Online distributed point-to-point electric energy management method and system for interconnected micro-grid

Abstract

The invention discloses an interconnected micro-grid online distributed point-to-point electric energy management method and system, relates to the technical field of interconnected micro-grid management, and aims to construct an electric energy transaction model by taking the minimum energy transaction cost as an optimization target and taking the electric energy of each micro-grid as a decision variable. Respectively inputting the working condition parameters of each micro-grid and the corresponding historical operation data into a preset electric energy optimization model to obtain initial electric energy corresponding to each micro-grid, and performing distributed iterative optimization on the electric energy transaction model according to the working condition parameters and the initial electric energy corresponding to each micro-grid, and obtaining the target energy transaction cost and the target electric energy corresponding to each micro-grid. The problem that a traditional micro-grid electric energy management method is generally low in convergence speed is solved, the real-time requirement of online management is difficult to meet in a large-scale interconnected micro-grid cluster scene, and the running reliability of a micro-grid is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of interconnected microgrid management, and particularly relates to an online distributed peer-to-peer electric energy management method and system for interconnected microgrids. BACKGROUND

[0002] Under the trend of low-carbon transformation of global energy structure, the penetration rate of distributed energy (such as rooftop photovoltaic, user-side energy storage, and distribution network flexible load) continues to increase. Microgrids, as the core carrier for integrating such distributed energy, not only have the ability to independently produce, store and consume electricity, but also can participate in energy interaction by selling excess electricity or purchasing electricity in shortage, and gradually become a key unit with flexibility and economy in the power system. To further tap the potential of energy sharing of microgrids, a peer-to-peer (P2P) electric energy trading mechanism has emerged. This mechanism allows interconnected microgrid groups to independently carry out multi-party electric energy trading without relying on traditional single distribution network dispatching, which not only improves the local consumption rate of distributed energy and reduces energy transmission loss, but also optimizes the overall energy allocation efficiency through market cooperation, providing a new path for the green transformation of the power system.

[0003] However, the operating environment of interconnected microgrids has significant dynamics and uncertainties. Renewable energy output (such as photovoltaic and wind power affected by weather), user load demand (such as time-dependent fluctuations of industrial and residential electricity), and electricity price signals all change dynamically over time. Therefore, the electric energy management method of microgrids needs to have real-time response capability to adapt to optimization decisions in time-varying scenarios.

[0004] Currently, traditional microgrid electric energy management methods (such as the alternating direction multiplier method and traditional gradient descent method) generally have slow convergence speed, which makes it difficult to meet the real-time requirements of online management in large-scale interconnected microgrid cluster scenarios, reducing the reliability of microgrid operation. SUMMARY

[0005] The present application provides an online distributed peer-to-peer electric energy management method and system for interconnected microgrids, which solves the technical problem of slow convergence speed of traditional microgrid electric energy management methods, which makes it difficult to meet the real-time requirements of online management in large-scale interconnected microgrid cluster scenarios, reducing the reliability of microgrid operation.

[0006] The present application provides an online distributed peer-to-peer electric energy management method for interconnected microgrids, comprising:

[0007] An electric energy trading model is constructed with the minimum energy trading cost as the optimization objective and the electric energy of each microgrid as the decision variable.

[0008] inputting the working condition parameters and the corresponding historical operation data of each micro-grid into a preset electric energy optimization model to obtain initial electric energy corresponding to each micro-grid;

[0009] performing distributed iterative optimization on the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain a target energy transaction cost and target electric energy corresponding to each micro-grid.

[0010] Optionally, the step of performing distributed iterative optimization on the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain a target energy transaction cost and target electric energy corresponding to each micro-grid comprises:

[0011] updating each initial electric energy according to the working condition parameters corresponding to each micro-grid based on a preset objective function to obtain multiple updated electric energies;

[0012] determining whether the number of updates of each updated electric energy is greater than or equal to a preset iteration threshold value;

[0013] If the number of updates is less than the iteration threshold value, the updated electric energy is taken as new initial electric energy, and the step of updating each initial electric energy according to the working condition parameters corresponding to each micro-grid based on a preset objective function to obtain multiple updated electric energies is executed again.

[0014] If the number of updates is greater than or equal to the iteration threshold value, the updated electric energy is determined as target updated electric energy.

[0015] determining the target electric energy corresponding to each micro-grid according to each target updated electric energy, and inputting each target electric energy into the electric energy transaction model to obtain a corresponding target energy transaction cost.

[0016] Optionally, the step of updating each initial electric energy according to the working condition parameters corresponding to each micro-grid based on a preset objective function to obtain multiple updated electric energies comprises:

[0017] performing local gradient estimation on each initial electric energy according to the working condition parameters corresponding to each micro-grid based on a preset objective function to obtain a local gradient estimation value corresponding to each micro-grid;

[0018] inputting the pre-acquired historical electric energy, each local gradient estimation value and the corresponding initial electric energy into a preset local update function to obtain multiple local estimation values;

[0019] constructing a micro-grid matrix by using each local estimation value;

[0020] According to the preset global updating function, the micro-grid matrix is updated according to the historical electric energy obtained in advance, and a plurality of updated electric energies are obtained.

