Distributed new energy efficient aggregation method and system

Abstract

The invention discloses a distributed new energy efficient aggregation method and system. The distributed new energy efficient aggregation method comprises the following steps: from a low-voltage power grid to a high-voltage power grid, obtaining the equivalent maximum power generation capacity of all grid frames in each level of power grid; from a low-voltage power grid to a high-point power grid, acquiring a cost quotation curve of all grid frames in each level of power grid; the equivalent maximum power generation capacity and the cost quotation curve are reported to a power grid for scheduling, so that a scheduling instruction is obtained and issued; and splitting output required in the scheduling instruction into equivalent output of each low-voltage power grid, and obtaining output conditions of power generation units of all grid frames in each level of power grid. By adopting the operation aggregation model of the step-by-step voltage level power grid and adding the consideration factor of the line loss into the model constraint, the aggregation process of the demand side response can consider the actual cost of power transmission, so that the price mechanism is more reasonable and fair.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and control, and particularly to a method and system for efficient aggregation of distributed new energy. Background Art

[0002] The demand-side response technology controls the user-side load units to cooperate with the power grid operation. The existing technologies mainly include methods such as price incentives and direct control. Although the regulation ability can be enhanced, when aggregating distributed new energy generation or controllable loads in a multi-level power grid, the impact of line losses is not considered, which may lead to problems such as supply-demand mismatch, affecting the stability and economy of the power grid. Summary of the Invention

[0003] The purpose of this application is to overcome the defects of the existing technology and provide a method and system for efficient aggregation of distributed new energy.

[0004] In the first aspect, this application provides a method for efficient aggregation of distributed new energy, and the method for efficient aggregation of distributed new energy includes the following steps: From the low-voltage power grid to the high-voltage power grid, obtain the equivalent maximum power generation capacity of all grid frameworks in each level of the power grid; From the low-voltage power grid to the high-voltage power grid, obtain the cost quotation curves of all grid frameworks in each level of the power grid; Report the equivalent maximum power generation capacity and the cost quotation curves to the power grid for scheduling to obtain a scheduling instruction and issue it; Split the required output in the scheduling instruction into the equivalent output of each low-voltage power grid, and obtain the output conditions of the power generation units of all grid frameworks in each level of the power grid.

[0005] Optionally, the step of obtaining the equivalent maximum power generation capacity of all grid frameworks in each level of the power grid from the low-voltage power grid to the high-voltage power grid includes: Obtain the parameter data of each level of the power grid, and determine the low-voltage power grid and the first grid framework of the low-voltage power grid; Input the active load, reactive load, active output, and reactive output of the first grid framework of the low-voltage power grid, optimize the first objective function using the first optimization algorithm, and use the first constraint condition for constraint to obtain the optimal value of the first objective function; When the active output and the reactive output of the first grid framework of the low-voltage power grid are zero, optimize the second objective function using the second optimization algorithm, and use the first constraint condition for constraint to obtain the optimal value of the second objective function, that is, the equivalent load of the low-voltage power grid; Based on the optimal value of the first objective function and the optimal value of the second objective function, obtain the equivalent maximum capacity of the first grid framework; Determine whether the first grid frame of the layer is the last grid frame of the layer grid. If not, return to operate on the next grid frame. If so, the active load input by the next layer grid is the equivalent load of the low-voltage grid. Determine whether the low-voltage grid is the highest-voltage grid. If not, return to operate on the next layer grid. If so, output the equivalent maximum capacity of all grid frames of each voltage level grid.

[0006] Optionally, the first constraint condition includes: power flow constraint, output constraint.

[0007] Optionally, from the low-voltage grid to the high-voltage grid, obtain the cost quotation curves of all grid frames in each level of grid, including: According to the parameter data of each level of grid, determine the first-end injection power and the equivalent power generation unit output range of the first grid frame of the low-voltage grid; Input the active power output and the reactive power output of each power generation unit in the low-voltage grid and the first grid frame, and cyclically take values within the output range to obtain the equivalent power generation unit output value; Based on the equivalent power generation unit output value, use the third optimization algorithm to optimize the third objective function and use the second constraint condition for constraint to obtain the optimization result of the third objective function, that is, the ordinate point; Repeat the above operations, and aggregate the ordinate points obtained by cyclically taking values through dotting and fitting to obtain the cost curve of the grid frame; Determine whether the first grid frame is the last grid frame of the layer grid. If not, return to operate on the next grid frame. If so, continue to determine whether the low-voltage grid is the highest-voltage grid. If not, return to continue cyclically taking values for the next layer grid. If so, output the quotation curves of each level of grid frames.

