Distributed power supply bearing capacity assessment method and device, electronic equipment and medium

Abstract

The invention discloses a distributed power supply bearing capacity assessment method and device, electronic equipment and a medium, and is used for solving the problem of how to better assess the bearing capacity of a power distribution network to a distributed power supply on the premise of considering power supply economy. At least one micro-grid is connected to a power distribution network side, each micro-grid corresponds to a schedulable equipment set, and the method comprises the following steps: performing economic scheduling based on various schedulable equipment in the schedulable equipment set, and obtaining power purchasing and selling power demands of the micro-grid; performing centralized optimization scheduling on various flexible resources in the power distribution network to obtain an optimization scheduling result; judging whether the optimized scheduling result fully meets the electricity purchasing and selling power demand or not; if yes, extracting system average power supply costs corresponding to installed capacities of different distributed power supplies in the micro-grid from the optimal scheduling result, and determining the optimal photovoltaic capacity of the micro-grid; and integrating all the optimal photovoltaic capacities as the overall optimal grid-connected capacity of the power distribution network so as to evaluate the bearing capacity of the power distribution network to the distributed power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of smart distribution network, and in particular to a method, device, electronic equipment and medium for evaluating the carrying capacity of a distributed power source. Background Art

[0002] With the vigorous development of renewable energy, more and more distributed power sources are connected to the distribution network. However, the output of renewable energy is highly uncertain. The continuous improvement of the grid-connected capacity of distributed power sources has led to an increase in the risk of node voltage and branch current exceeding the limit in the distribution network, making the safe and stable operation of the distribution network face challenges.

[0003] Investing in flexible resources can reduce the volatility of distributed power output and improve the distribution network's ability to absorb distributed power. These flexible resources mainly include the following categories: on the power supply side, the application of technologies such as electric energy storage has promoted the diversification of energy sources in the distribution network; on the grid side, with the support of distribution automation and power electronic equipment, the distribution network can improve its ability to absorb distributed power through flexible scheduling strategies; on the load side, the emergence of a large number of new loads such as smart homes and electric vehicles can achieve flexible demand response. Therefore, how to determine the maximum carrying capacity of the distribution network for distributed photovoltaics while taking into account the effect of massive flexible resources on the absorption of distributed power has become a research hotspot in recent years.

[0004] Investing in various flexible resources often requires a lot of costs. Or it is difficult to fully promote them in the short term. At the same time, the output of renewable energy units has significant seasonal and intermittent characteristics. The low proportion of power supply during the period of small-scale new energy generation and the difficulty of absorbing during the period of large-scale generation frequently occur alternately. If new energy units are allowed to appropriately abandon some peak power during the period of large-scale generation, although it will increase the power generation cost of wind and solar units, it can increase the proportion of power supply during the period of small-scale new energy generation, thereby improving the system's new energy power penetration rate and power supply economy throughout the year.

[0005] In view of the development needs of high-penetration wind and solar power generation, how to formulate reasonable operation and planning plans that take into account wind and solar power abandonment measures while taking into account the economy of power supply, so as to better evaluate the distribution network's carrying capacity for distributed power sources, is an engineering problem that the power system urgently needs to solve. Summary of the invention

[0006] The present invention provides a method, device, electronic device and medium for evaluating the carrying capacity of distributed power sources, which are used to solve or partially solve the technical problem of how to better evaluate the carrying capacity of the distribution network for distributed power sources while taking into account the economy of power supply.

[0007] The present invention provides a method for evaluating the carrying capacity of a distributed power source. At least one microgrid is connected to the distribution network side, and each microgrid corresponds to a set of dispatchable devices. The method comprises:

[0008] Perform economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid;

[0009] Centrally optimizing and dispatching various flexible resources in the distribution network to obtain optimized dispatching results;

[0010] Determine whether the optimization scheduling result fully meets the power demand for purchasing and selling electricity;

[0011] If satisfied, then extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determining the optimal photovoltaic capacity of the microgrid;

[0012] All optimal photovoltaic capacities are integrated as the optimal grid-connected capacity of the distribution network as a whole to evaluate the carrying capacity of the distribution network for distributed power sources.

[0013] Optionally, the types of dispatchable devices in the dispatchable device set include distributed power sources and energy storage devices, and the economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid includes:

[0014] For any of the microgrids, with the goal of minimizing the cost of electricity purchase, maximizing the revenue from electricity sales, minimizing the loss of distributed energy storage, and minimizing the power loss, a microgrid-side dispatch objective function is constructed, and at the same time, a tie line power balance constraint, a constraint that electricity cannot be purchased and sold at the same time, and an energy storage constraint are set;

[0015] Based on the microgrid-side dispatch objective function, the tie-line power balance constraint, the constraint that electricity cannot be purchased and sold simultaneously, and the energy storage constraint, a microgrid-side economic dispatch model is constructed;

[0016] Solving the microgrid-side economic dispatch model by introducing intermediate variables to obtain the power demand for purchasing and selling electricity of the microgrid;

[0017] Wherein, when the output of the distributed power source of the microgrid is lower than the preset minimum output threshold, during the off-peak electricity price stage, electricity is purchased from the distribution network and stored in the energy storage device of the microgrid;

[0018] When the output of the distributed power source of the microgrid is higher than the preset maximum output threshold, while meeting the load demand, electricity is sold to the distribution network to obtain economic benefits.

[0019] Optionally, the various types of flexible resources include power supply side flexible resources, grid side flexible resources and load side flexible resources, and the centralized optimization and scheduling of various types of flexible resources in the distribution network to obtain the optimization scheduling result includes:

[0020] Setting the power supply side scheduling constraints corresponding to the power supply side flexible resources, the grid side scheduling constraints corresponding to the grid side flexible resources, and the load side scheduling constraints corresponding to the load side flexible resources;

[0021] According to the annual value cost of equipment, electricity purchase and sale cost, network loss cost and compensation cost of demand response users of the distribution network, a function for calculating the unit electric energy supply cost of the system is constructed, and at the same time, a constraint on the total amount of distributed power sources allowed to be connected to the distribution network is set;

[0022] Taking the minimum unit power supply cost of the system as the goal, considering the voltage quality of the power grid, the distribution network dispatch objective function is constructed, and at the same time, the node injection power balance constraint, the adjacent node voltage drop constraint and the microgrid power purchase demand constraint are set;

[0023] According to the distribution network side dispatch objective function, the power supply side dispatch constraint, the grid side dispatch constraint, the load side dispatch constraint, the total amount of distributed power supply constraint, the node injection power balance constraint, the adjacent node voltage drop constraint and the microgrid power purchase demand constraint are considered to construct a distribution network dispatch model;

[0024] The distribution network dispatching model is solved based on second-order cone optimization to achieve centralized optimal dispatching of various flexible resources in the distribution network and obtain optimized dispatching results. The optimized dispatching results include the dispatchable purchased and sold electricity power in the distribution network and the system average power supply cost corresponding to the installed capacity of different distributed power sources in each of the microgrids.

[0025] Optionally, the power supply side scheduling constraints include photovoltaic / wind turbine operation constraints and energy storage operation constraints, the grid side scheduling constraints include capacitor bank operation constraints, static VAR generator operation constraints and on-load tap-changing transformer operation constraints, and the load side scheduling constraints include demand side response constraints.