[0021] Optionally, the step of determining the target electric energy corresponding to each micro-grid according to each target updated electric energy comprises:

[0022] A target micro-grid matrix is constructed by using each target updated electric energy.

[0023] According to the preset correction function, the target micro-grid matrix is corrected according to a preset forgetting factor, and the target electric energy corresponding to each micro-grid is obtained.

[0024] Optionally, the electric energy transaction model is specifically:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] wherein, is the transaction cost of the mth micro-grid at the tth moment, is the energy storage operation cost of the mth micro-grid at the tth moment, is the network service fee of the mth micro-grid at the tth moment, is the purchase cost of the mth micro-grid from the main grid at the tth moment, is a micro-grid set, is a cost coefficient of the micro-grid m, is the electric energy of the mth micro-grid at the tth moment, is the electrical distance between the micro-grid m and the micro-grid n, is the electricity transaction volume purchased by micro-grid m and n at time t, is the electricity transaction volume purchased by micro-grid n and m at time t, is the network service fee of the network operator for the unit electricity point distance of all P2P energy transactions, is the impedance of line l, is the jth node of micro-grid n, is the set of all lines on the shortest path from node i m to node , i m is the ith node of micro-grid m, is the unit purchase electricity price from the main grid at time t, is the power purchased by micro-grid m from the grid at time t, is the unit sale electricity price from the main grid at time t, is the power sold by micro-grid m from the grid at time t, is the carbon intensity of micro-grid m at time t, is the carbon intensity of micro-grid n at time t, is the carbon intensity of the grid at time t, is the set of consumers, is the set of energy storage, is the set of producers, is the carbon emission upper limit of micro-grid m, is the minimum value of electricity transaction between micro-grid m and micro-grid n, is the maximum value of electricity transaction between micro-grid m and micro-grid n, is the lower limit of voltage amplitude, is the upper limit of voltage amplitude, is the first linear function of , is the micro-grid transaction period, is the lower limit of line active power, is the second linear function of , is the upper limit of line active power, t is the index of time, m is the first index of micro-grid, n is the second index of micro-grid, l is the index of line, j is the first index of node, and i is the second index of node.

[0037] Optionally, the local update function is specifically:

[0038]

[0039]

[0040] wherein, an initial local estimate value of the mth micro-grid at the kth iteration at time t, an updated energy quantity of the mth micro-grid at the k-1th iteration at time t, a decreasing step size of the kth iteration, a local gradient estimate value of the mth micro-grid at the k-1th iteration at time t, an acceleration gain of the kth iteration, an updated energy quantity of the mth micro-grid at the k-2th iteration at time t, k is an iteration number, a local estimate value of the mth micro-grid at the kth iteration at time t, a projection mapping function for a projection mapping function for a corresponding closed convex set of the mth micro-grid at time t.

[0041] The second aspect of the present application provides an online distributed point-to-point energy quantity management system of interconnected micro-grids, comprising:

[0042] a construction module configured to construct an energy quantity transaction model by taking the minimum energy transaction cost as an optimization target and taking the energy quantity of each micro-grid as a decision variable;

[0043] an updating module configured to input the working condition parameters and the corresponding historical operation data of each micro-grid into a preset energy quantity optimization model respectively to obtain the initial energy quantity corresponding to each micro-grid;

[0044] a distributed optimization module configured to perform distributed iteration optimization according to the working condition parameters and the initial energy quantity corresponding to each micro-grid to obtain a target energy transaction cost and a target energy quantity corresponding to each micro-grid.

[0045] The third aspect of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the online distributed point-to-point energy quantity management method of interconnected micro-grids according to any one of the above aspects.

[0046] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the online distributed point-to-point energy quantity management method of interconnected micro-grids according to any one of the above aspects.

[0047] The fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the online distributed point-to-point energy quantity management method of interconnected micro-grids according to any one of the above aspects.

[0048] From the above technical solutions, the present application has the following advantages:

[0049] The present application constructs an electric energy transaction model by taking the minimum energy transaction cost as the optimization target and taking the electric energy of each micro-grid as the decision variable, inputs the working condition parameters and the corresponding historical operation data of each micro-grid into the preset electric energy optimization model respectively to obtain the initial electric energy corresponding to each micro-grid, and performs distributed iterative optimization on the electric energy transaction model according to the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain the target energy transaction cost and the target electric energy corresponding to each micro-grid. The present application overcomes the problem of slow convergence speed of the traditional micro-grid electric energy management method, and reduces the reliability of micro-grid operation. Compared with the traditional micro-grid electric energy management method, the present application distributes the iterative optimization operation process to the processors of each micro-grid, improves the optimization efficiency of interconnected micro-grids, and thus meets the real-time demand of online management of interconnected micro-grid clusters and improves the reliability of micro-grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Figure 1 A step flow chart of an online distributed point-to-point electric energy management method for interconnected micro-grids provided by the first embodiment of the present application is shown in the figure.

