Method and device for planning power distribution network containing large-scale hierarchical aggregation adjustable resources

Abstract

The invention provides a power distribution network planning method and device containing large-scale hierarchical aggregation adjustable resources, and relates to the technical field of power distribution network planning. The method comprises the steps that a grid-connected multi-microgrid system planning model is constructed, the grid-connected multi-microgrid system planning model comprises an upper-layer planning model and a lower-layer planning model, and the upper-layer planning model is constructed based on construction data, operation data, energy flow data and carbon tax data of a power distribution network; the lower-layer planning model is constructed based on construction data, operation data, demand side corresponding compensation data, carbon tax data and energy transaction data of a lower-layer micro-grid of the power distribution network; and solving the grid-connected multi-microgrid system planning model by adopting a target cascade method to obtain an optimal power distribution network planning scheme of the power distribution network, and the optimal power distribution network planning scheme comprises tie line installation configuration and microgrid unit configuration. According to the invention, the energy utilization rate and the economical efficiency of the system under power distribution network planning can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network planning, and particularly to a distribution network planning method and device including a large-scale hierarchical aggregation adjustable resource. Background Art

[0002] With a large number of renewable energy sources and related devices connected to the distribution network, the traditional distribution network is gradually evolving into an active distribution network with numerous controllable resources. Distributed power sources, especially renewable energy sources such as new energy, have randomness, intermittency, and volatility in their power generation output, and their prediction accuracy is relatively low, and the prediction error increases with time, bringing great challenges to the regulation of the power grid. Although existing research has effectively improved the ability of the distribution network to cope with the uncertainty of renewable energy through model predictive control, it mainly performs day-ahead optimal scheduling of the distribution network. However, as time goes by, the uncertain factors affecting the prediction of new energy and load increase, and the accuracy of the distribution network optimal scheduling will also decrease accordingly.

[0003] The invention with the publication number CN116488264A discloses an optimization scheduling method, device, equipment and storage medium for a distribution network. The method includes: in the day-ahead scheduling stage, taking the minimum operation cost of the distribution network and the maximum proportion of new energy as the objective function, constructing and solving a day-ahead planning model to obtain a day-ahead scheduling plan; in the intra-day scheduling stage, based on the day-ahead scheduling plan, taking the minimum network loss of the distribution network and the maximum active power output of distributed energy as the objective function to construct an intra-day rolling model, and solving the intra-day rolling model every preset time interval to obtain an intra-day scheduling plan; in the real-time scheduling stage, within each preset time interval, every preset sampling step, taking the minimum adjustment amount of the output of adjustable resources as the objective function, correcting the intra-day scheduling plan.

[0004] According to the above invention, the existing distribution network planning solves with the minimum operation cost of the distribution network and the maximum proportion of new energy as two objective functions to obtain the daily day-ahead scheduling plan, so as to make full use of new energy and reduce costs in the general direction. However, while making full use of new energy in this way, the utilization rate of traditional energy is reduced, resulting in low utilization rate of traditional energy and causing certain waste. Summary of the Invention

[0005] The present application provides a distribution network planning method and device including a large-scale hierarchical aggregation adjustable resource to solve the problem of low energy utilization rate under the existing distribution network planning.

[0006] In a first aspect, the present application provides a distribution network planning method including a large-scale hierarchical aggregation adjustable resource, including:

[0007] Construct a grid-connected multi-microgrid system planning model, where the grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids under the lower layer of the distribution network;

[0008] Use the objective cascading method to solve the grid-connected multi-microgrid system planning model, and obtain the optimal distribution network planning scheme of the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units.

[0009] In a second aspect, the present application provides a distribution network planning device containing large-scale hierarchical aggregated adjustable resources, including:

[0010] A model construction module for constructing a grid-connected multi-microgrid system planning model. The grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids under the lower layer of the distribution network;

[0011] A solving module for using the objective cascading method to solve the grid-connected multi-microgrid system planning model, and obtaining the optimal distribution network planning scheme of the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units.

[0012] The present application provides a distribution network planning method and device containing large-scale hierarchical aggregated adjustable resources. By constructing a grid-connected multi-microgrid system planning model, the grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids under the lower layer of the distribution network; use the objective cascading method to solve the grid-connected multi-microgrid system planning model, and obtain the optimal distribution network planning scheme of the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units. The present application establishes a grid-connected multi-microgrid system planning model by considering the electrical energy interaction between various entities, improves the subjective initiative of the system through integrated demand response, and improves the flexibility of system operation through the joint optimization of different forms of energy such as "source-storage-load", which not only improves the energy utilization rate under the distribution network planning, but also improves the economy of the system. Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a schematic flowchart of a distribution network planning method with large-scale hierarchical aggregation adjustable resources provided by an embodiment of the present application;

[0015] Figure 2 It is a schematic structural diagram of a grid-connected multi-microgrid system provided by an embodiment of the present application;

[0016] Figure 3 It is a schematic structural diagram of a distribution network planning device with large-scale hierarchical aggregation adjustable resources provided by an embodiment of the present application. Specific embodiments

[0017] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0018] To make the purpose, technical solutions, and advantages of the present application clearer, the following will be described through specific embodiments in conjunction with the drawings.

[0019] Figure 1 It is an implementation flowchart of a distribution network planning method with large-scale hierarchical aggregation adjustable resources provided by an embodiment of the present application, which is described in detail as follows:

[0020] In step 101, a grid-connected multi-microgrid system planning model is constructed. The grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side response compensation data, carbon tax data, and energy trading data of the microgrids at the lower layer of the distribution network.

[0021] Among them, the microgrids at the lower layer of the distribution network include wind turbine units, photovoltaic units, energy storage units, and small diesel generator sets.

[0022] In the embodiments of the present application, for a distribution network with large-scale grid connection, a grid-connected multi-microgrid system planning model is established. Among them, the grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids at the lower layer of the distribution network.

