Power distribution network optimal dispatching method and terminal considering multi-microgrid power interaction

Abstract

The application discloses a power distribution network optimization scheduling method and terminal considering power interaction of multiple microgrids, and microgrids are marked as different microgrid types according to source and load data, and microgrids of the same type are taken as a whole for optimization scheduling of the power distribution network. On this basis, a first optimization scheduling model is constructed to ensure that the operation cost of the microgrid is minimum. Considering charging uncertainty brought by large-scale growth of electric vehicles and electricity flexibility of user load, a charging load model and a flexible load model are constructed to realize scheduling constraint among multiple microgrids. In addition, considering power coupling between the power distribution network and the multiple microgrids, a second optimization scheduling model of the power distribution network is constructed to ensure safe operation of the power distribution network, and coupling constraint between the first and second optimization scheduling models is constructed, so that the source and load duality of the microgrid is fully utilized, safe operation of the power distribution network is ensured, and collaborative and efficient operation of the microgrid and the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and scheduling, and in particular to a distribution network optimization and scheduling method and terminal taking into account the power interaction of multiple microgrids. Background Art

[0002] Under the "dual carbon" goal, a large number of distributed energy sources are developing rapidly, and the penetration rate of new energy in new power systems is increasing. Its volatility and intermittency pose huge challenges to the safe operation of the power grid. The technology to absorb a high proportion of new energy is becoming increasingly important.

[0003] Microgrids aggregate regionally adjacent loads and distributed energy devices. By increasing the local consumption rate of distributed renewable energy, they improve energy self-sufficiency within the microgrid, reducing the overall power demand of the microgrid, thereby alleviating pressure on the distribution network and improving its operational safety. Furthermore, the optimized configuration of microgrids can effectively increase the consumption rate of distributed renewable energy and reduce its impact on the power grid.

[0004] With the continued development of distributed energy and the rapid growth of loads, each microgrid experiences source-load duality. This means that each microgrid has different installed capacity of renewable energy sources and different load energy characteristics. This diversity creates significant potential for energy complementarity between microgrids. A multi-microgrid system, consisting of multiple interconnected microgrids located in close proximity within the same distribution network area, effectively leverages this source-load duality through power exchange between these microgrids. However, while power exchange exists between microgrids, they also interact with the external grid. This demand for power exchange can cause power flow fluctuations in the distribution network. Therefore, while optimizing microgrid scheduling, the need for safe operation of the distribution network must also be considered. Currently, most microgrid scheduling research views the distribution network as a simple energy provider, ignoring the limitations of the distribution network's power flow transport capacity. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network optimization scheduling method and terminal taking into account the power interaction of multiple microgrids, synchronously considering the power interaction between multiple microgrids and the power interaction between multiple microgrids and the distribution network, while fully utilizing the source-load duality of microgrids and ensuring the safe operation of the distribution network.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A distribution network optimization scheduling method considering multi-microgrid power interaction, comprising:

[0008] Obtain source and load data of each microgrid in the same dispatch cycle;

[0009] Marking each of the microgrids as a different microgrid type according to the source-load data;

[0010] Construct a charging load model based on the charging behavior of electric vehicles;

[0011] Establishing a flexible load model of the microgrid;

[0012] Constructing a first optimization scheduling model with the goal of minimizing the total operating cost of multiple microgrids based on the labeling results of the microgrid types, and constructing a power constraint of the first optimization scheduling model based on the charging load model and the flexible load model;

[0013] With the goal of minimizing the network loss of the distribution network to which the microgrid is connected, a second optimization scheduling model is constructed, and safe operation constraints of the second optimization scheduling model are constructed;

[0014] Constructing a coupling constraint between the first optimization scheduling model and the second optimization scheduling model;

[0015] The first optimization scheduling model and the second optimization scheduling model are solved according to the power constraint, the safe operation constraint and the coupling constraint to obtain an optimal scheduling result.

[0016] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0017] A distribution network optimization and scheduling terminal that takes into account the power interaction of multiple microgrids includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, each step of the above-mentioned distribution network optimization and scheduling method that takes into account the power interaction of multiple microgrids is implemented.

[0018] The beneficial effects of the present invention are that: microgrids are marked as different microgrid types according to source-load data, so that the same type of microgrids are optimized and scheduled as a whole for the distribution network, effectively considering the power interaction between multiple microgrids and the distribution network. At the same time, in the first optimization scheduling model for power interaction among multiple microgrids, considering the large-scale growth of electric vehicles, a charging load model is constructed to characterize the uncertainty of electric vehicle charging in the microgrid, and a flexible load model is constructed considering the power consumption flexibility of user loads, thereby realizing the optimization scheduling of power interaction among multiple microgrids. In addition, when optimizing the scheduling of multiple microgrids, considering the power coupling between the distribution network and the multiple microgrids, a second optimization scheduling model for the distribution network connected to the multiple microgrid group is constructed, and coupling constraints are constructed between the first and second optimization scheduling models, fully considering the impact of the power interaction of multiple microgrids on the safe operation of the distribution network, while fully utilizing the source-load duality of the microgrid, ensuring the safe operation of the distribution network, and improving the coordinated and efficient operation of the microgrid and the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of the steps of a distribution network optimization scheduling method taking into account the power interaction of multiple microgrids provided by an embodiment of the present invention;

[0020] Figure 2 A flowchart of the solution of the alternating direction multiplication method provided in an embodiment of the present invention;

[0021] Figure 3 A flow chart of constructing a charging load model using the Monte Carlo sampling method provided in an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the structure of a distribution network optimization and dispatching terminal taking into account the power interaction of multiple microgrids provided by an embodiment of the present invention;

[0023] Description of labels:

[0024] 100. A distribution network optimization and dispatching terminal taking into account the power interaction of multiple microgrids; 101. Memory; 102. Processor. DETAILED DESCRIPTION

[0025] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0026] An embodiment of the present invention provides a distribution network optimization scheduling method taking into account the power interaction of multiple microgrids, including:

[0027] Obtain source and load data of each microgrid in the same dispatch cycle;

[0028] Marking each of the microgrids as a different microgrid type according to the source-load data;

[0029] Construct a charging load model based on the charging behavior of electric vehicles;

[0030] Establishing a flexible load model of the microgrid;

[0031] Constructing a first optimization scheduling model with the goal of minimizing the total operating cost of multiple microgrids based on the labeling results of the microgrid types, and constructing a power constraint of the first optimization scheduling model based on the charging load model and the flexible load model;

[0032] With the goal of minimizing the network loss of the distribution network to which the microgrid is connected, a second optimization scheduling model is constructed, and safe operation constraints of the second optimization scheduling model are constructed;

[0033] Constructing a coupling constraint between the first optimization scheduling model and the second optimization scheduling model;

[0034] The first optimization scheduling model and the second optimization scheduling model are solved according to the power constraint, the safe operation constraint and the coupling constraint to obtain an optimal scheduling result.