[0052] Figure 2 A step flow chart of an online distributed point-to-point electric energy management method for interconnected micro-grids provided by the second embodiment of the present application is shown in the figure.

[0053] Figure 3 A structure block diagram of an online distributed point-to-point electric energy management system for interconnected micro-grids provided by the third embodiment of the present application is shown in the figure.

[0054] Figure 4 A structure block diagram of an electronic device provided by the fourth embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0055] The embodiment of the present application provides an online distributed point-to-point electric energy management method and system of interconnected microgrids, and solves the problem of slow convergence speed of traditional microgrid electric energy management methods, and reduces the real-time requirement of online management in a large-scale interconnected microgrid cluster scene, and reduces the reliability of microgrid operation.

[0056] In order to make the application purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] Please refer to Figure 1 , Figure 1 The embodiment one of the present application provides a step flow chart of an online distributed point-to-point electric energy management method of interconnected microgrids.

[0058] The online distributed point-to-point electric energy management method of interconnected microgrids provided by the present application comprises:

[0059] The interconnected microgrids refer to a plurality of microgrids with independent power generation, energy storage and power consumption capacity interconnected through a distribution network, and can realize electric energy interaction between microgrids and between the microgrids and a main network.

[0060] Step 101: An electric energy transaction model is constructed with the minimum energy transaction cost as an optimization target and electric energy of each microgrid as a decision variable.

[0061] The energy transaction cost refers to the overall transaction cost of each microgrid.

[0062] In the embodiment of the present application, the electric energy transaction model is constructed with the minimum energy transaction cost as an optimization target and electric energy of each microgrid as a decision variable, and microgrid constraint conditions are set. The microgrid constraint conditions include operation constraints, transaction completion constraints and power network constraints.

[0063] It should be noted that the total transaction power of the microgrid m at time t is limited by the operation constraint, as shown below:

[0064]

[0065] Wherein, is the minimum operation power of the microgrid m, is the maximum operating power of the microgrid m. For consumers, the minimum power consumption represents the non-reducible load part, while the maximum power consumption represents the maximum load demand. For producers with dispatchable generators, the constraint limits the generation level within a certain range, while producers with renewable energy generators can operate between 0 and the predicted value. For the energy storage m, in the charging state, its operating range is:

[0066]

[0067] where, is the maximum charging power of the energy storage m, is the maximum electrical energy storage capacity of the energy storage m, is the energy storage capacity of the energy storage m at time t-1, is the energy efficiency of the energy storage m charging, is the time interval.

[0068] In the discharging state, its operating range is:

[0069]

[0070] where, is the maximum discharging power of the energy storage m, is the minimum electrical energy storage capacity of the energy storage m, is the energy efficiency of the energy storage m charging.

[0071] The generation / consumption power of the microgrid m should be equal to the sum of all P2P electrical energy transactions between the microgrid m and its interconnected microgrids, as well as the energy transactions with the main grid, as follows:

[0072]

[0073] In addition, considering the carbon emission obligations in terms of generation, consumption and energy storage, the operating constraints are as follows:

[0074]

[0075] The transaction achievement constraints are as follows:

[0076]

[0077]

[0078] In order to ensure that the network constraints are not violated during the electrical energy transaction process, the linearized DisFlow model (Distributed Flow Model) is used as follows:

[0079]

[0080]

[0081]

[0082] wherein, is the active power flow vector of the lines at time t, is the active power injection vector of the nodes at time t, is the reactive power flow vector of the lines at time t, is the reactive power injection vector of the nodes at time t, is the voltage magnitude vector of the nodes at time t, is the first diagonal matrix, is the second diagonal matrix, is the first matrix, is the second matrix. is the first active power flow of line l at time t, is the E-th active power flow of line l at time t, E is the first index, is the first reactive power flow of line l at time t, is the E-th reactive power flow of line l at time t, is the first active power injection of line l at time t, is the E-th active power injection of line l at time t, is the first reactive power injection of line l at time t, is the E-th reactive power injection of line l at time t, is the first voltage magnitude of node at time t, is the E-th voltage magnitude of node at time t, for a low-voltage radial distribution network connected with a microgrid, the present application uses an undirected graph to represent, the lines are represented by , is the first index of the node, j is the second index of the node, is the radial voltage of node j, here node i is closer to the root node 0 than node j. Note is the incidence matrix of the graph, the size is , wherein the entries of the j-th column are zero except that the i-th entry is 1 and the j-th entry is -1, the matrix is divided into a first matrix and a second matrix, wherein the first matrix is​​​​​​​​​​ The first row of the first matrix is the remaining part, and the second matrix is recorded as is a microgrid located at node i, is a node connected to the microgrid m. Wherein, the power injection of node i is represented as:

[0083]

[0084] is the power injection of node i.