[0023] Specifically, the upper-layer planning model can take the minimum total economic operation cost of the distribution network as the optimization objective. The total economic operation cost of the distribution network includes the construction cost operation cost energy flow cost and carbon tax cost

[0024] Correspondingly, the objective function of the upper-layer planning model is:

[0025]

[0026] Among them, f u is the total economic cost of the distribution network, is the construction cost of the distribution network, is the operation cost of the distribution network, is the carbon tax cost of the distribution network, is the energy flow cost of the distribution network, is the unit installation cost of the tie line of model θ, X θ,i is whether the tie line i of model θ is constructed, l i is the length of the tie line i, d is the interest rate, is the unit operation cost of the tie line, r θ,i is the unit impedance of the tie line i of model θ, is the active power of the tie line i at the hth hour of the sth quarter of the yth year, is the reactive power of the tie line i at the hth hour of the sth quarter of the yth year, U is the rated voltage of the distribution network, D ctax is the carbon tax, is the power of the diesel generator set at the hth hour of the sth quarter of the yth year, δ u is the carbon emission factor of the output power of the superior power grid, is the selling electricity price of the microgrid from the distribution network at the hth hour of the sth quarter of the yth year, is the purchase electricity price of the microgrid from the distribution network at the hth hour of the sth quarter of the yth year, y is the year, s is the quarter, h is the hour, θ is the model, is the set of all installable models of the tie lines, i is the tie line, and Ω1 is the set of all tie lines between the superior power grid and the microgrid.

[0027] Among them, X θ,i Indicates whether the connection line i of model θ is constructed. 1 means constructed, and vice versa is 0.

[0028] and It is defined that the direction from the upper-level power grid to the microgrid is the positive direction.

[0029] Correspondingly, the constraint conditions of the upper-level planning model include the transmission power constraint of the connection lines in the distribution network and the state variable constraint of the connection lines.

[0030] Among them, the transmission power constraint of the connection line is:

[0031] For

[0032]

[0033] Among them, is the length of the connection line i of model θ put into construction, is the upper limit of the active power transmitted by the connection line i of model θ, is the upper limit of the reactive power transmitted by the connection line i of model θ, respectively represent for any year, quarter, hour, connection line, and connection line model.

[0034] The state variable constraint of the connection line is:

[0035] Only one type will be selected for construction in a channel within the planning period and will not be demolished within the planning period. For The installation state during the planning period can be described as:

[0036]

[0037] The lower-level planning model can take the minimum operation cost of the microgrid at the lower layer of the distribution network as the optimization goal. The microgrid operation cost includes the construction cost operation cost demand-side response compensation cost carbon tax cost and energy trading cost

[0038] Correspondingly, the objective function of the lower-level planning model is:

[0039]

[0040] Among them, f m is the operation cost of the m-th microgrid at the lower layer of the distribution network, is the construction cost of the microgrid, is the operation cost of the microgrid, is the carbon tax cost of the microgrid, is the energy trading cost of the microgrid, is the demand-side response compensation cost of the microgrid, is the unit construction cost of the wind turbine generator, is the capacity growth of the wind turbine generator in the m-th microgrid in the y-th year, is the unit construction cost of the photovoltaic generator, is the capacity growth of the photovoltaic generator in the m-th microgrid in the y-th year, is the unit construction cost of the energy storage unit, is the capacity growth of the energy storage unit in the m-th microgrid in the y-th year, is the unit construction cost of the small diesel generator set, is the capacity growth of the small diesel generator set in the m-th microgrid in the y-th year, and are both the operation cost coefficients of the small diesel generator set in the m-th microgrid, is the power of the small diesel generator set at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operation cost of the photovoltaic generator in the m-th microgrid, is the power of the photovoltaic generator at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operation cost of the wind turbine generator in the m-th microgrid, is the power of the wind turbine generator at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operation cost of the energy storage unit in the m-th microgrid, is the charging power of the energy storage at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the discharging power of the energy storage at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, D ctax is the carbon tax, δ m is the carbon emission factor of the small diesel generator set in the m-th microgrid, is the purchase electricity price of the microgrid from the distribution network at the h-th hour of the s-th quarter of the y-th year, is the active power purchased from the superior power grid at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the selling electricity price of the microgrid to the distribution network at the h-th hour of the s-th quarter of the y-th year, is the unit compensation cost for load curtailment in the m-th microgrid, is the load curtailment amount at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the unit compensation cost for load transfer in the m-th microgrid, is the load transfer amount at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, d is the interest rate, y is the year, s is the quarter, and h is the hour.

[0041] Among them, Δsy = s y -s y-1 。

[0042] The positive direction of is from the superior power grid to the microgrid.

[0043] Correspondingly, the constraint conditions of the lower-layer planning model include energy balance constraint, diesel generator set constraint, distributed energy production infrastructure constraint, and energy storage constraint.

[0044] Among them, the energy balance constraint is:

[0045]

[0046] Among them, is the load before response at the hth hour of the sth quarter of the yth year in the mth microgrid, is the reactive power of the small diesel generator set at the hth hour of the sth quarter of the yth year in the mth microgrid, is the reactive power purchased from the superior power grid at the hth hour of the sth quarter of the yth year in the mth microgrid, is the reactive power of the photovoltaic generator set at the hth hour of the sth quarter of the yth year in the mth microgrid, is the reactive power of the wind turbine generator set at the hth hour of the sth quarter of the yth year in the mth microgrid, is the original reactive power load at the hth hour of the sth quarter of the yth year in the mth microgrid, is the transferred reactive power load at the hth hour of the sth quarter of the yth year in the mth microgrid, is the reduced reactive power load at the hth hour of the sth quarter of the yth year in the mth microgrid.

[0047] The diesel generator set constraint is:

[0048]

[0049] Among them, is the capacity of the small diesel generator set in the mth microgrid in the yth year, is the upper limit of the installed capacity of the small diesel generator set in the mth microgrid.

[0050] The distributed energy production infrastructure constraint is:

[0051]

[0052] Among them, is the capacity of the wind turbine generator set in the mth microgrid in the yth year, is the upper limit of the wind turbine generator set in the mth microgrid, is the capacity of the photovoltaic generator set in the mth microgrid in the yth year, is the upper limit of the installed PV capacity in the m-th microgrid, is the lower limit of the output power of the PV units in the m-th microgrid, is the upper limit of the output power of the PV units in the m-th microgrid, is the lower limit of the output power of the wind turbines in the m-th microgrid, is the upper limit of the output power of the wind turbines in the m-th microgrid.

