Multi-energy microgrid-power distribution network collaborative optimization method considering photovoltaic correlation

Abstract

The invention discloses a multi-energy micro-grid-power distribution network collaborative optimization method considering photovoltaic correlation, and the method comprises the steps: constructing a multi-energy micro-grid-power distribution network collaborative optimization model based on an improved distributed optimization theory, and considering the uncertainty and correlation influence of distributed photovoltaic output. In order to accurately characterize distributed photovoltaic correlation characteristics, a correlation polyhedron set is introduced, and parameters and boundaries of the correlation polyhedron set are remodeled. In combination with a micro-grid robust optimization theory, an improved alternating direction multiplier algorithm based on a Bregman distance is designed and is used for solving the problem of cooperative operation between a multi-energy micro-grid and a power distribution network. According to the method, an efficient solving method can be provided for the collaborative optimization problem of the multi-energy microgrid and the power distribution network based on the distributed photovoltaic correlation characteristics, scheduling personnel are effectively supported to formulate a scientific and reasonable scheduling strategy, and the method has important engineering practical value.

Description

Technical Field

[0001] The present invention relates to a multi - energy micro - grid - distribution network collaborative optimization method considering the correlation of photovoltaic power generation, and belongs to the technical field of power system dispatching. Background Art

[0002] With the rapid growth of the global economy and the continuous improvement of social demands, problems such as energy consumption, air pollution, and climate change have become increasingly prominent, and have become major challenges that need to be urgently solved globally. In this context, seeking more efficient and reliable energy utilization methods and achieving the sustainable development of the energy system have become important consensuses in the international community and academia.

[0003] Deeply integrating distributed energy into a multi - energy micro - grid and realizing collaborative and optimized operation with a traditional distribution network can significantly improve the overall energy utilization efficiency and operation reliability of the system. This mode not only realizes the in - situ production and consumption of energy, reduces the losses and environmental pollution caused by long - distance power transmission, but also effectively reduces the dependence on fossil energy and promotes the wide application of renewable energy.

[0004] At present, the penetration rate of renewable energy has increased significantly, but its power generation has high randomness and intermittency, which significantly increases the uncertainty of power supply on the source side and further enhances the complexity of the collaborative optimization problem between the multi - energy micro - grid and the distribution network. Among them, the correlation characteristics of photovoltaic power generation are particularly worthy of attention. This characteristic not only directly affects the prediction accuracy of photovoltaic power generation, but also poses higher requirements for the optimal utilization of energy scheduling within the multi - energy micro - grid.

[0005] Existing collaborative optimization methods for distribution networks and micro - grids mainly include centralized coordination control methods and distributed optimization algorithms. Although the centralized coordination control method can achieve global optimization of the system, it has significant deficiencies such as heavy communication burden, high computational complexity, and single - point failure risk. Although the distributed optimization algorithm can effectively reduce the communication burden and single - point failure risk of the system, it also has problems such as slow algorithm convergence speed, insufficient real - time performance, and poor stability in an environment of incomplete or delayed information. In addition, existing distributed optimization algorithms have limited ability to handle high - dimensional and non - convex problems and are difficult to ensure the efficient acquisition of the global optimal solution in a complex environment.

[0006] Driven by the "dual - carbon" goal and the power system reform, studying a multi - energy micro - grid - distribution network collaborative optimization method considering the correlation of photovoltaic power generation has important theoretical value and practical significance for improving the consumption ratio of renewable energy and achieving the efficient collaborative operation of multiple entities. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a collaborative optimization method for a multi - energy micro - grid and a distribution network considering the correlation of photovoltaic power generation. Based on the correlation characteristics of distributed photovoltaic power generation, an efficient solution is provided for the collaborative optimization problem of the multi - energy micro - grid and the distribution network, which effectively improves the stability and operation efficiency of the collaborative operation of the distribution network and the micro - grid system.

[0008] The present invention adopts the following technical solutions to solve the above - mentioned technical problems:

[0009] A collaborative optimization method for a multi - energy micro - grid and a distribution network considering the correlation of photovoltaic power generation includes the following steps:

[0010] Step 1: Considering the uncertainty and correlation of distributed photovoltaic power output in the multi - energy micro - grid, a multi - energy micro - grid model that meets various load demands of electricity, heat, and cold is constructed.

