Double-layer optimization scheduling method for power distribution microgrid

Abstract

The invention discloses a power distribution micro-grid double-layer optimization scheduling method, which is suitable for the technical field of power optimization scheduling, and comprises the following steps: S1, constructing an upper-layer power distribution network optimization scheduling model by taking the minimum network loss in an active power distribution network system as an objective function, and setting a corresponding upper-layer optimization constraint condition; s2, taking the minimum grid-connected operation cost in the micro-grid system as an objective function to construct a lower-layer micro-grid grid-connected optimization scheduling model, and setting corresponding lower-layer optimization constraint conditions; and S3, solving the grid-connected optimal scheduling model of the lower-layer micro-grid by adopting a second-order cone technology to obtain a tie line coordination parameter, determining a micro-grid access point type according to the tie line coordination parameter, and solving the optimal scheduling model of the upper-layer power distribution network by calling a Cplex solver and combining with a parameter corresponding to the micro-grid access point type to obtain a scheduling strategy. According to the invention, efficient collaborative optimization scheduling of the distribution microgrid system is realized, and the economical efficiency, robustness and flexibility of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power optimization dispatching, and in particular to a double-layer optimization dispatching method for a distribution microgrid. Background Art

[0002] With the rapid development of distributed energy (such as photovoltaics, wind power, etc.), the combination of distribution networks and microgrids has become an important direction for the development of future smart distribution networks. Distributed energy has brought great challenges to the optimal operation of distribution microgrids due to its strong volatility and uncertain output. Traditional microgrid optimization scheduling methods mainly rely on optimization control on a single time scale on the day before, which is heavily dependent on the prediction accuracy of renewable energy output and user load demand. However, due to the limitations of the prediction technology itself, as well as the rapid changes in distributed energy and user demand, there is a large difference between the actual operation and the scheduling plan, making it difficult to ensure the smooth access and efficient operation of the microgrid.

[0003] The existing single-time-scale optimization scheduling method is powerless when faced with the uncertainty of the output of distributed photovoltaic, wind power and other renewable energy sources. Once the prediction is biased, the actual operation of the microgrid will deviate from the optimal state, which will not only affect the economic benefits, but also pose a threat to the safe and stable operation of the power grid. In addition, the traditional method lacks timely adjustment of the daily and real-time operating status, and cannot effectively deal with the volatility of renewable energy and the randomness of load, making it difficult to achieve optimal allocation and efficient utilization of resources.

[0004] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to propose a two-layer optimization scheduling method for a distribution microgrid. By constructing a two-layer optimization model, adopting a multi-time scale optimization scheduling strategy, applying second-order cone technology and Cplex solver, and determining the type of microgrid access point to solve the two-layer optimization model, efficient collaborative optimization scheduling of the distribution microgrid system is achieved, and the economy, robustness and flexibility of the system are improved.

[0006] In a first aspect, a technical solution provided in an embodiment of the present invention is a two-layer optimization scheduling method for a distribution microgrid, comprising the following steps: S1. Taking the minimum network loss in the active distribution network system as the objective function, an upper-level distribution network optimization dispatching model is constructed and the corresponding upper-level optimization constraints are set; S2. Taking the minimization of grid-connected operation cost in the microgrid system as the objective function, a lower-level microgrid grid-connected optimization scheduling model is constructed and corresponding lower-level optimization constraints are set; S3. Use the second-order cone technology to solve the lower-level microgrid grid-connected optimization scheduling model to obtain the tie line coordination parameters, determine the microgrid access point type according to the tie line coordination parameters, call the Cplex solver to solve the upper-level distribution network optimization scheduling model in combination with the coordination parameters corresponding to the microgrid access point type to obtain the scheduling strategy.

