Energy management method and device of micro-grid, computer equipment and program product

Abstract

The invention relates to a micro-grid energy management method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing an energy management model based on a network topology structure of a micro-grid; constructing a robust optimization model based on the uncertain data of the DC load and the photovoltaic output of the micro-grid; converting a nonlinear constraint condition containing an uncertain variable in the robust optimization model into a deterministic linear constraint to obtain a reconstructed robust optimization model, and applying the reconstructed robust optimization model to an energy management model to obtain a final energy management model; and inputting the photovoltaic output data of each node in the micro-grid, the DC load data of each line, the AC load data of each line, the output parameter data of the energy storage equipment and the maximum capacity of the energy storage equipment into the final energy management model to obtain the output data of the micro-grid, the output data of the energy storage equipment and a day-ahead scheduling plan of the micro-grid. By adopting the method, safe and economical operation of the micro-grid can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical automation, in particular to a micro-grid energy management method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] With the wide access of high proportion of distributed new energy to distribution network, the original load-centered power supply and demand balance system is broken, and the distribution network gradually evolves from a single power consumption side system to a dual role structure with power supply and power consumption functions. This structural change not only speeds up the green transformation of energy, but also causes a series of problems such as reverse power flow, uneven load of multiple areas, and low power quality.

[0003] With the rapid development of power electronics technology, multiple areas are interconnected through low-voltage flexible interconnection devices to build interconnected micro-grids, which promote the formation of a new operation mode of capacity sharing, power mutual aid, and dynamic capacity expansion, and become one of the effective ways to solve the above problems. The flexible interconnection micro-grid enhances the operational coupling relationship between areas and improves the coordinated allocation of resources in a larger range, which not only helps to improve the local consumption rate of new energy, but also significantly enhances the adaptability of the distribution system to uncertain disturbances.

[0004] However, due to the significant uncertainty characteristics of distributed power and load in the micro-grid, if there is a lack of effective coordination mechanism and optimization strategy, new operation risks such as voltage fluctuation and power imbalance may still occur. Therefore, how to carry out micro-grid energy management optimization under the flexible interconnection architecture, fully tap the collaborative potential of energy storage and multi-area interconnection, and realize safe, economic and stable system operation has become an important direction of current research. SUMMARY

[0005] Therefore, it is necessary to provide a micro-grid energy management method, device, computer equipment, computer readable storage medium and computer program product capable of optimizing the safe operation of the micro-grid to solve the above technical problems.

[0006] In a first aspect, the present application provides a micro-grid energy management method, comprising:

[0007] Based on the network topology structure of the micro-grid, an energy management model of the micro-grid is constructed;

[0008] Based on the uncertain data of direct current load and photovoltaic output of the micro-grid, a robust optimization model of the micro-grid is constructed;

[0009] The nonlinear constraint condition containing uncertain variables in the robust optimization model is converted into a deterministic linear constraint without uncertain variables, and a reconstructed robust optimization model is obtained;

[0010] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0011] inputting the photovoltaic output data of each node in the micro-grid, the direct current load data of each line, the alternating current load data of each line, the energy storage device output parameter data, and the maximum capacity of the energy storage device into the final energy management model to obtain the output data of the micro-grid, the output data of the energy storage device, and the day-ahead scheduling plan of the micro-grid.

[0012] In one of the embodiments, the network topology based on the micro-grid is used to construct the energy management model of the micro-grid, which comprises:

[0013] The network topology based on the micro-grid is used to establish a power flow calculation model of the micro-grid.

[0014] On the basis of the power flow calculation model, a first objective function of minimizing the total operation cost of the micro-grid is constructed, and a constraint condition of balancing the load rate of each district in the micro-grid is constructed to establish the energy management model of the micro-grid.

[0015] In one of the embodiments, the network topology based on the micro-grid is used to establish a power flow calculation model of the micro-grid, which comprises:

[0016] Based on the number of each node and line, an association matrix between the nodes and the lines is determined.

[0017] Based on the association matrix, an initial power flow calculation model is established, line transmission power constraints and node voltage operation constraints are applied to the initial power flow calculation model, and a power flow calculation model of the micro-grid is obtained.

[0018] In one of the embodiments, the calculation formula of the first objective function is:

[0019] ;

[0020] wherein, is a minimization function calculation, is a charge-discharge power column vector of the energy storage device in the micro-grid, is an inflow power column vector of the voltage source converter, T is a set of micro-grid operation periods, represents the interaction cost of the micro-grid and the distribution network, represents the operation cost of the energy storage device in the micro-grid, represents the non-uniform load penalty cost of all districts in the micro-grid.

[0021] In one of the embodiments, the robust optimization model of the micro-grid is constructed based on the uncertain data of the direct current load and the photovoltaic output of the micro-grid, which comprises:

[0022] determining a prediction error of the uncertain data;

[0023] determining a DC node injection power in the microgrid based on the prediction error and a centralized energy storage regulation strategy;

[0024] based on the DC node injection power, constructing a robust optimization model of the microgrid based on a second objective function obtained by maximizing the uncertain data of the energy storage device in the microgrid, a DC line transmission power constraint, a DC bus voltage constraint, an output data constraint of the energy storage device, and a state of charge constraint of the energy storage device.

[0025] In one embodiment, the conversion of the nonlinear constraint condition containing uncertain variables in the robust optimization model into a deterministic linear constraint without uncertain variables includes:

[0026] characterizing the uncertain data in the form of a box-type uncertain set to obtain an uncertain set;

[0027] based on the uncertain set, converting the nonlinear constraint condition into a problem of solving the maximum value of uncertain items within the range of the uncertain set;

[0028] introducing an even variable vector to convert the nonlinear constraint condition into a deterministic linear constraint.

[0029] In a second aspect, the present application also provides an energy management device of a microgrid, comprising:

[0030] a construction module configured to construct an energy management model of the microgrid based on a network topology of the microgrid;

[0031] the construction module is configured to construct a robust optimization model of the microgrid based on uncertain data of DC loads and photovoltaic output of the microgrid;

[0032] a conversion module configured to convert a nonlinear constraint condition containing uncertain variables in the robust optimization model into a deterministic linear constraint without uncertain variables to obtain a reconstructed robust optimization model;

[0033] an application module configured to apply the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0034] a calculation module configured to input photovoltaic output data of each node in the microgrid, DC load data of each line, AC load data of each line, output parameter data of the energy storage device, and maximum capacity of the energy storage device into the final energy management model to obtain output data of the microgrid, output data of the energy storage device, and day-ahead scheduling plan of the microgrid.