[0011] Step 2: A distribution network model considering second - order cone relaxation constraints is constructed.

[0012] Step 3: Based on the models constructed in Step 1 and Step 2, combined with an improved alternating direction multiplier algorithm based on the Bregman distance, a collaborative scheduling strategy for the multi - energy micro - grid and the distribution network is designed.

[0013] Compared with the prior art, the present invention adopting the above - mentioned technical solutions has the following technical effects:

[0014] 1. In order to characterize the correlation characteristics of distributed photovoltaic power output, the present invention proposes an improved correlation polyhedron set for photovoltaic power generation. While covering the main range of photovoltaic power output fluctuations, this set effectively reduces the redundancy in the low - probability region of distributed photovoltaic power output fluctuations.

[0015] 2. In order to address the coordination and information sharing problems between the micro - grid and the distribution network, the present invention proposes an improved alternating direction multiplier method using the inertial Bregman distance. Compared with the traditional alternating direction multiplier method, it significantly improves the convergence speed while protecting data privacy and reducing communication pressure, provides better solution performance, and realizes the distributed coordination of the multi - energy micro - grid and the distribution network.

[0016] 3. The present invention can provide an efficient solution method for the collaborative optimization problem of the multi - energy micro - grid and the distribution network based on the correlation characteristics of distributed photovoltaic power generation, effectively supporting dispatchers to formulate scientific and reasonable scheduling strategies, and has important engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the topological diagram of the multi - energy micro - grid and the distribution network;

[0018] Figure 2 is the schematic diagram of the improved correlation polyhedron set;

[0019] Figure 3 is the distributed PV output under different uncertainties;

[0020] Figure 4 is the result diagram of the electric power equipment for the coordinated operation of the microgrid and the distribution network;

[0021] Figure 5 is the result diagram of the thermal power equipment for the coordinated operation of the microgrid and the distribution network. Detailed implementation manners

[0022] The following details the implementation manners of the present invention. Examples of the implementation manners are shown in the accompanying drawings. The implementation manners described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0023] The present invention proposes a coordinated optimization method for a multi - energy microgrid and distribution network considering PV correlation, aiming to design a coordinated dispatching scheme for the distribution network and microgrid under the condition of considering the uncertainty of renewable energy. The specific steps are as follows:

[0024] Step 1: Construct a multi - energy microgrid model considering various load demands of electricity, heat, and cooling.

[0025] The multi - energy microgrid system has the ability to purchase electricity from the superior distribution network. At the same time, the microgrid also consumes natural gas and uses equipment such as gas boilers, gas turbines, and heat pumps for efficient heat energy conversion to meet the heat demand of the microgrid. In addition, part of the natural gas can also be used to drive refrigerators to cope with the internal cooling load demand.

[0026] In the multi - energy microgrid, natural gas is an important form of energy and is often used in the operation of gas turbines, boilers, and heat pump equipment. To reasonably allocate the use of natural gas and ensure efficiency, the following constraints define its specific uses:

[0027] Gas t = P t gt / η gt + P t gb / η gb + P t hp / η hp

[0028] In the formula, Gas t represents the amount of natural gas purchased by the microgrid at time t; P t gt , P t gb and P t hpare the powers generated by the gas turbine, boiler, and heat pump at time t; η gt , η gb and η hp are the efficiency coefficients of the gas turbine, boiler, and heat pump, respectively.

[0029] P t gt represents the total output power of the gas turbine, including the power for power supply and the power for driving the refrigeration equipment:

[0030] P t gt =P t GT,p +P t ec

[0031] In the formula, P t GT,P represents the electric power generated by the gas turbine at time t; P t ec represents the power generated by the electric refrigeration equipment at time t;

[0032] To ensure that the microgrid can meet the demands of electricity, heat, and cooling simultaneously, its overall energy system must achieve a balance between supply and demand. The specific constraint conditions are as follows:

[0033] D t power +P t sell =P t GT,P +P t buy +P t wt +P t pv

[0034] D t heat =P t hp +P t gb

[0035] D t cool =P t ec η ec +P t hr

[0036] In the formula, D t power 、D t heat and D tcool respectively represent the demands for electricity, heat energy, and cooling capacity; P t wt represents the power generation power of distributed photovoltaics at time t; P t pv represents the power generation power of wind power generation at time t; P t buy represents the electricity quantity purchased by the microgrid from the superior power grid at time t; P t sell represents the electricity quantity sold by the microgrid to the superior power grid at time t; P t hr represents the recovered heat power of the heat recovery device at time t.

