaining multiple target constraints (step S003), obtaining optimization results (step S004), and adjusting the transmission power of the intelligent soft switch 11 and the switching state of the temperature control load 12 based on the optimization results (step S005).

[0055] In some examples, in step S001, the objective function that takes into account the network side cost and the user side cost may sometimes be referred to as an objective function, etc.

[0056] In some examples, the network side cost may include at least one of the distribution network loss cost, the intelligent soft switch 11 loss cost, and the switch 13 action cost; in some examples, the user side cost may include the temperature control load 12 cost. In this case, most of the operating costs (that is, the network side costs and the user side costs) can be covered, so that the objective function can further fit the use scenario of the active distribution network related to the electric water heater, and the accuracy of the objective function result can be improved, thereby reducing the operating cost of the active distribution network 1.

[0057] In some examples, the network loss cost of the distribution network may be the cost of the loss generated during the operation of the distribution network, such as line loss or power loss. In some examples, the loss cost of the smart soft switch 11 may be the cost of the power loss generated by configuring the smart soft switch 11, that is, the cost of the converter loss connected to the smart soft switch 11. In some examples, the action cost of the switch 13 may be the cost generated by the action change of the switch 13 in the active distribution network 1. In some examples, the cost of the temperature control load 12 may be the cost of the power loss generated by the temperature control load 12. Thus, the objective function can be constructed based on the above costs.

[0058] In some examples, the objective function may be:

[0059]

[0060] , where i is the i-th node, j is the j-th node, ij is the branch between the i-th node and the j-th node; T is time; F is the objective function; C loss is the network loss cost coefficient; C sop is the loss cost coefficient of the intelligent soft switch 11; C switch is the action loss coefficient of switch 13; C sell is the electricity price at the user side; B is the set of all branches in the active distribution network 1; N SOP N is a set of nodes connected to the intelligent soft switch 11; h is the set of nodes where the temperature control load 12 is located; I ij,t is the current flowing through the branch between the i-th node and the j-th node at time t; r ij is the equivalent resistance between the i-th node and the j-th node; is the converter loss generated by the converter on the side of the intelligent soft switch 11 connected to the i-th node at time t; is the total power of the temperature control load 12 used in the i-th node at time t; α ij The switch 13 of the branch between the i-th node and the j-th node after the active distribution network 1 is reconfigured; α ij,0The switch 13 of the branch between the ith node and the jth node is actuated before the network reconstruction of the active power distribution network 1; ΔT is the time interval.

[0061] Among them, when α ij = 0, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is disconnected. ij =1, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is closed; when α ij,0 = 0, the switch 13 of the branch between the i-th node and the j-th node is disconnected before the network reconstruction of the active distribution network 1. ij,0 =1, the switch 13 of the branch between the i-th node and the j-th node is closed before the network reconstruction of the active distribution network 1.

[0062] In some examples, the network reconstruction of the active distribution network 1 may refer to changing the topology of the network of the active distribution network 1. In other words, the network reconstruction of the active distribution network 1 may be performed by adjusting the action of the switch 13 in the active distribution network 1 (i.e., opening and / or closing). Among them, the switch 13 may be at least one of a section switch and a tie switch. In some examples, the active distribution network 1 may include a plurality of switches 13. In some examples, the plurality of switches 13 may be arranged between each node in the branch of the active distribution network 1 and / or between each branch. In this case, the connection state of each node and each branch in the active distribution network 1 can be adjusted based on the action of the switch 13 in the active distribution network 1, so as to facilitate the network reconstruction of the active distribution network 1.

[0063] In some examples, the objective function can be derived based on the network side cost and the user side cost of a preset time, and the preset time can be set based on demand. For example, the preset time can be 1 hour, 1 day, or 1 week. In this case, the preset time can be flexibly adjusted based on demand, so that the values ​​of the objective functions in multiple time dimensions can be derived.

[0064] In some examples, preferably, the objective function can be derived based on the daily network side cost and user side cost. In this case, the optimization interval of the active distribution network optimization method can be kept within a relatively appropriate range, the optimization cost can be reduced compared to a shorter optimization interval, and the calculation error caused by the longer time range can be reduced compared to a longer optimization interval, thereby improving the optimization effect.

[0065] In some examples, in step S002, multiple mathematical models can be constructed based on the objective function. In this case, the values ​​of various parameters (e.g., transmission power) in the active power distribution network 1 can be obtained more comprehensively based on multiple mathematical models, and the relationship between various parameters can be reflected, so that the subsequent optimization method can be adjusted better.

[0066] In some examples, the plurality of mathematical models may include a mathematical model of the smart soft switch 11. The mathematical model of the smart soft switch 11 may be used to calculate the loss cost of the smart soft switch 11.