[0007] In this scheme, the two-layer optimization model constructed by the upper distribution network optimization dispatching model and the lower microgrid grid-connected optimization dispatching model realizes the collaborative interaction between the upper and lower layers through the tie line, so that the distribution network and the microgrid can be optimized and dispatched as a whole, avoiding the limitations of single-level optimization. The upper model improves the economy of the distribution network by reducing network losses, and the lower model reduces the operating cost by optimizing the output of resources in the microgrid. The combination of the two further improves the overall economic benefits of the distribution microgrid system. Furthermore, through multi-time scale optimization dispatching, the forecast information and actual operation conditions of different time scales can be comprehensively considered, making the dispatch plan more accurate and reliable. Through the real-time feedback correction mechanism, the system can quickly respond to changes in renewable energy and load, adjust the dispatch plan, and enhance the robustness and adaptability of the system. Furthermore, the second-order cone technology transforms complex non-convex nonlinear problems into relatively simple second-order cone programming problems, greatly reducing the difficulty of solving and improving the efficiency of solving; Cplex solver, as the industry-leading mathematical optimization solver, can ensure the accuracy and reliability of the solution results, providing strong support for the optimization dispatch of the distribution microgrid system. Finally, by solving the lower-level microgrid grid-connected optimization dispatching model, the tie-line coordination parameters are obtained; based on these parameters, the microgrid access point type (such as power source or load) is determined, and the parameters of the upper-level distribution network optimization dispatching model are adjusted accordingly; the determination of the microgrid access point type enables the microgrid to flexibly access the distribution network according to different operating conditions, improving the flexibility and scalability of the system. By adjusting the parameters of the upper-level distribution network optimization dispatching model, the power flow distribution of the distribution network can be further optimized, network losses can be reduced, and the economy of the system can be improved.

[0008] Preferably, the expression of the objective function corresponding to the upper distribution network optimization dispatching model is: Among them, L is the network branch, Z ij is the branch impedance; I ij is the current between nodes i and j.

[0009] As a preferred embodiment, the operating cost of the generator set under the condition of minimum grid loss is also included, and the specific expression is: Where N represents the number of generator sets; α i , β i and γ iRepresents the consumption characteristic curve parameter of generator i; P Gi is the power generated by the i-th group of generators.

[0010] Preferably, the upper-level optimization constraint condition includes at least a power balance constraint of the distribution network, and the specific expression is as follows: Among them, P Li and Q Li represents the active and reactive power of the ith generator set; V i ,θ i represents the voltage amplitude and phase angle of node i; in the node admittance matrix, G ij , B ij are the real and imaginary parts of the elements in the i-th row and j-th column respectively.

[0011] Preferably, the lower-layer microgrid grid-connected optimization scheduling model includes: a microgrid day-ahead optimization scheduling model, a microgrid intraday scheduling stage model, and a microgrid real-time scheduling stage model; wherein: The microgrid day-ahead dispatching model takes the minimization of the microgrid day-ahead operation cost as its objective function; The microgrid intraday dispatch stage model takes the minimization of the microgrid intraday operation cost as the objective function; The objective function of the microgrid real-time dispatch model is to minimize the adjustment of adjustable resource output within the previous real-time stage time range.

[0012] As a preferred option, the objective function F1 corresponding to the microgrid day-ahead dispatching model DA The expression is: Among them, MT , CL are the node sets connected to the micro gas turbine and the controllable load, T DA is the time scale; is the power exchange cost of the interconnection line at time t a day ago; is the total operation and maintenance cost of the microgrid equipment at time t the day before; is the gas purchase cost for micro gas turbine power generation at node i at time t the day before; is the controllable load regulation cost at time t the day before; To compensate for the capacitor's all-day gear adjustment costs.

[0013] As a preferred option, the objective function F1 corresponding to the microgrid intraday dispatch stage model DR The expression is: In the formula, Ψ MT It is the set of nodes connected to MT; is the interconnection line exchange power cost at time t in the target phase; is the total operation and maintenance cost of the microgrid equipment at time t during the rolling period of the day; It is the gas purchase cost for power generation by micro gas turbine at node i at time t during the intra-day rolling phase.