[0085] The power network constraint limits the node voltage amplitude and line power flow, and the power network constraint is specifically:

[0086]

[0087]

[0088] Step 102, respectively, the working condition parameters and the corresponding historical operation data of each microgrid are input into the preset electric energy optimization model, and the initial electric energy corresponding to each microgrid is obtained.

[0089] The working condition parameters refer to the parameter set of the basic characteristics of the microgrid operation, and the parameter dimensions of different types of microgrids are different. The producer contains cost, power, and carbon emission related parameters, the consumer contains negative utility and power related parameters, and the energy storage contains degradation cost, charging and discharging, and capacity related parameters. It is the basic input of the electric energy optimization model and determines the operation boundary and cost characteristics of the microgrid.

[0090] The historical operation data refers to the recorded data actually generated by the microgrid in a specific time period in the past. The time granularity is consistent with the model calculation step, and key indicators such as power, cost, carbon emission, and energy storage state are covered. It is used to reflect the operation law of the microgrid and provide data support and law reference for solving the initial electric energy.

[0091] It should be noted that the electric energy optimization model can be a pre-trained neural network model.

[0092] In the embodiment of the application, the working condition parameters and the corresponding historical operation data of each microgrid are respectively input into the preset electric energy optimization model for optimization, and the initial electric energy corresponding to each microgrid (i.e. ).

[0093] Step 103, according to the working condition parameters and the initial electric energy corresponding to each microgrid, the electric energy transaction model is distributed and iteratively optimized, and the target energy transaction cost and the target electric energy corresponding to each microgrid are obtained.

[0094] The target energy transaction cost refers to the total energy transaction cost calculated by inputting the target electric energy of each micro-grid into the electric energy transaction model, and reflects the minimum transaction cost level of the interconnected micro-grid cluster.

[0095] The target electric energy refers to the optimal electric energy of each micro-grid obtained after distributed iterative optimization and forgetting factor correction.

[0096] In the embodiment of the present application, based on the preset target function, the initial electric energy of each micro-grid is updated according to the corresponding working condition parameters to obtain multiple updated electric energy. It is judged whether the update times of each updated electric energy are greater than or equal to the preset iteration threshold. If the update times are less than the iteration threshold, the updated electric energy is taken as the new initial electric energy, and the step of updating the initial electric energy of each micro-grid according to the corresponding working condition parameters based on the preset target function is jumped to execute to obtain multiple updated electric energy. If the update times are greater than or equal to the iteration threshold, the updated electric energy is determined as the target updated electric energy. The target electric energy of each micro-grid is determined according to each target updated electric energy, and each target electric energy is input into the electric energy transaction model to obtain the corresponding target energy transaction cost.

[0097] In the embodiment of the present application, by taking the minimum energy transaction cost as the optimization target and taking the electric energy of each micro-grid as the decision variable, an electric energy transaction model is constructed. The working condition parameters and the corresponding historical operation data of each micro-grid are input into the preset electric energy optimization model to obtain the initial electric energy corresponding to each micro-grid. The electric energy transaction model is distributed and iteratively optimized according to the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain the target energy transaction cost and the target electric energy corresponding to each micro-grid. The problem of slow convergence speed commonly existing in the traditional micro-grid electric energy management method is overcome. In the large-scale interconnected micro-grid cluster scenario, it is difficult to meet the real-time demand of online management, and the reliability of micro-grid operation is reduced. Compared with the traditional micro-grid electric energy management method, the initial electric energy corresponding to each micro-grid is obtained by inputting the working condition parameters and the corresponding historical operation data of each micro-grid into the preset electric energy optimization model, and then the electric energy transaction model is distributed and iteratively optimized according to the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain the target energy transaction cost and the target electric energy corresponding to each micro-grid. Thus, the iterative optimization operation process is distributed to the processors of each micro-grid, the optimization efficiency of the interconnected micro-grid is improved, the real-time demand of online management of the interconnected micro-grid cluster is met, and the reliability of micro-grid operation is improved.

[0098] Please refer to Figure 2 , Figure 2 The step flow chart of the online distributed point-to-point electric energy management method of the interconnected micro-grid provided in the second embodiment of the present application.

[0099] The application provides an online distributed point-to-point electric energy management method for interconnected microgrids, which comprises the following steps:

[0100] In step 201, an electric energy transaction model is constructed by taking the minimum energy transaction cost as an optimization target and taking the electric energy of each microgrid as a decision variable.

[0101] In the embodiment of the application, the electric energy transaction model is constructed by taking the minimum energy transaction cost as an optimization target, taking the electric energy of each microgrid as a decision variable, setting operation constraints, transaction achievement constraints and power network constraints.