[0035] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0036] constructing an energy management model of the micro-grid based on a network topology of the micro-grid;

[0037] constructing a robust optimization model of the micro-grid based on uncertain data of direct-current loads and photovoltaic output of the micro-grid;

[0038] transforming nonlinear constraint conditions containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables to obtain a reconstructed robust optimization model;

[0039] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0040] inputting photovoltaic output data of each node, direct-current load data of each line, alternating-current load data of each line, output parameter data of energy storage devices, and maximum capacity of energy storage devices in the micro-grid into the final energy management model to obtain output data of the micro-grid, output data of the energy storage devices, and day-ahead scheduling plan of the micro-grid.

[0041] In a fourth aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the following steps:

[0042] constructing an energy management model of the micro-grid based on a network topology of the micro-grid;

[0043] constructing a robust optimization model of the micro-grid based on uncertain data of direct-current loads and photovoltaic output of the micro-grid;

[0044] transforming nonlinear constraint conditions containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables to obtain a reconstructed robust optimization model;

[0045] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0046] inputting photovoltaic output data of each node, direct-current load data of each line, alternating-current load data of each line, output parameter data of energy storage devices, and maximum capacity of energy storage devices in the micro-grid into the final energy management model to obtain output data of the micro-grid, output data of the energy storage devices, and day-ahead scheduling plan of the micro-grid.

[0047] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0048] constructing an energy management model of the microgrid based on a network topology of the microgrid;

[0049] constructing a robust optimization model of the microgrid based on uncertain data of direct-current loads and photovoltaic output of the microgrid;

[0050] transforming nonlinear constraint conditions containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables to obtain a reconstructed robust optimization model;

[0051] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0052] inputting photovoltaic output data of each node, direct-current load data of each line, alternating-current load data of each line, output parameter data of energy storage devices, and maximum capacity of energy storage devices in the microgrid into the final energy management model to obtain output data of the microgrid, output data of energy storage devices, and day-ahead scheduling plan of the microgrid.

[0053] The above energy management method and device of the microgrid, computer equipment, computer readable storage medium, and computer program product first construct an energy management model of the microgrid based on a network topology of the microgrid, construct a robust optimization model of the microgrid based on uncertain data of direct-current loads and photovoltaic output of the microgrid, transform nonlinear constraint conditions containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables to obtain a reconstructed robust optimization model, apply the reconstructed robust optimization model to the energy management model to obtain a final energy management model, and input photovoltaic output data of each node, direct-current load data of each line, alternating-current load data of each line, output parameter data of energy storage devices, and maximum capacity of energy storage devices in the microgrid into the final energy management model to obtain output data of the microgrid, output data of energy storage devices, and day-ahead scheduling plan of the microgrid. In this way, the energy in the microgrid is managed by fully considering uncertain factors in the microgrid, day-ahead economic scheduling and day-ahead uncertain consumption of the microgrid are realized, and economic and safe operation of the microgrid system is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 An application environment diagram of the energy management method of the micro-grid in an embodiment;

[0056] Figure 2 A flowchart of the energy management method of the micro-grid in an embodiment;

[0057] Figure 3 A schematic diagram of the network topology of the micro-grid in an embodiment;

[0058] Figure 4 A flowchart of the energy management method of the micro-grid in another embodiment;

[0059] Figure 5 A structural block diagram of the energy management device of the micro-grid in an embodiment;

[0060] Figure 6 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0061] In order to make the purposes, technical solutions and advantages of the present application more clear, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0062] It should be noted that the terms "first", "second" and the like used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover the non-exclusive inclusion. The term "multiple" used in the present application refers to two and more than two. The term "and / or" used in the present application refers to one of the solutions, or any combination of multiple solutions.

[0063] The energy management method of the micro-grid provided by the embodiments of the present application can be applied to, for example Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, unmanned aerial vehicles, low-altitude aircraft, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a standalone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0064] In an exemplary embodiment, as shown in Figure 2 , an energy management method of a micro-grid is provided, which is applied to the terminal 102 in Figure 1 for example, including the following steps 202 to 210. Among them:

[0065] Step 202, based on the network topology of the micro-grid, an energy management model of the micro-grid is constructed.

[0066] Exemplarily, in the power distribution network, a plurality of devices are interconnected to form an AC / DC flexible interconnected micro-grid, all DC loads and rooftop photovoltaics in the micro-grid are connected to the DC bus of the micro-grid, and the energy management model of the micro-grid is constructed according to the network topology of the micro-grid.

[0067] Among them, the device includes an AC device, a rooftop photovoltaic device, an energy storage device, etc., and can also include other related devices, which are not limited by the embodiments of the present application.

[0068] Step 204, based on the uncertain data of the DC load and the photovoltaic output of the micro-grid, a robust optimization model of the micro-grid is constructed.

[0069] Optionally, the prediction error of the DC load and the photovoltaic output of the micro-grid leads to the uncertainty of the micro-grid, the photovoltaic output power and the DC load processing power containing the uncertainty in the micro-grid are determined, and the robust optimization model of the micro-grid is constructed according to the centralized energy storage regulation strategy and the target function containing the uncertainty.

[0070] Step 206, the nonlinear constraint condition containing the uncertain variable in the robust optimization model is converted into a deterministic linear constraint without uncertain variable, and a reconstructed robust optimization model is obtained.

[0071] Optionally, the constraint condition in the robust optimization model of the micro-grid contains uncertain data, the uncertain data is represented in the form of a box-type uncertain set, an uncertain set is obtained, a nonlinear constraint condition is converted into a maximum value problem of solving an uncertain item within the range of the uncertain set, an even variable vector is introduced, the nonlinear constraint condition is converted into a deterministic linear constraint, and a reconstructed robust model is obtained.

[0072] Step 208: applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model.

[0073] Optionally, the reconstructed robust optimization model is applied to the energy management model to obtain the final energy management model.