[0008] Optionally, the abscissa of the cost curve is the equivalent maximum capacity of the grid frame, and the ordinate is the optimization result of the third objective function.

[0009] Optionally, the second constraint condition includes: power flow constraint, first-end injection constraint.

[0010] Optionally, splitting the output required in the dispatching instruction into the equivalent output of each low-voltage grid and obtaining the output conditions of the power generation units of all grid frames in each level of grid, including: inputting the active power output and the reactive power output of the power generation unit of the last grid frame of the highest-voltage grid; Based on the active power output and the reactive power output, use the fourth optimization algorithm to optimize the fourth objective function and use the third constraint condition for constraint to obtain the optimal value of the fourth objective function; Determine whether the grid frame at this time is the first grid frame of the layer grid. If not, input the active power output and the reactive power output of the next grid frame. If so, determine whether the grid is the smallest layer grid. If not, return to perform the above operations on the next layer grid. If so, output the output conditions of all power generation units.

[0011] Optionally, the third constraint condition includes: power flow constraint, output constraint, and head-end injection constraint.

[0012] Optionally, each layer of the grid includes a plurality of grid frames, and each grid frame includes a plurality of distributed power generation units.

[0013] In a second aspect, the present application further provides a distributed new energy efficient aggregation system, including: A maximum capacity acquisition module for acquiring the maximum capacity of all grid frames of each level of grid from the low-voltage grid to the high-voltage grid; A quotation curve acquisition module for acquiring the quotation curves of all grid frames in each level of grid; A dispatching instruction execution module that splits the output required in the dispatching instruction into the output of each low-voltage grid according to the maximum capacity and the quotation curve, and obtains the output distribution matrix of the power generation units of all grid frames in each level of grid.

[0014] The present application provides a distributed new energy efficient aggregation method and system. Starting from the low-voltage grid, calculate the equivalent maximum power generation capacity and cost curve of each low-voltage distributed power generation unit at the nodes of the upper-level high-voltage grid for each voltage level grid; report the equivalent maximum power generation capacity and the cost quotation curve to the grid for dispatching to obtain a dispatching instruction and issue it; split the output required in the dispatching instruction into the equivalent output of each low-voltage grid, and obtain the output conditions of the power generation units of all grid frames in each level of grid. By adopting an operation aggregation model of grid networks at each voltage level and adding a consideration factor of line loss to the model constraints, the aggregation process of demand-side response can take into account the actual cost of power transmission, making the price mechanism more reasonable and fair, helping to optimize the geographical distribution of energy resources, promoting local production and consumption, and thus improving the efficiency of the overall energy system; it can also effectively guide the access and utilization of renewable energy, give priority to energy access points with lower losses, and improve the integration efficiency of renewable energy.

[0015] To make the above features and advantages of the invention more obvious and understandable, specific embodiments are hereinafter given and described in detail in conjunction with the accompanying drawings as follows. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required for use in the description of the embodiments or the related 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.

[0017] Figure 1 It is a flowchart of the distributed new energy efficient aggregation method provided in an embodiment of the present application.

[0018] Figure 2 It is a flowchart of step S10 in the distributed new energy efficient aggregation method provided in another embodiment of the present application.

[0019] Figure 3 It is a flowchart of step S20 in the distributed new energy efficient aggregation method provided in another embodiment of the present application.

[0020] Figure 4 It is a flowchart of step S40 in the distributed new energy efficient aggregation method provided in another embodiment of the present application.

[0021] Figure 5 It is a schematic structural diagram of the distributed new energy efficient aggregation system provided in an embodiment of the present application. Detailed implementation manners

[0022] To make the objectives and technical solutions of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present application in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0023] In one embodiment, please refer to Figure 1 , the present application also provides a distributed new energy efficient aggregation method, and the distributed new energy efficient aggregation method may include the following steps: S10 to S40.