[0026] Optionally, extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling result, and determining the optimal photovoltaic capacity of the microgrid, includes:

[0027] Extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and taking the system average power supply cost with the smallest value as the target system average power supply cost;

[0028] The installed capacity of distributed power sources corresponding to the average power supply cost of the target system is used as the optimal photovoltaic capacity of the microgrid.

[0029] Optionally, the method further comprises:

[0030] If the resource optimization scheduling cannot fully meet the power demand for purchasing and selling electricity, the load transfer optimization is performed through feeder reconstruction, and then the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid is recalculated based on centralized optimization scheduling.

[0031] Optionally, the load transfer optimization by feeder reconstruction includes:

[0032] Determine the target feeder network with interconnection relationship in the distribution network, take the active network loss and switch device operation cost of the target feeder network as the minimum within the optimization period as the goal, construct the power supply optimization objective function, and set the network topology no island constraint, feeder overload constraint, radial operation constraint and switch device operation frequency constraint;

[0033] Constructing a feeder-level load transfer optimization model according to the transfer optimization objective function, the network topology no-island constraint, the feeder no-overload constraint, the radial operation constraint, and the switch device operation frequency constraint;

[0034] The load transfer optimization model is solved by discrete particle swarm algorithm to obtain the optimization results of the tie switch and the section switch, and the optimization results of the tie switch and the section switch are used to optimize the load transfer of the distribution network.

[0035] The present invention also provides a distributed power supply carrying capacity evaluation device, wherein at least one microgrid is connected to the distribution network side, each of the microgrids corresponds to a set of dispatchable devices, and the device comprises:

[0036] A microgrid-side economic dispatch module, used to perform economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid;

[0037] A centralized optimization dispatching module is used to perform centralized optimization dispatching of various flexible resources in the distribution network to obtain optimization dispatching results;

[0038] A power purchase and sale power demand judgment module is used to judge whether the optimization scheduling result fully meets the power purchase and sale power demand;

[0039] An optimal photovoltaic capacity determination module is used to extract the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determine the optimal photovoltaic capacity of the microgrid;

[0040] The optimal grid-connected capacity integration module is used to integrate all optimal photovoltaic capacities as the optimal grid-connected capacity of the distribution network as a whole, so as to evaluate the carrying capacity of the distribution network for distributed power sources.

[0041] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0042] The memory is used to store program code and transmit the program code to the processor;

[0043] The processor is used to execute the distributed power supply carrying capacity evaluation method as described in any one of the above items according to the instructions in the program code.

[0044] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the distributed power supply carrying capacity evaluation method as described in any one of the above items.

[0045] It can be seen from the above technical solutions that the present invention has the following advantages:

[0046] In the case where at least one microgrid is connected to the distribution network side and each microgrid corresponds to a set of dispatchable devices, a method for evaluating the carrying capacity of distributed power sources is provided. First, economic dispatch is performed based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid; then, various flexible resources in the distribution network are centrally optimized and dispatched to obtain the optimized dispatch results; then, it is determined whether the optimized dispatch results fully meet the power demand for purchasing and selling electricity; if so, the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid is extracted from the optimized dispatch results, and the optimal photovoltaic capacity of the microgrid is determined; finally, all the optimal photovoltaic capacities are integrated as the optimal grid-connected capacity of the distribution network as a whole to evaluate the carrying capacity of the distribution network for distributed power sources. Therefore, through the economic dispatch on the microgrid side, combined with the centralized optimized dispatch of various flexible resources on the distribution network side, the carrying capacity of the distribution network for distributed power sources can be better evaluated under the premise of taking into account the economic efficiency of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flowchart of the steps of a method for evaluating the carrying capacity of distributed power sources;

[0049] Figure 2It is a schematic diagram of the optimal carrying capacity based on economic indicators;

[0050] Figure 3 It is a schematic diagram of the overall process of a distributed power supply carrying capacity evaluation method;

[0051] Figure 4 The structure block diagram of a distributed power supply carrying capacity evaluation device. DETAILED DESCRIPTION

[0052] The embodiments of the present invention provide a method, device, electronic device and medium for evaluating the carrying capacity of distributed power sources, which are used to solve or partially solve the technical problem of how to better evaluate the carrying capacity of the distribution network for distributed power sources while taking into account the economy of power supply.

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

[0054] As an example, in order to ensure safe and stable operation of the distribution network when distributed power sources are connected, domestic and foreign experts and scholars have done a lot of research on the optimal configuration of distributed power sources. A variety of calculation methods have been proposed, and the models built have different focuses. Specifically, the current joint optimization configuration model of distributed power sources and energy storage is generally constructed with the goal of minimizing the operating cost of the distribution network and the equipment investment cost, active power loss, or maximizing the capacity benefit of energy storage configuration and the utilization rate of renewable energy. At the same time, various grid constraints have been taken into account in the existing optimization planning models. For distributed power sources connected to the distribution network, voltage constraints and line capacity constraints have become key factors restricting the access of distributed power sources. However, since the node voltage constraints and line tide constraints are nonlinear constraints, it is difficult to solve the model.

[0055] In addition, when selecting the optimal configuration capacity of the best distributed power source based on the power supply cost, the current method fails to fully take into account the role of allowing the reasonable abandonment of the peak output of new energy units in improving the consumption of new energy, and fails to analyze the role of various flexible resources in absorbing new energy. The continuous and rapid access of distributed power sources poses challenges to the safe operation of the distribution network. Based on this, it is necessary to call on a variety of flexible resources to further improve the distribution network's carrying capacity for distributed power sources.

[0056] Therefore, one of the core invention points of the embodiment of the present invention is: from the perspective of economic optimization, based on the timing operation simulation, a distributed power supply carrying capacity evaluation method considering the interactive operation of flexible resources of the smart distribution network is proposed. First, the load demand, distributed energy storage, distributed power supply and other equipment within the microgrid are taken into account to realize local optimized economic dispatch, so as to propose the power demand for purchasing and selling electricity to the smart distribution network; then, the active distribution network side conducts centralized optimization dispatch of the flexible resources on the power supply side, the grid side and the load side while trying to meet the power demand for purchasing and selling electricity on the microgrid side, and judges whether the power demand for purchasing and selling electricity on the microgrid side can be fully met based on the optimization dispatch results; if the power demand for purchasing and selling electricity on the microgrid side cannot be met, the switching operation between adjacent feeders (feeder reconstruction) is performed to further explore the overall absorption potential of the local power grid for distributed power sources; finally, through the annual timing operation simulation, the lowest average power supply cost per unit of electricity is taken as the key indicator, and the optimal carrying capacity of the smart distribution network for distributed power sources is determined on the premise of taking into account the economic efficiency of power supply.

[0057] The technical solution proposed in the present invention takes into account the regulating effects of various flexible resources, allows for the adoption of wind and solar power abandonment measures, and adopts a distributed collaborative operation strategy in the operation simulation. It is suitable for the application scenario of using flexible resources to promote the carrying capacity of smart distribution networks for distributed power sources after the penetration rate of distributed power sources reaches a high level.