[0035] As can be seen from the above description, the beneficial effects of the present invention are: microgrids are marked as different microgrid types based on source-load data, so that microgrids of the same type are optimized and scheduled as a whole for the distribution network, effectively considering the power interaction between multiple microgrids and the distribution network. At the same time, in the first optimization scheduling model for power interaction among multiple microgrids, considering the large-scale growth of electric vehicles, a charging load model is constructed to characterize the uncertainty of electric vehicle charging in the microgrid, and a flexible load model is constructed considering the power consumption flexibility of user loads, thereby achieving optimized scheduling of power interaction among multiple microgrids. In addition, when optimizing the scheduling of multiple microgrids, considering the power coupling between the distribution network and multiple microgrids, a second optimization scheduling model is constructed for the distribution network connected to the multi-microgrid group, and coupling constraints are constructed between the first and second optimization scheduling models. This fully considers the impact of the power interaction of multiple microgrids on the safe operation of the distribution network, while fully utilizing the source-load duality of the microgrid, ensuring the safe operation of the distribution network, and improving the coordinated and efficient operation of the microgrid and distribution network.

[0036] Furthermore, the source-load data includes power output data and load data; the microgrid types include microgrid groups and independent microgrids;

[0037] The step of marking each microgrid as a different microgrid type according to the source-load data includes:

[0038] Generating a power output vector and a load vector based on the power output data and load data of each microgrid according to the same sorting rule, and calculating the Spearman rank correlation coefficient between the power output vector and the load vector;

[0039] Calculating the Euclidean distance between the power output data and the load data;

[0040] Calculating the source-load matching degree of the source-load data by a weight assignment method according to the Spearman rank correlation coefficient and the Euclidean distance;

[0041] Determining whether the source-load matching degree meets a preset matching value;

[0042] If yes, the microgrid corresponding to the source-load matching degree is marked as a microgrid group;

[0043] Otherwise, the microgrid corresponding to the source-load matching degree is marked as an independent microgrid.

[0044] From the above description, it can be seen that, considering that the Spearman rank correlation coefficient cannot characterize the data correlation and can only represent the rank size of the data sequence, while the Euclidean distance only considers the distance of the data. Therefore, the present invention calculates the Spearman rank correlation coefficient and the Euclidean distance respectively, and calculates the matching degree between the source and the load through the weight assignment method, divides the microgrid groups according to the matching degree, and can reasonably screen out the microgrid groups with high matching degree, and optimize the power interaction of multiple microgrids as a whole. For microgrids that do not meet the matching value, they participate in the subsequent distribution network optimization scheduling as independent individuals, without considering their power interaction with other microgrids, effectively improving the effectiveness of multi-microgrid scheduling.

[0045] Furthermore, the charging behavior of the electric vehicle includes a charging start time, a charging stop time, and an expected power amount;

[0046] The construction of a charging load model according to the charging behavior of the electric vehicle includes:

[0047] A charging probability density model is established based on the normal distribution according to the start time of charging of electric vehicles;

[0048] According to the stop charging time of electric vehicles, a stop charging probability density model is established based on the log-normal distribution;

[0049] According to the expected power consumption of electric vehicle users, an expected power probability density model is established based on the log-normal distribution;

[0050] The charging load model of the electric vehicle is obtained by calculating the charging probability density model, the charging stop probability density model and the expected power probability density model using the Monte Carlo sampling method.

[0051] As can be seen from the above description, the probability of an electric vehicle starting charging follows a normal distribution, while the probability of stopping charging and the probability of the expected power follow a lognormal distribution. Therefore, a corresponding probability density distribution model is established to characterize the uncertainty of electric vehicle charging behavior. Furthermore, Monte Carlo sampling can effectively simulate the changes in electric vehicle charging load, thereby optimizing the power interaction between multiple microgrids.

[0052] Furthermore, the flexible load model includes a translatable load model, a transferable load model and a reducible load model;

[0053] The flexible load model of the microgrid is specifically established as follows:

[0054] Establish a translatable load model for the microgrid:

[0055] ΔP shift (t) = f shift [t+Δt(Δδ shift (t))]-fshift (t);

[0056]

[0057] Where ΔP shift (t) represents the electric power that needs to be shifted during period t, i.e., the shifted load model; f shift (t) represents the initial load curve of the load that can be translated; Δt represents the time period required for translation; Δδ shift (t) represents the change in the relevant price during period t, and T is the scheduling period;

[0058] Establish the transferable load model of the microgrid:

[0059] ΔP trans (t) = f trans (Δδ(t),P trans (t),e trans (t))-f trans (t);

[0060]

[0061] Where ΔP trans (t) represents the electric power that needs to be transferred during period t, i.e., the transfer load model; P trans (t) represents the basic transfer load during period t; e trans (t) represents the self-transfer elastic coefficient; f trans (t) initial load curve representing the transferable load;

[0062] Establishing a curtailable load model for the microgrid;

[0063] ΔP cut (t) = f cut (P cut (t),Δδ(t),e cut (t))-f cut (t);

[0064] Where ΔP cut (t) represents the electric power that needs to be reduced during period t, i.e., the load reduction model; P cut (t) represents the basic load reduction during period t; e cut (t) represents the reduction elasticity coefficient during period t; f cut (t) represents the initial load curve of the load that can be reduced.

