Multi-microgrid cooperative operation optimization method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a multi-microgrid cooperative operation optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing micro-grid operation constraints of each micro-grid according to new energy prediction power, energy storage constraint parameters, demand response parameters and transmission constraint parameters of each micro-grid; constructing a cooperative operation cost function according to the main network exchange cost parameter, the energy storage cost parameter, the electric carbon factor parameter and the carbon emission cost parameter of each micro-grid; constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and each microgrid operation constraint; and solving the multi-microgrid cooperative operation model by using a distributed alternating direction multiplier method in which an adaptive parameter updating mechanism and an error correction mechanism are introduced by taking the minimum cooperative operation cost function as an optimization target to obtain target electric energy exchange parameters among the microgrids. By adopting the method, the target electric energy exchange parameters for realizing the optimal cooperative operation among the microgrids can be efficiently obtained.

Description

Technical Field

[0001] The present application relates to the technical field of power optimization, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing the cooperative operation of multiple microgrids. Background Art

[0002] With the development of power technology, parks can achieve more stable energy management by establishing independent microgrids, and can further ensure the balance of power supply and demand by exchanging electricity with the main grid and microgrids in other parks.

[0003] However, in the cooperative operation of multi-park microgrids, it is usually necessary to comprehensively consider multiple factors such as operating costs, supply and demand balance, and carbon emissions, which makes the decision of cooperative operation strategy complicated and difficult to obtain the optimal cooperative operation strategy efficiently and quickly. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-microgrid cooperative operation optimization method, device, computer equipment, computer-readable storage medium and computer program product to address the above technical problems.

[0005] In a first aspect, the present application provides a multi-microgrid cooperative operation optimization method, comprising:

[0006] Constructing microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0007] Constructing a cooperative operation cost function of each microgrid cooperative operation according to the main grid exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0008] Constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0009] Taking minimizing the cooperative operation cost function as the optimization goal, the multi-microgrid cooperative operation model is iteratively solved using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain the target power exchange parameters between the microgrids.

[0010] In one of the embodiments, the minimization of the cooperative operation cost function is taken as the optimization goal, and the distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism is used to iteratively solve the multi-microgrid cooperative operation model to obtain the target power exchange parameters between each of the microgrids, including: in each iteration, according to the difference between the current iteration parameter value and the previous iteration parameter value of the power exchange parameter between each of the microgrids, the current iteration parameter value is corrected; it is determined whether the original residual and the dual residual of the current iteration parameter value of each corrected power exchange parameter meet the preset convergence condition; if so, the iteration is stopped, and the target power exchange parameters between each of the microgrids are obtained according to the current iteration parameter value of each power exchange parameter; if not, the penalty factor of the distributed alternating direction multiplier method is adjusted according to the original residual and the dual residual, and the adjusted penalty factor is applied to the next iteration.

[0011] In one embodiment, the penalty factor of the distributed alternating direction multiplier method is adjusted according to the original residual and the dual residual, including: if the second norm of the original residual is greater than the product of the second norm of the dual residual and the first residual coefficient, the adjusted penalty factor is obtained according to the product of the second residual coefficient and the penalty factor; if the product of the second norm of the original residual and the first residual coefficient is greater than the second norm of the dual residual, the adjusted penalty factor is obtained according to the ratio of the penalty factor to the second residual coefficient.

[0012] In one of the embodiments, the microgrid operation constraints of each microgrid are constructed according to the predicted new energy power, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid, including: constructing new energy power generation constraints for new energy power generation equipment in the microgrid according to the predicted new energy power; constructing energy storage charging and discharging constraints for energy storage equipment in the microgrid according to energy storage capacity limitation parameters and charging and discharging efficiency parameters included in the energy storage constraint parameters; constructing a first transmission constraint for power exchange between the microgrid and the main grid according to the first power transmission upper limit between the microgrid and the main grid and the second power transmission upper limit between the microgrid and each cooperative microgrid included in the transmission constraint parameters, Construct a second transmission constraint for the microgrid to exchange electric energy with each cooperative microgrid; construct a load adjustment constraint for the adjustable load equipment in the microgrid according to the initial load parameters and the adjustable load parameters included in the demand response parameters; construct a balance constraint for the microgrid's new energy power generation, energy storage charging and discharging, user load, the first energy exchange amount with the main grid, and the second energy exchange amount with each cooperative microgrid according to the power balance requirement of the microgrid; obtain the microgrid operation constraint of the microgrid according to the new energy power generation constraint, the energy storage charging and discharging constraint, the first transmission constraint, the second transmission constraint, the load adjustment constraint and the balance constraint.

[0013] In one embodiment, the adjustable load device includes a curtailable load device; the load adjustment constraint of the adjustable load device in the microgrid is constructed based on the initial load parameters and the adjustable load parameters included in the demand response parameters, including: obtaining the curtailable load amount constraint of the curtailable load device in the microgrid based on the product of the initial load parameters and the curtailable coefficient included in the adjustable load parameters; and obtaining the load adjustment constraint based on the curtailable load amount constraint.

[0014] In one embodiment, the adjustable load device includes a transferable load device; the load adjustment constraint of the adjustable load device in the microgrid is constructed according to the adjustable load parameters included in the demand response parameters, including: obtaining the transferable load quantity constraint of the transferable load device in the microgrid according to the product of the initial load parameters and the transferable coefficient included in the adjustable load parameters; constructing a total load quantity unchanged constraint of the transferable load device; and obtaining the load adjustment constraint according to the transferable load quantity constraint and the total load quantity unchanged constraint.

[0015] In a second aspect, the present application also provides a multi-microgrid cooperative operation optimization device, comprising:

[0016] A constraint construction module, used to construct microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0017] A cost function construction module, used to construct a cooperative operation cost function of each microgrid cooperative operation according to the main network exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0018] A model building module, used for building a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0019] The model solving module is used to iteratively solve the multi-microgrid cooperative operation model by using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism, taking the minimization of the cooperative operation cost function as the optimization goal, and obtaining the target power exchange parameters between each of the microgrids.

[0020] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0021] Constructing microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0022] Constructing a cooperative operation cost function of each microgrid cooperative operation according to the main grid exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0023] Constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0024] Taking minimizing the cooperative operation cost function as the optimization goal, the multi-microgrid cooperative operation model is iteratively solved using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain the target power exchange parameters between the microgrids.