[0024] S10: From the low-voltage power grid to the high-voltage power grid, obtain the equivalent maximum power generation capacity of all grid frameworks in each level of the power grid.

[0025] S20: From the low-voltage power grid to the high-voltage power grid, obtain the cost quotation curves of all grid frameworks in each level of the power grid.

[0026] S30: Report the equivalent maximum power generation capacity and the cost quotation curves to the power grid for scheduling to obtain a scheduling instruction and issue it.

[0027] S40: Split the required output in the dispatching instruction into the equivalent outputs of each low-voltage power grid, and obtain the output conditions of the power generation units of all the grid frameworks in each hierarchical power grid.

[0028] In the distributed new energy efficient aggregation method of the present application, starting from the low-voltage power grid, calculate the equivalent maximum power generation capacity and cost curve of each low-voltage distributed power generation unit at the nodes of the upper-level high-voltage power grid for each voltage-level power grid, so as to realize the parameter equivalence of the main grid nodes; according to the power grid dispatching instruction, starting from the winning bid nodes of the high-voltage power grid, split the equivalent output required by the dispatching instruction into the equivalent outputs of each low-voltage power grid node, and calculate according to the voltage level from high to low, and finally obtain the output conditions of all the distributed power generation units under the aggregator. It can make the aggregation process of demand response take into account the actual cost of power transmission, make the price mechanism more reasonable and fair, make the formulated power grid dispatching and optimization strategies more accurate, reduce voltage fluctuations and frequency deviations, and enhance the stability and reliability of the power grid.

[0029] In step S10, please refer to Figure 1 step S10 in

[0030] As an example, please refer to Figure 2 , step S10 may include the following steps: S11~S16.

[0031] S11: Obtain the parameter data of each hierarchical power grid, and determine the low-voltage power grid and the first grid framework of the low-voltage power grid.

[0032] S12: Input the active load, reactive load, active output, and reactive output of the first grid framework of the low-voltage power grid, optimize the first objective function using the first optimization algorithm, and use the first constraint condition for constraint to obtain the optimal value of the first objective function.

[0033] S13: When the active output and reactive output of the first grid framework of the low-voltage power grid are 0, optimize the second objective function using the second optimization algorithm, and use the first constraint condition for constraint to obtain the optimal value of the second objective function, that is, the equivalent load of the low-voltage power grid.

[0034] S14: Based on the optimal value of the first objective function and the optimal value of the second objective function, obtain the equivalent maximum capacity of the first grid framework.

[0035] S15: Determine whether the first grid framework is the last grid framework of the layer power grid. If not, return to step S12 to operate on the next grid framework. If so, the active load of the next layer power grid is the equivalent load of the low-voltage power grid.

[0036] S16: Determine whether the low-voltage power grid is the highest-voltage power grid. If not, return to step S12 to operate on the next-level power grid. If so, output the maximum capacity of all the grid frameworks at each level of the power grid.

[0037] As an example, obtain the parameter data of each level of the power grid. Each level of the power grid includes several grid frameworks, and each grid framework includes several nodes. The parameter data may include: node data, distributed generation unit data, load data, branch data, etc. Among them, the node data of the grid frameworks at each level of the power grid may include boundary condition data such as resistance and reactance between nodes, upper and lower limits of branch power, etc., which are used for load equivalent calculation and power flow constraint analysis; the distributed generation unit data includes: active power output / reactive power output of the generation unit, quotation of the generation unit. The active power output / reactive power output of the generation unit is used as the basic data for calculating the maximum capacity, cost curve, and performing constraint judgment and calculation in each module. The quotation of the generation unit is used in the quotation curve weaving module to calculate the minimum generation cost and equivalent cost curve; the load data includes: active / reactive load on each node, which is used for power flow constraint calculation and load equivalent calculation; the branch data includes: resistance / reactance corresponding to the branches connected to each node, which is used for power flow constraint calculation to analyze situations such as line losses during the power transmission process.