[0058] Reference Figure 1 , shows a flow chart of the steps of a distributed power supply carrying capacity evaluation method provided by an embodiment of the present invention, wherein at least one microgrid is connected to the distribution network side, and each of the microgrids corresponds to a set of dispatchable devices. The method may specifically include the following steps:

[0059] Step 101, performing economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid;

[0060] In the specific implementation, for any microgrid connected to the distribution network, dispatch optimization control can be carried out on the microgrid side to obtain the power demand for purchasing and selling electricity.

[0061] The dispatchable devices of the microgrid are composed of distributed power sources and energy storage, and interact with the upper power grid through interconnection lines. In other words, the dispatchable device types in the dispatchable device set can include distributed power sources and energy storage devices.

[0062] Specifically, the process of performing economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand of the microgrid for purchasing and selling electricity can be achieved by executing the following steps S01 to S03:

[0063] Step S01: For any microgrid, with the goal of minimizing the power purchase cost, maximizing the power sales revenue, minimizing the distributed energy storage loss and minimizing the power loss, a microgrid-side dispatch objective function is constructed, and at the same time, the tie line power balance constraint, the constraint of not being able to purchase and sell power at the same time, and the energy storage constraint are set;

[0064] Step S02: constructing a microgrid-side economic dispatch model based on the microgrid-side dispatch objective function, the tie-line power balance constraint, the constraint that electricity cannot be purchased and sold simultaneously, and the energy storage constraint;

[0065] The constructed microgrid-side economic dispatch model is shown in the following formula:

[0066]

[0067] in, It represents the minimum value solution, which corresponds to the minimum value of the dispatch objective function on the microgrid side. The first formula in the constraint condition st is the tie line power balance constraint, the second to fourth formulas are the constraints that electricity cannot be purchased and sold at the same time, and the fifth formula is the energy storage constraint. and They represent the unit price of electricity purchased by the microgrid and the unit price of electricity sold to the distribution network operator considering the time-of-use electricity price at time t respectively; and They respectively represent the upper limit information of power sold / purchased by the tie line to node i at time t; represents the DG installed capacity (PV installed capacity) at node i at time t; It represents the cost of charging / discharging 1MWh of energy storage invested in the microgrid; represents the power of the distributed energy storage device at node i at time t, with discharge as positive and charge as negative; represents the square value of the voltage at the first node of the branch with node i as the first segment; R represents the equivalent resistance of the bus line and inverter on the microgrid side; and They respectively represent the upper limit information of power sold / purchased by the grid side to node i at time t; and 0-1 indicator variables representing the state of electricity purchase and sale; T represents a scheduling cycle, which is generally 24 hours; represents the user load on node i at time t; represents the installed capacity of the energy storage at node i at the first moment in the scheduling period T; It represents the installed capacity of the energy storage at node i at the last moment in the scheduling period T, i.e., time T.

[0068] Step S03: Solve the microgrid-side economic dispatch model by introducing intermediate variables to obtain the power purchase and sales demand of the microgrid.

[0069] The optimization model can be converted into a quadratic programming model by eliminating the absolute value term by introducing intermediate variables, and then solved using mature commercial software.

[0070] When the output of distributed power sources is low (i.e., when the output of distributed power sources of the microgrid is lower than a preset minimum output threshold), consider purchasing electricity from the distribution network and storing it in energy storage equipment during the off-peak electricity price stage; when the output of distributed power sources is large (i.e., when the output of distributed power sources of the microgrid is higher than a preset maximum output threshold), while meeting the load demand, it can sell electricity to the superior distribution network to obtain certain economic benefits. By solving the constructed microgrid-side economic dispatch model, the power demand for power purchase and sale proposed by the microgrid side to the superior distribution network can be obtained while meeting the minimum power purchase cost, maximum power sales revenue, minimum distributed energy storage loss and minimum power loss, while taking into account the power balance constraints of the interconnection line, the constraints of not being able to purchase and sell power at the same time, and the energy storage constraints.

[0071] Step 102, centrally optimizing and dispatching various flexible resources in the distribution network to obtain an optimized dispatching result;

[0072] Flexible resources exist on the source, grid and load sides. In other words, the various types of flexible resources referred to in the embodiments of the present invention can mainly include flexible resources on the power supply side, flexible resources on the grid side and flexible resources on the load side.

[0073] Specifically, the process of centrally optimizing and dispatching various flexible resources in the distribution network to obtain the optimized dispatching result can be achieved by executing the following sub-steps S11 to S15:

[0074] Step S11: setting the power supply side scheduling constraints corresponding to the power supply side flexible resources, the grid side scheduling constraints corresponding to the grid side flexible resources, and the load side scheduling constraints corresponding to the load side flexible resources;

[0075] When optimizing and dispatching various flexible resources in a centralized manner, it is necessary to establish corresponding dispatching constraints for various flexible resources. On the power supply side, the main components are modeled for distributed generation and electric energy storage, and it is necessary to consider the upper and lower limits of the output of distributed power sources (photovoltaic / wind turbine operation constraints), energy storage operation constraints, etc. On the grid side, the optimization dispatching constraints are established by considering the operation constraints of capacitor banks, static VAR generators, and on-load tap-changing transformers. On the load side, the dispatching constraints of various demand-side response resources that can participate in the peak load regulation of the grid are mainly established.

[0076] Furthermore, the photovoltaic / wind turbine operation constraints of the power supply side scheduling constraints are as follows:

[0077]

[0078] in, represents the active power generated by the uncontrollable distributed generation connected to node i at time t; It represents the upper limit of active power of the uncontrollable distributed generation connected to node i at time t. At this time, the output of the uncontrollable distributed generation can be reduced.

[0079] The energy storage operation constraints of the power supply side dispatch constraints mainly include power and electricity constraints, as shown in the following formula:

[0080]

[0081] in, represents the remaining power of the energy storage at node i at time t+1; represents the remaining power of the energy storage at node i at time t; represents the active power of the energy storage device controlled by the distribution network at node i at time t, with discharge as positive; represents the optimization step size; and They represent the rated capacity and maximum charging and discharging power of the energy storage at node i respectively.

[0082] For grid-side dispatching constraints, the capacitor bank adopts a group switching method in the operation and dispatching process of the smart distribution network. Therefore, the capacitor is a discrete decision variable. And it has a strict switching change number within the dispatching cycle. Therefore, the capacitor bank operation constraint can be established as shown in the following formula:

[0083]

[0084] in, represents the reactive power output of the grouped capacitor at node i at time t; represents the reactive output of a single capacitor at node i; It represents the number of capacitor groups put into operation at node i at time t; represents the maximum number of capacitors at node i; Indicates the maximum number of capacitor adjustments within the dispatch cycle; represents the number of adjustments of the capacitor at node i at time t; represents the number of adjustments of the capacitor at node i at time t+1; Represents the exclusive-or operation.

[0085] The static VAR generator can be continuously adjusted during the operation and dispatching of the smart distribution network. Therefore, the capacitor is a continuous decision variable. And it can absorb reactive power or generate reactive power during the dispatching cycle. Therefore, the static VAR generator operation constraint can be established as shown in the following formula:

[0086]

[0087] in, represents the reactive power output of the static reactive generator at node i at time t; Represents the reactive power output limit of the static reactive power generator at node i.

