Power distribution network energy management method, system, equipment and medium

Abstract

The invention belongs to the technical field of power distribution networks, and discloses a power distribution network energy management method, system and device and a medium, and the method comprises the steps: building a mobile energy storage space-time state model; establishing a flexible switch operation constraint condition based on the state of the flexible switch of the power distribution network, and establishing a network reconstruction model of the power distribution network based on the flexible switch operation constraint condition; performing integrated analysis on the mobile energy storage space-time state model and the power distribution network reconstruction model, and establishing a power distribution network bearing capacity improvement model according to an integrated analysis result and a predefined objective function; and evaluating the bearing capacity of the power distribution network to the distributed power supply by using the power distribution network bearing capacity improvement model, and optimizing a power distribution network energy management strategy according to a bearing capacity evaluation result. By establishing the mobile energy storage space-time state model, electric energy can be moved spatially, and support is provided for realizing peak clipping and valley filling under space-time multi-scale and solving the demand of supply and demand balance.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a power distribution network energy management method, system, equipment and medium. Background Art

[0002] The carrying capacity of the distribution network refers to the maximum load capacity that the distribution network can bear under the conditions of safe, reliable and economic operation. The carrying capacity of the distribution network is limited. The large-scale access of distributed power sources to the distribution network will have an adverse impact on the normal operation of the distribution network. Evaluating the carrying capacity of the distribution network for distributed power sources is conducive to its reasonable planning and deployment. By taking effective measures to reduce or even eliminate its adverse effects on the distribution network, the carrying capacity of the distribution network can be improved and the large-scale access of distributed power sources can be promoted. Under the premise that the system operates stably and the various electrical quantity indicators do not exceed the limit, the maximum capacity of distributed power sources that can be connected to the distribution network is called the carrying capacity of the distribution network for distributed power sources.

[0003] As an energy response resource, energy storage devices have the characteristics of fast response and can solve the problem of unbalanced energy distribution in time. At the same time, they can promote the consumption of wind power and photovoltaic power, solve the intermittent problem of new energy output, and improve the economy and reliability of power grid operation. As a fast response resource with spatiotemporal flexibility, mobile energy storage devices are more flexible than traditional energy storage devices and can quickly participate in the resource dispatch of distribution networks. They overcome geographical constraints, are more convenient to use, and occupy a small area. They are often used for short-term power supply repairs, temporary power supply after disasters, etc. At the same time, they have great prospects in short-term capacity expansion and seasonal power grid operation mode changes.

[0004] Distribution network reconstruction technology refers to the technology that improves reliability, reduces line loss, balances load and improves the quality of power supply voltage by changing the network topology of the distribution network. It is an important means to improve the safety and economy of the distribution network. By formulating a reasonable distribution network reconstruction strategy, the adverse effects of distributed power generation on the power grid can be reduced. There are two types of distribution network reconstruction: static reconstruction and dynamic reconstruction. Static reconstruction only reconstructs according to the state of the distribution network at a certain moment when optimizing the target, and does not change the topology when the subsequent operating state changes. Dynamic reconstruction is based on static reconstruction. When optimizing the target, the state of the distribution network within a certain period of time is considered, and the network is reconstructed according to the current state at the scheduling time point. Dynamic reconstruction can more effectively improve the carrying capacity of the distribution network for distributed power sources because it can adapt to changing operating conditions. However, dynamic reconstruction will cause the network topology to change frequently, reducing the stability and economy of the system. Therefore, when using dynamic reconstruction, the constraints of the number of switch operations should be considered at the same time.

[0005] With the large-scale grid connection of highly intermittent renewable energy, the high penetration of power electronic equipment and multi-energy coupling have greatly increased the complexity of distribution network optimization and scheduling. The strong randomness of renewable energy generation has also put forward higher requirements on the carrying capacity of distribution networks. However, it still faces the following difficulties and challenges:

[0006] 1) In the existing technology, most of the measures to improve the carrying capacity of the distribution network consider fixed energy storage. Compared with mobile energy storage, fixed energy storage has low flexibility in time scale and cannot flexibly respond to changes in distribution network demand. It has low dispatchability in space scale and cannot quickly provide energy support in emergency situations.

[0007] 2) In the past, when considering measures to improve the carrying capacity of distribution networks, distribution network reconstruction directly decided the switch state. However, with the large-scale access of distributed power sources to the distribution network, the distribution network needs to be actively reconstructed based on the flexible regulation technology of flexible devices. Compared with the two, the traditional distribution network reconstruction technology cannot timely optimize the electrical network flow and improve the node voltage distribution, thereby effectively improving the problem of distributed resource consumption in the distribution network.

[0008] Therefore, how to provide a distribution network energy management method, system, equipment and medium is a problem that needs to be solved urgently. Summary of the invention

[0009] The embodiments of the present invention provide a distribution network energy management method, system, device and medium to solve the problem in the prior art that fixed energy storage has low flexibility in time scale compared with mobile energy storage, cannot flexibly respond to changes in distribution network demand, has low dispatchability in space scale, and cannot provide energy support quickly in emergency situations.