[0038] As an example, starting from the low-voltage power grid, calculate the equivalent maximum generation capacity of each low-voltage distributed generation unit on the nodes of the upper-level high-voltage power grid for each voltage-level power grid. It is defined that there are α layers of power grids, and each layer of the power grid has grid frameworks, and each grid framework has nodes, distributed generation units, where . Each layer of the power grid represents a power grid corresponding to a voltage level. The smaller α is, the lower the voltage level. The low-voltage layer power grid is the power grid when α = 1. Starting from the first grid framework of the lowest voltage layer power grid, that is, set α = 1, , according to the obtained parameter data of each level of the power grid, obtain the active load on the i node of the / reactive load of the th grid framework of the αth layer of the power grid. Input the active power output i / reactive power output of the th distributed generation unit of the th grid framework of the αth layer of the power grid, and use the first optimization algorithm to optimize the first objective function obj1. The first objective function obj1 can be expressed by the following formula: where α is the αth layer of the power grid, a grid network is the injected power at the head node of the th grid network of the α - layer power grid (the output of the distributed generation unit is not zero), and the first objective function is converged to a stable optimal solution through the first constraint condition. When the active power output and reactive power output of the first grid network of the low - voltage power grid are 0, that is , the second optimization algorithm is used to optimize the second objective function obj2, and it is converged through the first constraint condition. The formula of the second objective function obj2 is as follows: where α is the α - layer power grid, is the th grid network, is the injected power at the head node of the th grid network of the α - layer power grid (the output of the distributed generation unit is zero). The data optimized by the second objective function obj2 is the equivalent load of the th grid network of the α - layer power grid equivalent to the high - voltage node. At this time, the active power output and reactive power output of the distributed generation unit are 0.

[0039] As an example, the first constraint condition can be a power flow constraint and an output constraint.

[0040] As an example, the formula of the power flow constraint (constrain1) is as follows: where is the voltage of node of the i th grid network of the α - layer power grid, is the voltage of node of the j th grid network of the α - layer power grid, is the resistance corresponding to the branch connecting the two nodes of the th grid network of the α - layer power grid, ij is the reactance corresponding to the branch connecting the two nodes of the th grid network of the α - layer power grid, is the active power corresponding to the flow on the branch connecting the two nodes of the ij th grid network of the α - layer power grid, is the th grid network of the α - layer power grid, ij is the active power flowing on the branch connecting the two nodes corresponding to it, is the reactive power corresponding to the th grid frame of the α-th layer power grid ij flowing on the branch connecting two nodes, is the current flowing on the branch connecting two nodes of the th grid frame of the α-th layer power grid ij ; is the active power output of the th generating unit in the j th grid frame of the α-th layer power grid, is the reactive power output of the th generating unit in the j th grid frame of the α-th layer power grid, is the active load on the th node of the j th grid frame of the α-th layer power grid, is the reactive load on the th node of the j th grid frame of the α-th layer power grid, is the total active injection power on the th node of the j th grid frame of the α-th layer power grid, is the total reactive injection power on the th node of the j th grid frame of the α-th layer power grid.

[0041] As an example, the output constraint (constrain2) of the generating unit can be: when α = 1, , ; when α ≠ 1, . Among them, is the active power output of the th generating unit in the i th grid frame of the α-th layer power grid, is the reactive power output of the th generating unit in the i th grid frame of the α-th layer power grid, is the upper limit of the output of the distributed generating unit of the th grid frame of the α-th layer power grid, is the lower limit of the output of the distributed generating unit of the th grid frame of the α-th layer power grid, is the maximum capacity of the th grid frame of the (α - 1)-th layer power grid.

[0042] Furthermore, based on the optimization results of the first objective function obj1 and the optimization results of the second objective function obj2, when α = 1, , calculate the maximum capacity of the first grid frame of the low-voltage power grid, the maximum capacity calculation formula is as follows: Wherein, is the maximum capacity of the th grid frame of the αth layer power grid, is the power injection at the head node of the th grid frame of the αth layer power grid (the output of the distributed generation unit is not zero), is the power injection at the head node of the th grid frame of the αth layer power grid (the output of the distributed generation unit is zero).

[0043] As an example, when α = 1, is calculated, and the maximum capacity of the th grid frame (the first one) of the αth layer power grid (low-voltage power grid) is obtained, and then it is judged whether the th grid frame is the last grid frame of the αth layer power grid. If not, return to step S12 and operate on the th grid frame; if so, the active load on the th node of the i th grid frame of the (α + 1)th layer power grid is the equivalent load of the th grid frame. And continue to judge whether the αth layer power grid is the highest-voltage power grid. If not, return to step S12 and continue the above steps for the (α + 1)th layer power grid until the maximum capacity of the highest layer power grid is obtained. If so, output the maximum capacities of all grid frames of each voltage level power grid.