[0025] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0026] Constructing microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0027] Constructing a cooperative operation cost function of each microgrid cooperative operation according to the main grid exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0028] Constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0029] Taking minimizing the cooperative operation cost function as the optimization goal, the multi-microgrid cooperative operation model is iteratively solved using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain the target power exchange parameters between the microgrids.

[0030] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0031] Constructing microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0032] Constructing a cooperative operation cost function of each microgrid cooperative operation according to the main grid exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0033] Constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0034] Taking minimizing the cooperative operation cost function as the optimization goal, the multi-microgrid cooperative operation model is iteratively solved using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain the target power exchange parameters between the microgrids.

[0035] The above-mentioned multi-microgrid cooperative operation optimization method, device, computer equipment, computer-readable storage medium and computer program product construct the microgrid operation constraints of each microgrid according to the new energy predicted power, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid, and construct the cooperative operation cost function of each microgrid cooperative operation according to the main network exchange cost parameters, energy storage cost parameters, electric carbon factor parameters, and carbon emission cost parameters of each microgrid, and then construct a multi-microgrid cooperative operation model according to the cooperative operation cost function and the operation constraints of each microgrid, and then use the minimization of the cooperative operation cost function as the optimization goal, use the distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to iteratively solve the multi-microgrid cooperative operation model to obtain the target power exchange parameters between each microgrid.

[0036] This scheme, by constructing the microgrid operation constraints of the microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters and transmission constraint parameters, can comprehensively consider the impact of various factors on the participation of microgrids in cooperative operation, such as the volatility of new energy generation in the microgrid, the charging and discharging capacity limit of energy storage equipment, the load demand response adjustment capability within the park, and the line transmission capacity limit between the microgrid in the park and the main grid and other microgrids, so as to ensure the safe and stable operation of each microgrid in cooperative operation. By incorporating the electric carbon factor parameters and carbon emission costs into the calculation of the cooperative operation cost, the carbon emission cost of the microgrid can be taken into consideration in the subsequent optimization, which is conducive to the comprehensive optimization of the economic cost and carbon emission cost of the cooperative operation of multiple microgrids and the realization of low-carbon scheduling of microgrids. At the same time, by using the distributed alternating direction multiplier method to iteratively solve the multi-microgrid cooperative operation model, the microgrids in each park can only share the necessary power exchange data to complete the optimization solution, so that the privacy of the privacy information of each participant can be effectively guaranteed while achieving the optimization solution. Among them, the introduction of adaptive parameter update mechanism in iterative solution can dynamically adjust the penalty factor in the solution process to accelerate the convergence speed, and the introduction of error correction mechanism can correct the errors that may occur in the iteration process, improve the solution accuracy and stability of the algorithm, and reduce the number of iterations. Based on this, the use of this scheme can efficiently obtain the target power exchange parameters that enable the optimal cooperative operation between microgrids, effectively ensure the safe and stable operation of microgrids, and reduce the overall economic cost and carbon emission cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0038] FIG1 is a schematic diagram of a flow chart of a multi-microgrid cooperative operation optimization method according to an embodiment;

[0039] FIG2 is a schematic diagram of a flow chart of an iterative solution process in one embodiment;

[0040] FIG3 is a schematic diagram of a process for obtaining target power exchange parameters in one embodiment;

[0041] FIG4 is a schematic diagram of a process for constructing microgrid operation constraints in one embodiment;

[0042] FIG5 is a structural block diagram of a multi-microgrid cooperative operation optimization device in one embodiment;

[0043] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] In one embodiment, as shown in FIG1 , a multi-microgrid cooperative operation optimization method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0046] Step S101, constructing the microgrid operation constraints of each microgrid according to the predicted new energy power, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid.

[0047] Exemplarily, a microgrid may include new energy power generation equipment (such as wind power generation equipment, photovoltaic power generation equipment), energy storage equipment (such as batteries) and user load equipment. Multiple microgrids can be connected through interconnection lines to form a multi-microgrid system, and each microgrid can also be connected to the main grid to achieve power exchange with the main grid. Among them, when a microgrid encounters power imbalance such as power surplus or power shortage, it can be adjusted through the charging and discharging of energy storage equipment in the park, demand response adjustment of user load equipment, etc., or it can choose to exchange power with the main grid, or conduct point-to-point power exchange with other microgrids in the multi-microgrid system to ensure the balance of power supply and demand of the microgrid itself.

[0048] Among them, the power supply and demand of the microgrid can be affected by the power generation capacity of the new energy power generation equipment in the system, the charging and discharging capacity of the energy storage equipment, the demand response capacity of the user load equipment, etc., and the power exchange between it and the main grid and other microgrids can be affected by the line transmission capacity. Based on this, in this step, the new energy predicted power, energy storage constraint parameters, demand response parameters and transmission constraint parameters corresponding to each microgrid can be obtained, and the microgrid operation constraints of each microgrid can be constructed based on these parameters.

[0049] Optionally, the predicted power of new energy sources corresponding to the microgrid may include the predicted power generation value of each new energy power generation device in the microgrid at each moment in the target period, the energy storage constraint parameters may include the energy storage capacity limit and charging and discharging efficiency of each energy storage device in the microgrid, the demand response parameters may include the initial load parameters of the user load device in the microgrid and the adjustable load parameters of each adjustable load device therein, and the transmission constraint parameters may include the upper limit of the power transmission of the power transmission line between the microgrid and the main grid and the upper limit of the power transmission of the power transmission line between the microgrid and other microgrids in the multi-microgrid system. Based on these parameters, the operating conditions that need to be met for the safe and stable operation of the microgrid can be determined, that is, the microgrid operation constraints corresponding to the microgrid.

[0050] Step S102, constructing a cooperative operation cost function of each microgrid according to the main network exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid.

[0051] Among them, the main grid exchange cost parameter of the microgrid may include the unit cost of the microgrid exchanging electric energy with the main grid, the energy storage cost parameter may include the unit cost of charging and discharging the energy storage device in the microgrid, the electric carbon factor parameter may include the unit carbon emission corresponding to the electric energy exchange between the microgrid and the main grid or other microgrids in the multi-microgrid system, and the carbon emission cost parameter may include the unit carbon emission cost of the microgrid. Based on these parameters, a cooperative operation cost function corresponding to the overall cost of cooperative operation of each microgrid in the multi-microgrid system can be constructed.