[0067] In some examples, the mathematical model of the intelligent soft switch 11 may be:

[0068]

[0069]

[0070]

[0071] in, is the active power of the converter connected to the i-th node by the intelligent soft switch 11 at time t, is the active power of the converter connected to the jth node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the ith node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the jth node by the intelligent soft switch 11 at time t; is the converter loss connected to the i-th node, is the converter loss connected to the jth node; γ SOP is the converter loss coefficient of the intelligent soft switch 11; i is the i-th node, j is the j-th node, and t is the time t. It should be noted that SOP can be used to represent relevant parameters of the intelligent soft switch 11 (for example, active power, reactive power or converter loss, etc.).

[0072] In some examples, the plurality of mathematical models may further include a mathematical model of the temperature control load 12. The mathematical model of the temperature control load 12 may be used to calculate the cost of the temperature control load 12.

[0073] In some examples, the temperature control load 12 may be an electric water heater, and the mathematical model of the temperature control load 12 may be:

[0074]

[0075] Where t is time t, T is temperature, Δt is time interval, T t d is the water temperature of the electric water heater at time t; T ts is the ambient temperature at time t; R d and C d are the thermal resistance and thermal capacity of the electric water heater respectively; L is the total capacity of the water tank of the electric water heater; l t is the unit water consumption of the user at time t. represents the working state of the electric water heater at time t, where When the electric water heater is in the insulation mode, When , it means the electric water heater is in heating mode; is the heat consumption coefficient of the electric water heater; P d It is the active power of the electric water heater during heating. It should be noted that d can be used to represent the relevant parameters of the above electric water heater (for example, the water temperature of the electric water heater, etc.).

[0076] In this case, the water temperature of the electric water heater at a certain time interval can be obtained based on the mathematical model of the temperature control load 12. In addition, the water temperature of the electric water heater based on the certain time interval can facilitate the adjustment of the working mode (i.e., insulation mode and heating mode) and working state (i.e., on and off) of the electric water heater, thereby reducing the loss of the active distribution network 1 and the cost of the temperature control load 12.

[0077] In some examples, the plurality of mathematical models may include an optimal power flow model for a power distribution network, wherein the optimal power flow model for a power distribution network may be used to calculate the network loss cost of the power distribution network and the operation cost of the switch 13 .

[0078] In some examples, the distribution network optimal power flow model may be:

[0079]

[0080]

[0081] Where p is the active power injected into the node, q is the reactive power injected into the node; P is the active power, Q is the reactive power; i is the i-th node, j is the j-th node, k is the k-th node; ij is the branch between the i-th node and the j-th node, jk is the branch between the j-th node and the k-th node; P jk,t is the active power of the branch between the jth node and the kth node at time t, P ij,t is the active power of the branch between the i-th node and the j-th node at time t; Q jk,t is the reactive power of the branch between the jth node and the kth node at time t, Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; I ij,t is the current flowing through the branch between the i-th node and the j-th node at time t, r ijis the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t, V j,t is the voltage of the jth node at time t; P j,t is the active power injected into the jth node at time t, Q j,t is the reactive power injected by the jth node at time t; δ(j) is the parent node set of the jth node in the active distribution network 1, π(j) is the child node set of the jth node in the active distribution network 1; g j,t are the equivalent conductance of the load connected to the jth node at time t, b j,t is the equivalent susceptance of the load connected to the jth node at time t; N is the node set. In this case, at least one of the active power, reactive power, current and voltage can be adjusted based on the optimal power flow model of the distribution network, thereby reducing the loss of the active distribution network 1.

[0082] In other examples, we can make the above model That is, the optimal power flow model of the distribution network can be:

[0083]

[0084]

[0085] Where p is the active power injected by the node, q is the reactive power injected by the node; P is the active power of the branch, Q is the reactive power of the branch; i is the i-th node, j is the j-th node, k is the k-th node; ij is the branch between the i-th node and the j-th node, jk is the branch between the j-th node and the k-th node; P jk,t is the active power of the branch between the jth node and the kth node at time t, P ij,t is the active power of the branch between the i-th node and the j-th node at time t; Q jk,t is the reactive power of the branch between the jth node and the kth node at time t, Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; is the square of the current in the branch between the i-th node and the j-th node at time t, r ij is the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t, is the square of the voltage of the jth node at time t; P j,t is the active power injected into the jth node at time t, Q j,tis the reactive power injected by the jth node at time t; δ(j) is the parent node set of the jth node in the active distribution network 1, π(j) is the child node set of the jth node in the active distribution network 1; gj ,t are the equivalent conductance of the load connected to the jth node at time t, b j,t is the equivalent susceptance of the load connected to the jth node at time t; N is the node set. In this case, the nonlinear mathematical model can be transformed into a linear mathematical model, thereby simplifying the calculation process and improving the accuracy of the calculation.

[0086] In some examples, in step S003, multiple mathematical models and multiple constraints may be processed to obtain multiple target constraints.