[0053] The energy storage constraint is:

[0054]

[0055] Among them, is the capacity of the energy storage unit in the m-th microgrid in the y-th year, is the upper limit of the installed capacity of the energy storage unit in the m-th microgrid.

[0056] In the embodiments of the present application, by comprehensively considering requirements such as system economy and environmental protection, and by establishing a grid-connected multi-microgrid system planning model including renewable energy, small diesel generators, energy storage units, and adjustable loads, the subjective initiative of the system is improved through integrated demand response, and the operation flexibility of the system is improved through the joint optimization of different forms of energy such as "source-storage-load".

[0057] In step 102, the Analysis Target Cascading (ATC) method is used to solve the grid-connected multi-microgrid system planning model, and the optimal distribution network planning scheme of the distribution network is obtained. The optimal distribution network planning scheme includes the installation configuration of the tie lines and the power of the microgrid units.

[0058] Among them, the Analysis Target Cascading (ATC) method is a method for solving non-centralized, hierarchical structure coordination problems. It allows each element in the hierarchical structure to make independent decisions, and the parent element coordinates and optimizes the decisions of the child elements to obtain the overall optimal solution of the problem.

[0059] In the embodiments of the present application, the ATC algorithm is used to solve the upper-layer planning model and the lower-layer planning model to obtain the optimal distribution network planning scheme of the distribution network. Among them, the optimal distribution network planning scheme includes the installation configuration of the tie lines in the upper-layer planning and the configuration of the microgrid units in the lower-layer planning. The configuration of the microgrid units can include the construction of the microgrid units, the power distribution and regulation of the units.

[0060] Based on the grid-connected microgrid system planning model, considering the characteristics of multi-parameters and multi-agents in the system, the ATC algorithm is adopted, and an external penalty function is introduced to add the consistency constraint to the objective function for solution, realizing the distributed autonomy and wide-area of the grid-connected multi-microgrid system, ensuring the safe, stable and reliable operation of the system. The distributed optimization planning based on ATC improves the decision-making autonomy and information privacy of the system, and verifies the effectiveness and economy of the grid-connected microgrid system planning model through simulation examples.

[0061] In a possible implementation manner, the target cascading method is used to solve the grid-connected multi-microgrid system planning model to obtain the optimal distribution network planning scheme of the distribution network, which may include:

[0062] Using the target cascading method and the penalty function to update the objective function of the upper-layer planning model and the objective function of the lower-layer planning model, and taking the updated objective function of the upper-layer planning model as the current upper-layer objective function, and taking the updated objective function of the lower-layer planning model as the current lower-layer objective function. The current upper-layer objective function includes the upper-layer penalty factor, and the current lower-layer objective function includes the lower-layer penalty factor;

[0063] Solving the current upper-layer objective function to obtain the installation configuration of the tie line at the current iteration number, and transferring the installation configuration of the tie line at the current iteration number to the lower-layer planning model to update the current lower-layer objective function as the next lower-layer objective function;

[0064] Solving the next lower-layer objective function to obtain the microgrid unit configuration at the current iteration number, and transferring the microgrid unit configuration at the current iteration number to the upper-layer planning model to update the current upper-layer objective function as the next upper-layer objective function;

[0065] Judge whether the sum of the next upper-layer objective function and the next lower-layer objective function meets the convergence condition;

[0066] If both are satisfied, then take the installation configuration of the tie line and the microgrid unit configuration at the current iteration number as the optimal distribution network planning scheme;

[0067] If not satisfied, then update the upper-layer penalty factor and the lower-layer penalty factor at the current iteration number respectively by using the upper-layer penalty factor update coefficient and the lower-layer penalty factor update coefficient, add 1 to the current iteration number, and return to the step of solving the current upper-layer objective function to obtain the installation configuration of the tie line at the current iteration number and continue to execute.

[0068] Since the basic idea of the ATC algorithm is to divide the multi-objective optimization problem into sub-problems at multiple levels, and each sub-problem only contains one optimization objective. In the optimization problem, the penalty function is a method for introducing constraint conditions into the objective function. When the original problem contains constraint conditions, the ATC algorithm can introduce the constraint conditions into the objective function by constructing a penalty function, thereby transforming the original problem into an unconstrained problem.

[0069] Optionally, use the ATC algorithm and the penalty function to update the objective function of the upper-level planning model and the objective function of the lower-level planning model, and use the updated objective function of the upper-level planning model as the current upper-level objective function, and use the updated objective function of the lower-level planning model as the current lower-level objective function.

[0070] Among them, the current upper-level objective function is:

[0071]

[0072] Among them, f u is the total economic cost of the distribution network, is the upper-level penalty factor, c ysh,m is the degree of inconsistency between the upper level and the lower level, y is the year, s is the quarter, h is the hour, is the element-by-element multiplication of the matrices.

[0073] The current lower-level objective function is:

[0074]

[0075] Among them, f m is the operating cost of the m-th microgrid in the lower layer of the distribution network, is the lower-level penalty factor.

[0076] Then solve the current upper-level objective function to obtain the tie-line installation configuration at the current iteration, and send the tie-line installation configuration at the current iteration to the lower-level planning model to update the current lower-level objective function as the next lower-level objective function. Then solve the next lower-level objective function to obtain the microgrid unit configuration at the current iteration, and send the microgrid unit configuration at the current iteration to the upper-level planning model to update the current upper-level objective as the next upper-level objective function.

[0077] Calculate the sum of the next upper-level objective function and the next lower-level objective function as the objective value, and determine whether the objective value meets the convergence condition.

[0078] Among them, the convergence condition is:

[0079]

[0080] Among them, is the total value of the objective function at the k-th iteration, is the total value of the objective function at the (k - 1)-th iteration, is the total economic cost of the distribution network at the k-th iteration, is the operating cost of the m-th microgrid in the lower layer of the distribution network at the k-th iteration, where m is the number of microgrids, is the change rate of the degree of inconsistency, α is the maximum allowable value of the cost change rate, and β is the maximum allowable value of the change rate of the degree of inconsistency.