[0052] Exemplarily, the cooperative operation cost function can be expressed as:

[0053]

[0054] In the formula, is the collection of microgrids in the multi-microgrid system, To control the cycle; is the energy exchange cost of the i-th microgrid at the t-th moment, is the working cost of the energy storage equipment of the i-th microgrid at the t-th time, is the carbon emission cost of the i-th microgrid at the t-th moment.

[0055] in, It can be expressed as: , is the power that the i-th microgrid obtains from the main grid in the t-th control time period, is the unit cost of obtaining electric energy from the main grid in the tth control time period, is the power that the i-th microgrid provides to the main grid in the t-th control time period, is the unit cost of providing power to the main grid during the tth control period. It can be understood that and This can be a parameter included in the mainnet exchange cost parameter.

[0056] in, It can be expressed as: , is the charging power of the energy storage device of the i-th microgrid in the t-th control time period, is the energy storage device discharge power of the i-th microgrid in the t-th control time period, The unit cost of charging and discharging the energy storage device. It is understandable that It can be obtained based on the energy storage cost parameters.

[0057] in, It can be expressed as: , is the carbon emission of the i-th microgrid in the t-th control time period, is the unit carbon emission cost in the tth control period. It can be understood that The carbon emissions of the microgrid can be obtained based on the carbon emission cost parameter. It can be obtained based on the electric carbon factor parameters of the microgrid and the amount of electric energy exchange between the microgrid and the main grid and other microgrids.

[0058] For example, carbon emissions It can be expressed as: ,in is the user load of the i-th microgrid in the t-th control time period, is the electric carbon factor of the i-th microgrid in the t-th control time period. Among them, the electric carbon factor of the microgrid can be calculated according to the carbon conservation law. First, consider the carbon flow input of the microgrid. The carbon flow input of each microgrid may include the carbon input of obtaining electricity from the main grid and the carbon input of obtaining electricity from other microgrids. In this embodiment, the carbon emissions of new energy equipment are considered to be zero. At the same time, the carbon flow output of each microgrid may include the carbon output of providing electricity to the main grid and the carbon output of providing electricity to other microgrids. According to the carbon conservation within the microgrid, it can be obtained:

[0059]

[0060]

[0061] In the formula, represents the electric carbon factor of the main grid in the tth control time period, and They represent the electric carbon factors of the j-th and i-th microgrids in the t-th control time period respectively. represents the electric energy transmitted from the jth microgrid to the ith microgrid in the tth control time period, and It represents the electric energy transmitted from the i-th microgrid to the j-th microgrid in the t-th control time period.

[0062] Based on this, the electric carbon factor of the microgrid can be transferred to the cooperative operation cost function as the basis for calculating carbon emissions, and then calculating the carbon cost.

[0063] Step S103: construct a multi-microgrid cooperative operation model according to the cooperative operation cost function and the operation constraints of each microgrid.

[0064] Among them, combined with the previously constructed cooperative operation cost function and the microgrid operation constraints of each microgrid, a multi-microgrid cooperative operation model for describing the overall cooperative operation of the multi-microgrid system can be constructed in this step.

[0065] In step S104, the cooperative operation model of multiple microgrids is iteratively solved by minimizing the cooperative operation cost function as the optimization goal using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain target power exchange parameters between the microgrids.

[0066] Among them, based on the multi-microgrid cooperative operation model, in this step, the distributed alternating direction method of multipliers (ADMM) that introduces an adaptive parameter update mechanism and an error correction mechanism can be used to iteratively solve the multi-microgrid cooperative operation model, and obtain the target power exchange parameters between the microgrids that can make the operating state of each microgrid meet the corresponding microgrid operation constraints and minimize the function value of the cooperative operation cost function.

[0067] Exemplarily, the objective function for iteratively solving the multi-microgrid cooperative operation model can be expressed as:

[0068]

[0069] in, It is the collection of microgrids in a multi-microgrid system. To control the cycle; is the energy exchange cost of the i-th microgrid in the t-th control time period, is the working cost of the energy storage equipment of the i-th microgrid in the t-th control time period, is the carbon emission cost of the i-th microgrid in the t-th control time period.

[0070] Since there are multiple couplings between microgrids in a multi-microgrid system, auxiliary variables can be introduced As a coupling variable, the coupling variable needs to satisfy the following constraints:

[0071]

[0072] In the formula, represents the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the kth iteration, It represents the amount of electricity that microgrid j expects to exchange with microgrid i in the tth control time period in the kth iteration.

[0073] Based on this, the augmented Lagrangian function can be constructed:

[0074]

[0075] In the formula, is the Lagrange multiplier, is the penalty factor, and its initial value can be 0.0001.

[0076] Optionally, in some embodiments, in each round of iterative solution, each microgrid may sequentially update the amount of electricity it wishes to exchange with other microgrids, wherein the microgrid updated first may provide the update result to the microgrid updated later.

[0077] For example, in the k+1th iteration, microgrid i can first update the amount of electricity it expects to exchange with each microgrid j in the multi-microgrid system according to the following formula: :

[0078]

[0079] In the formula, is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the k+1th iteration, is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the kth iteration, In the kth iteration, the amount of electricity that microgrid j expects to exchange with microgrid i in the tth control time period is: is the Lagrange multiplier of microgrid i in the kth iteration.

[0080] Then, microgrid j can receive the updated , update the expected amount of electricity exchanged with microgrid i according to the following formula :

[0081]

[0082] In the formula, is the amount of electricity that microgrid j expects to exchange with microgrid i in the tth control time period in the k+1th iteration, is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the k+1th iteration, In the kth iteration, the amount of electricity that microgrid j expects to exchange with microgrid i in the tth control time period is: is the Lagrange multiplier of microgrid j in the kth iteration.

[0083] Optionally, in other implementations, in each round of iterative solution, each microgrid may first update the amount of electricity it wishes to exchange with other microgrids based on the result of the previous iteration, and then exchange the updated results with each other to apply them to the next round of iteration.