[0044] In step S20, please refer to Figure 1 for the S20 step in it, and obtain the cost quotation curves of all grid frames in each hierarchical power grid from the low-voltage power grid to the high-voltage power grid.

[0045] As an example, please refer to Figure 3 , step S20 may include the following steps: S21~S25.

[0046] S21: Determine the power injection at the head end and the equivalent generation unit output range of the first grid frame of the low-voltage power grid according to the parameter data of each hierarchical power grid.

[0047] S22: Input the active power output and reactive power output of each generation unit in the low-voltage power grid and the first grid frame, and cyclically take values within the output range to obtain the equivalent generation unit output value.

[0048] S23: Based on the equivalent generation unit output value, optimize the third objective function using the third optimization algorithm and use the second constraint condition for constraint to obtain the optimization result of the third objective function, that is, the ordinate point.

[0049] S24: Repeat steps S22 - S23, aggregate the ordinate points obtained by loop value taking through dot fitting to obtain the cost curve of the grid frame.

[0050] S25: Determine whether the first grid frame is the last grid frame of the layer power grid. If not, return to step S22 to operate on the next grid frame. If so, continue to determine whether the first - layer power grid is the highest - voltage power grid. If not, return to step S22 to continue loop value taking for the next - layer power grid. If so, output the quotation curves of grid frames at each level.

[0051] As an example, it can be defined that there are α layers of power grids, each layer of power grid has grid frames, each grid frame has nodes, distributed generation units, where, . Define the injection power at the head end of the th grid frame of the α - th layer of power grid as , the equivalent generation unit output range is , initialize the low - voltage power grid and the first grid frame, that is, α = 2, , indicating that the calculation starts from the second - layer power grid. Since the quotation of the lowest - voltage - level generation unit is known and the equivalent cost needs to be calculated upward, input the active power output of the i th generation unit in the th grid frame of the (α - 1) - th layer of power grid, and the reactive power output of the i th generation unit in the th grid frame of the (α - 1) - th layer of power grid; loop value take the equivalent generation unit output of the th grid frame of the α - th layer from 0 to x ; use the third optimization algorithm to optimize the third objective function obj3 and use the second constraint condition for constraint to obtain the optimization result of the third objective function obj3. Repeat steps S22 - S23, aggregate the ordinate points obtained by loop value taking through dot fitting to obtain the cost curve of the active power output of the i th generation unit in the th grid frame of the α - th layer of power grid. As an example, the formula of the third objective function obj3 is as follows: Where, is the quotation of each generation unit in the th grid frame of the (α - 1) - th layer of power grid, is the active power output of the th grid frame of the (α - 1) - th layer of power grid and the

[0052] As an example, the third constraint condition may be a power flow constraint and an injection power constraint.

[0053] As an example, the injection power constraint (constrain3) can be expressed by the following formula: Where, is the injection power of the th grid frame of the (α - 1)-th layer power grid, is the maximum capacity of the th grid frame of the α-th layer power grid, is the injection power at the head node of the α th grid frame of the α-th layer power grid (the output of the distributed generation unit is 0). For the maximum capacity take values cyclically from 0 to max to obtain the cost curve by dot fitting.

[0054] As an example, the power flow constraint (constrain4) can be obtained by the following formula: Where, is the voltage of the th node of the i th grid frame of the α-th layer power grid, is the voltage of the th node of the j th grid frame of the α-th layer power grid, is the th grid frame of the α-th layer power grid ij corresponding resistance on the branch connecting the two nodes, is the th grid frame of the α-th layer power grid ij corresponding reactance on the branch connecting the two nodes, is the th grid frame of the α-th layer power grid ij corresponding active power flowing on the branch connecting the two nodes, is the th grid frame of the α-th layer power grid ij corresponding reactive power flowing on the branch connecting the two nodes, is the th grid frame of the α-th layer power grid ij corresponding current flowing on the branch connecting the two nodes, is the th grid frame of the α-th layer power grid, and the jThe active power output of a power generation unit is the th in the j th power grid of the α-th layer, and the reactive power output of the th power generation unit in the th grid of the j th power grid of the α-th layer. The active power load on the th node of the th grid of the j th power grid of the α-th layer. The reactive power load on the th node of the th grid of the j th power grid of the α-th layer. The total active injection power on the th node of the th grid of the j th power grid of the α-th layer. The total reactive injection power on the