[0081] If it is satisfied, the tie-line installation configuration and microgrid unit configuration at the current iteration number are used as the optimal distribution network division scheme; if not satisfied, the upper-layer penalty factor update coefficient and the lower-layer penalty factor update coefficient are used to update the upper-layer penalty factor and the lower-layer penalty factor at the current iteration number respectively, and the current iteration number is incremented by 1, and then return to solve the upper-layer objective function and the lower-layer objective function again.

[0082] Among them, the upper-layer penalty factor and the lower-layer penalty factor are updated using the first formula, and the first formula is:

[0083]

[0084] Among them, μ u is the upper-layer penalty factor update coefficient, and μ d is the lower-layer penalty factor update coefficient, m = i, and m usually takes values from 1 to 3.

[0085] The specific solution process is as follows:

[0086] Step 1, Initialize each parameter, set the iteration number k = 1, initialize the upper-layer penalty factor and the lower-layer penalty factor, and set the initial values of the penalty functions of the current upper-layer objective function and the current lower-layer objective function to 0 respectively.

[0087] Step 2, Solve the current upper-layer objective function to solve the upper-layer optimization problem, and obtain the distribution network configuration at the current iteration number. Among them, the distribution network configuration includes the active power of the tie-line i of the distribution network at the h-th hour of the s-th quarter of the y-th year and the reactive power of the tie-line i at the h-th hour of the s-th quarter of the y-th year and transfer and to each microgrid in the lower-layer planning model, and update the current lower-layer objective function to the next lower-layer objective function.

[0088] Step 3, Solve the next lower-layer objective function to solve the optimization problems of each lower-layer microgrid, obtain the configuration of each microgrid unit, transfer the configuration of each microgrid unit to the upper-layer planning model, and update the current upper-layer objective function to the next upper-layer objective function.

[0089] Step 4, determine whether the sum of the next upper-level objective function and the next lower-level objective function reaches the convergence condition If it reaches, go to Step 5; if it does not reach, go to Step 6.

[0090] Step 5, exit the iteration, and use the distribution network configuration and microgrid unit configuration at the current iteration number as the optimal distribution network planning scheme.

[0091] Step 6, use the first formula Update the upper-level penalty factor and the lower-level penalty factor at the current iteration number, the iteration number k = k + 1, and return to Step 2 to continue execution.

[0092] Exemplarily, the data optimized based on the ATC algorithm is as follows:

[0093] There are 3 microgrids in the system. The time-of-use electricity price is adopted for calculation, the peak-valley electricity price is A1, the valley-section electricity price is A2, and the flat-section electricity price is A3, where A1 > A3 > A2. The planning period is 10 years, implemented in two phases, 5 years for each phase. The equipment to be built in each microgrid is invested in the first year of each phase. Three types of tie-line models are set. The load growth rate in this area is 5%, and the investment discount is 5%. The unit compensation costs for penalty and load transfer are B1 and B2 respectively, where B1 > B2. The initial value of the penalty coefficient is 0.1, and the update coefficient is 1.5.

[0094] The programming environment is MATLAB2019, the Yalmip toolbox is used to call the gurobi9.5.0 solver, the PC main frequency is 3.2Ghz, and the memory is 16GB. The unit parameters of the system planning are shown in Table 1:

[0095] Table 1 Unit parameters of system planning

[0096]

[0097] For whether the multi-microgrid system is connected to the grid, independent planning and collaborative planning comparison tests are carried out, and the scenarios are set as follows:

[0098] Scenario 1: Each microgrid conducts independent planning;

[0099] Scenario 2: Each microgrid is connected to the superior power grid, and hierarchical distributed planning is considered with demand-side response.

[0100] Table 2 Comparison of the planning results of each microgrid in each scenario

[0101]

[0102]

[0103] The specific analysis results are as follows. After calculation, the microgrid planning results in each scenario are shown in Table 2.

[0104] In Scenario 1, since each microgrid is not connected to the distribution network and operates independently, in order to ensure the reliability of power supply within the microgrid and considering the fluctuations in the output of wind energy and solar energy, the installed capacity of diesel generators is approximately 1.5 times that of Scenario 2. In addition, diesel generators need to provide reactive power support for the system and ensure the most basic system reliability requirements. In Scenario 2, this function can be provided by the distribution network. Therefore, only a small number of diesel generators are equipped, greatly improving the environmental protection of the system and the new energy consumption rate. More energy storage is also configured in this scenario to cope with the randomness of renewable energy output and cooperate with demand-side response to achieve the purpose of peak shaving and valley filling.

[0105] The installed capacity of renewable energy in the two scenarios is similar, but the utilization rate of renewable energy in Scenario 2 is higher. The typical power conditions of each energy source in the 4th quarter of the 10th year of Microgrid 1 are selected for comparison. When the microgrid operates independently, diesel power generation provides the most basic energy supply, which can be provided by the distribution network in Scenario 2, while the diesel generators operate at a low output during the load valley in Scenario 1. In both scenarios, the output characteristics of other energy sources are the same. Photovoltaic mainly generates electricity during the day, and the power generation peak is from 12:00 to 14:00. Since wind power generation has the characteristics of peak shaving, it mainly generates electricity at night, and the peak is at 19:00, which can complement the output of photovoltaic units to a certain extent. The utilization rate of renewable energy in Scenario 2 is significantly higher than that in Scenario 1, enhancing the system's consumption level of renewable energy output. Energy storage charges during the power consumption valley and discharges during the power consumption peak to achieve peak shaving and valley filling.

[0106] Table 3 Microgrid planning costs in each scenario

[0107]

[0108] It can be seen from the cost comparison in Table 3 that the total cost of Scenario 1 is 8,416.2. Each microgrid operates independently, which saves the cost of purchasing electricity from the superior distribution network. However, to ensure the normal operation of each microgrid, a large number of wind turbines, photovoltaic power generation devices, and diesel generators need to be built. The construction cost is much higher than that of Scenario 2. A large number of diesel units have caused a substantial increase in operating costs and an increase in carbon emissions, which is not conducive to the economy and environmental protection of the system. Scenario 2 fully considers the electricity interaction between the main bodies. When the output of renewable energy is insufficient, power is supplied by the superior power grid, ensuring the reliability of the system. Compared with Scenario 1, the total cost of Scenario 2 is reduced by 19.5%. At the same time, it also ensures the maximization of the operating income of the superior power grid. The electricity purchased by the superior distribution network from the microgrid can be used for its own load power supply, and at the same time, carbon emissions are reduced. It can be seen that the model proposed in the present invention realizes the parallel independent optimization of each main body and ensures the optimal performance of the regional energy supply system.