[0010] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0011] According to a first aspect of an embodiment of the present invention, a method for energy management in a power distribution network is provided.

[0012] In one embodiment, the power distribution network energy management method includes:

[0013] Based on the energy storage timing characteristics and spatial transferability of mobile energy storage equipment, a mobile energy storage spatiotemporal state model is established;

[0014] Establish flexible switch operation constraints based on the flexible switch state of the distribution network, and establish a distribution network reconstruction model based on the flexible switch operation constraints;

[0015] The spatiotemporal state model of mobile energy storage and the distribution network reconstruction model are integrated and analyzed, and a distribution network carrying capacity improvement model is established based on the integrated analysis results and the predefined objective function;

[0016] The distribution network carrying capacity improvement model is used to evaluate the distribution network's carrying capacity for distributed generation, and the distribution network energy management strategy is optimized based on the carrying capacity assessment results.

[0017] In one embodiment, the step of establishing a flexible switch operation constraint condition based on the distribution network flexible switch state, and establishing a distribution network network reconstruction model based on the flexible switch operation constraint condition includes:

[0018] Obtain the state of the flexible switch in the distribution network, generate the flexible switch operation constraint conditions according to the flexible switch state, and build the distribution network reconstruction model based on the flexible intelligent switch operation constraint conditions;

[0019] The distribution network reconstruction model is analyzed, and the distribution network topology and distribution network operation status are adjusted based on the analysis results to achieve a reasonable allocation of loads and power sources among feeders in the distribution network.

[0020] In one embodiment, the distribution network reconstruction model is expressed as:

[0021]

[0022] Where N is the number of distribution network nodes; D is the number of distributed photovoltaic power sources; i and j are both distribution network nodes; G is the number of branches; α ij A closed branch.

[0023] In one embodiment, the mobile energy storage spatiotemporal state model and the distribution network reconstruction model are integrated and analyzed, and the distribution network carrying capacity improvement model is established according to the integrated analysis results and a predefined objective function, including:

[0024] The spatiotemporal state model of mobile energy storage and the network reconstruction model of distribution network are integrated and analyzed to obtain the integrated analysis results of mobile energy storage and network reconstruction. Based on the integrated analysis results and the predefined objective function, a distribution network carrying capacity improvement model is established.

[0025] The weighted summation technique is used to solve the distribution network carrying capacity improvement model, and the optimized distribution network carrying capacity improvement model is obtained, and constraint conditions are defined for the optimized distribution network carrying capacity improvement model.

[0026] In one embodiment, the objective function includes distributed photovoltaic, maximum energy storage capacity, and minimum total investment and operation cost consisting of additional reactive power compensation investment cost, active network loss cost of system operation after network reconstruction, and average annual energy consumption cost of energy storage;

[0027] The constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints and power flow balance constraints.

[0028] In one embodiment, the objective function is expressed as:

[0029]

[0030] minF2=F cost +F loss ;

[0031]

[0032] Where F1 is the total capacity of connected distributed photovoltaic and energy storage; F2 is the total investment and operation cost; F cost The investment cost for additional reactive power compensation; F loss F is the active network loss cost of system operation; soc is the average annual energy cost of energy storage; S N is the set of all nodes; C DG,i is the distributed photovoltaic capacity accessible to node i; C SOC,j is the distributed energy storage capacity accessible to node j; S C is the additional reactive power compensation set; ρ Q is the unit capacity price of additional reactive power compensation; τ is the discount rate; y is the operational life of reactive power compensation; Q C,b is the bth additional reactive power compensation capacity; r C,b is the bth additional reactive power compensation fixed installation investment cost; ρ P is the unit active network loss price; S L is the branch set; d l is the disconnection state of branch l; Z l is the impedance of branch l; I l is the current flowing through branch l; n SOC,j is the number of distributed energy storage installations; η m f is the system operation maintenance and cleaning factor; soc The installation investment cost of distributed energy storage.

[0033] In one embodiment, the expression of the optimized distribution network carrying capacity improvement model is:

[0034]

[0035] In the formula, f1 and f2 are the distributed photovoltaic capacity and total investment and operation cost in the optimization process respectively;

[0036] F 1,maxand F 1,min are the maximum and minimum values ​​of distributed photovoltaic capacity in the historical optimization process; F 2,max and F 2,min are the maximum and minimum values ​​of the total investment and operation costs in the historical optimization process; λ1 and λ2 are weight coefficients respectively.

[0037] According to a first aspect of an embodiment of the present invention, a power distribution network energy management system is provided.

[0038] In one embodiment, the power distribution network energy management system includes:

[0039] Mobile energy storage analysis module, used to establish a mobile energy storage spatiotemporal state model based on the energy storage timing characteristics and spatial transferability of mobile energy storage equipment;

[0040] A network reconstruction analysis module is used to establish flexible switch operation constraints based on the flexible switch state of the distribution network, and to establish a distribution network reconstruction model based on the flexible switch operation constraints;

[0041] The distribution network energy analysis module is used to integrate and analyze the spatiotemporal state model of mobile energy storage and the distribution network reconstruction model, and establish a distribution network carrying capacity improvement model based on the integrated analysis results and the predefined objective function;

[0042] The distribution network energy management module is used to evaluate the distribution network's carrying capacity for distributed power sources using the distribution network carrying capacity improvement model, and optimize the distribution network energy management strategy based on the carrying capacity evaluation results.