[0055] As an example, the reactive power output constraint (constrain5) can be: , . Among them, is the active power output of the th power generation unit in the i th grid of the th power grid of the α-th layer, is the reactive power output of the i th power generation unit in the th grid of the th power grid of the α-th layer,

[0056] As an example, the abscissa of the fitting curve takes values within the range of the maximum capacity in 0 to max , and the ordinate can be the optimization result of the third objective function obj3. By taking stepwise values of the maximum capacity from 0 to max , and using each value as the injection power constraint of the head node of the corresponding grid of the α-1 layer power grid, and solving the grid through the optimization algorithm, a set of can be obtained. Multiplying this set of output by the corresponding power generation unit bid , a vertical coordinate Cost in the curve can be obtained. By cycling in this way, a series of corresponding points can be obtained within the range of 0 to max . Through function approximation and function fitting of all points, the equivalent cost curve of the th power generation unit in the i th grid of the α-th layer can be obtained.

[0057] As an example, then judge the first grid (the whether the grid frame (the α-th grid frame) is the last grid frame of the first-layer power grid (the (α - 1)-th layer power grid). If not, return to step S22 to continue taking values for the output cycle of the equivalent power generation units of the grid frame ; if so, determine whether the α-th layer power grid is the highest layer power grid. If not, return to step S22 to continue the above steps for the (α + 1)-th layer power grid. If so, output the cost quotation curves of all grid frames of each level of power grid.

[0058] In step S30, please refer to Figure 1 step S30 in to report the maximum power generation capacity and the cost quotation curve to the power grid for scheduling, so as to obtain a scheduling instruction and issue it.

[0059] As an example, report the two final parameters of the maximum equivalent power generation capacity and the cost quotation curve to the power grid scheduling and the electricity market trading. The power grid scheduling or the electricity market receives this parameter, and the power grid scheduling instruction will issue the required output value, or the electricity market trading will issue the winning bid output value after completion.

[0060] In step S40, please refer to Figure 1 step S40 in to split the output required in the scheduling instruction into the equivalent output of each low-voltage power grid, and obtain the output conditions of the power generation units of all grid frames in each level of power grid.

[0061] As an example, please refer to Figure 4 , step S40 may include the following steps: S41 to S43.

[0062] S41: Input the active power output and reactive power output of the power generation unit of the last grid frame of the highest voltage level power grid.

[0063] S42: Based on the active power output and reactive power output, use the fourth optimization algorithm to optimize the fourth objective function and use the third constraint condition for constraint to obtain the optimal value of the fourth objective function.

[0064] S43: Determine whether the grid frame is the first grid frame of the layer power grid at this time. If not, return to step S41 to input the active power output and reactive power output of the next grid frame. If so, determine whether the power grid is the smallest layer power grid. If not, return to step S41 to perform the above operations on the next layer power grid. If so, output the output conditions of all power generation units.

[0065] As an example, input the highest layer power grid α and the last grid frame of the α-th layer power grid , the winning bid quantity of the highest layer power grid is , input the -th grid frame of the α-th layer power grid and the i -th active power output and reactive power output of the distributed power generation unit , based on the active power output and the reactive power output , use the fourth optimization algorithm to optimize the fourth objective function obj4, and use the third constraint condition for constraint to obtain the optimal solution of the fourth objective function obj4. The fourth objective function obj4 can be implemented by the following formula: where is the quotation of each power generation unit in the th grid frame of the αth layer of the power grid, is the active power output of the th power generation unit in the i th grid frame of the αth layer of the power grid.

[0066] As an example, the fourth constraint condition can be a power flow constraint, an output constraint, and a head-end injection constraint. Specifically, the head-end injection constraint (constrain6) can be: when , , where is the head-end node injection power of the th grid frame of the αth layer, is the head-end node injection power of the th grid frame of the αth layer of the power grid (the output of the distributed power generation unit is 0, obtained from step S10), is the output value or winning bid scalar required by the dispatching instruction. When , , where is the head-end node injection power of the th grid frame of the αth layer, is the output of the th power generation unit after optimization in the i th grid frame of the (α + 1)th layer, is the head-end node injection power of the β α th grid frame of the αth layer of the power grid when the output of the distributed power generation unit is 0 (obtained from step S10).