[0014] As a preferred option, the objective function ΔF1 corresponding to the microgrid real-time dispatch stage model RT The expression is: Among them, u RT,real To regulate the actual output feedback value of resources; Δu RT To adjust the output of resources; DR To help regulate resources, u DR To regulate the total output of resources.

[0015] Preferably, the lower-level optimization constraints include at least microgrid power balance constraints, micro gas turbine operation constraints, micro gas turbine ramp constraints, energy storage device charge constraints, and distributed power source output constraints.

[0016] Preferably, S3 further includes: The PCC power obtained by solving the lower-level microgrid grid-connected optimization scheduling model through the second-order cone technology is substituted into the upper-level distribution network optimization scheduling model and the positive and negative value of the result is judged. If it is negative, the node connected to the microgrid is taken as the load and substituted into the active power of the i-th generator set to participate in the model calculation; if the result is positive, the node connected to the microgrid is taken as the power source and substituted into the reactive power of the i-th generator set to participate in the model calculation.

[0017] Beneficial effects of the present invention: (1) This application constructs an optimal power flow model for the upper distribution network with the goal of minimizing network loss, and uses the second-order cone technology to solve it, effectively reducing the system network loss. At the same time, the lower microgrid model aims to minimize the grid-connected operation cost, comprehensively considers the optimal scheduling of multiple distributed energy sources and adjustable resources, and further reduces the operation cost; (2) The lower-level microgrid model of this application adopts the model predictive control (MPC) algorithm to achieve multi-time scale optimization scheduling in three stages: day-ahead optimization, intra-day rolling optimization, and real-time feedback correction. This strategy can continuously adjust the optimization scheduling plan according to the real-time changes of renewable energy and load, and effectively deal with its uncertainty. The multi-time scale optimization scheduling strategy significantly enhances the system's ability to cope with the uncertainty of renewable energy and load, and improves the system's robustness and stability. Through the real-time feedback correction mechanism, the system can respond to changes quickly to ensure the accuracy and reliability of the scheduling plan; (3) This application constructs a two-layer optimization and dispatching model for distribution and microgrids. The upper layer is the distribution network optimization and dispatching model, and the lower layer is the microgrid optimization and dispatching model. The collaborative interaction between the upper and lower layers is achieved through the interconnection line, so that the distribution network and the microgrid can be optimized and dispatched as a whole. At the same time, according to the type of microgrid access point determined by the optimization result of the lower layer, the optimization and dispatching strategy of the upper layer distribution network is further adjusted. The two-layer collaborative optimization operation realizes the effective coordination and overall optimization between the various levels of the distribution and microgrid system, which can not only improve the economy and operation efficiency of the system, but also enhance the flexibility and scalability of the system, providing strong support for the development of future smart distribution networks.

[0018] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0020] Figure 1 It is a flow chart of a two-layer optimization scheduling method for a distribution microgrid in an embodiment of the present invention.

[0021] Figure 2 It is a technical principle diagram of a two-layer optimization scheduling method for a distribution microgrid in an embodiment of the present invention.

[0022] Figure 3 It is a specific flow chart of lower layer optimization in an embodiment of the present invention.

[0023] Figure 4 It is a diagram of an improved IEEE33 node network structure in an embodiment of the present invention.

[0024] Figure 5 It is a curve diagram of load prediction results of a microgrid based on model predictive control in an embodiment of the present invention.