[0102] It should be noted that the electric energy transaction model is specifically as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] wherein, is the transaction cost of the mth microgrid at t moment, is the energy storage operation cost of the mth microgrid at t moment, is the network service fee of the mth microgrid at t moment, is the power purchase cost of the mth microgrid from the main grid at t moment, is a microgrid set, is a cost coefficient of the microgrid m, is the electric energy of the mth microgrid at t moment, is the electrical distance between the microgrids m and n, is the electric energy transaction amount purchased between the microgrids m and n at t moment, is the electricity transaction volume purchased by the micro-grid n and the micro-grid m at time t, is the network service fee of the network operator for the unit electricity point electrical distance of all P2P energy transactions, is the impedance of the line l, is the jth node of the micro-grid n, is the set of all lines on the shortest path from the node i m to the node , i m is the ith node of the micro-grid m, is the unit purchase electricity price of the main grid at time t, is the power purchased by the micro-grid m from the grid at time t, is the unit sale electricity price of the main grid at time t, is the power sold by the micro-grid m from the grid at time t, is the carbon intensity of the micro-grid m at time t, is the carbon intensity of the micro-grid n at time t, is the carbon intensity of the grid at time t, is the set of consumers, is the set of energy storages, is the set of producers, is the carbon emission upper limit of the micro-grid m, is the minimum value of the electricity transaction between the micro-grid m and the micro-grid n, is the maximum value of the electricity transaction between the micro-grid m and the micro-grid n, is the lower limit of the voltage amplitude, is the upper limit of the voltage amplitude, is the first linear function of , is the micro-grid transaction period, is the lower limit of the line active power, is the second linear function of , is the upper limit of the line active power, t is the index of time, m is the first index of the micro-grid, n is the second index of the micro-grid, l is the index of the line, j is the first index of the node, and i is the second index of the node.

[0115] In step 202, the working condition parameters and the corresponding historical operation data of each micro-grid are respectively input into a preset power optimization model to obtain the initial power of each micro-grid.

[0116] In the embodiment of the application, the working condition parameters and the corresponding historical operation data of each micro-grid are respectively input into a preset power optimization model to obtain the initial power of each micro-grid.

[0117] In step 203, based on the preset target function, the initial electric energy of each micro-grid is updated according to the working condition parameters corresponding to each micro-grid, and multiple updated electric energies are obtained.

[0118] Further, step 203 includes the following sub-steps:

[0119] In S11, based on the preset target function, the initial electric energy of each micro-grid is locally gradient estimated according to the working condition parameters corresponding to each micro-grid, and the local gradient estimation value corresponding to each micro-grid is obtained.

[0120] In the embodiment of the application, the working condition parameters corresponding to each micro-grid and the corresponding initial electric energy are input into the preset target function for local gradient estimation, and the local gradient estimation value corresponding to each micro-grid is obtained.

[0121] It should be noted that the target function is specifically:

[0122]

[0123] In S12, the pre-acquired historical electric energy, the local gradient estimation value and the corresponding initial electric energy are input into the preset local update function, and multiple local estimation values are obtained.

[0124] The historical electric energy refers to the updated electric energy of each micro-grid in the previous two iterations.

[0125] It should be noted that the local update function is specifically:

[0126]

[0127]

[0128] Wherein, is the initial local estimation value of the mth micro-grid at the kth iteration at t time, is the updated electric energy of the mth micro-grid at the k-1th iteration at t time, is the decreasing step of the kth iteration, is the k-1th local gradient estimation value of the mth micro-grid at t time, is the acceleration gain of the kth iteration, is the updated electric energy of the mth micro-grid at the k-2th iteration at t time, k is the iteration number, is the local estimation value of the mth micro-grid at the kth iteration at t time, is the projection mapping function for is the corresponding closed convex set of the mth micro-grid at t time.

[0129] ​In the embodiment of the present application, the local update function is preset for each local gradient estimation value and the initial electric energy input corresponding to each micro-grid before the first two iterations of each micro-grid, and a plurality of local estimation values are obtained.

[0130] It should be noted that, The initial electric energy is calculated by the electric energy optimization model. The initial electric energy is calculated by the electric energy optimization model.

[0131] It should be noted that S12 is operated in the terminal of each micro-grid.

[0132] S13, a micro-grid matrix is constructed by using each local estimation value.

[0133] In the embodiment of the present application, when the power distribution system operator receives each local estimation value, a micro-grid matrix is constructed by using each local estimation value. For example, Wherein, is the micro-grid matrix of the kth iteration at time t, is the local estimation value of the first micro-grid at the kth iteration at time t, is the local estimation value of the kth micro-grid at the kth iteration at time t. is the local estimation value of the kth micro-grid at the kth iteration at time t.

[0134] S14, based on the preset global update function, the micro-grid matrix is updated according to the pre-acquired historical electric energy, and a plurality of updated electric energies are obtained.