[0087] In some examples, the multiple constraints may include at least one of the capacity constraint of the intelligent soft switch 11, the upper and lower limit constraints of the water temperature of the electric water heater, the power flow constraint of the active distribution network 1, the safety constraint of the active distribution network 1, the output constraint of the photovoltaic power source 15 considering reactive power, the number of operation constraints of the switch 13, and the radial operation constraint of the distribution network. In this case, the values ​​of each parameter in the mathematical model of the intelligent soft switch 11, the mathematical model of the temperature control load 12, and the optimal power flow model of the distribution network can be further restricted, so that the calculation result of the objective function can be made more accurate, that is, the accuracy of the optimization result can be improved.

[0088] In some examples, the capacity constraint of the smart soft switch 11 may be:

[0089]

[0090]

[0091] in, is the active power of the converter connected to the i-th node by the intelligent soft switch 11 at time t, is the active power of the converter connected to the jth node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the ith node by the intelligent soft switch 11 at time t, is the reactive power of the converter of the intelligent soft switch 11 connected to the j-th node at time t; i is the i-th node, j is the j-th node, and t is the t-th time; is the capacity of the converter connected to the i-th node, is the capacity of the converter connected to the jth node. Therefore, the active power and reactive power can be limited.

[0092] In some examples, the upper and lower limit constraints of the water temperature of the electric water heater may be:

[0093] T d.min≤T t d ≤T d.max ;

[0094] Where, t is time t, T is temperature; is the water temperature of the electric water heater at time t, T d.max is the upper limit of the water temperature of the electric water heater, T d.min It is the lower limit of the water temperature of the electric water heater. Therefore, the water temperature of the electric water heater can be limited.

[0095] In some examples, the upper limit and lower limit of the water temperature of the electric water heater can be set based on the user's living habits. In this case, the water temperature of the electric water heater can be maintained in a suitable range, thereby improving the user's comfort.

[0096] In some examples, the insulation mode and the heating mode can be set based on the upper and lower limits of the water temperature of the electric water heater. In this case, the operating mode of the electric water heater (i.e., insulation mode and heating mode) can be adjusted based on the water temperature of the electric water heater, so that the water temperature of the electric water heater can be within the interval of the upper and lower limits of the water temperature.

[0097] Figure 4 is a schematic diagram showing the change of the working mode of the electric water heater in the active distribution network 1 optimization method involved in the example of the present disclosure over time. s is the starting temperature.

[0098] In some examples, reference Figure 4 When the water temperature of the electric water heater is lower than the lower limit of the water temperature of the electric water heater, the electric water heater can be in the heating mode to heat the water temperature of the electric water heater to the upper limit. When the water temperature of the electric water heater is higher than or equal to the upper limit of the water temperature of the electric water heater, the electric water heater can be in the insulation mode. In this case, based on the upper and lower limits of the water temperature of the electric water heater, the electric water heater can switch the working mode (that is, the insulation mode and the heating mode), so that the water temperature of the electric water heater can be maintained in a suitable range. Compared with the traditional electric water heater that is always in the heating mode, the loss of the electric water heater can be further reduced, thereby reducing the temperature control load 12 cost.

[0099] In some examples, the power flow constraints of the active power distribution network 1 may be:

[0100]

[0101]

[0102] Where i is the i-th node, j is the j-th node; ij is the branch between the i-th node and the j-th node, t is the t time; P ij,tis the active power of the branch between the i-th node and the j-th node at time t; Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; I ij,t is the current of the branch between the i-th node and the j-th node at time t, r ij is the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t; V i,t is the voltage of the ith node at time t, V j,t is the voltage of the jth node at time t; B is the branch set. In this case, the relationship between the various parameters in the active power distribution network 1 can be further defined.

[0103] In other examples, the above constraints can be set to That is, the power flow constraint of active distribution network 1 can be:

[0104]

[0105]

[0106] Where i is the i-th node, j is the j-th node; ij is the branch between the i-th node and the j-th node; t is the t time; P ij,t is the active power of the branch between the i-th node and the j-th node at time t; Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; is the square of the current in the branch between the i-th node and the j-th node at time t, r ij is the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t; is the square of the voltage of the ith node at time t, is the square of the voltage of the jth node at time t; B is the branch set.

[0107] In some examples, the power flow constraint of the active distribution network 1 may be processed based on the Big M method. In some examples, the power flow constraint of the active distribution network 1 processed by the Big M method may be:

[0108]

[0109]

[0110] Where i is the i-th node, j is the j-th node; ij is the branch between the i-th node and the j-th node; t is the t time; Pij,t is the active power of the branch between the i-th node and the j-th node at time t; Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; is the square of the current in the branch between the i-th node and the j-th node at time t, r ij is the equivalent resistance connected between the i-th node and the j-th node, x ij,t is the equivalent inductive reactance connected between the i-th node and the j-th node at time t; is the square of the voltage of the ith node at time t, is the square of the voltage of the jth node at time t; B is a branch set; M is an arbitrary integer. Thus, the calculation process of the power flow constraint of the active distribution network 1 can be simplified.