[0037] The traditional polyhedron set is used to represent the uncertainty of the distributed photovoltaic output of the multi - energy microgrid:

[0038]

[0039] In the above uncertainty set, i represents different regions of the distributed photovoltaic power generation of the multi - energy microgrid, and t represents different moments of a day. P i,t pv , respectively represent the active power, expected power, and output power fluctuation value of photovoltaic power generation, δ i,t is the uncertainty coefficient of the active power of photovoltaic power generation, and Γ is the uncertainty.

[0040] In the multi - energy microgrid, distributed photovoltaics in different regions are usually located within a relatively close geographical range. Therefore, they share similar meteorological conditions, and these factors directly affect the power generation of the photovoltaic system. In addition, photovoltaic power generation mainly depends on solar radiation and has the characteristic of power generation in obvious time periods. When the sun is the strongest at noon, the power generation power of all photovoltaic devices in all regions will reach the maximum, while at night, all photovoltaic devices in all regions will not generate electricity. Therefore, this leads to a correlation in the uncertainty coefficients of the active power of photovoltaic power generation in different regions.

[0041] The following set is an uncertainty set that takes into account the correlation between uncertainty coefficients. This model adjusts the boundary of the uncertainty set to reflect the change in the data correlation level, thereby changing the enclosed perturbation range. And in order to reduce the blank area in the uncertainty set and more effectively cover the space near the diagonal. By introducing a polyhedron set of photovoltaic output composed of correlation coefficients, the initial correlation polyhedron set is as follows:

[0042]

[0043] Among them, ρ ij,tis the correlation factor between the photovoltaic uncertainty coefficients in different regions, and n is the number of distributed photovoltaics.

[0044] The previously proposed correlation polyhedron set of distributed photovoltaic outputs takes into account the correlation between distributed photovoltaic outputs, but there are certain defects in the range it envelopes. On the one hand, the correlation polyhedron uncertainty set only envelopes a part of the area around the diagonal of the correlation distribution envelope diagram of the uncertain parameters, and does not envelope the part where the probability of distributed photovoltaic output fluctuations is high. This situation will reduce the robustness of the optimization results; on the other hand, the proportion of the part where the probability of distributed photovoltaic output fluctuations is low that it envelopes is still relatively high, and this situation will increase the conservatism of the optimization results.

[0045] To further address the problems of poor robustness and large conservatism of the above-mentioned correlation polyhedron uncertainty set, the above-mentioned correlation polyhedron uncertainty set will be improved to reduce the proportion of the blank area it envelopes and increase the proportion of the area around the diagonal. The method is as Figure 2 shown. Since in real life, the probability of extremely high correlation of distributed photovoltaics in different regions is relatively low, A1 is selected as the vertex of the set. Then, let the point with the largest ordinate among the points where the correlation distribution envelope diagram intersects the y-axis be D1; similarly, the point with the largest abscissa among the points where the correlation distribution envelope diagram intersects the x-axis is D2. When the point Y1 fluctuates between the point D1 and the point B1, and the point X1 fluctuates between the point D2 and the point B2, the set composed of the points A1, Y1, and X1 is the initially improved correlation polyhedron set.

[0046] In addition, the parameters in the uncertainty set are usually assumed to be precisely known. For example, if the uncertainty set of the future power demand of a certain node is defined as a box-type uncertainty set, then the lower and upper limits of this set are completely determined by the decision maker. However, the parameters of the uncertainty set often vary in the actual situation, so a suitable method is needed to accurately capture this uncertainty. Therefore, next, the parameters in the uncertainty set will be modeled, and it is assumed that the parameter fluctuates within the interval described below.