[0088] Assume that the root node of the distribution network is equipped with an on-load tap-changing transformer. The voltage of the root node can be changed by changing the tap during the dispatching cycle. The tap has a gear setting, so the transformer tap is a discrete decision variable. And it has a strict adjustment number during the dispatching cycle. Therefore, the operation constraint of the on-load tap-changing transformer can be established as shown in the following formula:

[0089]

[0090] in, and represents the voltage of the last node and the first node at time t; and The resistance and reactance parameters of the line where the first node and the last node are located; and Indicates the active power and reactive power flowing through the line; Indicates the current flowing through the line; Indicates the tap position of the transformer at the root node of the distribution network. The voltage at the root node changes by 2.5% for each adjustment. Indicates the maximum number of transformer tap adjustments within the dispatch cycle; Indicates the gear state of the transformer tap at time t; Indicates the gear status of the transformer tap at time t+1.

[0091] For the load-side dispatching constraints, it is assumed that all the loads participating in the demand response of the distribution network are currently shiftable loads, and there are some restrictions on the shift depth of the loads. Therefore, the demand-side response constraints shown in the following formula can be established:

[0092]

[0093] in, represents the original load of the user at node i participating in demand response at time t; It represents the load after user adjustment on node i participating in demand response at time t.

[0094] Step S12: construct a function for calculating the system unit power supply cost based on the annual value cost of equipment, electricity purchase and sales cost, network loss cost and compensation cost of demand response users of the distribution network, and set the total amount of distributed power sources allowed to be connected to the distribution network;

[0095] Then, a function is established to calculate the unit power supply cost of the system, and the total amount of distributed power sources allowed to be connected to the distribution network is set. Combining this function and the total amount of distributed power source constraints, it can also be understood as building a distributed power source carrying capacity assessment model based on day-ahead economic dispatch.

[0096] Specifically, the photovoltaic access capacity of each grid-connected node is taken as the object, and the unit electricity supply cost is used as the objective function to carry out optimization, and evaluate the optimal total access amount of the distribution network to distributed photovoltaics and the capacity allocation plan of each grid-connected node. Furthermore, the system unit electricity supply cost mainly includes the annual value cost of equipment, the cost of purchasing and selling electricity between the upper power grid, the network loss cost of the distribution network, and the compensation cost of demand response users. Among them, the cost of purchasing and selling electricity, the network loss cost, and the compensation cost of demand response users are closely related to the system operation plan and can be obtained by carrying out time series simulation operation.

[0097] Assume that there are N nodes in the distribution network, and the DG installed capacity (distributed power installed capacity, specifically photovoltaic installed capacity in the embodiment of the present invention) invested by the microgrid of the i-th node is recorded as , the installed capacity of energy storage is , the decision variable is the DG installed capacity vector and the installed capacity vector of energy storage .

[0098] The function used to calculate the system unit electric energy supply cost mainly includes the annual value cost of equipment, electricity purchase and sales cost, network loss cost of distribution network and compensation cost of demand response users.

[0099] 1. Annual cost of equipment (investment cost of the system)

[0100] The investment cost of the system is recorded as C1, which includes equipment investment cost and operation and maintenance cost. In the embodiment of the present invention, the equal annual value method is used to account for the equipment investment cost, which can be calculated by the following formula:

[0101]

[0102] in, and denote the bank discount rates for photovoltaic and energy storage equipment, respectively; and Represent the intended operating life of photovoltaic and energy storage equipment respectively; and Represent the operation and maintenance ratios of photovoltaic and energy storage equipment respectively; and Represent the unit capacity investment costs of photovoltaic and energy storage respectively; and Respectively represent the total installed capacity of photovoltaic and energy storage equipment in the distribution network, which can be calculated based on the installed capacity vector and Sum them separately.

[0103] (II) Cost of purchasing and selling electricity (cost-effectiveness of the distribution network purchasing / selling electricity to the upper-level power grid)

[0104] The distribution network is connected to the upper-level power grid through a transformer, and can purchase electricity from the upper-level power grid or sell electricity to the upper-level power grid. The cost of purchasing and selling electricity C2 can be calculated by the following formula:

[0105]

[0106] in, and They represent the power purchased and sold by the distribution network operator from the upper power grid at time t on day day; and They represent the time-of-use electricity prices for purchasing and selling electricity at time t respectively.

[0107] 3. Distribution network loss costs

[0108] The operation of the distribution network requires the reduction of network losses, which can be taken into account by means of network loss costs, denoted as C3, and the calculation method is as follows:

[0109]

[0110] in, Represents the set of all branches in the distribution network; It represents the branch resistance starting from node i and ending at node j; It represents the square value of the branch current at time t with node i as the first segment and node j as the last segment on day day; Indicates the cost per unit network loss.

[0111] 4. Compensation costs for demand response users

[0112] More and more users have the ability and willingness to participate in demand response, so the demand response call cost can be taken into account in the system unit power supply cost, recorded as the compensation cost C4 of the demand response user, and its calculation method is as follows:

[0113]

[0114] in, represents the unit compensation price for users who respond to demand at time t on day day; D represents the set of users who sign a response contract with the power grid company; represents the original load of the user at node i that does not participate in demand response at time t on day day; Represents the load after user adjustment on node i participating in demand response at time t on day day.

[0115] The unit power supply cost of the system It can be calculated by the following formula:

[0116]

[0117] Next, we build the system's unit power supply cost The constraints taken into account.

[0118] Due to the quota of distributed power capacity, the total amount of distributed power that can be connected to the distribution network is subject to an upper limit, as shown in the following formula:

[0119]

[0120] in, represents the DG installed capacity (PV installed capacity) connected to node i; Indicates the upper limit of the distributed generation quota allowed to be connected to the distribution network.

[0121] The installed capacity of distributed power generation at each node in the distribution network should be limited to a certain range, and a certain proportion of power supply side energy storage should be configured. Therefore, the following constraints can be further constructed:

[0122]

[0123] in, and They represent the minimum and maximum installed capacity of distributed generation at node i in the planning scheme respectively; Represents the installed capacity of energy storage at node i The proportion of distributed generation installed capacity is usually 0.1~0.2.

[0124] In addition, it is also necessary to consider the static safety constraints of the power grid, such as power balance constraints, node voltage constraints, and timing constraints such as line flow. Since these constraints are not the inventive points to be emphasized by the present invention, when setting them, they can be set by referring to the existing related technical settings, and will not be described in detail here.

[0125] Based on the above functions for calculating the system unit power supply cost and the total amount of distributed power supply constraints, the optimal access capacity of distributed power supply is determined from the perspective of economy as the carrying capacity of the distribution network for distributed power supply. Among them, only the annual cost of equipment The first item can be directly calculated based on the investment plan of distributed resources, and the remaining items need to be obtained through the day-ahead economic dispatch simulation, and the optimal investment plan needs to be selected based on the simulation results. The solution can be obtained by one-dimensional traversal.

[0126] Step S13: Taking the minimum unit power supply cost of the system as the goal, considering the grid voltage quality, constructing the distribution network side dispatch objective function, and setting the node injection power balance constraint, the adjacent node voltage drop constraint and the microgrid power purchase demand constraint;

[0127] After meeting all loads, the distribution network sells excess electricity to the upper grid. The distribution network needs to formulate scheduling plans for various flexible resources and send boundary interaction information to each microgrid under the premise of meeting safety constraints.