[0084] For example, a multi-microgrid system including microgrid 1, microgrid 2, and microgrid 3 is used as an example for explanation. The iterative solution process of the multi-microgrid cooperative operation model of the system using the ADMM algorithm can be shown in Figure 2. Among them, in the multi-microgrid system including microgrid 1, microgrid 2, and microgrid 3, each microgrid can update the amount of electricity it expects to exchange with other microgrids and exchange information with other microgrids. As shown in Figure 2, when performing the kth round of iteration, microgrid 1 can first update the amount of electricity that microgrids 2 and 3 expect to exchange with it according to the amount of electricity obtained in the k-1th round of iteration. and , and update the amount of electricity that it expects to exchange with microgrids 2 and 3, respectively, to obtain and Then, microgrid 2 can calculate the amount of electricity that microgrids 1 and 3 expect to exchange with microgrid 2 during the k-1th iteration. , , and update the amount of electricity that it expects to exchange with microgrids 1 and 3 respectively, and get and Then, microgrid 3 can calculate the amount of electricity that microgrids 1 and 2 expect to exchange with it during the k-1th iteration. and , and update the amount of electricity that it expects to exchange with microgrids 1 and 2 respectively, and get and Then, microgrids 1, 2, and 3 can exchange their updated results obtained in the kth iteration.

[0085] Based on this, during the iterative solution process, each microgrid only needs to exchange power information without exchanging other information, thereby effectively ensuring the security of privacy information within each microgrid.

[0086] It can be understood that after all microgrids have completed the update of their corresponding decision quantities, the Lagrange multiplier can be updated:

[0087]

[0088] in, is the Lagrange multiplier updated in the k+1th iteration, is the Lagrange multiplier updated in the kth iteration.

[0089] Then, it can be determined whether the obtained result meets the preset convergence condition. If it does, the result obtained in this round of iteration can be used as the target power exchange parameter between each microgrid. Otherwise, the next round of iteration can be entered until a result that meets the convergence condition is obtained.

[0090] In order to improve the accuracy and efficiency of iterative solution, this embodiment introduces an adaptive parameter update mechanism and an error correction mechanism based on the ADMM algorithm.

[0091] Specifically, by adopting the error correction mechanism, after each microgrid in the multi-microgrid system calculates the expected exchange power between it and other microgrids in each round of iteration, the expected exchange power obtained in this round of iteration can be adjusted according to the deviation between the result obtained in this round of iteration and the result obtained in the previous round of iteration, and the adjusted expected exchange power is used as the result of this round of iteration. Based on this, the errors that may occur during the iteration process can be effectively corrected, the solution accuracy and stability of the algorithm can be improved, and the number of iterations can be reduced.

[0092] By introducing an adaptive parameter update mechanism, the value of the penalty factor in the ADMM algorithm can be dynamically adjusted when the iterative solution result does not meet the convergence condition, which is beneficial to improving the convergence speed of the algorithm.

[0093] In the above multi-microgrid cooperative operation optimization method, by constructing the microgrid operation constraints of the microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters and transmission constraint parameters, it is possible to comprehensively consider the volatility of new energy generation in the microgrid, the charging and discharging capacity limit of the energy storage equipment, the load demand response adjustment capability within the park, the line transmission capacity limit between the microgrid in the park and the main grid and other microgrids, and other factors that affect the microgrid's participation in cooperative operation, thereby helping to ensure the safe and stable operation of each microgrid in cooperative operation. By incorporating the electric carbon factor parameters and carbon emission costs into the calculation of the cooperative operation cost, the carbon emission cost of the microgrid can be taken into consideration in the subsequent optimization, which is conducive to the comprehensive optimization of the economic cost and carbon emission cost of the cooperative operation of multiple microgrids, and the realization of low-carbon scheduling of microgrids. At the same time, in this scheme, by using the distributed alternating direction multiplier method to iteratively solve the multi-microgrid cooperative operation model, the microgrids in each park can only share the necessary power exchange data to complete the optimization solution, so that the privacy information of each participant can be effectively protected while achieving the optimization solution. Among them, the introduction of adaptive parameter update mechanism in iterative solution can dynamically adjust the penalty factor in the solution process to accelerate the convergence speed, and the introduction of error correction mechanism can correct the errors that may occur in the iteration process, improve the solution accuracy and stability of the algorithm, and reduce the number of iterations. Based on this, the use of this scheme can efficiently obtain the target power exchange parameters that enable the optimal cooperative operation between microgrids, effectively ensure the safe and stable operation of microgrids, and reduce the overall economic cost and carbon emission cost.

[0094] In an exemplary embodiment, as shown in FIG3 , the cooperative operation cost function is minimized as the optimization goal, and the distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism is used to iteratively solve the multi-microgrid cooperative operation model to obtain the target power exchange parameters between the microgrids, which may include:

[0095] Step S301: In each iteration, according to the difference between the parameter value of the current iteration and the parameter value of the previous iteration of the power exchange parameter between the microgrids, the parameter value of the current iteration is corrected.

[0096] The power exchange parameters between the microgrids may include the amount of electricity exchanged between each microgrid and other microgrids in the multi-microgrid system. In this step, after obtaining the current iteration parameter value of the power exchange parameters between the microgrids in each iteration, the current iteration parameter value may be corrected according to the difference between the current iteration parameter value and the corresponding previous iteration parameter value to obtain the corrected current iteration parameter value. Exemplarily, the correction process may be shown as follows:

[0097]

[0098] Among them, the left side of the equation is the corrected parameter value of the iteration, and the right side of the equation is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the k+1th iteration (i.e., the parameter value of the current iteration), is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the kth iteration (i.e., the parameter value of the previous iteration), is a constant greater than 0 and less than 1.

[0099] Step S302, determining whether the original residual and the dual residual of the current iteration parameter value of each corrected electric energy exchange parameter meet a preset convergence condition.

[0100] Among them, according to the current iteration parameter value of each corrected electric energy exchange parameter, the corresponding original residual and dual residual can be calculated respectively, and it can be judged whether the two meet the preset convergence condition.

[0101] Exemplarily, the preset convergence condition may be that the original residual is not greater than the first convergence criterion parameter, and the dual residual is not greater than the second convergence criterion parameter. Optionally, the preset convergence condition may be expressed as:

[0102]

[0103]

[0104] In the formula, is the original residual of the k+1th iteration, is the dual residual of the k+1th iteration, is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the k+1th iteration, is the amount of electricity that microgrid j expects to exchange with microgrid i in the tth control time period in the k+1th iteration, is the amount of electricity that microgrid i expects to exchange with microgrid j in the tth control time period in the kth iteration, is the first convergence criterion parameter, is the second convergence criterion parameter.

[0105] Step S303: If satisfied, the iteration is stopped, and the target power exchange parameters between the microgrids are obtained according to the current iteration parameter values ​​of the power exchange parameters.