[0047]

[0048] Substituting the above formula into the first line of the correlation polyhedron formula set, we can get:

[0049]

[0050] The formula for the improved correlation polyhedron set is as follows:

[0051]

[0052] In the formula, γ is a parameter that controls the position of the set boundary. and represent the predicted value, fluctuation value, and uncertainty coefficient of the expected photovoltaic power generation, respectively, and Γ pv,u is the uncertainty of the expected value of photovoltaic power generation.

[0053] Step 2: Construct a distribution network model considering second-order cone relaxation constraints.

[0054] To ensure that the injection and consumption of active and reactive power at each node are in dynamic balance, the specific constraint conditions are as follows:

[0055]

[0056] In the formula, P mn,t , Q mn,t represent the active and reactive power flowing out of node m at time t, and P lm,t , Q lm,t represent the active power flowing into node m at time t, respectively. L lm,t represents the square of the current in branch lm at time t, r lm , x lm are the resistance and reactance of branch lm, respectively. P m,t G , Q m,t G are the active and reactive power generated by the generator at node m at time t, respectively. P m,t load , Q m,t Load represent the active and reactive loads at node m at time t, respectively. P m,t DM is the power transferred from node m to the microgrid at time t, is the reactive power compensation at node m at time t.

[0057] The following formula represents the relationship between node voltage, branch power, and current:

[0058] U m,t = U l,t - 2(P lm,t r lm + Q lm,t x lm ) + (r lm 2 + x lm 2 )L lm,t

[0059]

[0060] In the formula, U m,t represents the voltage of node m at time t, and Ul,t Denote the voltage of node l at time t.

[0061] Step 3: Based on the multi - energy micro - grid model constructed in Step 1 and the distribution network model determined in Step 2, combined with the improved alternating direction method of multipliers (ADMM) based on Bregman distance, design a coordinated scheduling strategy for the multi - energy micro - grid and the distribution network.

[0062] The proposed optimal operation model of the multi - energy micro - grid - distribution network in this invention conducts unified scheduling within the region and does not involve the internal decoupling of electricity, gas, and heat. The augmented Lagrangian function forms of each item in the multi - energy micro - grid - distribution network distributed model are as follows:

[0063]

[0064] In the formula, λ l denotes the Lagrange multiplier, ρ l is the penalty factor, and are the coupling quantities introduced for equation decoupling.

[0065] The (s + 1)-th iteration formula for solving this problem based on the traditional alternating direction method of multipliers is:

[0066]

[0067] In the formula, μ l is the iteration coefficient, μ l ≥0. To accurately understand the improved ADMM multiplier method algorithm, the Bregman divergence is briefly introduced below:

[0068] B φ (x, y) = φ(x) - φ(y) - ▽φ(y) T (x - y)

[0069] where φ is a continuously differentiable and strictly convex function on the relative interior of the convex set, x and y are two different variables, ▽φ(y) represents the gradient of φ at y, and B φ (x, y) is called the Bregman divergence.

[0070] Compared with the classical ADMM algorithm, the improved ADMM algorithm has innovation in the variable update strategy. Its core idea is to replace the traditional quadratic penalty term with the Bregman divergence, thereby improving the flexibility and efficiency of the algorithm in dealing with complex optimization problems. In addition, the algorithm introduces an additional Bregman divergence for each variable update step. Specifically, for variables P m,t MD and P m,t DMDuring the update process, an additional Bregman divergence is incorporated respectively to construct a complete solution framework. This method can not only improve the convergence speed but also may exhibit better performance when solving non-linear problems.

[0071]

[0072] In the formula, the convex function 0.5x of the present invention 2 is used to represent φ(x) and can be linearized by the piecewise linear approximation method.

[0073] Embodiment

[0074] The simulation model consists of an IEEE-33 node distribution network and a multi-energy microgrid in a park in a certain area of East China. It is assumed that the multi-energy microgrid is connected to distribution network node 18, as Figure 1 shown.

[0075] In the improved correlation polyhedron uncertainty set, the change in uncertainty reflects the change in the disturbance range of distributed photovoltaic output. Therefore, it is particularly important to analyze the impact of different uncertainties on photovoltaic output and the overall system optimization.