[0111] In some examples, the active power distribution network 1 security constraints may be:

[0112]

[0113]

[0114] Among them, I ij,t is the current of the branch between the i-th node and the j-th node at time t, I ij,t is the lower limit of the current in the branch between the i-th node and the j-th node at time t, is the upper limit of the current in the branch between the i-th node and the j-th node at time t; V j,t is the voltage of the jth node at time t, V j,t is the lower limit of the voltage of the jth node at time t, is the upper limit of the voltage of the jth node at time t; I is the current, V is the voltage, j is the jth node; ij is the branch between the i-th node and the j-th node; t is time t; B is the branch set, N + In this case, by setting the upper limit and lower limit of the current and voltage in the active power distribution network 1 , the current and voltage of the active power distribution network 1 can be in a suitable range, thereby improving the safety of the active power distribution network 1 .

[0115] In other examples, the above constraints can be set to That is, the security constraint of the active distribution network 1 can be:

[0116]

[0117]

[0118] in, is the square of the current in the branch between the i-th node and the j-th node at time t, I ij,t is the lower limit of the current in the branch between the i-th node and the j-th node at time t, is the upper limit of the current in the branch between the i-th node and the j-th node at time t; V j,t is the lower limit of the voltage of the jth node at time t, is the upper limit of the voltage of the jth node at time t, is the square of the voltage of the jth node at time t; I is the current, V is the voltage, j is the jth node; ij is the branch between the i-th node and the j-th node; t is time t; B is the branch set, N + A collection of nodes.

[0119] In some examples, the output constraint of the photovoltaic power source 15 considering reactive power may be:

[0120]

[0121] Where, P is active power, Q is reactive power; i is the i-th node; t is time t; and are respectively the predicted active power (that is, the maximum value of the active power) and the actual active power of the photovoltaic power source 15 connected to the i-th node at time t; is the actual reactive power of the photovoltaic power source 15 connected to the i-th node at time t; is the power factor at the ith node (i.e., the ratio of actual reactive power to actual active power); N pv is a node set of the photovoltaic power source 15. In this case, since the reactive power of the photovoltaic power source 15 can affect the operation of the active power distribution network 1 (for example, output reactive power), the output constraint of the photovoltaic power source 15 based on reactive power considerations can further limit the active power and reactive power.

[0122] In some examples, the power factor may be a constant power factor or a variable power factor. In this embodiment, preferably, the power factor may be a constant power factor. Thus, the reactive power of the photovoltaic power source 15 can be easily regulated.

[0123] In some examples, the switch 13 operation times constraint may be:

[0124]

[0125] Among them, α ijThe switch 13 of the branch between the i-th node and the j-th node after the active distribution network 1 is reconfigured; α ij,0 is the switch 13 action of the branch between the ith node and the jth node before the network reconstruction of the active distribution network 1; ij is the branch between the ith node and the jth node; is the maximum number of actions (ie, opening and / or closing) of all switches 13 in the active power distribution network 1; and B is the branch set.

[0126] Among them, when α ij = 0, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is disconnected. ij =1, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is closed; when α ij,0 = 0, the switch 13 of the branch between the i-th node and the j-th node is disconnected before the network reconstruction of the active distribution network 1. ij,0 =1, the switch 13 of the branch between the i-th node and the j-th node is closed before the network reconstruction of the active distribution network 1.

[0127] In this case, the number of operations of the switch 13 (ie, at least one of the tie switch and the section switch) in the active power distribution network 1 can be limited, thereby reducing the operation cost of the switch 13.

[0128] In some examples, the distribution network radial operation constraints may be:

[0129]

[0130] β ij +β ji =α ij ,

[0131]

[0132] β 1j =0;

[0133] Where i is the i-th node, j is the j-th node, ij is the branch between the i-th node and the j-th node, and 1j is the branch between the 1st node and the j-th node; α ij The switch 13 of the branch between the i-th node and the j-th node is actuated after the active distribution network 1 is reconfigured. ij = 0, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is disconnected. ij=1, after the active distribution network 1 is reconfigured, the switch 13 of the branch between the i-th node and the j-th node is closed; δ(j) is the parent node set of the j-th node in the active distribution network 1; when β ij =1, it means that the i-th node is the parent node of the j-th node. ij =0, it means that the i-th node is the child node of the j-th node; η B is the total number of branches in active distribution network 1.

[0134] In this case, since the network topology of the active distribution network 1 will be changed after the network reconstruction, the action of the switch 13 in the active distribution network 1 can be limited based on the radial operation constraint of the distribution network, thereby reducing the occurrence of island and ring network phenomena in the active distribution network 1, thereby maintaining the operation of the active distribution network 1.

[0135] In some examples, in step S003, multiple mathematical models and multiple constraints can be processed based on the second-order cone programming relaxation method to obtain multiple target constraints. Among them, the second-order cone programming relaxation method can be used to convert mixed integer linear programming (that is, multiple mathematical models and multiple constraints) into mixed integer second-order cone programming (that is, multiple target constraints). In this case, compared with mixed integer linear programming, the calculation process of each parameter in the active distribution network 1 can be simplified, and the calculation results can be easily obtained.