[0106] Among them, when the original residual and the dual residual meet the preset convergence conditions, the algorithm can be considered to have converged, so the iteration can be stopped, and the current iteration parameter value of each power exchange parameter obtained in the current iteration is used as the target power exchange parameter between each microgrid.

[0107] Step S304: if not satisfied, the penalty factor of the distributed alternating direction multiplier method is adjusted according to the original residual and the dual residual, and the adjusted penalty factor is applied to the next iteration.

[0108] When the primal residual and the dual residual do not meet the preset convergence conditions, it can be considered that the algorithm has not converged and needs to continue to the next round of iteration.

[0109] Among them, due to the penalty factor in the ADMM algorithm The choice of will affect the convergence speed. If the value is too large or too small, it may cause more iterations. Based on the analysis of the algorithm, it can be seen that when the penalty factor is increased When , the original residual will be reduced and its dual residual will be increased. When , the original residual will be increased and its dual residual will be reduced. Based on this, in this step, the penalty factor can be adjusted according to the size relationship between the original residual and the dual residual. The value of is adjusted and applied to the next iteration to speed up the convergence.

[0110] Among them, in an exemplary embodiment, adjusting the penalty factor of the distributed alternating direction multiplier method according to the original residual and the dual residual can include: if the second norm of the original residual is greater than the product of the second norm of the dual residual and the first residual coefficient, then obtaining the adjusted penalty factor according to the product of the second residual coefficient and the penalty factor; if the product of the second norm of the original residual and the first residual coefficient is greater than the second norm of the dual residual, then obtaining the adjusted penalty factor according to the ratio of the penalty factor to the second residual coefficient.

[0111] Specifically, according to the aforementioned preset convergence conditions, when both the original residual and the dual residual are very small, the algorithm can reach a convergence state. Based on this, the penalty factor can be adjusted by calculating the second norm of the original residual and the dual residual respectively, and according to the relationship between the second norms. Exemplarily, the adjustment can be shown as follows:

[0112]

[0113] In the formula, is the adjusted penalty factor, is the penalty factor applied in the kth iteration, is the second norm of the original residual of the kth iteration, is the binary norm of the dual residual of the k-th iteration, is the first residual coefficient, is the second residual coefficient, , are all constants greater than 0.

[0114] In this embodiment, by introducing an error correction mechanism in the iterative process of the distributed ADMM algorithm, the result of the current iteration is dynamically adjusted according to the deviation between the result of the current iteration and the result of the previous iteration, which can effectively correct the errors that may occur in the iterative process, significantly improve the solution accuracy and stability of the algorithm, reduce the number of iterations, and make the final result more accurate and optimized. By introducing an adaptive parameter update mechanism in the iterative process, when the algorithm has not converged, the penalty factor of the ADMM algorithm is dynamically adjusted according to the size relationship between the dual residual and the original residual, combined with the first residual coefficient and the second residual coefficient, which can effectively speed up the convergence speed and further improve the overall efficiency of the solution process.

[0115] In an exemplary embodiment, as shown in FIG4 , constructing the microgrid operation constraints of each microgrid according to the predicted new energy power, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid may include:

[0116] Step S401, constructing new energy power generation constraints for new energy power generation equipment in a microgrid according to the new energy predicted power.

[0117] Among them, the microgrid may include new energy power generation equipment such as wind power generation equipment and photovoltaic power generation equipment. Since the power generation capacity of new energy power generation equipment is uncertain, the power generation power of new energy power generation equipment in the target period can be predicted, and the power generation power of new energy power generation equipment in the period can be set according to the predicted power of new energy. Exemplarily, the new energy power generation constraint can be shown as follows:

[0118]

[0119] In the formula, The new energy generation equipment in microgrid i in the target period The actual power generation (i.e., the power generation of new energy) in the tth control period, Forecast the power generation of renewable energy for microgrid i in the tth control period, For microgrid collection, Indicates the control period.

[0120] Step S402: constructing energy storage charging and discharging constraints for energy storage devices in the microgrid according to energy storage capacity limitation parameters and charging and discharging efficiency parameters included in energy storage constraint parameters.

[0121] Among them, energy storage equipment can be set up in the microgrid. When the new energy equipment in the microgrid generates surplus electricity, energy storage can be used to increase energy consumption and improve the new energy consumption rate; when the load demand is large, the demand can be met by discharging the energy storage equipment. For energy storage equipment, the corresponding energy storage charge and discharge constraints can be constructed according to the energy storage capacity limit parameters and charge and discharge efficiency parameters contained in the corresponding energy storage constraint parameters. Among them, the energy storage capacity limit parameters may include the upper limit and lower limit of the energy storage capacity of the energy storage equipment, and the charge and discharge efficiency parameters may include the charging efficiency parameters and discharge efficiency parameters of the energy storage equipment.

[0122] Exemplarily, the energy storage charging and discharging constraints of the energy storage device may be as follows:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, is the energy storage capacity of the energy storage device in microgrid i in the tth control period, , They represent the energy storage charging power and energy storage discharging power of the energy storage device in microgrid i in the tth control period, and They represent the charging efficiency parameter and discharging efficiency parameter of the energy storage device respectively, and They are respectively the minimum state of charge (i.e. the lower limit of energy storage capacity) and the maximum state of charge (i.e. the upper limit of energy storage capacity) of the energy storage device. and It is a state variable used to restrict the charging and discharging of energy storage devices from being performed simultaneously, and its value is 0 or 1.

[0130] Step S403, based on the first power transmission upper limit between the microgrid and the main grid and the second power transmission upper limit between the microgrid and each cooperative microgrid included in the transmission constraint parameters, a first transmission constraint for the microgrid to exchange power with the main grid is constructed, and a second transmission constraint for the microgrid to exchange power with each cooperative microgrid is constructed.

[0131] Among them, the electric energy that can be transmitted between the microgrid and the main grid is usually limited by the transmission line between the two. Therefore, in this step, the first transmission constraint for the microgrid and the main grid to exchange electric energy can be constructed according to the first electric energy transmission upper limit between the microgrid and the main grid contained in the transmission constraint parameter. Exemplarily, the first transmission constraint can be shown as follows:

[0132]

[0133]

[0134] In the formula, is the power that microgrid i obtains from the main grid in the tth control time period, is the maximum power of electric energy transmitted from the main grid to microgrid i, is the power that microgrid i provides to the main grid in the tth control time period, is the maximum power of electric energy transmitted from microgrid i to the main grid, For microgrid collection, Indicates the control period.