[0076] From Figure 3 it can be seen that as the uncertainty increases, the photovoltaic power generation shows an upward trend. This is because the increase in uncertainty expands the range of the polyhedron uncertainty set, covering more possible power values. From a statistical perspective, this means that the actual power of photovoltaic power generation approaches its upper limit value more frequently, thus increasing the overall power generation level. Since the photovoltaic power generation is directly consumed within the multi-energy microgrid, when its power increases, the power self-supply ability of the multi-energy microgrid increases, and its dependence on the external power grid decreases accordingly.

[0077] According to Figure 4 , it can be observed that the demand for natural gas gradually increases between time periods t8 and t12, finally reaching a peak, and there will be another peak during the night period. During the morning period, due to the start-up of more equipment, the demand for thermal power gradually increases, which in turn drives the increase in the demand for natural gas. At night, due to the further increase in the demand for thermal power, the purchase volume of natural gas reaches the peak again. During the time period from t1 to t6, due to the relatively low demand for thermal power, the gas turbine with higher thermal efficiency consumes more natural gas to generate thermal power during this period, while the thermal power generated by the gas boiler is relatively less. In addition, during this time period, the thermal energy storage system can also release more thermal power to maintain the thermal balance of the multi-energy microgrid. Therefore, the overall demand for natural gas decreases during this stage.

[0078] And from Figure 5It can be seen that from t8 to t12 are not only the peak periods of natural gas consumption, but also the peak periods of electricity demand. During this period, although the electric power generated by the gas turbine has increased significantly compared with the morning period, the microgrid still needs to transmit electricity from the distribution network to meet the electric power demand of the multi - energy microgrid. In contrast, in the time period from t13 to t15, due to the sufficient electric power in the microgrid, it can instead supply a certain amount of electricity to the distribution network.

[0079] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned multi - energy microgrid - distribution network collaborative optimization method considering photovoltaic correlation are implemented.

[0080] Based on the same inventive concept, an embodiment of the present application provides a computer - readable storage medium. The computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned multi - energy microgrid - distribution network collaborative optimization method considering photovoltaic correlation are implemented.

[0081] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk storage, CD - ROM, optical storage, etc.) containing computer - usable program code.

[0082] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general - purpose computer, a special - purpose computer, an embedded processor, or other programmable data - processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data - processing devices produce means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0083] These computer program instructions can also be stored in a computer - readable memory that can direct a computer or other programmable data - processing devices to work in a specific manner, such that the instructions stored in the computer - readable memory produce a manufactured article including instruction means, and the instruction means implements the functions specified in the process Figure 1One process or multiple processes and / or boxes Figure 1 The functions specified in one box or multiple boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process or multiple processes and / or boxes Figure 1 One process or multiple processes and / or boxes Figure 1 The steps for implementing the functions specified in one box or multiple boxes.

[0085] The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the present invention.

Claims

1. A multi - energy microgrid - distribution network collaborative optimization method considering photovoltaic correlation, characterized in that, It includes the following steps: Step 1: Considering the uncertainty and correlation of the distributed photovoltaic output of the multi - energy micro - grid, construct a multi - energy micro - grid model that meets the electricity, heat, and cooling load demands; Step 2: Construct a distribution network model considering second - order cone relaxation constraints; Step 3: Based on the models constructed in Step 1 and Step 2, combined with the improved alternating direction multiplier algorithm based on Bregman distance, design a coordinated scheduling strategy for the multi - energy micro - grid and the distribution network.