[0025] Figure 6 It is a network loss comparison diagram before and after the distribution network is connected to the microgrid in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc. Embodiment 1:

[0028] In view of the strong volatility of distributed photovoltaic power generation and the uncertainty of distribution network current, this embodiment constructs a two-layer optimization model for distribution and microgrids, with the upper layer being the distribution network optimization dispatching model and the lower layer being the microgrid grid-connected optimization dispatching model. Specifically, a lower-layer microgrid grid-connected optimization dispatching model including active and reactive resource models such as photovoltaics, hydropower, energy storage, adjustable loads, static VAR compensators, capacitors, etc. is constructed, and the topology change constraints of the distribution and microgrid are established in combination with feeder terminals, etc., and a multi-time scale microgrid optimization operation strategy based on model predictive control algorithms, taking into account day-ahead optimization, intraday rolling optimization, and real-time feedback correction is studied. Combined with the PCC interconnection line power obtained from the lower-layer optimization, the DG output of the distribution network is solved, and the optimization dispatching results of the active distribution network and the microgrid are finally obtained through the collaborative and joint interaction of the two-layer optimization dispatching model.

[0029] like Figure 1 As shown, a two-layer optimization scheduling method for a distribution microgrid is provided in an embodiment of the present invention, comprising the following steps: S1, constructing an upper-layer distribution network optimization scheduling model with minimizing the network loss in the active distribution network system as the objective function and setting corresponding upper-layer optimization constraints.

[0030] Specifically, the expression of the corresponding objective function of the upper distribution network optimization dispatching model is: Among them, L is the network branch, Z ij is the branch impedance; I ij is the current between nodes i and j.

[0031] Specifically, it also includes the operating cost of the generator set under the condition of minimum grid loss, and the specific expression is: Where N represents the number of generator sets; α i , β i and γ i Represents the consumption characteristic curve parameter of generator i; P Gi is the power generated by the i-th group of generators.

[0032] Specifically, the upper-level optimization constraints include at least the power balance constraint of the distribution network, and the specific expression is as follows: Among them, P Li and Q Li represents the active and reactive power of the ith generator set; V i ,θ i represents the voltage amplitude and phase angle of node i; in the node admittance matrix, G ij , B ij are the real and imaginary parts of the elements in the i-th row and j-th column respectively.

[0033] The lower model takes microgrid as the research object, studies the multi-time scale optimization operation strategy combined with (model predictive control) MPC algorithm, and establishes the dispatch model under the microgrid grid-connected mode based on this; optimizes the dispatch of each unit output and the interconnection line exchange power. The total dispatch cycle is 24 hours a day, the time scale is 1h, and the whole day is divided into 24 time periods.

[0034] Based on this, S2, taking the minimum grid-connected operation cost in the microgrid system as the objective function, constructs the lower-level microgrid grid-connected optimization scheduling model and sets the corresponding lower-level optimization constraints.

[0035] Furthermore, the lower-layer microgrid grid-connected optimization scheduling model includes: a microgrid day-ahead optimization scheduling model, a microgrid intraday scheduling stage model, and a microgrid real-time scheduling stage model; wherein: The microgrid day-ahead dispatching model takes the minimization of the microgrid day-ahead operation cost as its objective function; The microgrid intraday dispatch stage model takes the minimization of the microgrid intraday operation cost as the objective function; The objective function of the microgrid real-time dispatch model is to minimize the adjustment of adjustable resource output within the previous real-time stage time range.

[0036] Specifically, the objective function F1 corresponding to the microgrid day-ahead dispatch model is DA The expression is: Among them, MT ,Ψ CL are the node sets connected to the micro gas turbine and the controllable load, TDA is the time scale; is the power exchange cost of the interconnection line at time t a day ago; is the total operation and maintenance cost of the microgrid equipment at time t the day before; is the gas purchase cost for micro gas turbine power generation at node i at time t the day before; is the controllable load regulation cost at time t the day before; To compensate for the capacitor's all-day gear adjustment cost.

[0037] Furthermore, the power exchange cost of the tie line includes the cost of purchasing and selling electricity; the interaction cost between the microgrid and the main grid is expressed as follows: Where: c s (t), c p (t) are the prices of electricity sold and purchased by the microgrid from the distribution network during period t; p MG (t) represents the interaction power in period t; s MG is a 0-1 variable, s MG 0 or 1 indicates that the microgrid sells electricity to the distribution network or the microgrid purchases electricity from the distribution network.