[0065] As can be seen from the above description, due to the flexibility of user loads, the rational use of load flexibility during power system scheduling can effectively achieve load peak shaving and valley filling, thereby alleviating power supply pressure on the power grid. Among them, shiftable loads can be applied to industrial loads to adjust production shifts; transferable loads can ensure that total power consumption remains unchanged within the scheduling cycle, but the power consumption is shifted over time; and curtailable loads are loads that can be directly reduced at certain times, without the need for transfer to maintain total power.

[0066] Furthermore, the first optimization scheduling model based on the labeling results of the microgrid types with the goal of minimizing the total operating cost of multiple microgrids is specifically constructed as follows:

[0067]

[0068]

[0069]

[0070] Among them, F MG represents the total operating cost of multiple microgrids; F grid,i Represents microgrid MG i The cost of power interaction with the distribution network; if the microgrid MG i is marked as the microgrid group, then F mg,i Represents microgrid MG i The cost of power interaction with other microgrids; if the microgrid MG i is marked as the independent microgrid, then F mg,i is zero; p1(t) and p2(t) represent the price of electricity purchased from the distribution network and the price of electricity sold to the microgrid respectively; P grid,b,i (t) and P grid,s,i (t) represents the microgrid MG i The power purchased from the distribution network and the power sold; Δt represents the dispatch unit time interval; P i,j (t) represents the microgrid MG at time t i and microgrid MG j The interaction power, if P i,j (t) is positive, then the microgrid MG i Microgrid MG j Selling electricity, the opposite is buying electricity;

[0071] The power constraints of the first optimization scheduling model constructed according to the charging load model and the flexible load model are specifically:

[0072] P WT,i (t)+P PV,i (t)+X(t)P grid,b,i(t)-P i,j (t)-[1-X(t)]P grid,s,i (t) = P 1oad,i (t);

[0073] Among them, P WT,i (t) and P PV,i (t) represents the microgrid MG i Wind power output and photovoltaic power output at time t; X(t) represents a 0-1 binary variable. i When purchasing electricity from the distribution network, X(t) is 1, otherwise it is 0; P 1oad,i (t) represents the load power at time t, P load,i (t) = P MRC +P FLEX , P MRC Represents the charging load model, P FLEX Represents a flexible load model.

[0074] As can be seen from the above description, the first optimization scheduling model for multi-microgrids aims to improve their overall economic benefits. However, the optimization process is subject to constraints such as power balance. Therefore, it is necessary to fully consider the uncertainty of electric vehicle charging and the flexibility of flexible loads to establish power constraints. This approach minimizes the total operating cost of the multi-microgrid while fully utilizing the duality of source and load in microgrids for power interaction.

[0075] Furthermore, the safety operation constraints include power distribution network line flow constraints, distribution network operation voltage constraints, line current carrying capacity constraints, and substation output power constraints;

[0076] The second optimization scheduling model is constructed with the goal of minimizing the network loss of the distribution network to which the microgrid is connected as follows:

[0077]

[0078] Among them, I lm,t represents the square of the current in the circuit lm at time t; R lm Indicates the resistance of the circuit lm;

[0079] The safe operation constraints for constructing the second optimization scheduling model are specifically:

[0080] Constructing the distribution network line flow constraints of the second optimization scheduling model:

[0081]

[0082]

[0083] Where l represents the starting node of the line with node m as the end point, k represents the end node of the line with node m as the starting point; Ω(m,:) and Ω(:,m) represent the set of lines with m as the starting point and end point, respectively; P lm,t and P km,t They represent the active power of line lm and line km at time t respectively; Q lm,t and Q km,t They represent the reactive power of line lm and line km at time t respectively; I lm,t represents the square of the current in the circuit lm at time t; X lm Indicates the impedance of the line lm; and denote the active power and reactive power of the substation at node m respectively; and They represent the active load and reactive load of the user connected to the distribution network node m respectively; represents the power provided by the distribution network to the microgrid at node m;

[0084] The voltage constraints of the distribution network operation of the second optimization dispatch model are constructed as follows:

[0085]

[0086]

[0087] Among them, U m,t represents the square of the voltage at node m at time t; U l,t represents the square of the voltage at node l at time t; Indicates the lower limit of the voltage safety amplitude; Indicates the upper limit of the voltage safety amplitude;

[0088] Constructing line current carrying capacity constraints of the second optimization scheduling model;

[0089]

[0090]

[0091] in, Indicates the upper limit of the line's allowable current carrying capacity;

[0092] Construct the substation output power constraint of the second optimization dispatch model:

[0093]

[0094] in, represents the rated power of the substation at node m.

[0095] From the above description, it can be seen that since power interaction between multiple microgrids will cause power flow fluctuations in the distribution network, it is necessary to consider the safe operation of the distribution network while considering the optimal scheduling of microgrids. Therefore, the second optimal scheduling model is constructed by minimizing the network loss of the distribution network. The distribution network must meet the power flow constraints of the distribution network line, the voltage constraints of the distribution network operation, the line current carrying capacity constraints, and the substation output power constraints to ensure the safe operation of the distribution network.

[0096] Furthermore, the coupling constraints between the first optimization scheduling model and the second optimization scheduling model are specifically:

[0097]

[0098] Among them, P grid,b,i (t) and P grid,s,i (t) represents the microgrid MG i The power purchased from the distribution grid and the power sold; represents the power provided by the distribution network to the microgrid at node m; MG represents the microgrid; ω(m) represents the set of microgrids connected to the distribution network node m.

[0099] As can be seen from the above description, since multiple microgrids interact with each other in power, they also interact with the distribution network. The difference between the power a microgrid purchases from the distribution network and the power it sells is the microgrid's net power purchase, which is equal to the power the distribution network provides to the microgrid. Based on the consistency of power interaction between microgrids and the distribution network, coupling constraints are constructed for the first and second optimal scheduling models, enabling optimal scheduling between multiple microgrids while also considering the safe operation of the distribution network.

[0100] Furthermore, solving the first optimization scheduling model and the second optimization scheduling model according to the power constraint, the safe operation constraint, and the coupling constraint to obtain an optimal scheduling result includes:

[0101] Decoupling the first optimization scheduling model from the second optimization scheduling model according to the coupling constraint based on the alternating direction multiplier method, and iteratively solving the decoupled first optimization scheduling model and the second optimization scheduling model respectively;

[0102] Calculating the primal residual and the dual residual between the first optimization scheduling model and the second optimization scheduling model;

[0103] Determining whether the iterative solution result meets the convergence condition according to the original residual and the dual residual;

[0104] If so, the current iterative solution result is output as the optimal scheduling result;

[0105] Otherwise, the iterative solution is continued until the convergence condition is met or the maximum number of iterations is reached.