[0135] Among them, when performing point-to-point power exchange between microgrids in a multi-microgrid system, it is also necessary to consider the limitations of the transmission lines between microgrids. Therefore, in this step, the second transmission constraint for power exchange between the microgrid and each cooperative microgrid can be constructed according to the second power transmission upper limit between the microgrid and each cooperative microgrid contained in the transmission constraint parameter. Exemplarily, the second transmission constraint can be shown as follows:

[0136]

[0137] In the formula, is the power of energy exchanged between microgrid i and microgrid j in the tth control time period, It is the maximum power of point-to-point transmission of electric energy between microgrids in a multi-microgrid system.

[0138] Step S404: constructing load adjustment constraints for adjustable load devices in the microgrid according to the initial load parameters and adjustable load parameters included in the demand response parameters.

[0139] Among them, the user load equipment in the microgrid may include adjustable load equipment. By adjusting the operation plan of the adjustable load equipment, the overall power consumption plan on the user side can be adjusted, thereby achieving the carbon reduction needs on the user side and assisting in achieving the supply and demand balance of the microgrid.

[0140] The load adjustment range of the adjustable load device is usually limited by the initial load before adjustment and the adjustment parameters of the device itself. Therefore, in this step, the load adjustment constraints of the adjustable load device in the microgrid can be constructed according to the initial load parameters and adjustable load parameters included in the demand response parameters.

[0141] Exemplarily, the adjustable load equipment may include multiple load types such as transferable loads, curtailable loads, etc. In this step, each load type may be modeled according to its actual operating rules, and then the load adjustment constraints of the adjustable load equipment may be obtained.

[0142] In an exemplary embodiment, the adjustable load device may include a curtailable load device. According to the initial load parameters and the adjustable load parameters included in the demand response parameters, constructing the load adjustment constraint of the adjustable load device in the microgrid may include: obtaining the curtailable load constraint of the curtailable load device in the microgrid according to the product of the initial load parameters and the curtailable coefficient included in the adjustable load parameters; and obtaining the load adjustment constraint according to the curtailable load constraint.

[0143] Specifically, the curtailable load device can achieve flexible load adjustment by reducing non-critical power demand. When the microgrid includes curtailable load devices, the adjustable load parameters may include curtailable coefficients corresponding to the curtailable load devices. Exemplarily, based on the initial load parameters and the adjustable load parameters, the curtailable load amount constraint of the curtailable load device can be constructed as shown in the following formula:

[0144]

[0145] In the formula, is the initial load parameter of microgrid i in the tth control time period, is the curtailable coefficient of the curtailable load equipment in microgrid i, is the amount of load that can be reduced obtained by adjusting the load-reducible equipment in the microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0146] Then, according to the curtailable load constraint, the load adjustment constraint corresponding to the adjustable load device in the microgrid can be obtained. For example, when the load type of the adjustable load device in the microgrid is only the curtailable load device, the load adjustment constraint of the microgrid can be expressed as:

[0147]

[0148] In the formula, is the user load of microgrid i in the tth control time period, is the initial load parameter of microgrid i in the tth control time period, is the load that can be reduced in microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0149] In an exemplary embodiment, the adjustable load device includes a transferable load device. According to the adjustable load parameters included in the demand response parameters, constructing the load adjustment constraint of the adjustable load device in the microgrid may include: obtaining the transferable load constraint of the transferable load device in the microgrid according to the product of the initial load parameter and the transfer coefficient included in the adjustable load parameter; constructing the total load constant constraint of the transferable load device; and obtaining the load adjustment constraint according to the transferable load constraint and the total load constant constraint.

[0150] Specifically, the transferable load device can achieve demand response by changing the electricity consumption time. When the microgrid includes a transferable load device, the adjustable load parameter may include a transfer coefficient corresponding to the transferable load device. Exemplarily, based on the initial load parameter and the transfer coefficient, the transferable load quantity constraint of the transferable load device can be constructed as shown in the following formula:

[0151]

[0152] In the formula, is the initial load parameter of microgrid i in the tth control time period, is the transferability coefficient of the transferable load equipment in microgrid i, is the transferable load amount obtained by adjusting the power consumption time of the transferable load equipment in the microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0153] At the same time, the transferable load of the transferable load device also needs to satisfy the requirement that the total amount remains unchanged at all response times. Therefore, the total load amount constant constraint of the transferable load device can be constructed as shown in the following formula:

[0154]

[0155] In the formula, is the transferable load of microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0156] Then, according to the transferable load constraint and the total load constant constraint, the load adjustment constraint corresponding to the adjustable load device in the microgrid can be obtained. For example, when the load type of the adjustable load device in the microgrid is only the transferable load device, the load adjustment constraint of the microgrid can be expressed as:

[0157]

[0158] In the formula, is the user load of microgrid i in the tth control time period, is the initial load parameter of microgrid i in the tth control time period, is the transferable load of microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0159] It can be understood that when the adjustable load devices in the microgrid include multiple load types, the load adjustment constraints of the microgrid can be obtained by combining the constraints corresponding to the various load types. For example, when the microgrid includes both curtailable load devices and adjustable load devices, the load adjustment constraints of the microgrid can be expressed as:

[0160]

[0161] In the formula, is the user load of microgrid i in the tth control time period, is the initial load parameter of microgrid i in the tth control time period, is the load that can be reduced in microgrid i in the tth control time period, is the transferable load of microgrid i in the tth control time period, For microgrid collection, Indicates the control period.

[0162] Step S405, according to the power balance requirement of the microgrid, construct the balance constraints of the microgrid's new energy power generation, energy storage charging and discharging, user load, the first power exchange amount between the microgrid and the main grid, and the second power exchange amount between the microgrid and each cooperative microgrid.

[0163] Among them, since the safe and stable operation of the microgrid needs to maintain power balance, in this step, a balance constraint including the microgrid's new energy power generation, energy storage charging and discharging, user load, the first energy exchange amount between the microgrid and the main grid, and the second energy exchange amount between each cooperative microgrid can be constructed according to the power balance requirement of the microgrid. Among them, the energy storage charging and discharging amount of the microgrid can include the energy storage charging power and energy storage discharging power of the energy storage equipment in the microgrid, the first energy exchange amount can include the power of the microgrid to obtain electricity from the main grid and the power to provide electricity to the main grid, and the second energy exchange amount can include the power of the microgrid to exchange electricity with each cooperative microgrid in the multi-microgrid system.