[0136] In some examples, the plurality of target constraints may include at least one of a first target constraint, a second target constraint, and a third target constraint.

[0137] In some examples, the power flow constraint of the active distribution network 1 may be processed based on the second-order cone programming relaxation method to obtain a first target constraint condition, and the first target constraint condition may be:

[0138]

[0139] Where i is the i-th node, j is the j-th node, ij is the branch between the i-th node and the j-th node, and t is the t time; P ij,t is the active power of the branch between the i-th node and the j-th node at time t; Q ij,t is the reactive power of the branch between the i-th node and the j-th node at time t; is the square of the current in the branch between the i-th node and the j-th node at time t; is the square of the voltage of the jth node at time t. Thus, the calculation process of the power flow constraint of the active power distribution network 1 can be simplified.

[0140] In some examples, the mathematical model of the intelligent soft switch 11 may be processed based on a second-order cone programming relaxation method to obtain a second target constraint condition, and the second target constraint condition may be:

[0141]

[0142]

[0143] in, is the active power of the converter connected to the i-th node by the intelligent soft switch 11 at time t, is the active power of the converter connected to the jth node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the ith node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the jth node by the intelligent soft switch 11 at time t; is the converter loss connected to the i-th node, is the converter loss connected to the jth node; γ SOP is the converter loss coefficient of the intelligent soft switch 11; i is the ith node, j is the jth node, and t is the time t. Thus, the calculation process of the mathematical model of the intelligent soft switch 11 can be simplified.

[0144] In some examples, the capacity constraint of the intelligent soft switch 11 may be processed based on a second-order cone programming relaxation method to obtain a third target constraint condition, and the third target constraint condition may be:

[0145]

[0146]

[0147] in, is the active power of the converter connected to the i-th node by the intelligent soft switch 11 at time t, is the active power of the converter connected to the jth node by the intelligent soft switch 11 at time t, is the reactive power of the converter connected to the ith node by the intelligent soft switch 11 at time t, is the reactive power of the converter of the intelligent soft switch 11 connected to the j-th node at time t; i is the i-th node, j is the j-th node, and t is the t-th time; is the capacity of the converter connected to the i-th node, is the capacity of the converter connected to the jth node. Thus, the calculation process of the capacity constraint of the intelligent soft switch 11 can be simplified.

[0148] In some examples, in step S004, the objective function may be solved based on multiple constraints to minimize the objective function and obtain an optimization result. In this case, an optimization result with lower network side cost and user side cost may be obtained based on the minimum value of the objective function.

[0149] In some examples, the objective function can be solved based on a solver to minimize the objective function and obtain an optimization result. The solver can be a YALMIP+CPLEX platform. In this case, since the objective function and multiple objective constraints include multiple parameters, the minimum value of the objective function can be obtained more accurately and conveniently based on the YALMIP+CPLEX platform, thereby obtaining an optimization result.

[0150] In some examples, in step S005, the transmission power of the intelligent soft switch 11 and the switch state of the temperature-controlled load 12 can be adjusted based on the optimization result. In this case, since the active distribution network 1 optimization method includes adjusting the transmission power of the intelligent soft switch 11 and the switch state of the temperature-controlled load 12, the active distribution network 1 optimization method can be well adjusted based on the optimization result, thereby being able to well reduce the load and operation cost of the active distribution network 1.

[0151] The following describes step S005 in detail in conjunction with a specific example of this implementation.

[0152] Figure 5 is an exemplary structural diagram showing the active distribution network 1 in the active distribution network 1 optimization method involved in the present disclosure.

[0153] In some examples, reference Figure 5 , the active power distribution network 1 may include 33 nodes, such as node 1, node 2, ... and node 33. The solid line between the nodes indicates that the nodes are connected. For example, node 23 and node 24 are connected by a solid line, indicating that node 23 and node 24 are connected. The dotted line between the nodes indicates that there is a switch 13 between the nodes. When the switch 13 is closed, the nodes at both ends of the dotted line are in a connected state. When the switch 13 is open, the nodes at both ends of the dotted line are in a disconnected state.

[0154] In some examples, reference Figure 5 , the intelligent soft switch 11 can be connected between the node 8 and the node 14, and the parameter information of the intelligent soft switch 11 is shown in Table 1.

[0155] Table 1

[0156]

[0157]

[0158] In some examples, the temperature control load 12 can be connected to node 3, node 5, node 7, node 11, node 13, node 15, node 18, node 22, node 26 and node 31, and the parameter information of the temperature control load 12 is shown in Table 2.