[0106] From the above description, it can be seen that in order to protect the data privacy of the distribution network and multiple microgrids, the present invention decouples the first optimization scheduling model and the second optimization scheduling model based on the alternating direction multiplier method, and then performs an iterative solution. In the solution process, the original residual and the converged residual are used as convergence conditions, which effectively reduces the amount of solution calculation, and the solution process comprehensively considers the interaction between the microgrid and the distribution network.

[0107] Furthermore, the alternating direction multiplier method is used to decouple the first optimization scheduling model from the second optimization scheduling model according to the coupling constraint, and the decoupled first optimization scheduling model and the second optimization scheduling model are iteratively solved respectively as follows:

[0108]

[0109]

[0110] Where λ represents the Lagrange multiplier; ρ represents the penalty factor; u represents the current number of iterations; represents the value solved by the current iteration; and and They represent the values ​​of the corresponding variables iteratively solved during the u-1th iteration.

[0111] As can be seen from the above description, since the optimization subjects of the first and second optimization scheduling models are microgrids and distribution networks, respectively, there are multiple entities involved. If traditional solution methods are used, the distribution network and each microgrid must share their own source and load parameters and internal topology, which cannot protect the data privacy of each entity. In addition, with the development of microgrids, traditional solution methods cannot balance the huge computational workload and computational efficiency of optimization problems. The alternating direction multiplier method introduces Lagrange multiplier terms in the objective function to decouple the coupling constraints, achieving a distributed solution, which can effectively reduce the computational complexity of the entire problem. Moreover, since the entities only need to exchange key coupling variables, the privacy of each entity can be effectively protected.

[0112] Another embodiment of the present invention provides a distribution network optimization and scheduling terminal that takes into account the power interaction of multiple microgrids, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, it implements the various steps in the above-mentioned distribution network optimization and scheduling method that takes into account the power interaction of multiple microgrids.

[0113] As can be seen from the above description, the beneficial effects of the present invention are: microgrids are marked as different microgrid types based on source-load data, so that microgrids of the same type are optimized and scheduled as a whole for the distribution network, effectively considering the power interaction between multiple microgrids and the distribution network. At the same time, in the first optimization scheduling model for power interaction among multiple microgrids, considering the large-scale growth of electric vehicles, a charging load model is constructed to characterize the uncertainty of electric vehicle charging in the microgrid, and a flexible load model is constructed considering the power consumption flexibility of user loads, thereby achieving optimized scheduling of power interaction among multiple microgrids. In addition, when optimizing the scheduling of multiple microgrids, considering the power coupling between the distribution network and multiple microgrids, a second optimization scheduling model is constructed for the distribution network connected to the multi-microgrid group, and coupling constraints are constructed between the first and second optimization scheduling models. This fully considers the impact of the power interaction of multiple microgrids on the safe operation of the distribution network, while fully utilizing the source-load duality of the microgrid, ensuring the safe operation of the distribution network, and improving the coordinated and efficient operation of the microgrid and distribution network.

[0114] The embodiments of the present invention provide a distribution network optimization scheduling method and terminal that takes into account the power interaction of multiple microgrids. The method and terminal can be applied to the optimization scheduling scenario of a new distribution network with multiple microgrids connected. The method can simultaneously consider the power interaction between multiple microgrids and the power interaction between multiple microgrids and the distribution network, while fully utilizing the source-load duality of microgrids and ensuring the safe operation of the distribution network. The following is an illustration of the method through specific embodiments:

[0115] Please refer to Figure 1 and Figure 2 , embodiment 1 of the present invention is:

[0116] A distribution network optimization scheduling method considering multi-microgrid power interaction, comprising:

[0117] S1. Obtain the source and load data of each microgrid in the same scheduling cycle.

[0118] S2. Mark each of the microgrids as a different microgrid type according to the source-load data.

[0119] S3. Construct a charging load model based on the charging behavior of electric vehicles.

[0120] S4. Establishing a flexible load model of the microgrid.

[0121] S5. Construct a first optimization scheduling model with the goal of minimizing the total operating cost of multiple microgrids based on the marking results of the microgrid types, and construct a power constraint of the first optimization scheduling model according to the charging load model and the flexible load model.

[0122] In some embodiments, the first optimization scheduling model only minimizes the overall operating cost of the multi-microgrid and improves the economic benefits of the multi-microgrid, while being subject to constraints such as power balance during the optimization process.

[0123] In step S5, S52, based on the labeling results of the microgrid types, a first optimization scheduling model with the goal of minimizing the total operating cost of multiple microgrids is constructed as follows:

[0124]

[0125]

[0126]

[0127] Among them, F MG represents the total operating cost of multiple microgrids; F grid,i Represents microgrid MG i The cost of power interaction with the distribution network; if the microgrid MG i is marked as the microgrid group, then F mg,i Represents microgrid MG i The cost of power interaction with other microgrids; if the microgrid MG i is marked as the independent microgrid, then F mg,i is zero; p1(t) and p2(t) represent the price of electricity purchased from the distribution network and the price of electricity sold to the microgrid respectively; P grid,b,i (t) and P grid,s,i (t) represents the microgrid MG i The power purchased from the distribution network and the power sold; Δt represents the dispatch unit time interval; P i,j (t) represents the microgrid MG at time t i and microgrid MG j The interaction power, if P i,j (t) is positive, then the microgrid MG i Microgrid MG j Selling electricity, and vice versa is buying electricity.

[0128] In step S5, S53, the power constraints of the first optimization scheduling model are constructed according to the charging load model and the flexible load model, specifically:

[0129] P WT,i (t)+P PV,i (t)+X(t)P grid,b,i (t)-P i,j (t)-[1-X(t)]P grid,s,i (t) = P 1oad,i (t) (4)

[0130] Among them, P WT,i (t) and P PV,i (t) represents the microgrid MG i Wind power output and photovoltaic power output at time t; X(t) represents a 0-1 binary variable. i When purchasing electricity from the distribution network, X(t) is 1, otherwise it is 0; P 1oad,i (t) represents the load power at time t, P load,i (t) = P MRC +P FLEX , P MRC Represents the charging load model, P FLEX Represents a flexible load model.