[0164] Exemplarily, the balance constraint of the microgrid can be expressed as follows:

[0165]

[0166] In the formula, is the renewable energy generation of microgrid i in the tth control time period, is the power that microgrid i obtains from the main grid in the tth control time period, is the energy storage discharge power of the energy storage device in microgrid i in the tth control period, is the user load of microgrid i in the tth control time period, The power that microgrid i provides to the main grid in the tth control time period, is the energy storage charging power of the energy storage device in microgrid i in the tth control period, The power of electric energy exchanged between microgrid i and each cooperative microgrid j in the tth control time period, For microgrid collection, Indicates the control period.

[0167] Step S406, obtaining the microgrid operation constraints of the microgrid according to the renewable energy generation constraints, the energy storage charging and discharging constraints, the first transmission constraints, the second transmission constraints, the load adjustment constraints and the balance constraints.

[0168] Among them, according to the microgrid's renewable energy power generation constraints, energy storage charging and discharging constraints, first transmission constraints, second transmission constraints, load adjustment constraints and balance constraints, the microgrid operation constraints that can enable the microgrid to achieve safe and stable operation and power exchange can be obtained.

[0169] In this embodiment, for various influencing factors in the operation of the microgrid, the new energy generation constraints, energy storage charging and discharging constraints, first transmission constraints, second transmission constraints, load adjustment constraints and balance constraints of the microgrid are respectively constructed, and then the microgrid operation constraints of the microgrid as a whole are obtained by combining these constraints, so as to optimize and solve the multi-microgrid cooperative operation model containing the microgrid operation constraints, so that the target power exchange parameters obtained by the solution can meet the microgrid operation constraints of each microgrid in the multi-microgrid system, and effectively ensure the safe and stable operation of each microgrid. Among them, by constructing the new energy generation constraints corresponding to the new energy generation equipment, the impact of the volatility of new energy generation in the microgrid on the power supply and demand of the microgrid can be taken into consideration; by constructing the energy storage charging and discharging constraints corresponding to the energy storage equipment, the charging and discharging performance limitations of the energy storage equipment can be considered to avoid overloading or waste of the energy storage equipment; by constructing the first transmission constraint for the exchange of power between the microgrid and the main grid and the second transmission constraint between the microgrid and the cooperative microgrid, the impact of transmission over-limit on the line can be considered, effectively ensuring the safety and stability of power transmission and optimizing the rationality of power trading, and by considering the physical constraints and network of power transmission By taking advantage of the network characteristics, it can improve the system's operating efficiency and reduce transmission bottlenecks in energy transactions between multiple microgrids, thereby improving the reliability of distributed power transactions and promoting regional energy interconnection and optimal configuration; by constructing load adjustment constraints corresponding to adjustable load devices and performing classified modeling and differentiated control according to different load types, it can fully consider the characteristics of various loads in user-side demand response, effectively improve the refinement of demand response and scheduling efficiency, and enhance the flexibility and energy utilization of the system; and by constructing microgrid balance constraints, it can ensure the power balance of microgrids during cooperative operation, effectively ensuring the safe and stable operation of microgrids.

[0170] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0171] Based on the same inventive concept, the embodiment of the present application also provides a multi-microgrid cooperative operation optimization device for implementing the multi-microgrid cooperative operation optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more multi-microgrid cooperative operation optimization device embodiments provided below can refer to the limitations of the multi-microgrid cooperative operation optimization method above, and will not be repeated here.

[0172] In an exemplary embodiment, as shown in FIG5 , a multi-microgrid cooperative operation optimization device 500 is provided, comprising:

[0173] The constraint construction module 501 is used to construct the microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid;

[0174] The cost function construction module 502 is used to construct a cooperative operation cost function of each microgrid cooperative operation according to the main network exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid;

[0175] A model building module 503 is used to build a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints;

[0176] The model solving module 504 is used to iteratively solve the multi-microgrid cooperative operation model by using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism, taking the minimization of the cooperative operation cost function as the optimization goal, and obtaining the target power exchange parameters between each of the microgrids.

[0177] In an exemplary embodiment, the model solving module 504 is also used to: in each iteration, correct the parameter value of the current iteration according to the difference between the parameter value of the current iteration and the parameter value of the previous iteration of the power exchange parameter between the microgrids; determine whether the original residual and the dual residual of the corrected parameter value of the current iteration of each power exchange parameter meet the preset convergence condition; if satisfied, stop the iteration, and obtain the target power exchange parameter between the microgrids according to the parameter value of the current iteration of each power exchange parameter; if not satisfied, adjust the penalty factor of the distributed alternating direction multiplier method according to the original residual and the dual residual, and apply the adjusted penalty factor to the next iteration.

[0178] In an exemplary embodiment, the model solving module 504 is also used to: if the second norm of the original residual is greater than the product of the second norm of the dual residual and the first residual coefficient, then the adjusted penalty factor is obtained according to the product of the second residual coefficient and the penalty factor; if the product of the second norm of the original residual and the first residual coefficient is greater than the second norm of the dual residual, then the adjusted penalty factor is obtained according to the ratio of the penalty factor to the second residual coefficient.

[0179] In an exemplary embodiment, the constraint construction module 501 is also used to: construct a new energy power generation constraint for the new energy power generation equipment in the microgrid according to the predicted power of the new energy; construct an energy storage charging and discharging constraint for the energy storage equipment in the microgrid according to the energy storage capacity limitation parameter and the charging and discharging efficiency parameter included in the energy storage constraint parameter; construct a first transmission constraint for the microgrid to exchange energy with the main grid and a second transmission constraint for the microgrid to exchange energy with each cooperative microgrid according to the first upper limit of energy transmission between the microgrid and the main grid and the second upper limit of energy transmission between the microgrid and each cooperative microgrid included in the transmission constraint parameter. The second transmission constraint; constructing the load adjustment constraint of the adjustable load equipment in the microgrid according to the initial load parameters and the adjustable load parameters included in the demand response parameters; constructing the balance constraints of the microgrid's new energy power generation, energy storage charging and discharging, user load, the first energy exchange amount between the microgrid and the main grid, and the second energy exchange amount between the microgrid and each of the cooperative microgrids according to the power balance requirement of the microgrid; obtaining the microgrid operation constraint of the microgrid according to the new energy power generation constraint, the energy storage charging and discharging constraint, the first transmission constraint, the second transmission constraint, the load adjustment constraint and the balance constraint.