[0109] In the embodiment of the present application, a joint planning framework for a grid-connected multi-microgrid system is established by considering the electricity interaction between the main bodies, making full use of the flexibility on the load side, and realizing peak shaving and valley filling by moving or shrinking the supply-demand curve under the peak-valley electricity price, improving the economy of the system.

[0110] In addition, referring to Figure 2 , the grid-connected multi-microgrid system is composed of multiple microgrid units. Among them, the distribution network realizes the wide-area interaction and hierarchical coordination of the source, load, and storage, ensures the power balance within each microgrid system, meets the operation constraints of various controllable resources of the source, load, and storage, and conforms to the construction logic constraints during the planning period. The microgrid system realizes the local interaction and self-balance of the source, load, and storage.

[0111] The distribution network planning system (grid-connected multi-microgrid system planning model) is divided into a microgrid unit (lower-layer planning model) and a distribution network system (upper-layer planning model). The microgrid unit mobilizes distributed energy and exchanges energy with the superior system to meet energy demands and reduce costs. Autonomous energy management is realized within each microgrid. The distribution network system coordinates the power of each microgrid. The distribution network supplements / absorbs the deficit / surplus energy of the microgrid to ensure the voltage quality.

[0112] The distribution network system includes a data acquisition module and a processing module, which are used to collect, summarize, and calculate the power generation, energy storage, electricity consumption, and trading data of each microgrid.

[0113] The microgrid unit includes a power supply module, an energy storage module, and a load module, which are used to realize the local interaction and self-balance of the source, load, and storage.

[0114] The power supply module includes a wind turbine, a photovoltaic device, and a diesel generator. The energy storage module includes multiple energy storage modules. The load module includes a load that can be curtailed, a load that can be transferred, and a general load that does not participate in active scheduling.

[0115] Due to the different processing characteristics of wind power and photovoltaic power, their outputs can be complementary, maintaining the stable operation of the system. Renewable energy has randomness and volatility, which brings a certain degree of reliability decline to the system. Therefore, a certain amount of thermal power units must be equipped in the system to provide the most basic reliability guarantee, at least ensuring that the electricity demand of the primary load in the microgrid can be maintained under any circumstances.

[0116] Energy storage is the comprehensive link of the multi-energy system, paying more attention to the unidirectional conversion and storage technologies between electric energy and other energies, as well as the unidirectional or bidirectional conversion and storage technologies between other various energies. First, for the local distribution network capacity that does not meet the load demand, it can replace the user transformer capacity increase or reduce the transformer capacity configuration, reducing equipment costs and capacity occupancy fees. Second, for the large peak-valley electricity price difference in urban industry and commerce, charging during valley hours and discharging during peak hours can reduce the electricity cost. It can also improve the power quality and provide emergency power supply. Finally, it strengthens the interaction between users and the power grid, significantly enhancing the demand response ability on the user side.

[0117] Demand-side response can maximize the flexibility and initiative of the load in the microgrid. According to different response mechanisms, it can be divided into price-based demand-side response and incentive-based demand-side response. Users spontaneously transfer the energy consumption period according to the price signal, and at the same time, the system compensates the users. At the same time, according to the load type, the response type is divided into transfer type and reduction type, mobilizing users to participate in resource regulation from two aspects: time translation and space reduction, and stimulating the potential of users.

[0118] This application provides a distribution network planning method containing large-scale hierarchical aggregatable adjustable resources. By constructing a grid-connected multi-microgrid system planning model, the grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side response compensation data, carbon tax data, and energy trading data of the microgrids at the lower layer of the distribution network. The target cascading method is used to solve the grid-connected multi-microgrid system planning model to obtain the optimal distribution network planning scheme of the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units. This application establishes a grid-connected multi-microgrid system planning model by considering the electrical energy interaction between various entities, improves the subjective initiative of the system through comprehensive demand response, and improves the flexibility of system operation through the joint optimization of different forms of energy such as "source-storage-load", not only improving the energy utilization rate under the distribution network planning, but also improving the economy of the system.

[0119] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0120] The following is an apparatus embodiment of the present application. For details not described in detail herein, reference may be made to the corresponding method embodiments above.

[0121] Figure 3 The structural schematic diagram of a distribution network planning apparatus with large-scale hierarchical aggregation adjustable resources provided by an embodiment of the present application is shown. For the sake of convenience of description, only parts related to the embodiments of the present application are shown and are described in detail as follows:

[0122] As Figure 3 shown, the distribution network planning apparatus 3 with large-scale hierarchical aggregation adjustable resources includes:

[0123] A model construction module 31, configured to construct a grid-connected multi-microgrid system planning model. The grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids under the distribution network.

[0124] A solving module, configured to solve the grid-connected multi-microgrid system planning model by using the objective cascading method to obtain an optimal distribution network planning scheme for the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units.

[0125] The present application provides a distribution network planning apparatus with large-scale hierarchical aggregation adjustable resources. By constructing a grid-connected multi-microgrid system planning model, the grid-connected multi-microgrid system planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is constructed based on the construction data, operation data, energy flow data, and carbon tax data of the distribution network. The lower-layer planning model is constructed based on the construction data, operation data, demand-side corresponding compensation data, carbon tax data, and energy trading data of the microgrids under the distribution network. The grid-connected multi-microgrid system planning model is solved by using the objective cascading method to obtain an optimal distribution network planning scheme for the distribution network. The optimal distribution network planning scheme includes the installation configuration of tie lines and the power of microgrid units. By considering the electrical energy interaction between various entities, the present application constructs a grid-connected multi-microgrid system planning model, improves the subjective initiative of the system through integrated demand response, and improves the flexibility of system operation through the joint optimization of different forms of energy such as "source-storage-load", which not only improves the energy utilization rate under distribution network planning, but also improves the economy of the system.