[0135] In the embodiment of the present application, the pre-acquired historical electric energy and the micro-grid matrix are input into the preset global update function, and the target micro-grid matrix is obtained. The updated electric energy corresponding to each micro-grid is extracted from the target micro-grid matrix.

[0136] It should be noted that the global update function is specifically:

[0137]

[0138]

[0139]

[0140] Wherein, is the total number of micro-grids, is a preset positive integer, is the target micro-grid matrix of the kth iteration at time t.

[0141] Step 204, respectively judge whether the update times of each updated electric energy is greater than or equal to the preset iteration threshold value.

[0142] The iteration threshold refers to a minimum iteration number for terminating iteration, and is 100.

[0143] In the embodiment of the present application, it is judged whether the update number of each updated electric energy is greater than or equal to 100.

[0144] In step 205, if the update number is less than the iteration threshold, the updated electric energy is taken as a new initial electric energy, and the step of updating each initial electric energy based on the preset target function according to the working condition parameters corresponding to each micro-grid is executed.

[0145] In the embodiment of the present application, if the update number is less than 100, the updated electric energy is taken as a new initial electric energy, and step 203-step 205 is executed.

[0146] In step 206, if the update number is greater than or equal to the iteration threshold, the updated electric energy is determined as a target updated electric energy.

[0147] In the embodiment of the present application, if the update number is greater than or equal to 100, the updated electric energy is determined as a target updated electric energy.

[0148] In step 207, each target electric energy corresponding to each micro-grid is determined according to each target updated electric energy, and each target electric energy is input into an electric energy transaction model to obtain a corresponding target energy transaction cost.

[0149] Further, step 207 includes the following sub-steps:

[0150] S21, a target micro-grid matrix is constructed by using each target updated electric energy.

[0151] S22, according to a preset forgetting factor, the target micro-grid matrix is corrected based on a preset correction function to obtain each target electric energy corresponding to each micro-grid.

[0152] In the embodiment of the present application, a target micro-grid matrix is constructed by using each target updated electric energy. The target micro-grid matrix and the preset forgetting factor are input into the preset correction function to obtain a corrected micro-grid matrix, and each target electric energy corresponding to each micro-grid is extracted from the corrected micro-grid matrix.

[0153] It should be noted that the correction function is specifically:

[0154]

[0155] wherein, is a corrected micro-grid matrix at the Kth iteration at t+1, is a target micro-grid matrix at the Kth iteration at t, is a decay factor, is the attenuation factor at time t, is the attenuation factor at time t, is the energy-related quantity at time t, is the attenuation factor at time t, is the index of past time, and I is the past time period. It is worth mentioning that the optimization performance of steps 201-207 can be measured by the average value of the cumulative regret, and if the average value of the cumulative regret approaches zero, it means that the target energy of each micro-grid obtained by solving is close to the optimal result.

[0156] The expression of the average value of the cumulative regret is:

[0157]

[0158]

[0159] wherein, is the average value of the cumulative regret, is the cumulative regret value, is the corrected micro-grid matrix at time t.

[0160] It is worth mentioning that a differential privacy algorithm can be introduced to optimize steps 201-207.

[0161] In the embodiment of the present application, by taking the minimum energy transaction cost as the optimization target and taking the energy of each micro-grid as the decision variable, an energy transaction model is constructed, the working condition parameters and the corresponding historical operation data of each micro-grid are respectively input into the preset energy optimization model, the initial energy of each micro-grid is obtained, the distributed iterative optimization is performed on the energy transaction model according to the working condition parameters and the initial energy of each micro-grid, and the target energy transaction cost and the target energy of each micro-grid are obtained. The problem of slow convergence speed commonly existing in the traditional micro-grid energy management method is overcome, and in the large-scale interconnected micro-grid cluster scene, the real-time demand of online management is difficult to meet, and the reliability of micro-grid operation is reduced. Compared with the traditional micro-grid energy management method, the initial energy of each micro-grid is obtained by respectively inputting the working condition parameters and the corresponding historical operation data of each micro-grid into the preset energy optimization model, and then the distributed iterative optimization is performed on the energy transaction model according to the working condition parameters and the initial energy of each micro-grid, so as to obtain the target energy transaction cost and the target energy of each micro-grid. Thus, the iterative optimization operation process is distributed to the processor of each micro-grid, the optimization efficiency of the interconnected micro-grid is improved, the real-time demand of online management of the interconnected micro-grid cluster is met, and the reliability of micro-grid operation is improved.

[0162] Please refer to Figure 3 ​, Figure 3 A structural block diagram of an online distributed point-to-point electric energy management system of an interconnected micro-grid is provided for embodiment three of the present application.

[0163] The online distributed point-to-point electric energy management system of the interconnected micro-grid provided by the present application comprises:

[0164] The construction module 301 is configured to construct an electric energy transaction model by taking the minimum energy transaction cost as an optimization target and taking the electric energy of each micro-grid as a decision variable.