[0131] In some embodiments, the flexible load model includes multiple types, then P FLEX Represents the sum of all flexible load models.

[0132] In some embodiments, the transmission power between microgrids is limited by the power transmission capacity of the tie line. Therefore, it is necessary to construct the power interaction constraints of the first optimization scheduling model:

[0133]

[0134] Among them, P i,j (t) represents the microgrid MG at time t i and microgrid MG j The interaction power between and Represents microgrid MG i and microgrid MG j The lower and upper limits of the interaction power.

[0135] S6. With the goal of minimizing the network loss of the distribution network to which the microgrid is connected, construct a second optimization scheduling model, and construct safe operation constraints for the second optimization scheduling model.

[0136] Specifically, the safety operation constraints include power distribution network line flow constraints, distribution network operation voltage constraints, line current carrying capacity constraints, and substation output power constraints.

[0137] In step S6, S61, with the goal of minimizing the network loss of the distribution network to which the microgrid is connected, a second optimization scheduling model is constructed specifically as follows:

[0138]

[0139] Among them, I lm,t represents the square of the current in the circuit lm at time t; R lm Indicates the resistance of the circuit 1m.

[0140] In some embodiments, the distribution network operates using the Distflow equation, so its operation optimization goal is set to minimize network losses, and it must meet the distribution network flow constraints and line current carrying capacity safety constraints, and the microgrid's power purchase demand for the distribution network node on which it is mounted is used as the boundary condition of the distribution network operation to achieve coupling optimization between the microgrid and the distribution network.

[0141] In step S6, S62, the safe operation constraints of the second optimization scheduling model are specifically constructed as follows:

[0142] S621: Constructing the power distribution network line flow constraints of the second optimization scheduling model:

[0143]

[0144]

[0145] Where l represents the starting node of the line with node m as the end point, k represents the end node of the line with node m as the starting point; Ω(m,:) and Ω(:,m) represent the set of lines with m as the starting point and end point, respectively; P lm,t and P km,t They represent the active power of line lm and line km at time t respectively; Q lm,t and Q km,t They represent the reactive power of line lm and line km at time t respectively; I lm,t represents the square of the current in the circuit lm at time t; X lm Indicates the impedance of the line lm; and denote the active power and reactive power of the substation at node m respectively; and They represent the active load and reactive load of the user connected to the distribution network node m respectively; It represents the power provided by the distribution network to the microgrid at node m.

[0146] S622: Constructing voltage constraints for distribution network operation of the second optimization dispatch model:

[0147]

[0148]

[0149] Among them, U m,t represents the square of the voltage at node m at time t; U l,t represents the square of the voltage at node l at time t; Indicates the lower limit of the voltage safety amplitude; Indicates the upper limit of the voltage safety amplitude.

[0150] S623: Constructing line current carrying capacity constraints of the second optimization scheduling model;

[0151]

[0152]

[0153] in, Indicates the upper limit of the line's allowable current carrying capacity.

[0154] S624: Construct the substation output power constraint of the second optimization scheduling model:

[0155]

[0156] in, represents the rated power of the substation at node m.

[0157] S7. Construct a coupling constraint between the first optimization scheduling model and the second optimization scheduling model.

[0158] Step S7 is specifically as follows:

[0159]

[0160] Among them, P grid,b,i (t) and P grid,s,i (t) represents the microgrid MG i The power purchased from the distribution grid and the power sold; represents the power provided by the distribution network to the microgrid at node m; MG represents the microgrid; ω(m) represents the set of microgrids connected to the distribution network node m.

[0161] In some embodiments, P grid,b,i (t) and P grid,s,i The difference between (t) is the microgrid MG i The net amount of electricity purchased from the distribution network is assumed to be i If node m is connected to the distribution network, then P grid,b,i (t) and P grid,s,i The difference between (t) is equal to the distribution network at node m to the microgrid MG i The power provided. Therefore, there is a consistent coupling constraint between the microgrid and the distribution network. This coupling constraint is used as the coupling variable between the first optimization scheduling model corresponding to the microgrid and the second optimization scheduling model corresponding to the distribution network, facilitating the subsequent coupled solution of the first and second optimization scheduling models.

[0162] S8. Solve the first optimization scheduling model and the second optimization scheduling model according to the power constraint, the safe operation constraint and the coupling constraint to obtain an optimal scheduling result.

[0163] Specifically, step S8 includes:

[0164] S81. Decouple the first optimization scheduling model from the second optimization scheduling model according to the coupling constraint based on the alternating direction multiplier method, and iteratively solve the decoupled first optimization scheduling model and the second optimization scheduling model respectively.

[0165] The step S81 is specifically as follows:

[0166]

[0167]

[0168] Where λ represents the Lagrange multiplier; ρ represents the penalty factor; u represents the current number of iterations; represents the value solved by the current iteration; and and They represent the values ​​of the corresponding variables iteratively solved during the u-1th iteration.

[0169] In some embodiments, the alternating direction multiplication method is implemented by As auxiliary variables, the coupling constraints are decoupled. The value of each iterative solution is affected by the value of the coupling constraints in each subsystem in the previous iterative solution. Therefore, the auxiliary variables The update process is as follows:

[0170] S82. Calculate the primal residual and the dual residual between the first optimization scheduling model and the second optimization scheduling model.

[0171] In some embodiments, step S82 is specifically as follows:

[0172]

[0173] Among them, σ P,ADN , σ P,MG represents a set of original residuals, which is used to ensure that the coupling variables between connected subsystems are close enough; σ D Represents the dual residual, which is used to ensure the stability of the solution; ε P and ε D denote the primal and dual residual limits, respectively.

[0174] S83. Determine whether the iterative solution meets a convergence condition based on the original residual and the dual residual.

[0175] In some embodiments, when both the primal residual and the dual residual of the iterative solution result are less than a given threshold, it is determined that the current iterative solution result meets the convergence condition; otherwise, it is determined that the current iterative solution result does not meet the convergence condition.