[0126] In a possible implementation manner, the upper-layer planning model takes the minimization of the total economic cost of the distribution network as the optimization objective, and the objective function of the upper-layer planning model is:

[0127]

[0128] Among them, f u is the total economic cost of the distribution network, is the construction cost of the distribution network, is the operation cost of the distribution network, is the carbon tax cost of the distribution network, is the energy flow cost of the distribution network, is the unit installation cost of the tie line of model θ, X θ,i is whether the tie line i of model θ is constructed, l i is the length of the tie line i, d is the interest rate, is the unit operation cost of the tie line, r θ,i is the unit impedance of the tie line i of model θ, is the active power of the tie line i at the h-th hour of the s-th quarter of the y-th year, is the reactive power of the tie line i at the h-th hour of the s-th quarter of the y-th year, U is the rated voltage of the distribution network, D ctax is the carbon tax, is the power of the diesel generator set at the h-th hour of the s-th quarter of the y-th year, δ u is the carbon emission factor of the power output from the superior power grid, is the selling electricity price of the microgrid from the distribution network at the h-th hour of the s-th quarter of the y-th year, is the purchasing electricity price of the microgrid from the distribution network at the h-th hour of the s-th quarter of the y-th year, y is the year, s is the quarter, h is the hour, θ is the model, is the set of all installable models of the tie lines, i is the tie line, and Ω1 is the set of all tie lines between the superior power grid and the microgrid.

[0129] In a possible implementation, the constraint conditions of the upper-layer planning model may include the tie line transmission power constraint and the tie line state variable constraint;

[0130] The tie line transmission power constraint is:

[0131]

[0132] Among them, is the length of the tie line i of model θ put into construction, is the upper limit of the transmitted active power of the tie line i of model θ, is the upper limit of the transmitted reactive power of the tie line i of model θ;

[0133] The tie line state variable constraint is:

[0134]

[0135] In a possible implementation, the lower-level planning model aims to minimize the operating cost of the microgrid at the lower level of the distribution network. The objective function of the lower-level planning model is as follows:

[0136]

[0137] where f m is the operating cost of the m-th microgrid at the lower level of the distribution network, is the construction cost of the microgrid, is the operating cost of the microgrid, is the carbon tax cost of the microgrid, is the energy trading cost of the microgrid, is the demand-side response compensation cost of the microgrid, is the unit construction cost of the wind turbine, is the capacity growth of the wind turbines in the m-th microgrid in the y-th year, is the unit construction cost of the photovoltaic unit, is the capacity growth of the photovoltaic units in the m-th microgrid in the y-th year, is the unit construction cost of the energy storage unit, is the capacity growth of the energy storage units in the m-th microgrid in the y-th year, is the unit construction cost of the small diesel unit, is the capacity growth of the small diesel units in the m-th microgrid in the y-th year, and are both the operating cost coefficients of the small diesel units in the m-th microgrid, is the power of the small diesel unit at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operating cost of the photovoltaic units in the m-th microgrid, is the power of the photovoltaic units at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operating cost of the wind turbines in the m-th microgrid, is the power of the wind turbines at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the operating cost of the energy storage units in the m-th microgrid, is the charging power of the energy storage at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, is the discharging power of the energy storage at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, D ctax is the carbon tax, δ m is the carbon emission factor of the small diesel units in the m-th microgrid, is the purchase electricity price of the microgrid from the distribution network at the h-th hour of the s-th quarter of the y-th year, is the active power purchased from the superior power grid at the h-th hour of the s-th quarter of the y-th year in the m-th microgrid, The selling electricity price of the microgrid from the distribution network at the h-th hour of the s-th quarter in the y-th year. The unit compensation cost for load curtailment in the m-th microgrid. The amount of load curtailment at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. The unit compensation cost for load transfer in the m-th microgrid. The amount of load transfer at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. d is the interest rate, y is the year, s is the quarter, and h is the hour.

[0138] In a possible implementation, the constraint conditions of the lower-level planning model include energy balance constraints, diesel generator set constraints, distributed energy production infrastructure constraints, and energy storage constraints.

[0139] The energy balance constraint is:

[0140]

[0141] Where is the load before response at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the reactive power of the small diesel generator set at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the reactive power purchased from the superior power grid at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the reactive power of the photovoltaic generator set at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the reactive power of the wind turbine generator set at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the original reactive power load at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the transferred reactive power load at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid. is the curtailed reactive power load at the h-th hour of the s-th quarter in the y-th year in the m-th microgrid.

[0142] The diesel generator set constraint is:

[0143]

[0144] Where is the capacity of the small diesel generator set in the m-th microgrid in the y-th year. is the upper limit of the installed capacity of the small diesel generator set in the m-th microgrid.

[0145] The distributed energy production infrastructure constraint is:

[0146]

[0147] Where is the capacity of the wind turbine in the m-th microgrid in the y-th year, is the upper limit of the wind turbine in the m-th microgrid, is the capacity of the photovoltaic unit in the m-th microgrid in the y-th year, is the upper limit of the installed photovoltaic capacity in the m-th microgrid, is the lower limit of the output power of the photovoltaic unit in the m-th microgrid, is the upper limit of the output power of the photovoltaic unit in the m-th microgrid, is the lower limit of the output power of the wind turbine in the m-th microgrid, is the upper limit of the output power of the wind turbine in the m-th microgrid;

[0148] The energy storage constraint is:

[0149]

[0150] Among them, is the capacity of the energy storage unit in the m-th microgrid in the y-th year, is the upper limit of the installed capacity of the energy storage unit in the m-th microgrid.

[0151] In a possible implementation manner, the solving module can be used for:

[0152] Using the objective cascade method and penalty function to update the objective function of the upper-level planning model and the objective function of the lower-level planning model, and taking the updated objective function of the upper-level planning model as the current upper-level objective function, and taking the updated objective function of the lower-level planning model as the current lower-level objective function, the current upper-level objective function includes the upper-level penalty factor, and the current lower-level objective function includes the lower-level penalty factor;

[0153] Solving the current upper-level objective function to obtain the tie-line installation configuration at the current iteration number, and transmitting the tie-line installation configuration at the current iteration number to the lower-level planning model to update the current lower-level objective function as the next lower-level objective function;

[0154] Solving the next lower-level objective function to obtain the microgrid unit configuration at the current iteration number, and transmitting the microgrid unit configuration at the current iteration number to the upper-level planning model to update the current upper-level objective function as the next upper-level objective function;

[0155] Judging whether the sum of the next upper-level objective function and the next lower-level objective function meets the convergence condition;

[0156] If both are satisfied, then taking the tie-line installation configuration and the microgrid unit configuration at the current iteration number as the optimal distribution network planning scheme;

[0157] If not satisfied, update the upper-layer penalty factor and the lower-layer penalty factor at the current iteration using the upper-layer penalty factor update coefficient and the lower-layer penalty factor update coefficient respectively, increment the current iteration count by 1, and return to solve the current upper-layer objective function to continue with the tie-line installation and configuration steps at the current iteration count.