[0074] Step 210: inputting the photovoltaic output data of each node in the micro-grid, the direct-current load data of each line, the alternating-current load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device into the final energy management model to obtain output data of the micro-grid, output data of the energy storage device, and a day-ahead scheduling plan of the micro-grid.

[0075] Optionally, the photovoltaic output data of each node in the micro-grid, the direct-current load data of each line, the alternating-current load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device are obtained and input into the final energy management model, a Cplex solver is called in Matlab by using Yalmip, and the output data of the micro-grid, the output data of the energy storage device, and the day-ahead scheduling plan of the micro-grid are obtained by solving, and the energy in the micro-grid is regulated and controlled according to the day-ahead scheduling plan of the micro-grid.

[0076] The solving manner in the energy management model can also be other manners, and the embodiments of the present application do not limit this; and the solving result of the energy management model can also include a centralized allocation coefficient vector and an active power column vector of a VSC (voltage source converter) flowing into a direct-current area.

[0077] The centralized allocation coefficient vector is a proportion of responsibility allocation of different energy storage devices to the total uncertainty of the micro-grid.

[0078] In one of the embodiments, when the micro-grid is running in a day, assuming that the total uncertainty in the micro-grid is 100 kW, the "centralized allocation coefficient vector" is (0.1, 0.2, 0.3, 0.4), indicating that the first energy storage device absorbs 10% of the uncertainty, i.e. 10 kW, the second energy storage device absorbs 20%, i.e. 20 kW, the third energy storage device absorbs 30%, i.e. 30 kW, and the fourth energy storage device absorbs 40%, i.e. 40 kW, so that the energy storage device can absorb the total uncertainty in the micro-grid according to the "allocation coefficient vector" obtained by optimization; if the total uncertainty generated in the system is 20 kW, according to the proportion, the proportion of the uncertainty absorbed by different energy storage devices is still determined according to the "centralized allocation coefficient vector of energy storage", the first energy storage is 0.1*20kW=2kW, the second one… and so on; when the micro-grid is running in real time, the uncertainty generated is unknown, but the proportion of the responsibility for consumption can be determined in advance according to the capacity of the energy storage device, i.e. "centralized allocation coefficient vector of energy storage".

[0079] In the above energy management method of the micro-grid, an energy management model of the micro-grid is constructed based on a network topology structure of the micro-grid; a robust optimization model of the micro-grid is constructed based on uncertain data of direct current loads and photovoltaic outputs of the micro-grid; nonlinear constraint conditions containing uncertain variables in the robust optimization model are converted into deterministic linear constraints without uncertain variables, to obtain a reconstructed robust optimization model; the reconstructed robust optimization model is applied to the energy management model, to obtain a final energy management model; photovoltaic output data of each node in the micro-grid, direct current load data of each line, alternating current load data of each line, output parameter data of the energy storage device, and maximum capacity of the energy storage device are input into the final energy management model, to obtain output data of the micro-grid, output data of the energy storage device, and a day-ahead scheduling plan of the micro-grid. In this way, by fully considering uncertain factors in the micro-grid, the energy in the micro-grid is managed, the day-ahead economic scheduling and the intra-day uncertainty consumption of the micro-grid are realized, and the economic and safe operation of the micro-grid system is ensured.

[0080] In one exemplary embodiment, an energy management model of a micro-grid is constructed based on a network topology structure of the micro-grid, including: based on the network topology structure of the micro-grid, establishing a power flow calculation model of the micro-grid; based on the power flow calculation model, taking the minimum total operation cost of the micro-grid as a first objective function, and taking the load rate balance of each district in the micro-grid as a constraint condition, constructing the energy management model of the micro-grid.

[0081] In one exemplary embodiment, based on the network topology of the micro-grid, a power flow calculation model of the micro-grid is established, including: based on the number of each node and line, determining the association matrix between the nodes and lines; based on the association matrix, establishing an initial power flow calculation model, imposing line transmission power constraints and node voltage operation constraints on the initial power flow calculation model, to obtain the power flow calculation model of the micro-grid.

[0082] In actual implementation, the topology structure of the DC area of the micro-grid is a radial type, and a specific structure diagram is as shown in Figure 3 The first end node is set as a reference node, and is numbered as 0. Except for the reference node, each node has a parent node, and it is stipulated that the node number of the parent node must be less than the node number of the node; each node can have a child node, and it is stipulated that the node number of the node must be less than the node number of the child node. In addition, the line number from the parent node to the child node x is x. In the DC topology of the micro-grid, the set composed of all DC nodes is , which satisfies , and there are nodes in the micro-grid except for the 0 node; the set composed of all lines is , which satisfies . m is the total number of nodes of the micro-grid including the 0 node.

[0083] The association matrix between the initial nodes and lines , wherein each element is defined as shown in formula (1).

[0084] (1)

[0085] wherein, , is the first column of the association matrix, is a full-rank node-line matrix.

[0086] The linear power flow model of the DC area in the micro-grid is shown in formula (2) to formula (6).

[0087] (2)

[0088] (3)

[0089] (4)

[0090] (5)

[0091] (6)

[0092] wherein, , respectively represent the active transmission power of the xth, yth transmission line in the DC area of the microgrid at time t, is the set of child nodes of node x; , respectively represent the node voltage amplitude of node , x at time t, represents the resistance of the xth transmission line; , are the minimum transmission power and the maximum transmission power of the xth transmission line, respectively; , are the minimum value and the maximum value of the square of the node voltage amplitude. Formula (2) and formula (3) are the DC power balance constraints ignoring network loss; formula (4) is the node voltage drop constraint; to ensure the safe and stable operation of the system, formula (5) and formula (6) are the transmission power constraint of the xth line and the voltage constraint of node x, respectively.

[0093] In actual implementation, the DC line loss in the microgrid is small, the above formula (2) to formula (6) are written in a compact form, which is shown in formula (7) to formula (11).

[0094] (7)

[0095] (8)

[0096] (9)

[0097] (10)

[0098] (11)

[0099] wherein, is the line transmission power column vector of all lines at time t, m is the total AC / DC node number of the microgrid containing node 0, it is worth noting that according to formula (2), the first element in must be 0; is the node injection active power column vector of all nodes at time t; is the extended node-line association matrix, , is the full rank node-line matrix; is the node voltage amplitude column vector of all nodes, is the extended line resistance matrix, , is the diagonal matrix of all line resistances, that is, , represents taking the diagonal matrix, The resistance of line y; is the reference voltage amplitude, is the all-one column vector of m dimensions; is the column vector composed of all line resistances, with diagonal elements being all elements of the column vector; , are the minimum and maximum column vectors of line transmission power of all lines, respectively; , are the minimum and maximum column vectors of node voltage amplitude of all nodes, respectively.