[0176] S84: If yes, output the current iterative solution result as the optimal scheduling result.

[0177] S85. Otherwise, continue iterating until the convergence condition is met or the maximum number of iterations is reached.

[0178] In some embodiments, as Figure 2 As shown in the figure, before iteratively solving the decoupled first and second optimization scheduling models based on the alternating direction multiplier method, the average value of the coupling constraints, the Lagrange multiplier, the convergence threshold of the primal and dual residuals, the maximum solution time, and the maximum number of iterations should be initialized. During the iterative solution process, each subsystem (i.e., the decoupled first and second optimization scheduling models) first solves the optimization problem of the model in parallel to obtain the value of the coupling constraints in the model; then, each subsystem exchanges the value of the coupling constraints of the latest iteration with the connected subsystems and calculates the average value of the coupling constraints for each subsystem, thereby protecting the data privacy of each subsystem; then, the primal and dual residuals are used to determine whether the iterative result meets the convergence condition. If not, the auxiliary variables are updated iteratively and the optimization problem of the model is solved again to obtain the value of the coupling constraints after the latest iteration in the model until the convergence condition is met, the iteration times out, or the maximum number of iterations is reached, and the current optimized scheduling solution is output.

[0179] The second embodiment of the present invention is:

[0180] A distribution network optimization scheduling method taking into account the power interaction of multiple microgrids is provided, which differs from the first embodiment in that step S2 is limited.

[0181] Specifically, the source-load data includes power output data and load data; the microgrid types include microgrid groups and independent microgrids.

[0182] The step S2 comprises:

[0183] S21. Generate a power output vector and a load vector based on the same sorting rule for the power output data and load data of each microgrid, and calculate the Spearman rank correlation coefficient between the power output vector and the load vector.

[0184] In some embodiments, the Spearman rank correlation coefficient between the power output vector and the load vector is calculated as follows:

[0185]

[0186] Where ρ' represents the Spearman rank correlation coefficient between the power output vector and the load vector; r i It represents the numerical difference between the two vectors at each moment; D represents the vector dimension, i.e., the selected historical scheduling period.

[0187] S22: Calculate the Euclidean distance between the power output data and the load data.

[0188] In some embodiments, step S22 is specifically as follows:

[0189]

[0190] Among them, s t represents the Euclidean distance; y 1_t and y 2_t Respectively represent the timing values ​​of power output data and load data.

[0191] S23, calculating the source-load matching degree of the source-load data by a weight assignment method according to the Spearman rank correlation coefficient and the Euclidean distance;

[0192] In some embodiments, the Spearman rank correlation coefficient and the Euclidean distance obtained in step S21 and step S22 are normalized to eliminate the dimension difference, and the source-charge matching degree is obtained according to formula (20) by the weight assignment method. Specifically:

[0193]

[0194] Among them, ε represents the source-charge matching degree; λ' represents the weight coefficient; s min and s max They represent the minimum and maximum values ​​of the Euclidean distance respectively; T is the total scheduling period.

[0195] S24: Determine whether the source-load matching degree meets a preset matching value.

[0196] S25: If yes, mark the microgrid corresponding to the source-load matching degree as a microgrid group.

[0197] S26. Otherwise, mark the microgrid corresponding to the source-load matching degree as an independent microgrid.

[0198] In some embodiments, a microgrid group includes an output group and a receiving group. The output group is configured with an output matching value, and the receiving group is configured with a receiving matching value, wherein the output matching value is higher than the receiving matching value. When the source-load matching degree is greater than the output matching value, the microgrid is marked as an output group; when the source-load matching degree is less than or equal to the output matching value and greater than the receiving matching value, the microgrid is marked as a receiving group; when the source-load matching degree is less than or equal to the receiving matching value, the microgrid is marked as an independent microgrid.

[0199] Specifically,

[0200]

[0201] Where Z(i) represents the microgrid type of the i-th microgrid, 2 represents the output group, 1 represents the receiving group, and 0 represents the independent microgrid; ε i represents the source-load matching degree of the i-th microgrid; C2 represents the output matching value, and C1 represents the receiving matching value.

[0202] Please refer to Figure 3 , the third embodiment of the present invention is:

[0203] A distribution network optimization scheduling method taking into account the power interaction of multiple microgrids is provided, which differs from the first embodiment in that step S3 is limited.

[0204] Specifically, the charging behavior of the electric vehicle includes a charging start time, a charging stop time, and an expected power amount.

[0205] The step S3 comprises:

[0206] S31. Establish a charging probability density model based on a normal distribution according to the start time of charging of the electric vehicle.

[0207] In some embodiments, the charging probability density model is specifically:

[0208]

[0209] Where f(t) represents the charging probability density model, μ and σ represent the expectation and standard deviation of the normal distribution respectively; t represents the time when the electric vehicle starts charging.

[0210] S32. Establish a charging stop probability density model based on the log-normal distribution according to the charging stop time of the electric vehicle.

[0211] In some embodiments, the charging stop probability density model is specifically:

[0212]

[0213] Where f'(t') represents the probability density model of charging stop, μ' and σ' represent the expectation and standard deviation of the log-normal distribution of the charging stop time, respectively; t' represents the time when the electric vehicle stops charging.

[0214] S33. Establish an expected power probability density model based on the log-normal distribution according to the expected power of the electric vehicle user.

[0215] In some embodiments, the expected power probability density model is specifically:

[0216]

[0217] Among them, f s (x) represents the expected power probability density model, μ s , σ s They represent the expectation and standard deviation of the log-normal distribution of expected power respectively; x represents the expected power of the electric vehicle.

[0218] S34. Calculate the charging load model of the electric vehicle using the Monte Carlo sampling method according to the charging probability density model, the charging stop probability density model, and the expected power probability density model.

[0219] In some embodiments, as Figure 3 As shown, step S34 is specifically as follows: first, the parameters such as the start charging time, stop charging time and expected charging time of the electric vehicle to be sampled are initialized, and the start charging time is randomly extracted from the charging probability density model, the stop charging time is randomly extracted from the stop charging probability density model, and the expected power is extracted from the expected power probability density model, and the charging time (the time interval between the start charging time and the stop charging time) and the load (the load required for the expected power) are superimposed to obtain the charging load model of the electric vehicle.