[0043] In one embodiment, the network reconstruction analysis module includes a network reconstruction model building module and a network reconstruction model analysis module, wherein:

[0044] A network reconstruction model building module is used to obtain the state of the flexible switch in the distribution network, generate the flexible switch operation constraint conditions according to the flexible switch state, and build the distribution network reconstruction model based on the flexible intelligent switch operation constraint conditions;

[0045] The network reconstruction model analysis module is used to analyze the distribution network reconstruction model and adjust the distribution network topology and distribution network operation status based on the analysis results to achieve a reasonable allocation of loads and power sources among feeders in the distribution network.

[0046] In one embodiment, the distribution network reconstruction model is expressed as:

[0047]

[0048] Where N is the number of distribution network nodes; D is the number of distributed photovoltaic power sources; i and j are both distribution network nodes; G is the number of branches; αij A closed branch.

[0049] In one embodiment, the distribution network energy analysis module includes a capacity enhancement model building module and a capacity enhancement model optimization module, wherein:

[0050] The module for establishing a model for improving the carrying capacity is used to integrate and analyze the spatiotemporal state model of mobile energy storage and the network reconstruction model of the distribution network, obtain the integrated analysis results of mobile energy storage and network reconstruction, and establish a distribution network carrying capacity improvement model based on the integrated analysis results and the predefined objective function;

[0051] The carrying capacity improvement model optimization module is used to solve the distribution network carrying capacity improvement model using the weighted summation technology to obtain the optimized distribution network carrying capacity improvement model and define constraint conditions for the optimized distribution network carrying capacity improvement model.

[0052] In one embodiment, the objective function includes distributed photovoltaic, maximum energy storage capacity, and minimum total investment and operation cost consisting of additional reactive power compensation investment cost, active network loss cost of system operation after network reconstruction, and average annual energy consumption cost of energy storage;

[0053] The constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints and power flow balance constraints.

[0054] In one embodiment, the objective function is expressed as:

[0055]

[0056] minF2=F cost +F loss ;

[0057]

[0058] Where F1 is the total capacity of connected distributed photovoltaic and energy storage; F2 is the total investment and operation cost; F cost The investment cost for additional reactive power compensation; F loss F is the active network loss cost of system operation; soc is the average annual energy cost of energy storage; S N is the set of all nodes; C DG,i is the distributed photovoltaic capacity accessible to node i; C SOC,j is the distributed energy storage capacity accessible to node j; S C is the additional reactive power compensation set; ρ Q is the unit capacity price of additional reactive power compensation; τ is the discount rate; y is the operational life of reactive power compensation; Q C,bis the bth additional reactive power compensation capacity; r C,b is the bth additional reactive power compensation fixed installation investment cost; ρ P is the unit active network loss price; S L is the branch set; d l is the disconnection state of branch l; Z l is the impedance of branch l; I l is the current flowing through branch l; n SOC,j is the number of distributed energy storage installations; η m f is the system operation maintenance and cleaning factor; soc The installation investment cost of distributed energy storage.

[0059] In one embodiment, the expression of the optimized distribution network carrying capacity improvement model is:

[0060]

[0061] In the formula, f1 and f2 are the distributed photovoltaic capacity and total investment and operation cost in the optimization process respectively;

[0062] F 1,max and F 1,min are the maximum and minimum values ​​of distributed photovoltaic capacity in the historical optimization process; F 2,max and F 2,min are the maximum and minimum values ​​of the total investment and operation costs in the historical optimization process; λ1 and λ2 are weight coefficients respectively.

[0063] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0064] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0065] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0066] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0067] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0068] 1. The present invention establishes a distribution network carrying capacity improvement model that takes into account mobile energy storage and network reconstruction, with the objective function being to maximize the distributed photovoltaic and energy storage capacity, minimize the total investment and operation cost consisting of the additional reactive power compensation investment cost, the system operation active network loss cost after network reconstruction, and the average annual energy consumption cost of energy storage, thereby effectively improving the level of distributed power source access to the distribution network.

[0069] 2. The present invention can move electric energy in space by establishing a mobile energy storage spatiotemporal state model, providing support for achieving peak shaving and valley filling at multiple spatiotemporal scales and solving the problem of supply and demand balance. At the same time, a distribution network reconstruction model is established to reduce the adverse effects of distributed power sources on the distribution network by changing flexible intelligent switches.

[0070] 3. The distribution network energy management method provided by the present invention solves the problem of poor flexibility and adjustability in time and space in configuring fixed energy storage in the prior art. It also solves the problem of poor network reconstruction in the prior art in terms of the ability to timely optimize the flow of the distribution network connected to distributed power sources by directly deciding the switch state, and the low ability to absorb renewable energy.