[0100] The AC area of the microgrid has AC loads, which are linked with the DC bus through VSC devices to meet the active power, and the specific formula is shown in formula (12) and formula (13).

[0101] (12)

[0102] (13)

[0103] wherein, is the active power injected from the distribution network to the microgrid at node x at time t, is the active power flowing into the DC area of the VSC device at node x at time t, is the active power of the AC load at node x at time t. is the reactive power injected from the distribution network to the microgrid at node x at time t, is the reactive power flowing into the DC area of the VSC device at node x at time t, is the reactive power of the AC load at node x at time t.

[0104] Formula (12) and formula (13) are written in a compact form, and the specific formula is shown in formula (14) and formula (15).

[0105] (14)

[0106] (15)

[0107] wherein, is the column vector of active power injected from the distribution network to the microgrid at all nodes at time t, is the column vector of active power flowing into the DC network of the VSC at all nodes at time t, is the column vector of active power of the AC load at all nodes at time t. is the column vector of reactive power injected from the distribution network to the microgrid at all nodes at time t, is the column vector of reactive power flowing into the DC network of the VSC at all nodes at time t, is the reactive power column vector of all AC loads on all nodes at time t.

[0108] In the above embodiment, by establishing the power flow calculation model of the micro-grid, the balance of power, voltage and other data in the energy management model can be better managed, so that the final micro-grid energy regulation result is more in line with the actual situation.

[0109] In one of the embodiments, the injection power calculation formula of the micro-grid DC bus is shown as formula (16).

[0110] (16)

[0111] wherein, is a photovoltaic-node association matrix, if the xth photovoltaic device is connected to node y, then the element in the yth row and the xth column in the matrix is 1, and other elements are 0; is the active power column vector of all photovoltaic devices at time t; is a DC load-node association matrix, if the xth DC load is connected to node y, then the element in the yth row and the xth column in the matrix is 1, and other elements are 0, is the active power column vector of all DC loads at time t; is a storage-node association matrix, if the xth storage device is connected to node y, then the element in the yth row and the xth column in the matrix is 1, and other elements are 0, is the charge-discharge active power column vector of all storage devices, and the storage charging is defined as the positive direction; is the active power column vector of all VSCs flowing into the DC network at time t.

[0112] In an exemplary embodiment, the energy management optimization target of the micro-grid is to minimize the total operation cost of the micro-grid, i.e., to minimize the interaction cost of the micro-grid with the distribution network and the operation cost of the storage device, and to balance the load rate of all sub-areas in the micro-grid, i.e., to minimize the average value of the load rate difference between each sub-area and other sub-areas. The specific formula of the first objective function is shown as formula (17).

[0113] (17)

[0114] wherein, is the minimum function calculation, is the charge-discharge power column vector of the storage device in the micro-grid, is the flow-in power column vector of the voltage source converter (VSC device), and T is the set of micro-grid operation periods, represents the interaction cost of the micro-grid with the distribution network, represents the operation cost of the storage device in the micro-grid, denotes the non-uniform load penalty cost of all areas in the microgrid.

[0115] In one of the embodiments, the expansion of each cost function is shown in equations (18) to (20).

[0116] (18)

[0117] (19)

[0118] (20)

[0119] wherein, denotes the set of all AC nodes in the microgrid, is the cost coefficient of the microgrid purchasing unit power from the distribution network, is the revenue coefficient of the microgrid selling unit power to the distribution network, , denote the micro-grid injection power of the i-th and j-th AC node at time t, respectively, denotes the non-negative part of the expression; denotes the set of all energy storage devices in the microgrid, is the operating cost coefficient of the unit power output of the energy storage, denotes the active power output of the i-th energy storage device at time t; is the unit load rate deviation penalty coefficient, , are the capacities of the transformers mounted on the i-th and j-th AC node, respectively, is the number of AC / DC nodes in the microgrid.

[0120] An auxiliary variable is introduced, and the interaction cost function can be further simplified into the following linear form, as shown in equation (21).

[0121] (21)

[0122] wherein, denotes the set of optimization scheduling periods, denotes the set of all AC nodes in the microgrid; is an auxiliary variable introduced in the equivalent transformation process of the interaction cost; is the cost coefficient of the microgrid purchasing unit power from the distribution network, is the revenue coefficient of the microgrid selling unit power to the distribution network, denotes the micro-grid injection active power of the i-th AC node at time t.

[0123] An auxiliary variable is introduced, and the interaction cost function which can be further simplified into a linear form as shown in equation (22).

[0124] (22)

[0125] wherein, denotes a set of microgrid operation time periods, denotes a set of all energy storage devices in the microgrid; is an auxiliary variable introduced in the equivalent conversion process of interaction cost, is an operation cost coefficient of the unit output power of the energy storage, denotes the output power of the jth energy storage device at time t.

[0126] Further, the calculation formula of the first objective function of the microgrid is shown in equation (23).

[0127] (23)

[0128] In the above embodiment, by calculating the first objective function, the optimal regulation strategy can be determined to ensure that the devices in the microgrid operate at the best operating point, thereby improving the operation efficiency of the microgrid.

[0129] In actual implementation, the load rate of each district in the microgrid is balanced, and the energy storage devices, VSC devices, transformers and other devices in the microgrid must meet the physical constraint conditions.

[0130] The energy storage device must meet the active power output constraint and the state of charge constraint, and the specific formula is shown in equations (24), (25) and (26).

[0131] (24)

[0132] (25)

[0133] (26)

[0134] wherein, denotes a charge and discharge power column vector of all energy storage devices, , denote minimum and maximum output column vectors of the energy storage device, respectively; , denote the state of charge of the energy storage device at t+1 and t, respectively, denotes a self-discharge coefficient of the energy storage device, is a dispatch time interval; , denote maximum and minimum state of charge column vectors of the energy storage device, respectively.

[0135] The safe and stable operation of the VSC device needs to meet the capacity constraint, and the specific formula is shown as formula (27).