[0159] Table 2

[0160]

[0161] Among them, Class A users may refer to users who are at home all day, Class B users may refer to users who are at home after get off work, and Class C users may refer to users whose home time is between Class A users and Class B users; the distribution situation may refer to the average number of users connected to each node, for example, Class A users can access nodes 3, node 5, node 7, node 11, node 13, node 15, node 18, node 22, node 26 and node 31 on average, that is, the number of Class A users in each of the above nodes is 160.

[0162] In some examples, reference Figure 5 , the tie switch and the sectionalizing switch in the active distribution network 1 can be set between the node 7 and the node 20, between the node 11 and the node 21, between the node 17 and the node 32, and between the node 24 and the node 28, and the switch 13 action can refer to the opening and / or closing of the tie switch and the sectionalizing switch. In this case, the network reconstruction of the active distribution network 1 can be adjusted based on the switch 13 action.

[0163] In some examples, reference Figure 5 , the node 33 can be connected to the AC power source 14, and the nodes 5, 11, 15, 22 and 31 can be connected to the photovoltaic power source 15. Thus, the active power distribution network 1 can be provided with electric energy.

[0164] In order to further demonstrate the effect of the active distribution network 1 optimization method involved in the present disclosure, the present disclosure optimizes the active distribution network 1 in different ways, including Example 1, Example 2 and Example 3.

[0165] Embodiment 1 is a time-divided optimization, in which the objective function is constructed and minimized based on the user side cost in the first half of the preset time, and the objective function is constructed and minimized based on the network side cost in the second half of the preset time, and the network side includes an intelligent soft switch 11;

[0166] Embodiment 2 is to construct an objective function based on the network side cost and the user side cost and minimize the objective function, and the network side does not include the intelligent soft switch 11;

[0167] Embodiment 3 is a specific embodiment of the active distribution network 1 optimization method involved in the present disclosure, that is, the objective function is constructed based on the network side cost and the user side cost and the objective function is minimized, and the network side includes an intelligent soft switch 11.

[0168] Based on Example 1, Example 2 and Example 3, the objective functions are processed respectively to obtain optimization results.

[0169] In some examples, the preset time can be divided into a first half and a second half based on a preset time point. In some examples, the preset time point can be set arbitrarily based on demand, wherein the first half and the second half of the preset time in Example 1 have the same duration.

[0170] In some examples, the optimization result includes at least one of the operation time of the electric water heater, the load of the electric water heater, the transmission power of the intelligent soft switch 11 , and the voltage value of each node in the active power distribution network 1 .

[0171] Figure 6 1 is an exemplary comparison diagram showing the operating time of the electric water heaters in Embodiment 1, Embodiment 2 and Embodiment 3 involved in the present disclosure. Figure 7 1 is an exemplary comparison diagram showing load curves of electric water heaters in Embodiment 1, Embodiment 2 and Embodiment 3 of the present disclosure. Figure 8 1 is an exemplary comparison diagram showing the transmission power of the intelligent soft switch 11 in Embodiment 1 and Embodiment 3 of the present disclosure. Fig. 9 1 is an exemplary comparison diagram showing voltage values ​​of nodes in the active power distribution network 1 in Embodiment 1, Embodiment 2 and Embodiment 3 of the present disclosure.

[0172] In some examples, the total cost of Embodiment 1, Embodiment 2, and Embodiment 3 is shown in Table 3, where the total cost may include the distribution network loss cost, the smart soft switch 11 loss cost, and the temperature control load 12 cost.

[0173] Table 3

[0174]

[0175]

[0176] In some examples, the total cost can be calculated based on the optimization results. The running time and load of the electric water heater can be used to calculate the cost of the temperature control load 12, the voltage value of each node in the active distribution network 1 can be used to calculate the network loss cost of the distribution network, and the transmission power of the smart soft switch 11 can be used to calculate the loss cost of the smart soft switch 11.

[0177] For some examples, see Figure 6, the total operation time may refer to the time the electric water heater is in the heating mode. In this case, since Embodiment 1 constructs and minimizes the objective function with the network side cost in the second half of the preset time, in other words, the influence of the user side is not considered in the second half of the preset time, the electric water heater in Embodiment 1 is in the heating mode for a longer period of time. At the same time, the electric water heater in Embodiment 3 can adjust the working state of the electric water heater based on the upper and lower limits of the water temperature, so the total operation time of Embodiment 1 is the longest, and the total operation time of Embodiment 3 is the shortest.

[0178] For some examples, see Figure 7 , when the electric water heater load (ie, power) is 0, the electric water heater is in the insulation mode. In some examples, the electric water heater loads in Embodiment 2 and Embodiment 3 are substantially the same, and at the same time, the electric water heater load in Embodiment 1 fluctuates greatly over time.

[0179] For some examples, see Figure 8 , the intelligent soft switch 11 can be connected between the node 8 and the node 14. In other words, the power (ie, active power and reactive power) of the node 8 and the node 14 can be the transmission power of the converter at both ends of the intelligent soft switch 11.