[0128] Among them, the costs / benefits taken into account by the distribution network mainly include the annual cost of equipment (investment cost of the system), the cost-effectiveness of the distribution network purchasing / selling electricity to the upper-level power grid, the network loss cost of the distribution network, the compensation cost of demand response users, and the voltage quality of the power grid. Among them, except for the annual cost of equipment, the remaining items need to be obtained through economic dispatch. Therefore, the dispatch objective function on the distribution network side can be constructed as shown in the following formula:

[0129]

[0130] in, and Represent the weight coefficients of cost and voltage respectively; It represents the sum of the algebraic differences between the node voltage and the reference value; represents the voltage value of node i at time t; represents the reference voltage of node i; Represents the algebraic difference between the node voltage and the reference value; and They represent the power purchased and sold by the distribution network operator from the upper power grid at time t respectively; It represents the square value of the branch current at time t with node i as the first segment and node j as the last segment; It represents the unit compensation electricity price for users who perform demand response at time t.

[0131] The node injection power should meet the power balance constraint. Based on the DistFlow (Distribution Flow, a power flow calculation method designed specifically for distribution networks) power flow model, the following node injection power balance constraint can be established:

[0132]

[0133]

[0134] in, and They represent the branch active and reactive power flowing from node j to node i at time t respectively; and They represent the branch active and reactive power flowing from node i to node k at time t respectively; represents the branch reactance between node i and node j; represents the set of nodes associated with node i; represents the reactive load at node i at time t; represents the reactive output of the energy storage configured by the distribution network operator at node i at time t; represents the set of nodes connected to node i through SOP; and They represent the active and reactive power flowing from node m to node i through the SOP device at time t, respectively, with power inflow being recorded as positive.

[0135] The voltage between adjacent nodes should satisfy the voltage drop constraint. In order to characterize the physical relationship between branch current, node voltage and line active power and reactive power, the following cone-shaped adjacent node voltage drop constraint is established:

[0136]

[0137]

[0138] in, It represents the square value of the voltage at the end node of the branch with node j as the terminal; represents the resistance of the line at the first node i and the last node j; and They represent the branch active and reactive power flowing from node i to node j at time t respectively.

[0139] When formulating boundary interaction information with the microgrid, the power purchase demand of the microgrid should be met as much as possible, and the power purchase limit of the distribution network should be set. The constraints of the microgrid power purchase demand are shown in the following formula:

[0140]

[0141] Step S14: According to the distribution network side dispatch objective function, the distribution network dispatch model is constructed by taking into account the power supply side dispatch constraints, the grid side dispatch constraints, the load side dispatch constraints, the total amount of distributed power sources constraints, the node injection power balance constraints, the adjacent node voltage drop constraints and the microgrid power purchase demand constraints;

[0142] Step S15: Solve the distribution network dispatching model based on the second-order cone optimization to achieve centralized optimization dispatching of various flexible resources in the distribution network and obtain the optimization dispatching result.

[0143] The dispatch model on the distribution network side is a second-order cone optimization model, which can be solved by the CVX solver (a convex optimization toolkit).

[0144] Among them, the optimized scheduling results include the dispatchable power purchase and sales in the distribution network, as well as the system average power supply cost corresponding to the different distributed power generation installed capacities in each microgrid.

[0145] Step 103, determining whether the optimization scheduling result fully meets the power demand for purchasing and selling electricity;

[0146] According to the dispatchable purchase and sale power of the distribution network in the optimization dispatching results, it is judged that the dispatchable purchase and sale power can fully meet the purchase and sale power demand.

[0147] It is understandable that at this time, one or more microgrids may make a request for power purchase and sale to the distribution network. When only one microgrid makes a request for power purchase and sale to the distribution network, it is only necessary to determine whether the dispatchable power purchase and sale can fully meet the power purchase and sale demand of the microgrid, that is, whether the dispatchable power purchase and sale is greater than or equal to the power purchase and sale demand of the microgrid.

[0148] When multiple microgrids submit power purchase and sales demands to the distribution network, it is necessary to determine whether the dispatchable power purchase and sales can fully meet the power purchase and sales demands of multiple microgrids, that is, whether the dispatchable power purchase and sales is greater than or equal to the sum of the power purchase and sales demands of these microgrids.

[0149] Step 104: if satisfied, extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determining the optimal photovoltaic capacity of the microgrid;

[0150] In order to make the evaluation results more accurate, effective and reliable, the embodiment of the present invention adopts the annual time-series simulation method, selects one year as the entire scheduling cycle, and further divides one year into multiple single scheduling cycles T for scheduling calculation with one day as the division interval. Finally, the annual time-series simulation is used to determine the optimal carrying capacity of the smart distribution network for distributed power sources with the lowest power supply cost per unit of electric energy as the key indicator.

[0151] Among them, annual time series operation simulation refers to a detailed time series simulation of a system or process within a year to evaluate its performance in different time periods throughout the year.

[0152] When it is judged that the optimized dispatching results fully meet the power demand for purchasing and selling electricity, and the annual time series operation simulation is completed, the annual operation economic indicators are counted and the optimal carrying capacity is determined. When it is judged that the optimized dispatching results fully meet the power demand for purchasing and selling electricity, the optimized dispatching results of this dispatching cycle are output (that is, the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid of this dispatching cycle, and the optimal photovoltaic capacity is determined). When it is further judged that the annual time series operation simulation has not been completed, jump to the next dispatching cycle, execute the economic dispatch of the microgrid side and the centralized optimization dispatching steps of flexible resources on the distribution network side of the next dispatching cycle, until the dispatch of all dispatching cycles is completed, that is, the annual time series operation simulation is completed.

[0153] Combined with the centralized optimization scheduling related content introduced earlier, for a given set of photovoltaic capacities, that is, the installed capacities of different distributed power sources in the microgrid, for each scheduling cycle, the system average power supply cost corresponding to each distributed power source installed capacity can be calculated, and then the optimal photovoltaic capacity can be selected according to the system average power supply cost indicator.

[0154] Specifically, the system average power supply costs corresponding to different distributed power installed capacities in the microgrid are extracted from the optimization scheduling results, and the optimal photovoltaic capacity of the microgrid is determined. The system average power supply costs corresponding to different distributed power installed capacities in the microgrid are extracted from the optimization scheduling results, and the system average power supply cost with the smallest value is used as the target system average power supply cost; the distributed power installed capacity corresponding to the target system average power supply cost is used as the optimal photovoltaic capacity of the microgrid.

[0155] Step 105 , integrating all optimal photovoltaic capacities as the optimal grid-connected capacity of the entire distribution network to evaluate the carrying capacity of the distribution network for distributed power sources.

[0156] For a single dispatching cycle, after selecting the optimal photovoltaic capacity of each microgrid node, the optimal grid-connected capacity of the system as a whole and the optimal photovoltaic capacity of each microgrid node (distributed photovoltaic grid-connected capacity) can be output. Among them, the optimal grid-connected capacity can be used as the optimal grid-connected capacity of the smart distribution network in the dispatching cycle, so as to evaluate the distribution network's carrying capacity for distributed power sources from the short-term level of a single dispatching cycle. The selection effect of the optimal carrying capacity based on economic indicators is as follows: Figure 2 As shown. Figure 2 It can be seen that, taking into account the key indicator of unit electricity supply cost, combined with flexible resources and the proportion of wind and solar power abandonment, the best point corresponding to the optimal carrying capacity is D2.