[0136] (27)

[0137] wherein, , and are all the in-circle polygon coefficients of a circle, and w represents the number of side lengths of the approximate polygon; represents the active power of the VSC device flowing into the DC network at the node i at the time t, represents the active power of the VSC device flowing into the DC network at the node i at the time t, represents the reactive power of the VSC device flowing into the AC network at the node i at the time t, represents the maximum capacity of the VSC at the node i.

[0138] The transformer in the AC area needs to meet the capacity constraint, and the specific formula is shown as formula (28).

[0139] (28)

[0140] wherein, , and are all the in-circle polygon coefficients of a circle, and w represents the number of side lengths of the approximate polygon; is a micro-injection active power column vector of the node i at the time t, is a micro-injection reactive power column vector of the node i at the time t, is the capacity of the transformer hung on the i-th AC node.

[0141] In summary, the energy management model of the micro-grid without considering the uncertainty of the energy consumption in the present application is shown as formula (29).

[0142] (29)

[0143] wherein, represents the set of the optimization scheduling period, is a full 1 column vector of m dimensions, and m represents the total number of nodes in the micro-grid, is an auxiliary variable of the interaction cost at the time t; is a full 1 column vector of y dimensions, and y represents the total number of energy storage devices in the micro-grid, is an auxiliary variable of the energy storage operation cost at the time t; represents the set of all AC nodes in the micro-grid, is a unit load rate deviation penalty coefficient, , respectively represent the capacity of the transformer mounted on the i-th and j-th alternating current node, is the total alternating current / direct current node number of the micro-grid.

[0144] In the above embodiment, by establishing an energy management model, the power distribution in the micro-grid is optimized, unnecessary energy transmission loss is reduced, and the operation cost of the micro-grid is reduced.

[0145] In one exemplary embodiment, based on uncertain data of direct current load and photovoltaic output of the micro-grid, a robust optimization model of the micro-grid is constructed, including: determining the prediction error of the uncertain data; determining the direct current node injection power in the micro-grid based on the prediction error and the centralized energy storage regulation strategy; and based on the direct current node injection power, constructing a robust optimization model of the micro-grid based on a second objective function obtained by maximizing the uncertainty data of the energy storage device in the micro-grid, a direct current line transmission power constraint, a direct current bus voltage constraint, an energy storage device output data constraint, and an energy storage device state of charge constraint.

[0146] In actual implementation, the prediction error of the direct current load and the photovoltaic output leads to the uncertainty of the micro-grid, wherein the specific formula of the uncertain photovoltaic output power is shown as formula (30), and the specific formula of the direct current load output power is shown as formula (31).

[0147] (30)

[0148] (31)

[0149] wherein, , represent the actual output power of the i-th photovoltaic device and the j-th direct current load, , respectively represent the predicted output power of the i-th photovoltaic device and the j-th direct current load, , respectively represent the prediction error of the i-th photovoltaic and the j-th direct current load, i.e. the uncertainty.

[0150] In the micro-grid, the node injection power brought by the uncertainty source can be defined as:

[0151] (32)

[0152] wherein, is a column vector of the injection power of all nodes in the micro-grid containing uncertainty, is a node-photovoltaic association matrix, is a node-load association matrix, , are the predicted power output vectors of all photovoltaic devices and DC loads at time t, respectively, 、 are the predicted power output error vectors of all photovoltaic devices and DC loads, respectively.

[0153] To simplify the derivation of the subsequent model, the simplified form of formula (32) is shown in formula (33):

[0154] (33)

[0155] wherein, is the injection power vector of all nodes in the microgrid with uncertainty, is the uncertainty source-node association matrix, wherein m represents the total number of nodes in the microgrid, and n represents the total number of uncertainty sources in the system, i.e., the total number of photovoltaic devices and DC loads; is the predicted power output vector of uncertainty sources, is the predicted error vector of uncertainty sources.

[0156] The centralized energy storage regulation strategy of energy storage devices in the microgrid can be used to handle uncertainty. The specific strategy is shown in formula (34), and the real-time output power of the energy storage device is shown in formula (34). Figure 4

[0157] (34)

[0158] wherein, is the real-time output power vector of all energy storage devices, and the charging of the energy storage device is defined as the positive direction, is the day-ahead output reference vector of all energy storage devices; is the intra-day correction power vector of all energy storage devices, is the centralized apportionment ratio of energy storage devices at time t, and its specific meaning is that different energy storage devices are apportioned to the total uncertainty of the microgrid. represents an all-“1” column vector with a dimension of n, is the total uncertainty error of the uncertainty source.

[0159] wherein, must satisfy the following constraints as shown in formula (35) and formula (36):

[0160] (35)

[0161] (36)

[0162] ​wherein, denotes the uncertainty allocation coefficient of the kth energy storage device, which must be non-negative, and the sum of all coefficients is less than or equal to 1, i.e. to prevent the energy storage device from over-absorbing the uncertainty in the system.

[0163] Based on the centralized energy storage regulation strategy, the formula of the DC node injection power containing uncertainty in the microgrid is shown in equation (37).

[0164] (37)

[0165] wherein, denotes the column vector of all DC node injection powers containing uncertainty, is the column vector of all node injection powers containing uncertainty, is the energy storage-node association matrix, is the column vector of real-time output power of all energy storage devices, is the column vector of active power flowing into the DC network of all node VSC devices at time t; is the uncertainty source-node association matrix, is the column vector of predicted output power of uncertainty sources, is the column vector of day-ahead output reference of all energy storage devices, is the centralized allocation coefficient vector of energy storage, denotes the full "1" column vector with dimension n, is the column vector of prediction error of uncertainty sources.

[0166] In actual implementation, the microgrid contains uncertainty, but the influence of uncertainty on each node is relatively scattered, and the second objective function obtained by requiring the microgrid to absorb the total uncertainty data of energy storage is required, and the specific formula is shown in equation (38).