[0071] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0073] Figure 1 is a flow chart of a distribution network energy management method according to an exemplary embodiment;

[0074] Figure 2 is a principle block diagram of a distribution network energy management system according to an exemplary embodiment;

[0075] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0076] Figure 4 The invention is an architecture diagram of a power distribution network energy management system according to an exemplary embodiment. DETAILED DESCRIPTION

[0077] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0078] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0079] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0080] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0081] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0082] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0083] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0084] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0085] Figure 1 An embodiment of a distribution network energy management method of the present invention is shown.

[0086] In this optional embodiment, the power distribution network energy management method comprises:

[0087] Step S101, establishing a mobile energy storage spatiotemporal state model based on the energy storage time sequence characteristics and spatial transferability characteristics of the mobile energy storage device;

[0088] Step S103, establishing a flexible switch operation constraint condition based on the distribution network flexible switch state, and establishing a distribution network network reconstruction model based on the flexible switch operation constraint condition;

[0089] Step S105, integrating and analyzing the mobile energy storage spatiotemporal state model and the distribution network reconstruction model, and establishing a distribution network carrying capacity improvement model based on the integrated analysis results and a predefined objective function;

[0090] Step S107, using the distribution network carrying capacity improvement model to evaluate the distribution network's carrying capacity for distributed power sources, and optimizing the distribution network energy management strategy according to the carrying capacity evaluation result.

[0091] In this optional embodiment, the step of establishing a flexible switch operation constraint condition based on the distribution network flexible switch state, and establishing a distribution network network reconstruction model based on the flexible switch operation constraint condition includes:

[0092] The flexible switch status in the distribution network is obtained, the flexible switch operation constraints are generated according to the flexible switch status, and the distribution network reconstruction model is constructed based on the flexible intelligent switch operation constraints; the distribution network reconstruction model is analyzed, and the distribution network topology and the distribution network operation status are adjusted based on the analysis results to achieve a reasonable distribution of loads and power sources among feeders in the distribution network.

[0093] In this optional embodiment, the mobile energy storage spatiotemporal state model and the distribution network reconstruction model are integrated and analyzed, and the distribution network carrying capacity improvement model is established according to the integrated analysis results and the predefined objective function, including:

[0094] The spatiotemporal state model of mobile energy storage and the distribution network reconstruction model are integrated and analyzed to obtain the integrated analysis results of mobile energy storage and network reconstruction, and based on the integrated analysis results and the predefined objective function, a distribution network carrying capacity improvement model is established; the distribution network carrying capacity improvement model is solved using the weighted summation technique to obtain the optimized distribution network carrying capacity improvement model, and constraints are defined for the optimized distribution network carrying capacity improvement model.

[0095] In this optional embodiment, the objective function includes distributed photovoltaics, maximum energy storage capacity, and the minimum total investment and operation cost consisting of additional reactive power compensation investment cost, system operation active network loss cost after network reconstruction, and average annual energy consumption cost of energy storage; the constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints, and power flow balance constraints.

[0096] Figure 2 An embodiment of a power distribution network energy management system of the present invention is shown.

[0097] In this optional embodiment, the distribution network energy management system includes:

[0098] The mobile energy storage analysis module 201 is used to establish a mobile energy storage spatiotemporal state model based on the energy storage time series characteristics and spatial transferability characteristics of the mobile energy storage device;

[0099] The network reconstruction analysis module 203 is used to establish the flexible switch operation constraint conditions based on the distribution network flexible switch state, and to establish the distribution network network reconstruction model based on the flexible switch operation constraint conditions;

[0100] The distribution network energy analysis module 205 is used to integrate and analyze the mobile energy storage spatiotemporal state model and the distribution network reconstruction model, and establish a distribution network carrying capacity improvement model based on the integrated analysis results and a predefined objective function;

[0101] The distribution network energy management module 207 is used to evaluate the distribution network's carrying capacity for distributed power sources using the distribution network carrying capacity improvement model, and optimize the distribution network energy management strategy according to the carrying capacity evaluation result.

[0102] In this optional embodiment, the network reconstruction analysis module 203 includes a network reconstruction model establishment module (not shown in the figure) and a network reconstruction model analysis module (not shown in the figure), wherein:

[0103] The network reconstruction model establishment module is used to obtain the state of flexible switches in the distribution network, generate flexible switch operation constraints according to the flexible switch state, and build a distribution network reconstruction model based on the flexible intelligent switch operation constraints; the network reconstruction model analysis module is used to analyze the distribution network reconstruction model, and adjust the distribution network topology and distribution network operation status based on the analysis results to achieve a reasonable distribution of loads and power sources among feeders in the distribution network.