[0157] Furthermore, the optimal grid-connected capacity with the largest capacity can be selected from all scheduling cycles, or the average sum of the optimal grid-connected capacities of all scheduling cycles can be taken as the annual optimal grid-connected capacity, so as to more comprehensively evaluate the distribution network's carrying capacity for distributed power sources from a long-term perspective throughout the year.

[0158] In another case, if the power demand of the microgrid cannot be met, the feeder reconstruction can be carried out through the smart distribution network. Specifically, the opening and closing states of the section switches and the tie switches are used as optimization variables, and the total number of switch operations, the switch operation frequency and other constraints are taken into account to flexibly formulate a flexible dispatching plan for the active distribution network, and further tap the overall absorption potential of the local power grid for distributed power sources.

[0159] Specifically, if resource optimization scheduling cannot fully meet the power demand for purchasing and selling electricity, the load transfer optimization is carried out through feeder reconstruction, and then the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid is recalculated based on centralized optimization scheduling.

[0160] Furthermore, the specific process of optimizing load transfer by feeder reconstruction can be achieved by executing the following sub-steps S21 to S23:

[0161] Step S21: Determine the target feeder network with interconnection in the distribution network, and take the minimization of active network loss and switch device operation cost of the target feeder network within the optimization period as the goal, construct the power transfer optimization objective function, and set the network topology no island constraint, feeder overload constraint, radial operation constraint and switch device operation frequency constraint;

[0162] Specifically, for the interconnected medium and low voltage feeder networks, the power supply optimization objective function minF is constructed with the goal of minimizing the active network loss and the operation cost of the switchgear in the optimization cycle, as shown in the following formula:

[0163]

[0164] in, Indicates the number of network nodes of the medium and low voltage feeder networks that are interconnected; represents the active network loss of transformer node y at time t; represents the active power of transformer node y at time t; represents the reactive power of transformer node y at time t; represents the node voltage of transformer node y at time t; represents the resistance of transformer node y; Represents the total number of operations of all switch devices within the dispatch period T; Indicates the cost of a single action of the switching device.

[0165] In order to meet the safe operation requirements of the distribution system as much as possible, the following spatial and temporal constraints need to be met when optimizing load transfer:

[0166] (a) Network topology has no island constraints

[0167] In the process of changing the on / off state of the switchgear, some feeder segments are not allowed to be isolated (i.e. not connected to any distribution transformer). This constraint can be expressed by the following equation:

[0168]

[0169] Among them, PL represents the load node set; represents the active power of node i at time t; represents the active output of the distributed generation at node i at time t; this formula indicates that the sum of the loads supplied by the transformer node is equal to the sum of the net loads of other load nodes.

[0170] (b) Feeder non-overload constraints

[0171] After the switch device changes its open or closed state, feeder overload caused by load transfer is not allowed. Therefore, the following feeder overload constraints can be set:

[0172]

[0173] in, It represents the magnitude of the line transmission power between node i and node j at time t; Indicates the state of the controllable switch of the line between node i and node j at time t; It represents the upper limit of the line transmission power between node i and node j.

[0174] (c) Radial operation constraints

[0175] The normal state of the distribution network reconstruction needs to ensure that the reconstructed system still maintains a radial structure. Therefore, at any time, the radial operation constraints shown in the following formula need to be met:

[0176]

[0177] in, and is a binary variable, Indicates that at time t, node j is the upstream node of node i, otherwise ; Indicates that at time t, node i is the upstream node of node j, otherwise ; Indicates the section switch status of the first node i and the last node j line at time t; Represents the set of nodes connected to node i; in a power grid with a transformer cluster, the root node is the transformer node. Represents the set of root nodes; during network reconstruction, the number of switches from open to closed is equal to the number of switches from closed to open.

[0178] (d) Switching device operating frequency constraints

[0179] The change of the opening and closing state of the switch device has an impact on the life of the device and the reliability of the system power supply. In engineering, it is generally required that the operating frequency should not be too high. Therefore, the switch device operating frequency constraint can be set as shown in the following formula:

[0180]

[0181] In the formula, and They represent the maximum allowed number of actions of each controllable switch and the total maximum allowed number of actions of all controllable switches within the scheduling period T respectively; represents the number of actions of a single controllable switch responsible for connecting node i to node j at time t; It represents the number of actions of a single controllable switch responsible for connecting node i to node j at time t+1.

[0182] Step S22: constructing a feeder-level load transfer optimization model according to the transfer optimization objective function, the network topology no island constraint, the feeder no overload constraint, the radial operation constraint, and the switch device operation frequency constraint;

[0183] Step S23: solving the load transfer optimization model by discrete particle swarm algorithm to obtain the optimization results of the tie switch and the section switch. The optimization results of the tie switch and the section switch are used to optimize the load transfer of the distribution network.

[0184] The working state of the output section switch and the tie switch in the circuit. Specifically refers to the closed state or the open state.

[0185] After the load transfer optimization is completed, determine whether the annual time series operation simulation is completed. If not, recalculate and update the optimized scheduling results after the optimization of this scheduling cycle, and then refer to the above-mentioned related steps to perform the scheduling calculation of the next scheduling cycle until the scheduling of all scheduling cycles is completed.

[0186] In an embodiment of the present invention, a distributed power supply carrying capacity evaluation method considering the interactive operation of flexible resources in the smart distribution network is proposed. First, the load demand, distributed energy storage, distributed power supply and other equipment within the microgrid are taken into account to realize local optimized economic dispatch, so as to propose the power demand for purchasing and selling electricity to the smart distribution network; then, the active distribution network side conducts centralized optimization dispatch of the flexible resources on the power supply side, the grid side and the load side while trying to meet the power demand for purchasing and selling electricity on the microgrid side, and judges whether the power demand for purchasing and selling electricity on the microgrid side can be fully met based on the optimization dispatch results; if the power demand for purchasing and selling electricity on the microgrid side cannot be met, the feeder reconstruction is performed to further explore the overall consumption potential of the local power grid for distributed power sources; finally, through the annual time series operation simulation, the lowest average power supply cost per unit of electricity is taken as the key indicator, and the optimal carrying capacity of the smart distribution network for distributed power sources is determined on the premise of taking into account the economic efficiency of power supply. The technical solution proposed in the present invention takes into account the regulating effects of various flexible resources, allows for the adoption of wind and solar power abandonment measures, and adopts a distributed collaborative operation strategy in the operation simulation. It is suitable for the application scenario of using flexible resources to promote the carrying capacity of smart distribution networks for distributed power sources after the penetration rate of distributed power sources reaches a high level.

[0187] For better explanation, refer to Figure 3 , showing a schematic diagram of the overall process of a distributed power supply carrying capacity evaluation method provided by an embodiment of the present invention. It should be pointed out that this embodiment only briefly describes the general process of distributed power supply carrying capacity evaluation, and the specific implementation process of each step can be understood by referring to the relevant content in the aforementioned embodiment, which will not be described here. It can be understood that the present invention is not limited to this.