[0167] (38)

[0168] wherein, is the column vector of real-time output power of all energy storage devices, is the column vector of active power flowing into the DC network of all node VSC at time t, denotes the set of optimization scheduling periods, denotes the remaining uncertainty penalty function in the microgrid, and its specific definition is shown in equation (39):

[0169] (39)

[0170] In the formula, is the penalty coefficient of the remaining uncertainty in the microgrid, is the uncertainty source-node incidence matrix, is the energy storage-node incidence matrix, is the energy storage centralized allocation coefficient vector, denotes an all-ones column vector of dimension n. denotes an all-ones column vector of dimension m. The uncertainty in the microgrid has an impact on the DC line transmission power, the DC node voltage, the real-time output power of the energy storage device, and the state of charge of the energy storage device. The DC node injection power formula (37) containing uncertainty is substituted into formula (7) to obtain formula (40).

[0171] (40)

[0172] wherein, denotes the DC line transmission power containing uncertainty at time t, is the extended node-line incidence matrix, is the uncertainty source-node incidence matrix, is the uncertainty source predicted output power column vector, is the active power column vector of all nodes VSC flowing into the DC network at time t, is the energy storage-node incidence matrix, is the day-ahead output reference column vector of all energy storage devices, is the energy storage centralized allocation coefficient vector, denotes an all-ones column vector of dimension n, is the uncertainty source prediction error column vector. Substituting this formula into formula (10), the DC line transmission power constraint containing uncertainty can be obtained as:

[0173] (41)

[0174] wherein, , are the minimum and maximum column vectors of the line transmission power of all lines, respectively.

[0175] Similarly, formula (37) is substituted into the DC bus voltage expression formula (8) to obtain the expression of the DC bus voltage containing uncertainty, which is substituted into formula (11) to obtain the DC bus voltage constraint containing uncertainty:

[0176] (42)

[0177] wherein, , are the minimum and maximum column vectors of the square of the node voltage amplitude of all nodes, respectively, is the extended line resistance matrix.

[0178] Similarly, the real-time output power expression (34) of the energy storage is brought into equation (24), and the energy storage output constraint with uncertainty can be obtained:

[0179] (43)

[0180] wherein, , respectively represent the minimum and maximum output column vectors of the energy storage device.

[0181] The formula (34) is brought into equation (25), and then into equation (26), and the energy storage state of charge constraint with uncertainty can be obtained:

[0182] (44)

[0183] wherein, , respectively represent the maximum and minimum state of charge column vectors of the energy storage device, represents the self-discharge coefficient of the energy storage device, is the initial state of charge column vector of the energy storage device, is the dispatch time interval.

[0184] In summary, the robust optimization model considering uncertainty consumption is:

[0185] (45)

[0186] wherein, represents the set of microgrid operation periods, is a full 1 column vector of m dimensions, and m represents the total number of nodes in the microgrid, is an auxiliary variable of the interaction cost at time t; is a full 1 column vector of y dimensions, and y represents the total number of energy storage devices in the microgrid, is an auxiliary variable of the energy storage operation cost at time t; represents the non-uniform load penalty cost of all areas, represents the remaining uncertainty penalty function in the microgrid system.

[0187] In the above embodiment, the distributed energy (such as photovoltaic, wind energy) and load in the microgrid have significant uncertainty, and the model considering uncertainty consumption can effectively cope with these uncertainties and reduce the operation risk caused by prediction error.

[0188] In one exemplary embodiment, converting a nonlinear constraint condition containing uncertain variables in a robust optimization model into a deterministic linear constraint without uncertain variables comprises: using a form of a box-type uncertainty set to represent uncertain data, obtaining an uncertainty set; based on the uncertainty set, converting the nonlinear constraint condition into a problem of solving a maximum value of uncertain items within a range of the uncertainty set; introducing an even variable vector, and converting the nonlinear constraint condition into a deterministic linear constraint.

[0189] In actual implementation, a box-type uncertainty set is used to represent uncertainty, and uncertainty generated by the i-th uncertain source in the microgrid , satisfies:

[0190] (46)

[0191] wherein, is a positive number, representing a proportion of uncertainty generated by the i-th uncertain source at time t to the predicted active power of the i-th uncertain source, is the predicted active power of the i-th uncertain source.

[0192] To simplify the model derivation process, it is assumed that the proportions of uncertainty generated by all uncertain sources at all times are the same, and therefore, formula (46) can be further described as:

[0193] (47)

[0194] (48)

[0195] wherein, is a column vector of prediction error of the uncertain source, is the proportion of uncertainty generated by all uncertain sources at all times, is a column vector of predicted output power of the uncertain source; further, the constraint of uncertainty is:

[0196] (49)

[0197] (50)

[0198] wherein, represents an uncertainty coefficient matrix, is a unit diagonal matrix, and n represents the total number of uncertain sources in the microgrid; is a boundary matrix of uncertainty at time t, is the proportion of uncertainty generated by all uncertain sources at all times, is a column vector of predicted output power of the uncertain source;

[0199] ​Based on (41)-(44), it can be found that the constraints with uncertainty can be uniformly described as:

[0200] (51)

[0201] where, is the uniform structure matrix related to the uncertainty and the allocation coefficient vector, is the general expression matrix of the uncertainty, is the matrix of constant terms without the part of decision variables, , and,

[0202] Based on the robustness idea, formula (51) must be constantly true under the constraints of formula (50), then formula (51) can be further described as:

[0203] (52)

[0204] where, represents the maximum value of the function under the condition of The left term of this formula is a robust problem:

[0205] (53)

[0206] Based on the duality theorem, formula (53) can be equivalently transformed into:

[0207] (54)

[0208] where, is the introduced dual variable.

[0209] Bring formula (54) into formula (52), then formula (52) can be equivalently transformed into the following linear constraints:

[0210] (55)

[0211] Therefore, according to the above, lemma 1 is:

[0212] The original problem can be equivalently transformed into the dual problem .

[0213] Based on lemma 1, by introducing a dual variable, formula (41) can be equivalently transformed into:

[0214] (56)

[0215] (57)

[0216] where, , is the dual variable introduced for the power constraint of DC transmission line.

[0217] Based on Lemma 1, by introducing the dual variable, formula (42) can be equivalently transformed as:

[0218] (58)

[0219] (59)

[0220] where, , is the dual variable introduced for the voltage constraint of DC bus.

[0221] Based on Lemma 1, by introducing the dual variable, formula (43) can be equivalently transformed as:

[0222] (60)

[0223] (61)

[0224] where, , is the dual variable introduced for the voltage constraint of DC bus.