[0220] The fourth embodiment of the present invention is:

[0221] A distribution network optimization scheduling method taking into account the power interaction of multiple microgrids is provided, which differs from the first embodiment in that step S4 is limited.

[0222] Specifically, the flexible load model includes a translatable load model, a transferable load model, and a reducible load model.

[0223] The step S4 is specifically as follows:

[0224] S41. Establish a shiftable load model for the microgrid:

[0225] ΔP shift (t) = f shift [t+Δt(Δδ shift (t))]-fshift (t) (25)

[0226]

[0227] Where ΔP shift (t) represents the electric power that needs to be shifted during period t, i.e., the shifted load model; f shift (t) represents the initial load curve of the load that can be translated; Δt represents the time period required for translation; Δδ shift (t) represents the change in relevant prices during period t, and T is the scheduling period.

[0228] S42. Establish a transferable load model of the microgrid:

[0229] ΔP trans (t) = f trans (Δδ(t),P trans (t),e trans (t))-f trans (t) (27)

[0230]

[0231] Where ΔP trans (t) represents the electric power that needs to be transferred during period t, i.e., the transfer load model; P trans (t) represents the basic transfer load during period t; e trans (t) represents the self-transfer elastic coefficient; f trans (t) represents the initial load curve of the transferable load.

[0232] S43, establishing a load curtailment model for the microgrid;

[0233] ΔP cut (t) = f cut (P cut (t),Δδ(t),e cut (t))-f cut (t) (29)

[0234] Where ΔP cut (t) represents the electric power that needs to be reduced during period t, i.e., the load reduction model; P cut (t) represents the basic load reduction during period t; e cut (t) represents the reduction elasticity coefficient during period t; f cut (t) represents the initial load curve of the load that can be reduced.

[0235] Thus, we can get P in step S5 FLEX =ΔP shift (t)+ΔP trans (t)+ΔPcut (t).

[0236] Please refer to Figure 4 , the fifth embodiment of the present invention is:

[0237] A distribution network optimization and scheduling terminal 100 that takes into account the power interaction of multiple microgrids includes a memory 101, a processor 102, and a computer program stored on the memory 101 and running on the processor 102. When the processor 102 executes the computer program, a distribution network optimization and scheduling method that takes into account the power interaction of multiple microgrids according to embodiments 1 to 4 is implemented.

[0238] In summary, the present invention provides a distribution network optimization scheduling method and terminal that considers the power interaction between multiple microgrids. Taking into account the complementary energy consumption characteristics of multiple microgrids, the source-load matching degree of microgrids is used as a criterion for dividing microgrid groups into independent microgrids. Based on the microgrid group, a first optimization scheduling model for multiple microgrids is established to minimize the overall operating cost of the multiple microgrids and improve their economic benefits. Furthermore, the optimization process of the first optimization scheduling model needs to consider the power interaction between multiple microgrids, thus creating power balance constraints. Within the power balance constraints, the uncertainty of current electric vehicle charging behavior and the significant impact of load-side flexibility on the power grid are considered. An electric vehicle charging load model and a flexible load model are constructed to constrain the microgrids. Furthermore, while considering the power interaction constraints between microgrids, the first optimization scheduling model also considers the impact of the microgrid group on the safe operation of the distribution network. Therefore, a second optimization scheduling model for the distribution network is constructed, and coupling constraints are established between the first and second optimization scheduling models to constrain the power interaction between the distribution network and the microgrid group. Finally, the alternating direction multiplication method is used to reduce the computational complexity, achieve decoupled solution for each subject, and effectively protect the privacy of each subject. While fully utilizing the complementary characteristics of microgrid energy consumption, the present invention ensures the safe operation of the distribution network, improves the coordinated and efficient operation of the microgrid and distribution network, and can provide an effective reference for the optimization and scheduling problem of the new distribution network with multiple microgrids connected.

[0239] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network optimization scheduling method considering multi-microgrid power interaction, characterized in that: include: Obtain source and load data of each microgrid in the same dispatch cycle; Marking each of the microgrids as a different microgrid type according to the source-load data; Construct a charging load model based on the charging behavior of electric vehicles; Establishing a flexible load model of the microgrid; Constructing a first optimization scheduling model with the goal of minimizing the total operating cost of multiple microgrids based on the labeling results of the microgrid types, and constructing a power constraint of the first optimization scheduling model based on the charging load model and the flexible load model; With the goal of minimizing the network loss of the distribution network to which the microgrid is connected, a second optimization scheduling model is constructed, and safe operation constraints of the second optimization scheduling model are constructed; Constructing a coupling constraint between the first optimization scheduling model and the second optimization scheduling model; Solving the first optimization scheduling model and the second optimization scheduling model according to the power constraint, the safe operation constraint, and the coupling constraint to obtain an optimal scheduling result; The source-load data includes power output data and load data; the microgrid types include microgrid groups and independent microgrids; The step of marking each microgrid as a different microgrid type according to the source-load data includes: Generating a power output vector and a load vector based on the power output data and load data of each microgrid according to the same sorting rule, and calculating the Spearman rank correlation coefficient between the power output vector and the load vector; Calculating the Euclidean distance between the power output data and the load data; Calculating the source-load matching degree of the source-load data by a weight assignment method according to the Spearman rank correlation coefficient and the Euclidean distance; Determining whether the source-load matching degree meets a preset matching value; If yes, the microgrid corresponding to the source-load matching degree is marked as a microgrid group; Otherwise, the microgrid corresponding to the source-load matching degree is marked as an independent microgrid; The charging behavior of the electric vehicle includes the start time of charging, the stop time of charging and the expected power; The construction of a charging load model according to the charging behavior of the electric vehicle includes: A charging probability density model is established based on the normal distribution according to the start time of charging of electric vehicles; According to the stop charging time of electric vehicles, a stop charging probability density model is established based on the log-normal distribution; According to the expected power consumption of electric vehicle users, an expected power probability density model is established based on the log-normal distribution; Calculating the charging load model of the electric vehicle using the Monte Carlo sampling method according to the charging probability density model, the charging stop probability density model, and the expected power probability density model; Solving the first optimization scheduling model and the second optimization scheduling model according to the power constraint, the safe operation constraint, and the coupling constraint to obtain an optimal scheduling result includes: Decoupling the first optimization scheduling model from the second optimization scheduling model according to the coupling constraint based on the alternating direction multiplier method, and iteratively solving the decoupled first optimization scheduling model and the second optimization scheduling model respectively; Calculating the primal residual and the dual residual between the first optimization scheduling model and the second optimization scheduling model; Determining whether the iterative solution result meets the convergence condition according to the original residual and the dual residual; If so, the current iterative solution result is output as the optimal scheduling result; Otherwise, the iterative solution is continued until the convergence condition is met or the maximum number of iterations is reached.