[0188] Step 1: Consider the load demand, distributed energy storage, distributed power supply and other equipment within the microgrid for economic dispatch to obtain the power demand for purchasing and selling electricity of the microgrid; jump to step 2;

[0189] Step 2: Centrally optimize and dispatch various flexible resources in the distribution network to obtain optimized dispatch results; jump to step 3;

[0190] Step 3: Determine whether the optimized dispatch result fully meets the power demand for purchasing and selling electricity; if so, jump to step 5; if not, jump to step 4:

[0191] Step 4: First, optimize the load transfer by reconfiguring the feeder, and then obtain a new optimized dispatch result by recalculating based on the centralized optimized dispatch; jump to step 5;

[0192] Step 5: Output the optimized scheduling results of this scheduling cycle and determine whether the annual time series operation simulation is completed; if so, jump to step 6; if not, jump to step 1 to perform the scheduling calculation for the next scheduling cycle;

[0193] Step 6: For each scheduling cycle, extract the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid from the optimized scheduling results, determine the optimal photovoltaic capacity of the microgrid, and further output the optimal grid-connected capacity of the distribution network as a whole; jump to step 7;

[0194] Step 7: Select the best grid-connected capacity with the largest capacity from all the best grid-connected capacities as the best grid-connected capacity of the entire distribution network under the annual time series operation simulation to evaluate the distribution network's carrying capacity for distributed generation.

[0195] Reference Figure 4 , shows a structural block diagram of a distributed power supply carrying capacity evaluation device provided by an embodiment of the present invention, wherein at least one microgrid is connected to the distribution network side, and each of the microgrids corresponds to a set of dispatchable devices. The device may specifically include:

[0196] The microgrid-side economic dispatch module 401 is used to perform economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid;

[0197] A centralized optimization dispatching module 402 is used to perform centralized optimization dispatching of various flexible resources in the distribution network to obtain optimization dispatching results;

[0198] The power purchase and sale power demand judgment module 403 is used to judge whether the optimization scheduling result fully meets the power purchase and sale power demand;

[0199] The optimal photovoltaic capacity determination module 404 is used to extract the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determine the optimal photovoltaic capacity of the microgrid;

[0200] The optimal grid-connected capacity integration module 405 is used to integrate all the optimal photovoltaic capacities as the optimal grid-connected capacity of the distribution network as a whole, so as to evaluate the carrying capacity of the distribution network for distributed power sources.

[0201] In an optional embodiment, the dispatchable device types in the dispatchable device set include distributed power sources and energy storage devices, and the microgrid-side economic dispatch module 401 includes:

[0202] A microgrid-side dispatch objective function construction module is used to construct a microgrid-side dispatch objective function for any of the microgrids, with the goal of minimizing the power purchase cost, maximizing the power sales revenue, minimizing the distributed energy storage loss, and minimizing the power loss, and at the same time setting the tie line power balance constraint, the constraint of not being able to purchase and sell power at the same time, and the energy storage constraint;

[0203] A microgrid-side economic dispatch model construction module, used to construct a microgrid-side economic dispatch model based on the microgrid-side dispatch objective function, the tie line power balance constraint, the constraint that electricity cannot be purchased and sold simultaneously, and the energy storage constraint;

[0204] A microgrid-side economic dispatch model solving module is used to solve the microgrid-side economic dispatch model by introducing intermediate variables to obtain the power demand for purchasing and selling electricity of the microgrid;

[0205] Wherein, when the output of the distributed power source of the microgrid is lower than the preset minimum output threshold, during the off-peak electricity price stage, electricity is purchased from the distribution network and stored in the energy storage device of the microgrid;

[0206] When the output of the distributed power source of the microgrid is higher than the preset maximum output threshold, while meeting the load demand, electricity is sold to the distribution network to obtain economic benefits.

[0207] In an optional embodiment, the various types of flexible resources include power supply side flexible resources, grid side flexible resources and load side flexible resources, and the centralized optimization scheduling module 402 includes:

[0208] A flexible resource scheduling constraint setting module, used to set the power supply side scheduling constraints corresponding to the power supply side flexible resources, the grid side scheduling constraints corresponding to the grid side flexible resources, and the load side scheduling constraints corresponding to the load side flexible resources;

[0209] A system unit electric energy supply cost function construction module is used to construct a function for calculating the system unit electric energy supply cost according to the annual value cost of the equipment, electricity purchase and sale cost, network loss cost and compensation cost of demand response users of the distribution network, and at the same time set the total amount constraint of distributed power sources allowed to be connected to the distribution network;

[0210] The distribution network side dispatch objective function construction module is used to construct the distribution network side dispatch objective function with the goal of minimizing the system unit power supply cost, considering the grid voltage quality, and setting the node injection power balance constraint, adjacent node voltage drop constraint and microgrid power purchase demand constraint;

[0211] A distribution network dispatching model building module is used to build a distribution network dispatching model according to the distribution network side dispatching objective function, while taking into account the power supply side dispatching constraints, the grid side dispatching constraints, the load side dispatching constraints, the total amount of distributed power supply constraints, the node injection power balance constraints, the adjacent node voltage drop constraints and the microgrid power purchase demand constraints;

[0212] The distribution network dispatching model solving module is used to solve the distribution network dispatching model based on second-order cone optimization to achieve centralized optimization dispatching of various flexible resources in the distribution network and obtain optimization dispatching results. The optimization dispatching results include the dispatchable purchase and sale power in the distribution network and the system average power supply cost corresponding to the installed capacity of different distributed power sources in each of the microgrids.

[0213] In an optional embodiment, the power supply side scheduling constraints include photovoltaic / wind turbine operation constraints and energy storage operation constraints, the grid side scheduling constraints include capacitor bank operation constraints, static VAR generator operation constraints and on-load tap-changing transformer operation constraints, and the load side scheduling constraints include demand side response constraints.

[0214] In an optional embodiment, the optimal photovoltaic capacity determination module 404 includes:

[0215] A system average power supply cost extraction module is used to extract the system average power supply costs corresponding to different distributed power generation installed capacities in the microgrid from the optimization scheduling results, and take the system average power supply cost with the smallest value as the target system average power supply cost;

[0216] The optimal photovoltaic capacity determination submodule is used to use the distributed power supply installed capacity corresponding to the average power supply cost of the target system as the optimal photovoltaic capacity of the microgrid.

[0217] In an optional embodiment, the device further includes:

[0218] The load transfer optimization module is used to optimize the load transfer through feeder reconstruction when the resource optimization scheduling cannot fully meet the power demand for purchasing and selling electricity, and then recalculate the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid based on centralized optimization scheduling.

[0219] In an optional embodiment, the load transfer optimization module:

[0220] A power transfer optimization objective function construction module is used to determine the target feeder network with interconnected relationships in the distribution network, and to construct a power transfer optimization objective function with the goal of minimizing the active network loss and switch device operation cost of the target feeder network within the optimization period, and to set the network topology no island constraint, feeder overload constraint, radial operation constraint, and switch device operation frequency constraint;

[0221] A load transfer optimization model building module, used to build a feeder-level load transfer optimization model according to the transfer optimization objective function, the network topology no island constraint, the feeder no overload constraint, the radial operation constraint and the switch device operation frequency constraint;

[0222] The load transfer optimization model solving module is used to solve the load transfer optimization model through a discrete particle swarm algorithm to obtain the optimization results of the tie switch and the section switch. The optimization results of the tie switch and the section switch are used to optimize the load transfer of the distribution network.