[0067] As an example, the formula for the power flow constraint (constrain7) is as follows: where is the voltage of node in the i th grid frame of the αth layer of the power grid, is the The node of a grid frame j voltage of For the th grid frame of the α - layer power grid ij The corresponding resistance on the branch connecting two nodes For the th grid frame of the α - layer power grid ij The corresponding reactance on the branch connecting two nodes For the th grid frame of the α - layer power grid ij The corresponding active power flowing on the branch connecting two nodes For the th grid frame of the α - layer power grid ij The corresponding reactive power flowing on the branch connecting two nodes For the th grid frame of the α - layer power grid ij The current flowing on the branch connecting two nodes For the th grid frame of the α - layer power grid, the j active power output of the For the th grid frame of the α - layer power grid, the j reactive power output of the For the th grid frame of the α - layer power grid, the j active load on the For the th grid frame of the α - layer power grid, the j reactive load on the For the th grid frame of the α - layer power grid, the j total active injection power on the For the th grid frame of the α - layer power grid, the j total reactive injection power on the

[0068] As an example, determine whether the th grid frame is the first grid frame of the α - layer power grid. If not, return to step S41 to continue optimizing the th grid frame; if so, determine whether the α - layer power grid is the lowest - layer power grid. If not, return to step S41 to continue the above steps for the (α - 1) - layer power grid until the output of all grid frames of the lowest - layer power grid is obtained. If so, output the execution status of the output of all generating units of each hierarchical grid frame.

[0069] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0070] In the distributed new energy efficient aggregation method of the present application, from the low-voltage power grid to the high-voltage power grid, the equivalent maximum power generation capacity and cost quotation curve of each low-voltage distributed power generation unit at the upper-level high-voltage power grid node is calculated for each voltage level power grid, and the winning bid amount issued after the power grid dispatching instruction is issued or the power market transaction is completed, starting from the winning node of the high-voltage power grid, the equivalent output required by the winning bid amount or the dispatching instruction is split into the equivalent output of each low-voltage power grid node, and calculated from high to low according to the voltage level, and finally the output of all distributed power generation units under the jurisdiction of the aggregator is obtained. By adopting the operation aggregation model of the voltage level grid step by step, and adding the consideration of line loss in the model constraints, the aggregation process of the demand-side response can take into account the actual cost of power transmission, making the price mechanism more reasonable and fair. It helps to optimize the geographical distribution of energy resources, promote localized production and consumption, and thus improve the efficiency of the overall energy system.

[0071] In another embodiment, see Figure 5 The present application provides a distributed new energy efficient aggregation system, which may include: a maximum capacity calculation module 10, a quotation curve compilation module 20, and a dispatch instruction execution module 40. Among them, the maximum capacity calculation module 10 is used to obtain the maximum capacity of all grids of each level of power grid from the low-voltage power grid to the high-voltage power grid; the quotation curve compilation module 20 is used to obtain the quotation curves of all grids in the power grids of each level; the dispatch instruction execution module 40 splits the output required in the dispatch instruction into the output of each low-voltage power grid according to the maximum capacity and the quotation curve, and obtains the output distribution matrix of the power generation units of all grids in the power grids of each level.

[0072] As an example, the distributed new energy efficient aggregation system may further include: a data acquisition module (not shown) and a communication module 30. The data acquisition module is used to acquire data of the power generation units in each level of the power grid, status information of each controllable load unit, and electrical parameters of each node of the power grid; the communication module 30 is used for data communication between each module within the system and communication with an external power grid system.