[0180] For some examples, see Fig. 9 The voltage value may refer to the voltage value of each node in Embodiment 1, Embodiment 2 and Embodiment 3. In particular, since the intelligent soft switch 11 is not included in Embodiment 2, the reactive power of some nodes is insufficient, resulting in a lower voltage value of some nodes.

[0181] It can be concluded from Table 3 that, compared with Example 1 and Example 2, the total cost of Example 3 is lower. In other words, the optimization method of Example 3 is better.

[0182] Although the present disclosure is specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the above description does not limit the present disclosure in any form. Those skilled in the art may modify and change the present disclosure as needed without departing from the essential spirit and scope of the present disclosure, and these modifications and changes all fall within the scope of the present disclosure.

Claims

1. A terminal device for adjusting an active distribution network, the terminal device being configured to execute an active distribution network optimization method, the active distribution network optimization method being optimized based on a network side having an intelligent soft switch and a user side having a temperature-controlled load, characterized in that: The active distribution network optimization method comprises: Constructing an objective function that considers network-side costs and user-side costs, wherein the network-side costs include at least one of distribution network loss costs, intelligent soft switch loss costs, and switch action costs, and the user-side costs include temperature control load costs, wherein the temperature control load is an electric water heater; Constructing a plurality of mathematical models based on the objective function, the plurality of mathematical models including at least one of an intelligent soft switch mathematical model for calculating the loss cost of the intelligent soft switch and an optimal power flow model of a distribution network for calculating the network loss cost of the distribution network and the switch action cost, and the plurality of mathematical models also including a temperature control load mathematical model for calculating the temperature control load cost; Solving the objective function under multiple constraints to minimize the objective function and obtain an optimization result, wherein the optimization result includes the running time of the electric water heater, the load of the electric water heater, the transmission power of the intelligent soft switch, and the voltage value of each node in the active power distribution network, and the running time of the electric water heater and the load of the electric water heater are used to calculate the temperature control load cost; and The transmission power of the intelligent soft switch and the switch state of the temperature control load are adjusted based on the optimization result, wherein: The objective function is: in, i For the i nodes, j For the j nodes, ij For the i Nodes and j branches between nodes; T For time; F is the objective function; is the network loss cost coefficient; is the intelligent soft switch loss cost coefficient; is the switching loss coefficient; The electricity selling price at the user side; B is the collection of all branches in the active distribution network; is a set of nodes connected to the intelligent soft switch; is the set of nodes where the temperature control load is located; for t Moment i Nodes and j The current flowing through the branches between the nodes; For the i The node and j The equivalent resistance connected between the nodes; Connect the intelligent soft switch to the i The converter on one side of each node is t Converter losses generated at all times; for t Moment i The total power of the temperature control load used in each node; The active distribution network is reconfigured after the i Nodes and j The switching action of the branch between nodes; The active distribution network is reconfigured before the i Nodes and j The switching action of the branch between nodes; is the time interval, The optimal power flow model of the distribution network is: ， Where p is the active power injected by the node, q is the reactive power injected by the node; P is the active power, Q is the reactive power; i is the i-th node, j is the j-th node, k is the k-th node; ij is the branch between the i-th node and the j-th node, jk is the branch between the j-th node and the k-th node; is the active power of the branch between the jth node and the kth node at time t, is the active power of the branch between the i-th node and the j-th node at time t; is the reactive power of the branch between the jth node and the kth node at time t, is the reactive power of the branch between the i-th node and the j-th node at time t; is the current flowing through the branch between the i-th node and the j-th node at time t, is the equivalent resistance connected between the i-th node and the j-th node, is the equivalent inductive reactance connected between the i-th node and the j-th node at time t, is the voltage of the jth node at time t; is the active power injected into the jth node at time t, is the reactive power injected into the jth node at time t; is the parent node set of the jth node in the active distribution network, is the set of child nodes of the jth node in the active distribution network; are the equivalent conductance of the load connected to the jth node at time t, is the equivalent susceptance of the load connected to the jth node at time t; N is the node set.

2. The terminal device for adjusting the active power distribution network according to claim 1, characterized in that: The temperature control load mathematical model is: in, t for t time, T is the temperature, is the time interval, for t The water temperature of the electric water heater at the moment; for t Ambient temperature at the moment; and are respectively the thermal resistance and thermal capacity of the electric water heater; L is the total capacity of the water tank of the electric water heater; for t The user's unit water consumption at the moment, express t The working state of the electric water heater at the moment, wherein: When , it means that the electric water heater is in the insulation mode. When , it indicates that the electric water heater is in heating mode; is the heat consumption coefficient of the electric water heater; It is the active power of the electric water heater during heating.

3. The terminal device for adjusting the active power distribution network according to claim 2, characterized in that: The multiple constraints include at least one of intelligent soft switch capacity constraints, upper and lower limit constraints on electric water heater water temperature, active distribution network flow constraints, active distribution network safety constraints, photovoltaic power output constraints considering reactive power, switch action times constraints, and distribution network radial operation constraints.