2. The collaborative optimization method for a multi - energy microgrid - distribution network considering photovoltaic correlation according to claim 1, characterized in that The specific process of Step 1 is as follows: Introduce a polyhedral set considering correlation to describe the uncertainty of the distributed photovoltaic output of the multi - energy micro - grid, that is: Where, U new_correlated represents the uncertainty set of photovoltaic power generation, P i,t pv , respectively represent the active power and the output power fluctuation value of photovoltaic power generation, and respectively represent the predicted value, the fluctuation value and the uncertainty coefficient of the expected value of photovoltaic power generation, δ i,t is the uncertainty coefficient of the active power of photovoltaic power generation, γ is the parameter of the control set boundary position, Γ is the uncertainty, ρ ij,t is the correlation factor between the photovoltaic uncertainty coefficients in different regions, Γ pv,u is the uncertainty of the expected value of photovoltaic power generation, i represents the area of distributed photovoltaic power generation in the multi - energy micro - grid, and t represents the time; For natural gas in the multi - energy micro - grid, define the constraint equation for natural gas use: Where Gas t represents the amount of natural gas purchased by the microgrid at time t, and are the powers generated by the gas turbine, gas boiler, and heat pump at time t, respectively. η gt , η gb and η hp are the efficiency coefficients of the gas turbine, gas boiler, and heat pump, respectively; The power generated by the gas turbine at time t includes the power for power supply and the power for driving the refrigeration equipment, that is: Wherein, represents the electric power output by the gas turbine at time t, represents the power required for the gas turbine to drive the electric refrigeration equipment to operate at time t; Construct a multi - energy micro - grid model that meets the electricity, heat, and cooling load demands as follows: Wherein, and respectively represent the demands for electricity, heat, and cooling, represents the electricity quantity purchased by the microgrid from the superior power grid at time t, represents the power generation of the distributed photovoltaic at time t, represents the power generation of the wind power generation at time t, represents the electricity quantity sold by the microgrid to the superior power grid at time t, η ec represents the efficiency coefficient of the electric refrigeration equipment, represents the recovered heat power of the heat recovery equipment at time t.

3. The collaborative optimization method for a multi - energy micro - grid and distribution network considering photovoltaic correlation according to claim 2, wherein, The specific process of Step 2 is as follows: Based on the fact that the active power and reactive power injection and consumption at each node of the distribution network are in dynamic balance, construct a distribution network model considering second - order cone relaxation constraints as follows: where, P mn,t , Q mn,t respectively represent the active power and reactive power flowing out of node m at time t, P lm,t , Q lm,t respectively represent the active power and reactive power flowing into node m at time t, L lm,t represents the square of the current of branch lm at time t, r lm , x lm respectively represent the resistance and reactance of branch lm, P m,t G , Q m,t G respectively represent the active power and reactive power generated by the generator at node m at time t, P m,t load , Q m,t Load respectively represent the active load and reactive load at node m at time t, P m,t DM represents the power transferred from node m to the microgrid at time t, represents the reactive power compensation at node m at time t; The relationship between the voltage, branch power, and current at each node of the distribution network is as follows: U m,t = U l,t - 2(P lm,t r lm + Q lm,t x lm ) + (r lm 2 + x lm 2 )L lm,t where U m,t and U l,t represent the voltages of node m and node l at time t, respectively.

4. The multi - energy microgrid - distribution network collaborative optimization method considering photovoltaic correlation according to claim 3, characterized in that The specific process of Step 3 is as follows: Establish the augmented Lagrangian function corresponding to the multi - energy micro - grid model and the distribution network model as follows: where, L DN , L MEM respectively represent the Lagrangian functions of the microgrid and the distribution network, T represents the total number of hours in a day, λ l represents the Lagrange multiplier, P m,t MD represents the power transmitted from the microgrid to the distribution network, ρ l is the penalty factor of the Lagrangian function, and represent the coupling quantities introduced for function decoupling; Use the improved alternating direction multiplier algorithm based on Bregman distance to solve the above - mentioned augmented Lagrangian function. The (s + 1)-th iteration formula for solving the above - mentioned augmented Lagrangian function is: where P m,t MD,s+1 , P m,t MD,s respectively represent the power transmitted from the microgrid to the distribution network at the (s + 1)-th and s-th iterations at time t, P m,t DM,s+1 , P m,t DM,s respectively represent the power transmitted from the distribution network to the microgrid at the (s + 1)-th and s-th iterations at time t, B φ,mem represents the Bregman divergence of the microgrid, B φ,dn represents the Bregman divergence of the distribution network, respectively represent the Lagrange multipliers at the (s + 1)-th and s-th iterations at time t, ρ φ represents the penalty factor of the Bregman function; When the preset maximum number of iterations is reached, the iteration terminates. According to all the power parameters of the multi - energy micro - grid model and the distribution network model obtained from the last iteration, design a coordinated scheduling strategy for the multi - energy micro - grid and the distribution network.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it realizes the steps of the multi - energy micro - grid - distribution network coordinated optimization method considering photovoltaic correlation as described in any one of claims 1 to 4.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the multi - energy micro - grid - distribution network coordinated optimization method considering photovoltaic correlation as described in any one of claims 1 to 4.

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