[0104] In this optional embodiment, the distribution network energy analysis module 205 includes a capacity enhancement model building module (not shown in the figure) and a capacity enhancement model optimization module (not shown in the figure), wherein:

[0105] The capacity enhancement model establishment module is used to integrate and analyze the mobile energy storage spatiotemporal state model and the distribution network reconstruction model to obtain the integrated analysis results of mobile energy storage and network reconstruction, and establish the distribution network capacity enhancement model based on the integrated analysis results and the predefined objective function; the capacity enhancement model optimization module is used to solve the distribution network capacity enhancement model using the weighted summation technology to obtain the optimized distribution network capacity enhancement model, and define constraints for the optimized distribution network capacity enhancement model.

[0106] In this optional embodiment, the objective function includes distributed photovoltaics, maximum energy storage capacity, and the minimum total investment and operation cost consisting of additional reactive power compensation investment cost, system operation active network loss cost after network reconstruction, and average annual energy consumption cost of energy storage; the constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints, and power flow balance constraints.

[0107] like Figure 4As shown, the present invention proposes a distribution network capacity improvement model considering mobile energy storage and network reconstruction. The model combines the advantages of mobile energy storage with higher flexibility and network reconstruction to improve distribution network reliability and load balancing. First, a distribution network capacity improvement model considering mobile energy storage and network reconstruction is established, with the maximum distributed photovoltaic and energy storage capacity and the minimum total investment and operation cost as the objective function, effectively improving the level of distributed power source access to the distribution network; then, modeling and analysis are carried out for mobile energy storage, and electric energy is moved in space, which can effectively solve the problems of insufficient power consumption and unsatisfied electricity demand caused by imbalanced supply and demand in different places, realize peak shaving and valley filling on a spatial scale, and assist the distribution network in coping with the integration of a large number of renewable energy sources and the large-scale access of new loads to the network; finally, modeling and analysis are carried out for distribution network reconstruction, and by formulating a reasonable distribution network reconstruction strategy, the adverse effects of distributed power source access on the distribution network can be reduced.

[0108] In order to facilitate understanding of the above technical solution of the present invention, the distribution network energy management method provided by the present invention is described in detail below.

[0109] Step 1: Construct a multi-objective optimization model for improving the carrying capacity of the distribution network:

[0110] (1) Objective function:

[0111] The objective function of the present invention is to maximize the distributed photovoltaic and energy storage capacity, minimize the total investment and operation cost consisting of the additional reactive power compensation investment cost, the active network loss cost of the system operation after network reconstruction, and the average annual energy consumption cost of energy storage, and the expression is:

[0112]

[0113] minF2=F cost +F loss ;

[0114]

[0115] Where F1 is the total capacity of connected distributed photovoltaic and energy storage; F2 is the total investment and operation cost; F cost The investment cost for additional reactive power compensation; F loss F is the active network loss cost of system operation; soc is the average annual energy cost of energy storage; S N is the set of all nodes; C DG,i is the distributed photovoltaic capacity accessible to node i; C SOC,j is the distributed energy storage capacity accessible to node j; S C is the additional reactive power compensation set; ρ Q is the unit capacity price of additional reactive power compensation; τ is the discount rate; y is the operational life of reactive power compensation; Q C,bis the bth additional reactive power compensation capacity; r C,b is the bth additional reactive power compensation fixed installation investment cost; ρ P is the unit active network loss price; S L is the branch set; d l is the breaking state of branch l, 1 is the closing state of branch, and 0 is the disconnecting state of branch; Z l is the impedance of branch l; I l is the current flowing through branch l; n SOC,j is the number of distributed energy storage installations; η m is the system operation maintenance and cleaning coefficient, which is 0.5% in the present invention; f soc The installation investment cost of distributed energy storage.

[0116] In the process of configuring the photovoltaic storage capacity, it is necessary to balance the impact of different optimization objectives on the results. The present invention uses a weighted summation method to transform the above multi-objective optimization model (equivalent to the distribution network carrying capacity improvement model) into a single-objective problem for solution. Since the dimensions of different objectives are different, normalization is required. Therefore, the optimization model of the present invention (equivalent to the optimized distribution network carrying capacity improvement model) is defined as:

[0117]

[0118] In the formula, f1 and f2 are the distributed photovoltaic capacity and total investment and operation cost in the optimization process respectively;

[0119] F 1,max and F 1,min are the maximum and minimum values ​​of distributed photovoltaic capacity in the historical optimization process; F 2,max and F 2,min are the maximum and minimum values ​​of the total investment and operation costs in the historical optimization process; λ1 and λ2 are weight coefficients respectively.

[0120] (2) Constraints:

[0121] Distributed photovoltaic output constraints:

[0122]

[0123] Where P DG,i and Q DG,i They are the current active and reactive power output of distributed photovoltaic connected to node i (equivalent to the distribution network node), i∈S N ;P DG,i,max is the upper limit of the output active power of distributed photovoltaic on node i; Q DG,i,max is the upper limit of the output reactive power of distributed photovoltaic at node i.

[0124] Node voltage safety constraints:

[0125] Ui,min≤Ui≤Ui,max;

[0126]

[0127] Where U i is the voltage amplitude of node i, i∈S N ; U i,max and U i,min are the upper and lower limits of the voltage amplitude at node i respectively; U i,N is the rated voltage of the busbar in this area; R L , X L is the resistance and reactance components of the grid impedance; P i,max , Q i,max and P i ' ,max , Q i ' ,max is the active and reactive amplitude of node i, and the power factor The required value is calculated.