[0165] The update module 302 is configured to input the working condition parameters and the corresponding historical operation data of each micro-grid into a preset electric energy optimization model respectively to obtain the initial electric energy corresponding to each micro-grid.

[0166] The distributed optimization module 303 is configured to perform distributed iterative optimization on the electric energy transaction model according to the working condition parameters and the initial electric energy corresponding to each micro-grid to obtain the target energy transaction cost and the target electric energy corresponding to each micro-grid.

[0167] Further, the distributed optimization module 303 comprises:

[0168] The first update submodule is configured to update each initial electric energy according to the working condition parameters corresponding to each micro-grid based on the preset target function to obtain a plurality of updated electric energies.

[0169] The first analysis submodule is configured to determine whether the number of updates of each updated electric energy is greater than or equal to a preset iteration threshold value.

[0170] The jump submodule is configured to, if the number of updates is less than the iteration threshold value, take the updated electric energy as a new initial electric energy and jump to execute the step of updating each initial electric energy according to the working condition parameters corresponding to each micro-grid based on the preset target function to obtain a plurality of updated electric energies.

[0171] The target updated electric energy submodule is configured to, if the number of updates is greater than or equal to the iteration threshold value, determine the updated electric energy as a target updated electric energy.

[0172] The second analysis submodule is configured to determine the target electric energy corresponding to each micro-grid according to each target updated electric energy and input each target electric energy into the electric energy transaction model to obtain the corresponding target energy transaction cost.

[0173] Further, the first update submodule comprises:

[0174] The local gradient estimation unit is configured to perform local gradient estimation on each initial electric energy according to the working condition parameters corresponding to each micro-grid based on a preset target function, to obtain a local gradient estimation value corresponding to each micro-grid;

[0175] The local estimation unit is configured to input the pre-acquired historical electric energy, each local gradient estimation value and the corresponding initial electric energy into a preset local update function, to obtain a plurality of local estimation values;

[0176] The first construction unit is configured to construct a micro-grid matrix by using each local estimation value;

[0177] The global update unit is configured to update the micro-grid matrix according to the pre-acquired historical electric energy based on a preset global update function, to obtain a plurality of updated electric energies.

[0178] Further, the second analysis sub-module comprises:

[0179] The second construction unit is configured to construct a target micro-grid matrix by using each target updated electric energy;

[0180] The correction unit is configured to correct the target micro-grid matrix according to a preset forgetting factor based on a preset correction function, to obtain a target electric energy corresponding to each micro-grid.

[0181] Further, the electric energy transaction model is specifically:

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] wherein, the transaction cost of the mth microgrid at time t, the energy storage operation cost of the mth microgrid at time t, the network service fee of the mth microgrid at time t, the electricity purchase cost of the mth microgrid from the main grid at time t, the set of microgrids, the cost coefficient of the mth microgrid, the electrical energy of the mth microgrid at time t, the electrical distance between the mth microgrid and the nth microgrid, the electrical energy transaction amount purchased between the mth microgrid and the nth microgrid at time t, the electrical energy transaction amount purchased between the mth microgrid and the nth microgrid at time t, the network service fee of the network operator for the unit electrical distance of the point-to-point energy transaction, the impedance of the line l, the jth node of the nth microgrid, the set of all lines on the shortest path from the ith node m to the node , i m the ith node of the mth microgrid, the unit electricity purchase price from the main grid at time t, the power purchased by the mth microgrid from the grid at time t, the unit electricity sale price from the main grid at time t, the power sold by the mth microgrid from the grid at time t, the carbon intensity of the mth microgrid at time t, the carbon intensity of the nth microgrid at time t, the carbon intensity of the grid at time t, the set of consumers, the set of energy storages, the set of producers, the carbon emission upper limit of the mth microgrid, the minimum value of the electrical energy transaction between the mth microgrid and the nth microgrid, the maximum value of the electrical energy transaction between the mth microgrid and the nth microgrid, the lower limit of the voltage amplitude, the upper limit of the voltage amplitude, the first linear function of , i the microgrid transaction period, the lower limit of the line active power, the second linear function of , i Here, t represents the upper limit of the active power of the line, t represents the time index, m represents the first index of the microgrid, n represents the second index of the microgrid, l represents the line index, j represents the first index of the node, and i represents the second index of the node.

[0194] Furthermore, the local update function is specifically as follows:

[0195]

[0196]

[0197] in, Let be the initial local estimate of the m-th microgrid at time t during the k-th iteration. Let m be the updated electrical energy of the m-th microgrid in the (k-1)-th iteration at time t. Let be the decreasing step size for the k-th iteration. Let be the local gradient estimate of the m-th microgrid at time t (k-1th time). The acceleration gain for the k-th iteration is... Let k be the updated electrical energy of the m-th microgrid in the (k-2)-th iteration at time t, where k is the iteration number. Let be the local estimate of the m-th microgrid at time t during the k-th iteration. For The projection mapping function, Let be the closed convex set corresponding to the m-th microgrid at time t.