[0225] Based on Lemma 1, by introducing the dual variable, formula (44) can be equivalently transformed as:

[0226] (62)

[0227] (63)

[0228] where, , is the dual variable introduced for the voltage constraint of DC bus.

[0229] In summary, the equivalent model of microgrid energy management considering uncertainty accommodation is:

[0230] (64)

[0231] where, denotes the set of microgrid operation periods, is a full 1 column vector of m dimensions, m represents the total number of AC / DC nodes in the microgrid, is an auxiliary variable of the interaction cost at time t; is a full 1 column vector of y dimensions, y represents the total number of energy storage devices in the microgrid, is an auxiliary variable of the operation cost of energy storage devices at time t; This is the penalty coefficient for unit load rate deviation. , These are the capacities of the transformers mounted on the i-th and j-th AC nodes, respectively. This is the penalty coefficient for the residual uncertainty in the microgrid. For the uncertainty source-node correlation matrix, The energy storage-node correlation matrix, This is the vector of centralized energy storage allocation coefficients. This represents a column vector of all "1"s with dimension n, where n represents the total number of uncertainty sources in the system.

[0232] In the above embodiments, by simplifying the uncertain constraints into linear deterministic forms, the constraints in linear deterministic forms are easier to handle by existing optimization solvers, which can significantly simplify the solution process and improve the solution efficiency.

[0233] To illustrate the energy management method for microgrids in this application in detail, an embodiment is described below. For example, this application describes the energy management method for microgrids in a specific scenario.

[0234] First, in the distribution network, multiple devices are interconnected to form a flexible AC / DC interconnected microgrid. All DC loads and rooftop photovoltaics in the microgrid are connected to the DC bus of the microgrid. Based on the network topology of the microgrid, an energy management model for the microgrid is constructed.

[0235] The prediction errors of DC load and photovoltaic output in microgrids lead to uncertainties in microgrids. To determine the photovoltaic output power and DC load handling power in microgrids that contain uncertainties, a robust optimization model of the microgrid is constructed based on a centralized energy storage control strategy and an objective function containing uncertainties.

[0236] The constraints in the robust optimization model of microgrids contain uncertain data. The uncertain data is represented by the form of a box uncertainty set, and the uncertainty set is obtained. The nonlinear constraints are transformed into a problem of finding the maximum value of the uncertain terms within the uncertainty set. Even variable vectors are introduced to transform the nonlinear constraints into deterministic linear constraints, resulting in the reconstructed robust model.

[0237] The reconstructed robust optimization model is applied to the energy management model to obtain the final energy management model.

[0238] The photovoltaic output data of each node in the micro-grid, the DC load data of each line, the AC load data of each line, the energy storage device output parameter data and the maximum capacity of the energy storage device are input into the final energy management model, and the output data of the micro-grid, the output data of the energy storage device and the day-ahead scheduling plan of the micro-grid are solved by using Yalmip to call Cplex solver in Matlab. The energy in the micro-grid is regulated and controlled according to the day-ahead scheduling plan of the micro-grid.

[0239] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0240] Based on the same inventive concept, the embodiments of the present application also provide a micro-grid energy management device for implementing the above-mentioned micro-grid energy management method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more micro-grid energy management device embodiments provided below can refer to the limitations of the micro-grid energy management method in the above text, which will not be repeated here.

[0241] In one exemplary embodiment, as shown in Figure 5 A micro-grid energy management device is provided, comprising: a construction module 501, a transformation module 502, an application module 503 and a calculation module 504, wherein:

[0242] The construction module is configured to construct an energy management model of the micro-grid based on a network topology of the micro-grid.

[0243] The construction module is configured to construct a robust optimization model of the micro-grid based on uncertain data of DC load and photovoltaic output of the micro-grid.

[0244] The transformation module is configured to transform nonlinear constraint conditions containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables, to obtain a reconstructed robust optimization model.

[0245] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model.

[0246] a calculation module configured to input photovoltaic output data of each node in the microgrid, direct-current load data of each line, alternating-current load data of each line, output parameter data of energy storage devices, and maximum capacity of the energy storage devices into the final energy management model to obtain output data of the microgrid, output data of the energy storage devices, and day-ahead scheduling plan of the microgrid.

[0247] In one of the example embodiments, the construction module is further configured to establish a power flow calculation model of the microgrid based on a network topology of the microgrid.

[0248] The energy management model of the microgrid is constructed based on the power flow calculation model, with minimization of total operation cost of the microgrid as a first objective function, and load rate balance of each area in the microgrid as a constraint condition.

[0249] In one of the example embodiments, the construction module is further configured to determine an association matrix between nodes and lines based on numbers of each node and line.

[0250] The initial power flow calculation model is established based on the association matrix, line transmission power constraints and node voltage operation constraints are imposed on the initial power flow calculation model, and the power flow calculation model of the microgrid is obtained.

[0251] In one of the example embodiments, the calculation module is further configured to calculate the first objective function, and the calculation formula is:

[0252]

[0253] wherein, is a minimization function calculation, is a charge-discharge power column vector of the energy storage devices in the microgrid, is an inflow power column vector of the voltage source converter, and T is a set of microgrid operation time periods, represents an interaction cost of the microgrid and the distribution network, represents an operation cost of the energy storage devices in the microgrid, represents a non-uniform load penalty cost of all areas in the microgrid.

[0254] In one of the example embodiments, the construction module is further configured to determine a prediction error of the uncertain data.

[0255] Based on the prediction error and the centralized energy storage regulation strategy, direct-current node injection power in the microgrid is determined.​

[0256] On the basis of the power injected into the direct current node, a robust optimization model of the microgrid is constructed based on a second objective function obtained by maximizing uncertain data of energy storage devices in the microgrid, a direct current line transmission power constraint, a direct current bus voltage constraint, an output data constraint of the energy storage device, and a state of charge constraint of the energy storage device.

[0257] In one of the example embodiments, the conversion module is further configured to represent the uncertain data in the form of a box-type uncertain set, and obtain an uncertain set;

[0258] Based on the uncertain set, the nonlinear constraint condition is converted into a maximum value problem of solving the uncertain term within the range of the uncertain set;

[0259] An even variable vector is introduced to convert the nonlinear constraint condition into a deterministic linear constraint.

[0260] The modules in the microgrid energy management device can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the modules.