2. The method according to claim 1, characterized in that The flexible load model includes a translatable load model, a transferable load model and a reducible load model; The flexible load model of the microgrid is specifically established as follows: Establish a translatable load model for the microgrid: ； ； in, It represents the electric power that needs to be shifted during period t, i.e. the shifted load model; Initial load curve representing the translatable load; Indicates the time period that needs to be shifted; represents the change in relevant prices during period t, where T is the scheduling period; Establish the transferable load model of the microgrid: ； ； in, It represents the electric power that needs to be transferred during period t, i.e. the transfer load model; represents the base transfer load during period t; represents the self-transfer elastic coefficient; Initial load curve representing transferable load; Establish a curtailable load model for the microgrid: ； in, It represents the electric power that needs to be reduced during period t, i.e. the load reduction model; represents the base load reduction during period t; represents the reduction elasticity coefficient during period t; This shows the initial load curve of the load that can be reduced.

3. The method according to claim 1, characterized in that The first optimization scheduling model based on the labeling results of the microgrid types with the goal of minimizing the total operating cost of multiple microgrids is specifically constructed as follows: ； ； ； in, F MG represents the total operating cost of the multi-microgrid; F grid,i Represents microgrid MG i The cost of power interaction with the distribution network; if the microgrid MG i is marked as the microgrid group, then F mg,i Represents microgrid MG i The cost of power interaction with other microgrids; if the microgrid MG i is marked as the independent microgrid, then F mg,i is zero; p 1( t )and p 2( t ) represent the price of electricity purchased from the microgrid and the price of electricity sold to the distribution network; P grid,b,i ( t )and P grid,s,i ( t ) represent the microgrid MG i The power purchased from the distribution grid and the power sold; Indicates the scheduling unit time interval; P i,j ( t ) represents the microgrid MG at time t i and microgrid MG j The interaction power, if P i,j ( t ) is positive, then the microgrid MG i Microgrid MG j Selling electricity, the opposite is buying electricity; The power constraints of the first optimization scheduling model constructed according to the charging load model and the flexible load model are specifically: ； in, P WT,i ( t )and P PV,i ( t ) represent the microgrid MG i Wind power output and photovoltaic power output at time t; X ( t ) represents a 0-1 binary variable. When the microgrid MG i When purchasing electricity from the distribution network, X ( t ) is 1, otherwise it is 0; P load,i ( t ) represents the load power at time t, P load,i ( t )= P MRC + P FLEX , P MRC represents the charging load model, P FLEX Represents a flexible load model.

4. The method according to claim 1, wherein The safety operation constraints include power distribution network line flow constraints, distribution network operation voltage constraints, line current carrying capacity constraints and substation output power constraints; The second optimization scheduling model is constructed with the goal of minimizing the network loss of the distribution network to which the microgrid is connected as follows: ； in, I lm,t Represents the line at time t lm The square of the current; R lm Indicates line lm resistance; The safe operation constraints for constructing the second optimization scheduling model are specifically: Constructing the distribution network line flow constraints of the second optimization scheduling model: ； ； in, l Represents a node m The starting node of the line with the destination, k Represents a node m The end node of the line with the starting point; 、 Respectively represent the set of routes with m as the starting point and end point; P lm,t and P mk,t Represent the lines at time t lm and lines mk Active power; Q lm,t and Q mk,t Represent the lines at time t lm and lines mk Reactive power; I lm,t Represents the line at time t lm The square of the current; X lm Indicates line lm Impedance; and denote the active power and reactive power of the substation at node m respectively; and They represent the active load and reactive load of the user connected to the distribution network node m respectively; represents the power provided by the distribution network to the microgrid at node m; The voltage constraints of the distribution network operation of the second optimization dispatch model are constructed as follows: ； ； in, U m,t represents the square of the voltage at node m at time t; U l,t represents the square of the voltage at node l at time t; Indicates the lower limit of the voltage safety amplitude; Indicates the upper limit of the voltage safety amplitude; Constructing line current carrying capacity constraints of the second optimization scheduling model; ； ； in, Indicates the upper limit of the line's allowable current carrying capacity; Construct the substation output power constraint of the second optimization dispatch model: ； in, represents the rated power of the substation at node m.

5. The method according to claim 3, characterized in that The coupling constraints between the first optimization scheduling model and the second optimization scheduling model are specifically: ； in, and Represents microgrid MG i The power purchased from the distribution grid and the power sold; represents the power provided by the distribution network to the microgrid at node m; MG represents the microgrid; Represents the set of microgrids connected to the distribution network node m.

6. The method according to claim 1, characterized in that The method of decoupling the first optimization scheduling model from the second optimization scheduling model based on the alternating direction multiplier method according to the coupling constraint, and iteratively solving the decoupled first optimization scheduling model and the second optimization scheduling model respectively is specifically as follows: ； ； in, represents the Lagrange multiplier; represents the penalty factor; u Indicates the current iteration number; represents the value solved by the current iteration; and , 、 and They represent the values ​​of the corresponding variables iteratively solved in the u-1th iteration process; F MG represents the total operating cost of the multi-microgrid; F grid,i Represents the cost of power interaction between the microgrid and the distribution grid; F mg,i Represents microgrid MG i The cost of power interaction with other microgrids.

7. A distribution network optimization dispatching terminal taking into account the power interaction of multiple microgrids, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, each step of the distribution network optimization scheduling method taking into account the power interaction of multiple microgrids as described in any one of claims 1 to 6 is implemented.

Images