[0128] Short circuit current limitation:

[0129] Il≤Il,max;

[0130]

[0131] In the formula, I l,max is the short-circuit current limit allowed for branch l, and the minimum value of the breaking current limit of all devices connected to the busbar and the corresponding circuit breakers on the feeder line should be selected; k is the peak factor; R L , X L are the resistance and reactance components of the grid impedance respectively; U N is the rated voltage of the branch.

[0132] Transformer reverse load rate constraint:

[0133]

[0134] Where P L,i is the equivalent load output on node i; S T,max is the maximum transmission capacity of transformer T, Among them, S T is the transformer set; max is the maximum value of the transformer reverse load rate, which is 80%.

[0135] Network topology constraints: Network reconstruction needs to meet the requirements of network topology connectivity and radiation. For any node i in the network, there is a connected closed path from the node to the source node. At the same time, network reconstruction must prevent the disconnection of radial branches, and no islands or rings should occur. The network topology connectivity and radiation constraints are:

[0136]

[0137] In the formula, l i,0 is the set of branches contained in the path connecting the i-th node to the source node, i∈S N ; N is the total number of nodes in the network; L is the total number of branches in the network.

[0138] Flexible switch operation constraints:

[0139] P s,i +P s,j =0;

[0140]

[0141] Where P s,i , P s,j and Q s,i , Q s,j are the active power and reactive power injected into the node at both ends of the flexible switch respectively; S s is the capacity of the flexible switch.

[0142] Power flow balance constraints:

[0143]

[0144] In the formula, Q Li is the reactive power of the load on node i, i∈S N ;Q ci is the reactive compensation added to node i; δ ij is the voltage phase angle between nodes i and j; G ij and B ij are the conductance and susceptance of the branch between nodes i and j, respectively.

[0145] Step 2: Mobile energy storage modeling analysis:

[0146] Mobile energy storage is mainly composed of energy storage equipment and mobile equipment. The energy storage equipment is generally composed of a lithium battery pack and is connected to the DC link of the intelligent soft switch through a DC / DC converter. The DC / DC converter can adjust the charging and discharging power according to the system setting value. Compared with fixed energy storage, mobile energy storage can be flexibly connected to the grid between nodes to improve the flexibility of operation. At present, the design of mobile energy storage is mostly a structure in which fuel vehicles are equipped with lithium batteries. However, considering the pollution caused by exhaust gas and noise generated by fuel to the environment and the continuous development of power electronics technology, the structure of electric vehicles equipped with lithium batteries will gradually become the mainstream. Therefore, the present invention will perform physical modeling and mathematical modeling of mobile energy storage according to the design idea of ​​electric vehicles equipped with lithium batteries.

[0147] In the modeling of mobile energy storage, it is necessary to consider the energy storage timing characteristics and spatial transferability characteristics, which refer to the energy storage charge state constraints and mobile energy storage mobility state constraints, respectively, as follows:

[0148] The state of charge constraints are:

[0149]

[0150] Υ t ∈{0,1};

[0151]

[0152] In the formula, It is the flag of the mobile energy storage connected to the distribution network node. If the mobile energy storage is connected to the grid at the node at all times, is 1, otherwise it is 0; P N is the rated power of MES; is the power injected into node i by mobile energy storage at the moment; Δt is the time step; Υ t is the flag of the mobile energy storage; Ξ is the electric energy consumed by the mobile energy storage per unit distance; v is the moving speed of the mobile energy storage; and They are the maximum and minimum permissible values ​​of the state of charge of the mobile energy storage, respectively; is the charge state of the mobile energy storage device at time t; and They are the charge states of the mobile energy storage at the start and end of the operation cycle respectively.

[0153] The mobile state constraints of mobile energy storage are:

[0154]

[0155] h∈{1,2,···,min(Γij,Tt)},i,j∈Ω M ;

[0156] In the formula, Ω M is the set of traffic nodes that can be connected to mobile energy storage; h is the mobile energy storage movement time; T is the total operation cycle time; Γ ij The shortest travel time required for the mobile energy storage to move from traffic node to traffic node can be obtained by the Dijkstra algorithm.

[0157] Step 3: Network reconstruction modeling and analysis:

[0158] The distribution network has a complex structure and has a large number of interconnecting switches and section switches. Network reconstruction adjusts the distribution network topology and optimizes the system operation status by operating the open or closed state of the switch, so that the load and power supply in the system are reasonably distributed between the feeders. When reconstructing the network, it is necessary to meet the requirements of distribution network connectivity and radiation to avoid the generation of islands and ring networks. On the one hand, network reconstruction can change the power flow distribution of the distribution network operation mode and improve the voltage quality of the power grid; on the other hand, network reconstruction can effectively reduce network operation losses and improve the economy of the system. Therefore, network reconstruction can be used as an effective means to further improve the carrying capacity of distributed photovoltaics.