[0261] In one example embodiment, a computer device, which can be a server, has an internal structure as shown in Figure 6 The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. The processor of the computer device is configured 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store microgrid operation data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a microgrid energy management method.

[0262] The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0263] Those skilled in the art can understand that, Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0264] In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps:

[0265] Based on the network topology of the microgrid, an energy management model of the microgrid is constructed;

[0266] Based on the uncertain data of the direct current load and photovoltaic output of the microgrid, a robust optimization model of the microgrid is constructed;

[0267] The nonlinear constraint condition containing uncertain variables in the robust optimization model is converted into a deterministic linear constraint without uncertain variables, to obtain a reconstructed robust optimization model;

[0268] The reconstructed robust optimization model is applied to the energy management model to obtain a final energy management model;

[0269] The photovoltaic output data of each node, the direct current load data of each line, the alternating current load data of each line, the output parameter data of the energy storage device and the maximum capacity of the energy storage device in the microgrid are input into the final energy management model to obtain the output data of the microgrid, the output data of the energy storage device and the day-ahead scheduling plan of the microgrid.

[0270] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0271] Based on the network topology of the microgrid, an energy management model of the microgrid is constructed;

[0272] Based on the uncertain data of the direct current load and photovoltaic output of the microgrid, a robust optimization model of the microgrid is constructed;

[0273] transforming the nonlinear constraint condition containing uncertain variables in the robust optimization model into a deterministic linear constraint without uncertain variables to obtain a reconstructed robust optimization model;

[0274] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0275] inputting the photovoltaic output data of each node, the direct-current load data of each line, the alternating-current load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device in the micro-grid into the final energy management model to obtain the output data of the micro-grid, the output data of the energy storage device, and the day-ahead scheduling plan of the micro-grid.

[0276] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the following steps:

[0277] constructing an energy management model of the micro-grid based on the network topology structure of the micro-grid;

[0278] constructing a robust optimization model of the micro-grid based on the uncertain data of the direct-current load and the photovoltaic output of the micro-grid;

[0279] transforming the nonlinear constraint condition containing uncertain variables in the robust optimization model into a deterministic linear constraint without uncertain variables to obtain a reconstructed robust optimization model;

[0280] applying the reconstructed robust optimization model to the energy management model to obtain a final energy management model;

[0281] inputting the photovoltaic output data of each node, the direct-current load data of each line, the alternating-current load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device in the micro-grid into the final energy management model to obtain the output data of the micro-grid, the output data of the energy storage device, and the day-ahead scheduling plan of the micro-grid.

[0282] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0283] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0284] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0285] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A microgrid energy management method, characterized in that, The method includes: Based on the network topology of the microgrid, an energy management model for the microgrid is constructed. Based on the uncertain data of DC load and photovoltaic output of the microgrid, a robust optimization model of the microgrid is constructed. The nonlinear constraints containing uncertain variables in the robust optimization model are transformed into deterministic linear constraints without uncertain variables to obtain the reconstructed robust optimization model. The reconstructed robust optimization model is applied to the energy management model to obtain the final energy management model; The photovoltaic output data of each node in the microgrid, the DC load data of each line, the AC load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device are input into the final energy management model to obtain the output data of the microgrid, the output data of the energy storage device, and the day-ahead scheduling plan of the microgrid.

2. The method according to claim 1, characterized in that, The microgrid-based network topology is used to construct the energy management model of the microgrid, including: Based on the network topology of the microgrid, a power flow calculation model for the microgrid is established. Based on the power flow calculation model, the energy management model of the microgrid is constructed with the minimum total operating cost of the microgrid as the first objective function and the load rate balance of each transformer area in the microgrid as the constraint.

3. The method according to claim 2, characterized in that, The network topology based on the microgrid, and the establishment of the power flow calculation model for the microgrid, include: Based on the number of each node and line, determine the association matrix between nodes and lines; Based on the correlation matrix, an initial power flow calculation model is established, and line transmission power constraints and node voltage operation constraints are applied to the initial power flow calculation model to obtain the power flow calculation model of the microgrid.

4. The method according to claim 2, characterized in that, The formula for calculating the first objective function is: ； in, To minimize the function computation, Let be the column vector of charging and discharging power of energy storage devices in a microgrid. Let T be the column vector of the incoming power to the voltage source converter, and T be the set of operating segments of the microgrid. This represents the interaction cost between the microgrid and the distribution network. This indicates the operating cost of energy storage devices within a microgrid. This represents the uneven load penalty cost for all transformer substations in a microgrid.

5. The method according to claim 1, characterized in that, The robust optimization model for the microgrid, constructed based on the uncertain data of its DC load and photovoltaic output, includes: Determine the prediction error of the uncertain data; Based on the prediction error and the centralized energy storage control strategy, the DC node injection power in the microgrid is determined; Based on the injected power at the DC node, a robust optimization model for the microgrid is constructed using the second objective function obtained by maximizing the uncertainty data of energy storage devices in the microgrid, constraints on DC line transmission power, DC bus voltage, output data constraints of energy storage devices, and state of charge constraints of energy storage devices.

6. The method according to claim 1, characterized in that, The step of transforming the nonlinear constraints containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables includes: Uncertain data is represented using the form of a box-type uncertainty set, resulting in an uncertainty set. Based on the aforementioned uncertain set, the nonlinear constraint is transformed into a problem of finding the maximum value of the uncertain term within the range of the uncertain set; By introducing an even variable vector, the nonlinear constraint is transformed into a deterministic linear constraint.

7. An energy management device for a microgrid, characterized in that, The device includes: A construction module is used to build an energy management model for the microgrid based on its network topology. The construction module is used to construct a robust optimization model for the microgrid based on the uncertain data of the DC load and photovoltaic output of the microgrid. The conversion module is used to convert the nonlinear constraints containing uncertain variables in the robust optimization model into deterministic linear constraints without uncertain variables, so as to obtain the reconstructed robust optimization model. The application module is used to apply the reconstructed robust optimization model to the energy management model to obtain the final energy management model; The calculation module is used to input the photovoltaic output data of each node in the microgrid, the DC load data of each line, the AC load data of each line, the output parameter data of the energy storage device, and the maximum capacity of the energy storage device into the final energy management model to obtain the output data of the microgrid, the output data of the energy storage device, and the day-ahead scheduling plan of the microgrid.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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