4. The terminal device for adjusting the active power distribution network according to claim 1, characterized in that: Based on the second-order cone programming relaxation method, the multiple mathematical models and the multiple constraints are processed to obtain multiple target constraints, and the multiple target constraints are solved to obtain the optimization result.

5. The terminal device for adjusting the active power distribution network according to claim 4, characterized in that: The target constraint condition includes a first target constraint condition, and the first target constraint condition is: ； in, i For the i nodes, j For the j nodes, ij For the i Nodes and j The branches between nodes, t for t time; for t Moment i Nodes and j Active power of the branch between nodes; for t Moment i Nodes and j Reactive power of the branches between nodes; for t Moment i Nodes and j The square of the current in the branch between the nodes; for t Moment j The square of the voltage at the node.

6. The terminal device for adjusting the active power distribution network according to claim 4, characterized in that: The target constraint condition includes a second target constraint condition, and the second target constraint condition is: ， ； in, for t At this moment, the intelligent soft switch is connected to the i The active power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the j The active power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the i The reactive power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the j Reactive power of the converter of each node; To access i The converter loss of each node is To access j Converter loss of each node; is the converter loss coefficient of the intelligent soft switch; i For the i nodes, j For the j nodes, t for t time.

7. The terminal device for adjusting the active power distribution network according to claim 4, characterized in that: The target constraint condition includes a third target constraint condition, and the third target constraint condition is: ， ； in, for t At this moment, the intelligent soft switch is connected to the i The active power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the j The active power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the i The reactive power of the converter of each node, for t At this moment, the intelligent soft switch is connected to the j Reactive power of the converter of each node; i For the i nodes, j For the j nodes, t for t time; To follow in i The capacity of the converter of each node, To follow in j The capacity of the converter of each node.

8. An active distribution network optimization method, wherein the active distribution network optimization method is based on the network side with intelligent soft switches and the user side with temperature-controlled loads for optimization, characterized in that: include: Constructing an objective function that considers network-side costs and user-side costs, wherein the network-side costs include at least one of distribution network loss costs, intelligent soft switch loss costs, and switch action costs, and the user-side costs include temperature control load costs, wherein the temperature control load is an electric water heater; Constructing a plurality of mathematical models based on the objective function, the plurality of mathematical models including at least one of an intelligent soft switch mathematical model for calculating the loss cost of the intelligent soft switch and an optimal power flow model of a distribution network for calculating the network loss cost of the distribution network and the switch action cost, and the plurality of mathematical models also including a temperature control load mathematical model for calculating the temperature control load cost; Solving the objective function under multiple constraints to minimize the objective function and obtain an optimization result, wherein the optimization result includes the running time of the electric water heater, the load of the electric water heater, the transmission power of the intelligent soft switch, and the voltage value of each node in the active power distribution network, and the running time of the electric water heater and the load of the electric water heater are used to calculate the temperature control load cost; and The transmission power of the intelligent soft switch and the switch state of the temperature control load are adjusted based on the optimization result, wherein: The objective function is: in, i For the i nodes, j For the j nodes, ij For the i Nodes and j branches between nodes; T For time; F is the objective function; is the network loss cost coefficient; is the intelligent soft switch loss cost coefficient; is the switching loss coefficient; The electricity selling price at the user side; B is the collection of all branches in the active distribution network; is a set of nodes connected to the intelligent soft switch; is the set of nodes where the temperature control load is located; for t Moment i Nodes and j The current flowing through the branches between the nodes; For the i The node and j The equivalent resistance connected between the nodes; Connect the intelligent soft switch to the i The converter on one side of each node is t Converter losses generated at all times; for t Moment i The total power of the temperature control load used in each node; The active distribution network is reconfigured after the i Nodes and j The switching action of the branch between nodes; The active distribution network is reconfigured before the i Nodes and j The switching action of the branch between nodes; is the time interval, The optimal power flow model of the distribution network is: ， Where p is the active power injected by the node, q is the reactive power injected by the node; P is the active power, Q is the reactive power; i is the i-th node, j is the j-th node, k is the k-th node; ij is the branch between the i-th node and the j-th node, jk is the branch between the j-th node and the k-th node; is the active power of the branch between the jth node and the kth node at time t, is the active power of the branch between the i-th node and the j-th node at time t; is the reactive power of the branch between the jth node and the kth node at time t, is the reactive power of the branch between the i-th node and the j-th node at time t; is the current flowing through the branch between the i-th node and the j-th node at time t, is the equivalent resistance connected between the i-th node and the j-th node, is the equivalent inductive reactance connected between the i-th node and the j-th node at time t, is the voltage of the jth node at time t; is the active power injected into the jth node at time t, is the reactive power injected into the jth node at time t; is the parent node set of the jth node in the active distribution network, is the set of child nodes of the jth node in the active distribution network; are the equivalent conductance of the load connected to the jth node at time t, is the equivalent susceptance of the load connected to the jth node at time t; N is the node set.

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