[0038] Furthermore, the total cost of operation and maintenance of microgrid equipment is expressed as follows: Where: R PV , R MT , R ES , R HY They are the unit power maintenance costs of photovoltaic, micro gas turbine, energy storage equipment and small hydropower respectively; They represent the output power of photovoltaic, micro gas turbine, energy storage equipment and small hydropower respectively; The expression of gas purchase cost for micro gas turbine power generation at node i at time t is as follows: In the formula, C g (t) is the natural gas price; is the gas power of the interconnection line.

[0039] Furthermore, the controllable load regulation cost function expression at time t the day before is as follows: Where: Indicates whether the node i is controlled at time t a day ago; and They represent the load compensation cost and controlled active power respectively.

[0040] The cost of all-day gear adjustment of the compensation capacitor is expressed as follows: In the formula, ρ c is the unit adjustment cost of the compensation capacitor; ΔU c To compensate for the number of times the capacitor switching position is adjusted throughout the day.

[0041] Specifically, the microgrid intraday dispatching phase cycle corresponding to the microgrid intraday dispatching phase model in the grid-connected mode is set to 15 minutes, and the objective function F1 corresponding to the microgrid intraday dispatching phase model is DR The expression is: In the formula, Ψ MT It is the set of nodes connected to MT; is the interconnection line exchange power cost at time t in the target phase; is the total operation and maintenance cost of the microgrid equipment at time t during the rolling period of the day; It is the gas purchase cost for power generation by micro gas turbine at node i at time t during the intra-day rolling phase.

[0042] Specifically, the start-up period of the microgrid real-time feedback correction optimization corresponding to the microgrid real-time dispatch stage model in the grid-connected mode is 5 minutes. In order to cope with the uncertainty of renewable energy, the objective function of the current stage is set to minimize the adjustment amount of adjustable resource output within the current time range. Therefore, the objective function ΔF1 corresponding to the microgrid real-time dispatch stage model RT The expression is: U1=[P G ,Q RE,i ,P CDG,i ,Q CDG,i ,P ESS,i ,Q ESS,i ,Q SVC,i ] Where: Q RE,i Represents the reactive power output of renewable energy; P CDG,i , Q CDG,i are the active and reactive outputs of controllable distributed power sources; P ESS,i , Q ESS,i Indicates the active and reactive output of energy storage, Q SVC,i output for SVC; in addition, the change in the interconnection line exchange power P in the grid-connected mode is considered G The minimum optimization goal; u RT,real To regulate the actual output feedback value of resources; Δu RT To adjust the output of resources; DR To help regulate resources, u DR To regulate the total output of resources.

[0043] Furthermore, the lower-level optimization constraints include at least microgrid power balance constraints, micro gas turbine operation constraints, micro gas turbine ramp constraints, energy storage device charge constraints, and distributed power output constraints.

[0044] Specifically, the microgrid power balance constraint is expressed as follows: P WT (t)+P PV (t)+P ES (t)+P HY (t)+P PCC (t) = P load (t) Where P WT (t), P PV (t), P ES (t), P HY (t), P PCC (t), P load (t) are the power of the micro gas turbine device, photovoltaic power generation device, energy storage device, small hydropower device, interconnection line between microgrid and distribution network, and load in the microgrid at time t.

[0045] Microturbine operating constraints: Where: P MT,t,max、 P MT,t,min are the corresponding maximum and minimum power values ​​of the gas turbine respectively; ΔP MT,t + , P MT,t - Respectively represent the upper and lower limits of the gas turbine's ramp power; M onMT is the shortest start-up time of the gas turbine; T onMT,t-1 is the corresponding continuous operation time of the gas turbine; M offMT is the shortest downtime of the gas turbine; T offMT,t-1 is the corresponding continuous shutdown time of the gas turbine.

[0046] Specifically, the microturbine ramping constraints are: Where: are the upward and downward climbing rates of the i-th gas turbine unit respectively.