[0159] The network reconstruction model of the distribution network is shown as follows:

[0160]

[0161] The above formula indicates that the network topology should be maintained as a radial network, that is, the number of closed branches should not be less than the difference between the number of distribution network nodes and the number of distributed power sources, where N is the number of distribution network nodes, including the balancing node with node number 1, D is the number of distributed photovoltaic power sources, G is the number of branches, and α ij A closed branch.

[0162]

[0163] The above formula indicates that when there is a unique parent-child relationship between nodes i and j, the branch is closed, ε ij is an auxiliary variable of the radial network used to represent the parent-child relationship between nodes. If the parent node of i is ij is 1; if i is a child node of j, then ε ji is 1; otherwise ε ji A value of 0 indicates that there is no parent-child relationship between the two nodes, that is, the two nodes are not connected.

[0164]

[0165] The above formula means that each node has at most one parent node.

[0166]

[0167] The above formula indicates that after the network is reconstructed, the node where the distributed generation is located has no parent node, that is, the distributed generation is directly connected to the network, and the tie line does not act as a branch.

[0168] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0169] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0171] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0172] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0173] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distribution network energy management method, characterized in that: The method includes: Based on the energy storage timing characteristics and spatial transferability of mobile energy storage equipment, a mobile energy storage spatiotemporal state model is established; Establish flexible switch operation constraints based on the flexible switch state of the distribution network, and establish a distribution network reconstruction model based on the flexible switch operation constraints; The spatiotemporal state model of mobile energy storage and the distribution network reconstruction model are integrated and analyzed, and a distribution network carrying capacity improvement model is established based on the integrated analysis results and the predefined objective function; The distribution network carrying capacity improvement model is used to evaluate the distribution network's carrying capacity for distributed generation, and the distribution network energy management strategy is optimized based on the carrying capacity assessment results.

2. The power distribution network energy management method according to claim 1, characterized in that: The method of establishing a flexible switch operation constraint condition based on the distribution network flexible switch state and establishing a distribution network network reconstruction model based on the flexible switch operation constraint condition includes: Obtain the state of the flexible switch in the distribution network, generate the flexible switch operation constraint conditions according to the flexible switch state, and build the distribution network reconstruction model based on the flexible intelligent switch operation constraint conditions; The distribution network reconstruction model is analyzed, and the distribution network topology and distribution network operation status are adjusted based on the analysis results to achieve a reasonable allocation of loads and power sources among feeders in the distribution network.

3. The power distribution network energy management method according to claim 2, characterized in that: The expression of the distribution network reconstruction model is: Where N is the number of distribution network nodes; D is the number of distributed photovoltaic power sources; i and j are both distribution network nodes; G is the number of branches; α ij A closed branch.

4. The power distribution network energy management method according to claim 3, characterized in that: The said integrating and analyzing the mobile energy storage spatiotemporal state model and the distribution network reconstruction model, and establishing a distribution network carrying capacity improvement model according to the integrated analysis results and a predefined objective function includes: The spatiotemporal state model of mobile energy storage and the network reconstruction model of distribution network are integrated and analyzed to obtain the integrated analysis results of mobile energy storage and network reconstruction. Based on the integrated analysis results and the predefined objective function, a distribution network carrying capacity improvement model is established. The weighted summation technique is used to solve the distribution network carrying capacity improvement model, and the optimized distribution network carrying capacity improvement model is obtained, and constraint conditions are defined for the optimized distribution network carrying capacity improvement model.

5. The power distribution network energy management method according to claim 4, characterized in that: The objective function includes the maximum distributed photovoltaic and energy storage capacity, as well as the minimum total investment and operation cost consisting of the additional reactive power compensation investment cost, the active network loss cost of the system operation after network reconstruction, and the average annual energy consumption cost of energy storage; The constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints and power flow balance constraints.

6. The power distribution network energy management method according to claim 5, characterized in that: The expression of the objective function is: minF2=F cost +F loss ; Where F1 is the total capacity of connected distributed photovoltaic and energy storage; F2 is the total investment and operation cost; F cost The investment cost for additional reactive power compensation; F loss F is the active network loss cost of system operation; soc is the average annual energy cost of energy storage; S N is the set of all nodes; C DG,i is the distributed photovoltaic capacity accessible to node i; C SOC,j is the distributed energy storage capacity accessible to node j; S C is the additional reactive power compensation set; ρ Q is the unit capacity price of additional reactive power compensation; τ is the discount rate; y is the operational life of reactive power compensation; Q C,b is the bth additional reactive power compensation capacity; r C,b is the bth additional reactive power compensation fixed installation investment cost; ρ P is the unit active network loss price; S L is the branch set; d l is the disconnection state of branch l; Z l is the impedance of branch l; I l is the current flowing through branch l; n SOC,j The number of distributed energy storage installations; η m f is the system operation maintenance and cleaning factor; soc The installation investment cost of distributed energy storage.