[0158] In one possible implementation, the current upper-layer objective function is:

[0159]

[0160] where f u is the total economic cost of the distribution network, is the upper-layer penalty factor, c ysh,m is the degree of inconsistency between the upper layer and the lower layer, y is the year, s is the quarter, h is the hour, and ° represents element-wise multiplication of matrices;

[0161] The current lower-layer objective function is:

[0162]

[0163] where f m is the operating cost of the m-th microgrid in the lower layer of the distribution network, is the lower-layer penalty factor.

[0164] In one possible implementation, the convergence condition is:

[0165]

[0166] where, is the total value of the objective function at the k-th iteration, is the total value of the objective function at the (k - 1)-th iteration, is the total economic cost of the distribution network at the k-th iteration, is the operating cost of the m-th microgrid in the lower layer of the distribution network at the k-th iteration, m is the number of microgrids, is the rate of change of the degree of inconsistency, α is the maximum allowable value of the cost change rate, and β is the maximum allowable value of the rate of change of the degree of inconsistency.

[0167] In one possible implementation, the solving module can also be used to:

[0168] Calculate the updated upper-layer penalty factor and lower-layer penalty factor using the first formula, and the first formula is:

[0169]

[0170] where μ u is the upper-layer penalty factor update coefficient, and μ d is the lower-layer penalty factor update coefficient.

[0171] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0172] Those of ordinary skill in the art can realize that the templates, units, and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0173] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described embodiments of the distribution network planning method with large-scale hierarchical aggregation adjustable resources can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0174] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A distribution network planning method with large-scale hierarchical aggregated adjustable resources, characterized in that: include: Constructing a grid-connected multi-microgrid system planning model, the grid-connected multi-microgrid system planning model comprising an upper-layer planning model and a lower-layer planning model, the upper-layer planning model being constructed based on construction data, operation data, energy flow data and carbon tax data of the distribution network, and the lower-layer planning model being constructed based on construction data, operation data, corresponding compensation data on the demand side, carbon tax data and energy trading data of the microgrids at the lower layer of the distribution network; The target cascade method is used to solve the grid-connected multi-microgrid system planning model to obtain the optimal distribution network planning scheme of the distribution network. The optimal distribution network planning scheme includes the installation configuration of the tie line and the configuration of the microgrid units.

2. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 1 is characterized in that: The upper-level planning model takes minimizing the total economic cost of the distribution network as the optimization goal, and the objective function of the upper-level planning model is: Among them, f u is the total economic cost of the distribution network, is the construction cost of the distribution network, is the operating cost of the distribution network, is the carbon tax cost on the distribution network, is the energy flow cost of the distribution network, is the unit installation cost of the tie line of model θ, X θ,i is whether the tie line i of model θ is constructed, l i is the length of tie line i, d is the interest rate, is the unit operating cost of the tie line, r θ,i is the unit impedance of tie line i of model θ, is the active power of tie line i at hour h in quarter s of year y, is the reactive power of tie line i in the hth hour of the sth quarter of the yth year, U is the rated voltage of the distribution network, D ctax For carbon tax, is the power of the diesel generator set at the hth hour in the sth quarter of the yth year, δ u is the carbon emission factor of the upper grid output power, is the electricity price sold by the microgrid from the distribution network at the hth hour in the sth quarter of the yth year, is the electricity price purchased by the microgrid from the distribution network at the hth hour of the sth quarter of the yth year, where y is year, s is quarter, h is hour, and θ is the model. is the set of installable models of all tie lines, i is the tie line, and Ω1 is the set of tie lines between all upper-level power grids and microgrids.

3. The distribution network planning method containing large-scale hierarchical aggregated adjustable resources according to claim 2 is characterized in that: The constraint conditions of the upper-level planning model include tie line transmission power constraint and tie line state variable constraint; The tie line transmission power constraint is: in, The length of the tie line i of model θ is put into construction, is the upper limit of active power transmission of tie line i of model θ, is the upper limit of the transmission reactive power of the tie line i of model θ; The state variable constraints of the tie line are:

4. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 1 is characterized in that: The lower-level planning model takes minimizing the microgrid operation cost of the lower-level distribution network as the optimization goal, and the objective function of the lower-level planning model is: Among them, f m is the operating cost of the mth microgrid in the lower layer of the distribution network, is the construction cost of the microgrid, is the operating cost of the microgrid, is the carbon tax cost of the microgrid, is the energy transaction cost of the microgrid, Compensate for the demand-side response costs of the microgrid, is the unit construction cost of the wind turbine, is the capacity growth of the wind turbine in the mth microgrid in the yth year, is the unit construction cost of the photovoltaic unit, is the capacity growth of the PV unit in the mth microgrid in year y, is the unit construction cost of the energy storage unit, is the capacity growth of the energy storage unit in the mth microgrid in year y, is the unit construction cost of a small diesel generator set, is the capacity growth of the small diesel generator set in the mth microgrid in year y, and are the operating cost coefficients of the small diesel generator sets in the mth microgrid, is the power of the small diesel generator set in the mth microgrid at the hth hour in the yth quarter, is the operating cost of the PV unit in the mth microgrid, is the power of the photovoltaic unit in the mth microgrid at the hth hour in the sth quarter of the yth year, is the operating cost of the wind turbine in the mth microgrid, is the power of the wind turbine in the mth microgrid at the hth hour in the sth quarter of the yth year, is the operating cost of the energy storage unit in the mth microgrid, is the energy storage charging power in the mth microgrid at the hth hour in the yth quarter, is the energy storage discharge power in the mth microgrid at the hth hour in the sth quarter of the yth year, D ctax is the carbon tax, δ m is the carbon emission factor of the small diesel unit in the mth microgrid, is the electricity price purchased by the microgrid from the distribution network at the hth hour in the sth quarter of the yth year, is the active power purchased from the upper grid in the mth microgrid at the hth hour in the sth quarter of the yth year, is the electricity price sold by the microgrid from the distribution network at the hth hour in the sth quarter of the yth year, is the unit compensation cost for load reduction in the mth microgrid, is the load reduction in the mth microgrid at the hth hour in the sth quarter of the yth year, is the unit compensation cost of load transfer within the mth microgrid, is the load transfer in the mth microgrid at the hth hour in the yth quarter of the yth year, where d is the interest rate, y is year, s is quarter, and h is hour.

5. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 4 is characterized in that: The constraints of the lower-level planning model include energy balance constraints, diesel unit constraints, distributed energy production infrastructure constraints and energy storage constraints; The energy balance constraint is: in, is the load before response at hour h in quarter s in year y in the mth microgrid, is the reactive power of the small diesel generator set in the mth microgrid at the hth hour in the yth quarter, is the reactive power purchased from the upper grid in the mth microgrid at the hth hour in the yth quarter of the yth year, is the reactive power of the photovoltaic unit in the mth microgrid at the hth hour in the sth quarter of the yth year, is the reactive power of the wind turbine in the mth microgrid at the hth hour in the sth quarter of the yth year, is the original reactive load at the hth hour in the sth quarter of the yth year in the mth microgrid, is the transferred reactive load at the hth hour in the sth quarter of the yth year in the mth microgrid, The reactive load reduction in the mth microgrid at the hth hour in the sth quarter in the yth year; The diesel group constraints are: in, is the capacity of the small diesel generator set in the mth microgrid in year y, The upper limit of the installed capacity of small diesel units in the mth microgrid; The distributed energy production infrastructure constraints are: in, is the capacity of the wind turbine in the mth microgrid in year y, is the upper limit of wind turbines in the mth microgrid, is the capacity of the PV unit in the mth microgrid in year y, is the upper limit of the installed PV capacity in the mth microgrid, is the lower limit of the output power of the photovoltaic unit in the mth microgrid, is the upper limit of the output power of the photovoltaic unit in the mth microgrid, is the lower limit of the wind turbine output power in the mth microgrid, is the upper limit of the output power of the wind turbine in the mth microgrid; The energy storage constraint is: in, is the capacity of the energy storage unit in the mth microgrid in year y, is the upper limit of the installed capacity of the energy storage unit in the mth microgrid.

6. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 1 is characterized in that: The target cascade method is used to solve the grid-connected multi-microgrid system planning model to obtain the optimal distribution network planning scheme of the distribution network, including: Using the target cascade method and the penalty function to update the target function of the upper-level planning model and the target function of the lower-level planning model, and using the updated target function of the upper-level planning model as the current upper-level target function, and using the updated target function of the lower-level planning model as the current lower-level target function, the current upper-level target function includes an upper-level penalty factor, and the current lower-level target function includes a lower-level penalty factor; Solving the current upper layer objective function to obtain the tie line installation configuration under the current iteration number, and transferring the tie line installation configuration under the current iteration number to the lower layer planning model, and updating the current lower layer objective function to the next lower layer objective function; Solve the next lower-level objective function to obtain the microgrid unit configuration under the current iteration number, and transfer the microgrid unit configuration under the current iteration number to the upper-level planning model, and update the current upper-level objective function as the next upper-level objective function; Determine whether the sum of the next upper layer objective function and the next lower layer objective function meets the convergence condition; If all conditions are met, the tie line installation configuration and microgrid unit configuration under the current iteration number are used as the optimal distribution network planning scheme; If it is not satisfied, the upper penalty factor and the lower penalty factor at the current iteration number are updated using the upper penalty factor update coefficient and the lower penalty factor update coefficient respectively, and the current iteration number is increased by 1, and the solution of the current upper objective function is returned to obtain the tie line installation configuration step at the current iteration number and continue to execute.

7. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 6 is characterized in that: The current upper layer objective function is: Among them, f u is the total economic cost of the distribution network, is the upper layer penalty factor, c ysh,m is the inconsistency between the upper and lower layers, y is year, s is quarter, h is hour, Multiply the corresponding elements of the matrix; The current lower layer objective function is: Among them, f m is the operating cost of the mth microgrid in the lower layer of the distribution network, is the lower layer penalty factor.

8. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 6 is characterized in that: The convergence condition is: in, is the total value of the objective function at the kth iteration, is the total value of the objective function at the k-1th iteration, is the total economic cost of the distribution network at the kth iteration, is the operating cost of the mth microgrid in the lower layer of the distribution network at the kth iteration, m is the number of microgrids, is the rate of change of inconsistency degree, α is the maximum allowable value of cost change rate, and β is the maximum allowable value of inconsistency degree change rate.

9. The distribution network planning method with large-scale hierarchical aggregated adjustable resources according to claim 6 is characterized in that: The updating of the upper layer penalty factor and the lower layer penalty factor at the current number of iterations by using the upper layer penalty factor update coefficient and the lower layer penalty factor update coefficient respectively includes: The updated upper layer penalty factor and lower layer penalty factor are calculated by the first formula, and the first formula is: Among them, μ u is the upper layer penalty factor update coefficient, μ d is the update coefficient of the lower layer penalty factor.

10. A distribution network planning device containing large-scale hierarchical aggregated adjustable resources, characterized in that: include: A model building module, used to build a grid-connected multi-microgrid system planning model, wherein the grid-connected multi-microgrid system planning model includes an upper-level planning model and a lower-level planning model, wherein the upper-level planning model is built based on the construction data, operation data, energy flow data and carbon tax data of the distribution network, and the lower-level planning model is built based on the construction data, operation data, corresponding compensation data on the demand side, carbon tax data and energy trading data of the microgrids at the lower layer of the distribution network; The solution module is used to solve the grid-connected multi-microgrid system planning model by using the target cascade method to obtain the optimal distribution network planning scheme of the distribution network, wherein the optimal distribution network planning scheme includes the installation configuration of the tie line and the power of the microgrid unit.

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