[0047] Tie line power constraints: P PCC,min ≤P PCC,i (t)≤P PCC,max Where P PCC,min , P PCC,maxThey are the upper and lower limits of the interconnection line power respectively.

[0048] Specifically, the energy storage device charge constraints are: SOC min ≤SOC≤SOC max .

[0049] Specifically, the output constraints of distributed generation are: In the formula, is the active and reactive power of i DG at time t; The upper and lower limits of the ramp rate.

[0050] S3. Use the second-order cone technology to solve the lower-level microgrid grid-connected optimization scheduling model to obtain the tie line coordination parameters, determine the microgrid access point type according to the tie line coordination parameters, call the Cplex solver to solve the upper-level distribution network optimization scheduling model in combination with the coordination parameters corresponding to the microgrid access point type to obtain the scheduling strategy.

[0051] like Figure 2 and Figure 3 As shown in the figure, specifically, the PCC power obtained by solving the lower-level microgrid grid-connected optimization scheduling model through the second-order cone technology is substituted into the upper-level distribution network optimization scheduling model and the positive or negative value of the result is judged. If it is negative, the node connected to the microgrid is taken as the load and substituted into the active power of the i-th generator set to participate in the model calculation; if the result is positive, the node connected to the microgrid is taken as the power source and substituted into the reactive power of the i-th generator set to participate in the model calculation. Embodiment 2:

[0052] The technical framework of this embodiment is similar to that of Embodiment 1. As an adaptive replacement scheme, the difference lies in that the microgrid grid-connected mode is converted into the microgrid island mode. Therefore, the lower model of this application is also adapted to the microgrid island mode. Since the microgrid has no electrical connection with the large grid in the island mode, there is no interconnection line to exchange power in this mode. The solution method of the lower-layer microgrid island optimization scheduling model is the same as that of the embodiment.

[0053] Specifically, the objective function of the microgrid day-ahead optimization dispatching in the island mode is the minimum total economic operation cost as the objective function of the lower-layer microgrid island optimization dispatching model, which is expressed as follows: The objective function of the intraday optimization dispatch of the microgrid island mode is still to minimize the total economic operation cost, and does not include the power exchange cost between the microgrid and the main grid tie line. Its objective function is expressed as follows: The objective function of the real-time feedback phase of the microgrid island mode does not include the power change of the main grid tie line exchange P G : U2=[P G ,Q RE,i ,P CDG,i ,Q CDG,i ,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ] Except that the tie line power is 0, the other constraints in the microgrid island mode are the same as those in the grid-connected mode.

[0054] It should be noted that the solution process of the two-layer optimization model in the microgrid island mode in this embodiment is the same as that in Embodiment 1. Specifically, the second-order cone technology is used to define the variables c ij 、c ii and ij , and the variables satisfy the relationship: In the formula, e i 、f i 、e j 、f j Represent the real and imaginary parts of the voltage vector at node i and node j respectively.

[0055] Then the power balance constraint is: Translates to: Based on the multi-time scale optimization of the microgrid, the PCC power (power at the common connection point) is obtained, and the result is substituted into the upper-level distribution network line parameters. The Cplex solver is called to solve the model converted by the aforementioned second-order cone technology. The microgrid has no electrical connection with the large power grid in the island mode, so there is no tie line to exchange power in this mode.

[0056] In order to illustrate the validity of the invention patent, a simulation analysis is conducted on a microgrid in a certain region. The microgrid system includes distributed power generation units such as micro gas turbines, energy storage, wind power, and photovoltaics. A comparative analysis is conducted based on the microgrid optimization scheduling model based on model predictive control at multiple time scales established in the invention patent.

[0057] In order to verify the accuracy and effectiveness of the invention patent, the improved IEEE33 node system is selected for verification. The improved system structure is as follows: Figure 4The voltage of the node system is 12.66kV, and the reference power is 10MW. In the incorporated microgrid, the energy storage charging and discharging efficiency are set to 93%, the initial value of the state of charge is 50%, and the upper and lower limits are 85% and 25% respectively.