[0198] Please see Figure 4 , Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0199] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 performs the online distributed point-to-point power management method for interconnected microgrids as described in any of the above embodiments.

[0200] The memory 401 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. The memory 401 has a storage space 403 for program codes 413 for performing any of the method steps in the above-described methods. For example, the storage space 403 for program codes can include individual program codes 413 for implementing various steps in the above-described methods, respectively. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disc (CD), a memory card, or a floppy disk. The program codes can be compressed in a suitable form, for example. These codes, when run by a computing processing device, cause the computing processing device to perform individual steps in the above-described methods. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disc (CD), a memory card, or a floppy disk. The program codes can be compressed in a suitable form, for example. These codes, when run by a computing processing device, cause the computing processing device to perform individual steps in the above-described methods of online distributed point-to-point electric energy management for interconnected microgrids.

[0201] The embodiment five of the present application further provides a computer readable storage medium, which has stored thereon a computer program, and the computer program is executed by a processor to implement the method of online distributed point-to-point electric energy management for interconnected microgrids according to any of the above-described embodiments.

[0202] The embodiment six of the present application further provides a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, and the computer program includes program instructions, and when the program instructions are executed by a computer, the computer performs the method of online distributed point-to-point electric energy management for interconnected microgrids according to any of the above-described embodiments.

[0203] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0204] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0205] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0206] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0207] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0208] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An online distributed peer-to-peer power energy management method for interconnected microgrids, characterized in that, The application relates to a method for optimizing energy transaction of multiple microgrids. The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The online distributed peer-to-peer electrical energy management method for interconnected microgrids of claim 1, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

3. The online distributed peer-to-peer electrical energy management method for interconnected microgrids of claim 2, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

4. The online distributed peer-to-peer electrical energy management method for interconnected microgrids of claim 2, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

5. The online distributed peer-to-peer electrical energy management method for interconnected microgrids of claim 1, wherein, The method comprises the following steps: wherein, is the trading cost of the mth microgrid at time t, is the energy storage operation cost of the mth microgrid at time t, is the network service fee of the mth microgrid at time t, is the electricity purchase cost of the mth microgrid from the main grid at time t, is the set of microgrids, is the cost coefficient of the microgrid m, is the electrical energy of the mth microgrid at time t, is the electrical distance between the mth and nth microgrids, is the electricity transaction volume purchased between the mth and nth microgrids at time t, is the electricity transaction volume purchased between the mth and nth microgrids at time t, is the network service fee set by the network operator for the unit electrical distance of the P2P energy transaction, is the impedance of the line l, is the jth node of the nth microgrid, is the set of all lines on the shortest path from the ith node m to the node , i m is the ith node of the mth microgrid, is the unit purchase price from the main grid at time t, is the power purchased by the mth microgrid from the grid at time t, is the unit sale price from the main grid at time t, is the power sold by the mth microgrid from the grid at time t, is the carbon intensity of the mth microgrid at time t, is the carbon intensity of the nth microgrid at time t, is the carbon intensity of the grid at time t, is the set of consumers, is the set of energy storages, is the set of producers, is the upper limit of carbon emissions of the mth microgrid, is the minimum value of the electricity transaction between the mth and nth microgrids, is the maximum value of the electricity transaction between the mth and nth microgrids, is the lower limit of the voltage amplitude, is the upper limit of the voltage amplitude, is the first linear function of , is the microgrid transaction period, is the lower limit of the line active power, is the second linear function of , Pmax, line, t, m, n, l, j, i = Pmax, line, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, n, l, j, i - Pline, t, m, 6. The online distributed peer-to-peer electrical energy management method for interconnected microgrids of claim 3, wherein, The method comprises the following steps: wherein, is the initial local estimate of the mth microgrid at time t for the kth iteration, is the updated energy of the mth microgrid at time t for the k-1th iteration, is the decreasing step size for the kth iteration, is the local gradient estimate of the mth microgrid at time t for the k-1th iteration, is the acceleration gain for the kth iteration, is the updated energy of the mth microgrid at time t for the k-2th iteration, k is the iteration number, is the local estimate of the mth microgrid at time t for the kth iteration, is the projection mapping function for is the corresponding closed convex set of the mth microgrid at time t.​ 7. 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the initial electric energy corresponding to each micro-grid, so as to obtain the target energy transaction cost and the target electric energy corresponding to each micro-grid.

8. An electronic device, comprising: The computer program is executed by the processor to make the processor execute the steps of the online distributed point-to-point electric energy management method for interconnected micro-grids according to any one of claims 1-6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the online distributed point-to-point electric energy management method for interconnected micro-grids according to any one of claims 1-6.

10. A computer program product, characterised in that, The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the online distributed point-to-point electric energy management method for interconnected micro-grids according to any one of claims 1-6.

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