7. The power distribution network energy management method according to claim 1, characterized in that: The expression of the optimized distribution network carrying capacity improvement model is: In the formula, f1 and f2 are the distributed photovoltaic capacity and total investment and operation cost in the optimization process respectively; F 1,max and F 1,min are the maximum and minimum values ​​of distributed photovoltaic capacity in the historical optimization process; F 2,max and F 2,min are the maximum and minimum values ​​of the total investment and operation cost in the historical optimization process respectively; λ1 and λ2 are weight coefficients respectively.

8. A distribution network energy management system, characterized in that: The system includes: Mobile energy storage analysis module, used to establish a mobile energy storage spatiotemporal state model based on the energy storage timing characteristics and spatial transferability of mobile energy storage equipment; A network reconstruction analysis module is used to establish flexible switch operation constraints based on the flexible switch state of the distribution network, and to establish a distribution network reconstruction model based on the flexible switch operation constraints; The distribution network energy analysis module is used to integrate and analyze the spatiotemporal state model of mobile energy storage and the distribution network reconstruction model, and establish a distribution network carrying capacity improvement model based on the integrated analysis results and the predefined objective function; The distribution network energy management module is used to evaluate the distribution network's carrying capacity for distributed power sources using the distribution network carrying capacity improvement model, and optimize the distribution network energy management strategy based on the carrying capacity evaluation results.

9. The power distribution network energy management system according to claim 8, characterized in that: The network reconstruction analysis module includes a network reconstruction model establishment module and a network reconstruction model analysis module, wherein: A network reconstruction model building module is used to obtain the state of the flexible switch in the distribution network, generate the flexible switch operation constraint conditions according to the flexible switch state, and build the distribution network reconstruction model based on the flexible intelligent switch operation constraint conditions; The network reconstruction model analysis module is used to analyze the distribution network reconstruction model and adjust the distribution network topology and distribution network operation status based on the analysis results to achieve a reasonable allocation of loads and power sources among feeders in the distribution network.

10. The power distribution network energy management system according to claim 9, characterized in that: The expression of the distribution network reconstruction model is: Where N is the number of distribution network nodes; D is the number of distributed photovoltaic power sources; i and j are both distribution network nodes; G is the number of branches; α ij A closed branch.

11. The power distribution network energy management system according to claim 10, characterized in that: The distribution network energy analysis module includes a load-carrying capacity improvement model establishment module and a load-carrying capacity improvement model optimization module, wherein: The module for establishing a model for improving the carrying capacity is used to integrate and analyze the spatiotemporal state model of mobile energy storage and the network reconstruction model of the distribution network, obtain the integrated analysis results of mobile energy storage and network reconstruction, and establish a distribution network carrying capacity improvement model based on the integrated analysis results and the predefined objective function; The carrying capacity improvement model optimization module is used to solve the distribution network carrying capacity improvement model using the weighted summation technology to obtain the optimized distribution network carrying capacity improvement model and define constraint conditions for the optimized distribution network carrying capacity improvement model.

12. The power distribution network energy management system according to claim 11, characterized in that: The objective function includes the maximum distributed photovoltaic and energy storage capacity, as well as the minimum total investment and operation cost consisting of the additional reactive power compensation investment cost, the active network loss cost of the system operation after network reconstruction, and the average annual energy consumption cost of energy storage; The constraints include distributed photovoltaic output constraints, node voltage safety constraints, short-circuit current constraints, transformer reverse load rate constraints, network topology constraints, flexible switch operation constraints and power flow balance constraints.

13. The power distribution network energy management system according to claim 12, characterized in that: The expression of the objective function is: minF2=F cost +F loss ; Where F1 is the total capacity of connected distributed photovoltaic and energy storage; F2 is the total investment and operation cost; F cost The investment cost for additional reactive power compensation; F loss F is the active network loss cost of system operation; soc is the average annual energy cost of energy storage; S N is the set of all nodes; C DG,i is the distributed photovoltaic capacity accessible to node i; C SOC,j is the distributed energy storage capacity accessible to node j; S C is the additional reactive power compensation set; ρ Q is the unit capacity price of additional reactive power compensation; τ is the discount rate; y is the operational life of reactive power compensation; Q C,b is the bth additional reactive power compensation capacity; r C,b is the bth additional reactive power compensation fixed installation investment cost; ρ P is the unit active network loss price; S L is the branch set; d l is the disconnection state of branch l; Z l is the impedance of branch l; I l is the current flowing through branch l; n SOC,j The number of distributed energy storage installations; η m f is the system operation maintenance and cleaning factor; soc The installation investment cost of distributed energy storage.

14. The distribution network energy management system according to claim 13, characterized in that: The expression of the optimized distribution network carrying capacity improvement model is: In the formula, f1 and f2 are the distributed photovoltaic capacity and total investment and operation cost in the optimization process respectively; F 1,max and F 1,min are the maximum and minimum values ​​of distributed photovoltaic capacity in the historical optimization process; F 2,max and F 2,min are the maximum and minimum values ​​of the total investment and operation cost in the historical optimization process respectively; λ1 and λ2 are weight coefficients respectively.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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