[0058] In the lower model, based on the model predictive control model under multiple time scales, the load prediction in the microgrid is as follows: Figure 5 As shown in the figure, it can be seen that on the basis of open-loop rolling optimization, adding feedback correction link to realize closed-loop optimization control can effectively track system load and cope with the impact of prediction error and renewable energy fluctuation.

[0059] Figure 6 It reflects the network loss defense situation before and after the microgrid is incorporated into the active distribution network. It can be found that in the 7:00-20:00 period, the network loss shows a significant downward trend. This is because in this period, the microgrid reversely supplies power to the distribution network, which reduces the output of other distributed power sources in the distribution network, and ultimately effectively reduces the power generation cost of the distribution network and reduces network losses.

[0060] From the comparison of the economic operation costs of microgrid connection and island mode in Table 1, it can be seen that compared with island operation, the economic operation cost of the coordinated operation of distribution microgrids is reduced by about 33.62%. It can be concluded that the two-layer joint operation can effectively improve the economy. Table 1. Comparison of economic operation costs between microgrid connection and island mode Island Mode Grid-connected mode Economic operation cost / yuan 2561 1700

[0061] This embodiment has at least the following substantial technical effects: the two-layer optimization model constructed by the upper distribution network optimization dispatching model and the lower microgrid grid-connected optimization dispatching model realizes the collaborative interaction between the upper and lower layers through the tie line, so that the distribution network and the microgrid can be optimized and dispatched as a whole, avoiding the limitations of single-level optimization. The upper model improves the economy of the distribution network by reducing network losses, and the lower model reduces the operating cost by optimizing the output of resources in the microgrid. The combination of the two further improves the overall economic benefits of the distribution microgrid system. Furthermore, through multi-time scale optimization dispatching, the forecast information and actual operation conditions of different time scales can be comprehensively considered, making the dispatch plan more accurate and reliable. Through the real-time feedback correction mechanism, the system can quickly respond to changes in renewable energy and load, adjust the dispatch plan, and enhance the robustness and adaptability of the system. Furthermore, the second-order cone technology transforms complex non-convex nonlinear problems into relatively simple second-order cone programming problems, greatly reducing the difficulty of solving and improving the efficiency of solving; Cplex solver, as the industry-leading mathematical optimization solver, can ensure the accuracy and reliability of the solution results, and provide strong support for the optimization dispatch of the distribution microgrid system. Finally, by solving the lower-level microgrid grid-connected optimization dispatching model, the tie-line coordination parameters are obtained; based on these parameters, the microgrid access point type (such as power source or load) is determined, and the parameters of the upper-level distribution network optimization dispatching model are adjusted accordingly; the determination of the microgrid access point type enables the microgrid to flexibly access the distribution network according to different operating conditions, improving the flexibility and scalability of the system. By adjusting the parameters of the upper-level distribution network optimization dispatching model, the power flow distribution of the distribution network can be further optimized, network losses can be reduced, and the economy of the system can be improved.

[0062] The specific implementation described above is a preferred implementation of a two-layer optimization scheduling method for a distribution microgrid of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A two-layer optimization dispatching method for a distribution microgrid, characterized by: The steps include: S1. Taking the minimum network loss in the active distribution network system as the objective function, an upper-level distribution network optimization dispatching model is constructed and the corresponding upper-level optimization constraints are set; S2. Taking the minimization of grid-connected operation cost in the microgrid system as the objective function, a lower-level microgrid grid-connected optimization scheduling model is constructed and corresponding lower-level optimization constraints are set; S3. Use the second-order cone technology to solve the lower-level microgrid grid-connected optimization scheduling model to obtain the tie line coordination parameters, determine the microgrid access point type according to the tie line coordination parameters, call the Cplex solver to solve the upper-level distribution network optimization scheduling model in combination with the coordination parameters corresponding to the microgrid access point type to obtain the scheduling strategy.