[0180] In an exemplary embodiment, the adjustable load device includes a curtailable load device; the constraint construction module 501 is also used to: obtain the curtailable load quantity constraint of the curtailable load device in the microgrid according to the product of the initial load parameter and the curtailable coefficient included in the adjustable load parameter; and obtain the load adjustment constraint according to the curtailable load quantity constraint.

[0181] In an exemplary embodiment, the adjustable load device includes a transferable load device; the constraint construction module 501 is also used to: obtain the transferable load quantity constraint of the transferable load device in the microgrid according to the product of the initial load parameter and the transferable coefficient included in the adjustable load parameter; construct a total load quantity unchanged constraint of the transferable load device; and obtain the load adjustment constraint according to the transferable load quantity constraint and the total load quantity unchanged constraint.

[0182] Each module in the above-mentioned multi-microgrid cooperative operation optimization device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0183] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as new energy predicted power, energy storage constraint parameters, demand response parameters, transmission constraint parameters, main network exchange cost parameters, energy storage cost parameters, electric carbon factor parameters, and carbon emission cost parameters. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-microgrid cooperative operation optimization method is implemented.

[0184] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0185] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0186] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0187] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0188] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0189] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0190] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0191] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-microgrid cooperative operation optimization method, characterized in that: The method comprises: Constructing microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid; Constructing a cooperative operation cost function of each microgrid cooperative operation according to the main grid exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid; Constructing a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints; Taking minimizing the cooperative operation cost function as the optimization goal, the multi-microgrid cooperative operation model is iteratively solved using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism to obtain the target power exchange parameters between the microgrids.

2. The method according to claim 1, characterized in that: The minimization of the cooperative operation cost function is taken as the optimization goal, and the distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism is used to iteratively solve the multi-microgrid cooperative operation model to obtain the target power exchange parameters between the microgrids, including: In each iteration, according to the difference between the parameter value of the current iteration and the parameter value of the previous iteration of the electric energy exchange parameter between the microgrids, the parameter value of the current iteration is corrected; Determine whether the original residual and the dual residual of the current iteration parameter value of each of the corrected electric energy exchange parameters meet a preset convergence condition; If satisfied, the iteration is stopped, and the target power exchange parameter between the microgrids is obtained according to the current iteration parameter value of each power exchange parameter; If not, the penalty factor of the distributed alternating direction multiplier method is adjusted according to the original residual and the dual residual, and the adjusted penalty factor is applied to the next iteration.

3. The method according to claim 2, characterized in that The step of adjusting the penalty factor of the distributed alternating direction multiplier method according to the original residual and the dual residual includes: If the second norm of the original residual is greater than the product of the second norm of the dual residual and the first residual coefficient, the adjusted penalty factor is obtained according to the product of the second residual coefficient and the penalty factor; If the product of the second norm of the original residual and the first residual coefficient is greater than the second norm of the dual residual, the adjusted penalty factor is obtained according to the ratio of the penalty factor to the second residual coefficient.

4. The method according to claim 1, characterized in that The microgrid operation constraints of each microgrid are constructed according to the predicted new energy power, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid, including: According to the predicted power of the new energy, constructing a new energy power generation constraint for the new energy power generation equipment in the microgrid; According to the energy storage capacity limitation parameter and the charge and discharge efficiency parameter included in the energy storage constraint parameter, construct the energy storage charge and discharge constraint of the energy storage device in the microgrid; According to the first upper limit of electric energy transmission between the microgrid and the main grid and the second upper limit of electric energy transmission between the microgrid and each cooperative microgrid included in the transmission constraint parameter, a first transmission constraint for electric energy exchange between the microgrid and the main grid is constructed, and a second transmission constraint for electric energy exchange between the microgrid and each cooperative microgrid is constructed; Constructing load adjustment constraints for adjustable load devices in the microgrid according to the initial load parameters and adjustable load parameters included in the demand response parameters; According to the power balance requirements of the microgrid, balance constraints are established for the microgrid's renewable energy power generation, energy storage charging and discharging, user load, the first power exchange amount between the microgrid and the main grid, and the second power exchange amount between the microgrid and each of the cooperative microgrids; The microgrid operation constraint of the microgrid is obtained according to the new energy power generation constraint, the energy storage charging and discharging constraint, the first transmission constraint, the second transmission constraint, the load adjustment constraint and the balance constraint.

5. The method according to claim 4, characterized in that The adjustable load equipment includes load-reducible equipment; The step of constructing load adjustment constraints for adjustable load devices in the microgrid according to the initial load parameters and the adjustable load parameters included in the demand response parameters includes: Obtaining a curtailable load constraint of curtailable load equipment in the microgrid according to the product of the initial load parameter and the curtailable coefficient included in the adjustable load parameter; The load adjustment constraint is obtained according to the reducible load constraint.

6. The method according to claim 4, characterized in that The adjustable load device includes a transferable load device; The step of constructing load adjustment constraints for adjustable load devices in the microgrid according to the adjustable load parameters included in the demand response parameters includes: Obtaining a transferable load quantity constraint of a transferable load device in the microgrid according to the product of the initial load parameter and the transferable coefficient included in the adjustable load parameter; Constructing a total load invariance constraint for the transferable load device; The load adjustment constraint is obtained according to the transferable load constraint and the total load unchanged constraint.

7. A multi-microgrid cooperative operation optimization device, characterized in that: The device comprises: A constraint construction module, used to construct microgrid operation constraints of each microgrid according to the predicted power of new energy, energy storage constraint parameters, demand response parameters, and transmission constraint parameters of each microgrid; A cost function construction module, used to construct a cooperative operation cost function of each microgrid cooperative operation according to the main network exchange cost parameter, energy storage cost parameter, electric carbon factor parameter, and carbon emission cost parameter of each microgrid; A model building module, used for building a multi-microgrid cooperative operation model according to the cooperative operation cost function and the microgrid operation constraints; The model solving module is used to iteratively solve the multi-microgrid cooperative operation model by using a distributed alternating direction multiplier method that introduces an adaptive parameter update mechanism and an error correction mechanism, taking the minimization of the cooperative operation cost function as the optimization goal, and obtaining the target power exchange parameters between each of the microgrids.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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