[0223] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0224] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0225] The memory is used to store the program code and transmit the program code to the processor;

[0226] The processor is used to execute the distributed power supply carrying capacity evaluation method of any embodiment of the present invention according to the instructions in the program code.

[0227] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the distributed power supply carrying capacity evaluation method of any embodiment of the present invention.

[0228] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0229] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0230] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0232] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0233] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the carrying capacity of a distributed power source, characterized in that: At least one microgrid is connected to the distribution network side, each of the microgrids corresponds to a set of dispatchable devices, and the method includes: Perform economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid; Centrally optimizing and dispatching various flexible resources in the distribution network to obtain optimized dispatching results; Determine whether the optimization scheduling result fully meets the power demand for purchasing and selling electricity; If satisfied, then extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determining the optimal photovoltaic capacity of the microgrid; All optimal photovoltaic capacities are integrated as the optimal grid-connected capacity of the distribution network as a whole to evaluate the carrying capacity of the distribution network for distributed power sources.

2. The distributed power supply carrying capacity evaluation method according to claim 1 is characterized in that: The types of dispatchable devices in the dispatchable device set include distributed power sources and energy storage devices. The economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power purchase and sale demand of the microgrid includes: For any of the microgrids, with the goal of minimizing the cost of electricity purchase, maximizing the revenue from electricity sales, minimizing the loss of distributed energy storage, and minimizing the power loss, a microgrid-side dispatch objective function is constructed, and at the same time, a tie line power balance constraint, a constraint that electricity cannot be purchased and sold at the same time, and an energy storage constraint are set; Based on the microgrid-side dispatch objective function, the tie-line power balance constraint, the constraint that electricity cannot be purchased and sold simultaneously, and the energy storage constraint, a microgrid-side economic dispatch model is constructed; Solving the microgrid-side economic dispatch model by introducing intermediate variables to obtain the power demand for purchasing and selling electricity of the microgrid; Wherein, when the output of the distributed power source of the microgrid is lower than the preset minimum output threshold, during the off-peak electricity price stage, electricity is purchased from the distribution network and stored in the energy storage device of the microgrid; When the output of the distributed power source of the microgrid is higher than the preset maximum output threshold, while meeting the load demand, electricity is sold to the distribution network to obtain economic benefits.

3. The distributed power supply carrying capacity evaluation method according to claim 1 is characterized in that: The various types of flexible resources include flexible resources on the power supply side, flexible resources on the grid side, and flexible resources on the load side. The centralized optimization and scheduling of various types of flexible resources in the distribution network to obtain the optimization and scheduling results includes: Setting the power supply side scheduling constraints corresponding to the power supply side flexible resources, the grid side scheduling constraints corresponding to the grid side flexible resources, and the load side scheduling constraints corresponding to the load side flexible resources; According to the annual value cost of equipment, electricity purchase and sale cost, network loss cost and compensation cost of demand response users of the distribution network, a function for calculating the unit electric energy supply cost of the system is constructed, and at the same time, a constraint on the total amount of distributed power sources allowed to be connected to the distribution network is set; Taking the minimum unit power supply cost of the system as the goal, considering the voltage quality of the power grid, the distribution network dispatch objective function is constructed, and at the same time, the node injection power balance constraint, the adjacent node voltage drop constraint and the microgrid power purchase demand constraint are set; According to the distribution network side dispatch objective function, the power supply side dispatch constraint, the grid side dispatch constraint, the load side dispatch constraint, the total amount of distributed power supply constraint, the node injection power balance constraint, the adjacent node voltage drop constraint and the microgrid power purchase demand constraint are considered to construct a distribution network dispatch model; The distribution network dispatching model is solved based on second-order cone optimization to achieve centralized optimal dispatching of various flexible resources in the distribution network and obtain optimized dispatching results. The optimized dispatching results include the dispatchable purchased and sold electricity power in the distribution network and the system average power supply cost corresponding to the installed capacity of different distributed power sources in each of the microgrids.

4. The method for evaluating the carrying capacity of distributed power sources according to claim 3 is characterized in that: The power supply side scheduling constraints include photovoltaic / wind turbine operation constraints and energy storage operation constraints, the grid side scheduling constraints include capacitor bank operation constraints, static VAR generator operation constraints and on-load tap-changing transformer operation constraints, and the load side scheduling constraints include demand side response constraints.

5. The method for evaluating the carrying capacity of distributed power sources according to claim 3 or 4, characterized in that: The step of extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results and determining the optimal photovoltaic capacity of the microgrid includes: Extracting the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and taking the system average power supply cost with the smallest value as the target system average power supply cost; The installed capacity of distributed power sources corresponding to the average power supply cost of the target system is used as the optimal photovoltaic capacity of the microgrid.

6. The method for evaluating the carrying capacity of distributed power sources according to claim 3, characterized in that: Also includes: If the resource optimization scheduling cannot fully meet the power demand for purchasing and selling electricity, the load transfer optimization is performed through feeder reconstruction, and then the system average power supply cost corresponding to the installed capacity of different distributed power sources in the microgrid is recalculated based on centralized optimization scheduling.

7. The method for evaluating the carrying capacity of distributed power sources according to claim 6, characterized in that: The load transfer optimization by feeder reconstruction includes: Determine the target feeder network with interconnection relationship in the distribution network, take the active network loss and switch device operation cost of the target feeder network as the minimum within the optimization period as the goal, construct the power supply optimization objective function, and set the network topology no island constraint, feeder overload constraint, radial operation constraint and switch device operation frequency constraint; Constructing a feeder-level load transfer optimization model according to the transfer optimization objective function, the network topology no-island constraint, the feeder no-overload constraint, the radial operation constraint, and the switch device operation frequency constraint; The load transfer optimization model is solved by discrete particle swarm algorithm to obtain the optimization results of the tie switch and the section switch, and the optimization results of the tie switch and the section switch are used to optimize the load transfer of the distribution network.

8. A distributed power supply carrying capacity evaluation device, characterized in that: At least one microgrid is connected to the distribution network side, each of the microgrids corresponds to a set of dispatchable devices, and the device includes: A microgrid-side economic dispatch module, used to perform economic dispatch based on various dispatchable devices in the dispatchable device set to obtain the power demand for purchasing and selling electricity of the microgrid; A centralized optimization dispatching module is used to perform centralized optimization dispatching of various flexible resources in the distribution network to obtain optimization dispatching results; A power purchase and sale power demand judgment module is used to judge whether the optimization scheduling result fully meets the power purchase and sale power demand; An optimal photovoltaic capacity determination module is used to extract the system average power supply costs corresponding to the installed capacities of different distributed power sources in the microgrid from the optimization scheduling results, and determine the optimal photovoltaic capacity of the microgrid; The optimal grid-connected capacity integration module is used to integrate all optimal photovoltaic capacities as the optimal grid-connected capacity of the distribution network as a whole, so as to evaluate the carrying capacity of the distribution network for distributed power sources.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed power supply carrying capacity evaluation method described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the distributed power supply carrying capacity evaluation method described in any one of claims 1-7.

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