2. A two-layer optimization dispatching method for a distribution microgrid according to claim 1, characterized in that: The expression of the objective function corresponding to the upper distribution network optimization dispatching model is: Among them, L is the network branch, Z ij is the branch impedance; I ij is the current between nodes i and j.

3. A two-layer optimization dispatching method for distribution microgrid according to claim 2, characterized in that: It also includes the operating cost of the generator set under the condition of minimum grid loss, expressed as: Where N represents the number of generator sets; α i , β i and γ i Represents the consumption characteristic curve parameter of generator i; P Gi is the power generated by the i-th group of generators.

4. A two-layer optimization dispatching method for distribution microgrid according to claim 2, characterized in that: The upper-level optimization constraints include at least the power balance constraint of the distribution network, which is expressed as follows: Among them, P Li and Q Li represents the active and reactive power of the ith generator set; V i ,θ i represents the voltage amplitude and phase angle of node i; in the node admittance matrix, G ij , B ij are the real and imaginary parts of the elements in the i-th row and j-th column respectively.

5. A two-layer optimization dispatching method for distribution microgrid according to claim 1, characterized in that: The lower-layer microgrid grid-connected optimization dispatching model includes: a microgrid day-ahead optimization dispatching model, a microgrid intraday dispatching stage model, and a microgrid real-time dispatching stage model; wherein: The microgrid day-ahead dispatching model takes the minimization of the microgrid day-ahead operation cost as its objective function; The microgrid intraday dispatch stage model takes the minimization of the microgrid intraday operation cost as the objective function; The objective function of the microgrid real-time dispatch model is to minimize the adjustment of adjustable resource output within the real-time time range.

6. A two-layer optimization dispatching method for distribution microgrid according to claim 5, characterized in that: The objective function F1 corresponding to the microgrid day-ahead dispatch model DA The expression is: Among them, MT , CL are the node sets connected to the micro gas turbine and the controllable load, T DA is the time scale; is the power exchange cost of the interconnection line at time t a day ago; is the total operation and maintenance cost of the microgrid equipment at time t the day before; is the gas purchase cost for micro gas turbine power generation at node i at time t the day before; is the controllable load regulation cost at time t the day before; To compensate for the capacitor's all-day gear adjustment costs.

7. A two-layer optimization dispatching method for distribution microgrid according to claim 6, characterized in that: Objective function F1 corresponding to the microgrid intraday dispatch model DR The expression is: In the formula, Ψ MT It is the set of nodes connected to MT; is the interconnection line exchange power cost at time t in the target phase; is the total operation and maintenance cost of the microgrid equipment at time t during the rolling period of the day; It is the gas purchase cost for power generation by micro gas turbine at node i at time t during the intra-day rolling phase.

8. A two-layer optimization dispatching method for distribution microgrid according to claim 7, characterized in that: Objective function ΔF1 corresponding to the microgrid real-time dispatch model RT The expression is: Among them, u RT,real To regulate the actual output feedback value of resources; Δu RT To adjust the output of resources; DR To help regulate resources, u DR To regulate the total output of resources.

9. A two-layer optimization dispatching method for a distribution microgrid according to claim 1 or 5, characterized in that: The lower-level optimization constraints include at least microgrid power balance constraints, micro gas turbine operation constraints, micro gas turbine ramp constraints, energy storage device charge constraints, and distributed power output constraints.

10. A two-layer optimization dispatching method for distribution microgrid according to claim 1, characterized in that: S3 also includes: The PCC power obtained by solving the lower-level microgrid grid-connected optimization scheduling model through the second-order cone technology is substituted into the upper-level distribution network optimization scheduling model and the positive and negative value of the result is judged. If it is negative, the node connected to the microgrid is taken as the load and substituted into the active power of the i-th generator set to participate in the model calculation; if the result is positive, the node connected to the microgrid is taken as the power source and substituted into the reactive power of the i-th generator set to participate in the model calculation.

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