[0073] In the above-mentioned distributed new energy efficient aggregation system, through the maximum capacity calculation module 10, from the low-voltage power grid to the high-voltage power grid, the equivalent maximum power generation capacity of each low-voltage distributed power generation unit at the high-voltage power grid node of the previous level is calculated for each voltage level power grid; through the bid price curve compilation module 20, from the low-voltage power grid to the high-voltage power grid, the cost curve of each low-voltage distributed power generation unit at the high-voltage power grid node of the previous level is calculated for each voltage level power grid, so as to realize the equivalent maximum power generation capacity and cost bid price curve at the main grid node. When the grid dispatch instruction is issued or the winning bid quantity is issued after the completion of the power market transaction, starting from the winning bid node of the high-voltage power grid, through the dispatch instruction execution module 40, the winning bid quantity or the equivalent output required by the dispatch instruction is split into the equivalent output of each low-voltage power grid node, and calculated in descending order according to the voltage level. Finally, the output situation of all distributed power generation units under the aggregator is obtained. It can take into account the actual cost of power transmission during the aggregation process of demand response, make the price mechanism more reasonable and fair, help optimize the geographical distribution of energy resources, promote local production and consumption, and thus improve the efficiency of the overall energy system.

[0074] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0075] Although this application has been disclosed above with embodiments, it is not intended to limit this application. Any person with ordinary knowledge in the technical field to which this application belongs can make some changes and modifications without departing from the spirit and scope of this application. Therefore, the protection scope of this application shall be subject to the definition of the appended patent application scope.

Claims

1. A distributed new energy efficient aggregation method, characterized in that: The distributed new energy efficient aggregation method comprises the following steps: From low-voltage power grid to high-voltage power grid, obtain the equivalent maximum power generation capacity of all grids in each level of power grid; From the low voltage power grid to the high point power grid, obtain the cost quotation curve of all grid frames in the power grids at each level; Reporting the equivalent maximum power generation capacity and the cost quotation curve to the power grid for dispatching, so as to obtain and issue dispatching instructions; The output required in the dispatching instruction is split into equivalent outputs of each low-voltage power grid, and the output of the power generation units of all grids in each level of the power grid is obtained.

2. The distributed new energy efficient aggregation method according to claim 1 is characterized in that: The method of obtaining the equivalent maximum power generation capacity of all grids in each level of the power grid from the low voltage power grid to the high voltage power grid includes: Obtain parameter data of power grids at each level, determine the low-voltage power grid and the first grid frame of the low-voltage power grid; Input the active load, reactive load, active output, and reactive output of the first grid of the low-voltage power grid, optimize the first objective function using a first optimization algorithm, and constrain it using a first constraint condition to obtain an optimal value of the first objective function; When the active output and the reactive output of the first grid of the low-voltage power grid are 0, the second objective function is optimized using a second optimization algorithm, and the first constraint condition is used to constrain the second objective function to obtain an optimal value of the second objective function, that is, the equivalent load of the low-voltage power grid; Based on the optimal value of the first objective function and the optimal value of the second objective function, obtaining the equivalent maximum capacity of the first grid; Determine whether the first grid is the last grid of the layer power grid, if not, return to operate the next grid, if yes, the active load input by the next layer power grid is the equivalent load of the low-voltage power grid; Determine whether the low-voltage power grid is the highest-voltage power grid. If not, return to operate the next-level power grid. If so, output the equivalent maximum capacity of all grids of each level of power grid.

3. The distributed new energy efficient aggregation method according to claim 2 is characterized in that: The first constraint condition includes: power flow constraint and output constraint.

4. The distributed new energy efficient aggregation method according to claim 1 is characterized in that: The cost quotation curves of all grids in each level of the power grid are obtained from the low-voltage power grid to the high-point power grid, including: Determine the injection power at the head end of the first grid of the low-voltage grid and the output range of the equivalent power generation unit according to the parameter data of the power grids at each level; Input the active output and reactive output of each power generation unit in the low-voltage power grid and the first grid, and cyclically obtain values ​​within the output range to obtain the output value of the equivalent power generation unit; Based on the output value of the equivalent power generation unit, the third objective function is optimized using the third optimization algorithm and constrained using the second constraint condition to obtain the optimization result of the third objective function, that is, the ordinate point; Repeat the above operation, aggregate the vertical coordinate points obtained by cyclic value taking by point fitting, and obtain the cost curve of the grid; Determine whether the first grid is the last grid of the layer of power grid. If not, operate on the next grid. If so, continue to determine whether the low-voltage power grid is the highest-voltage power grid. If not, continue to loop and take values ​​for the next layer of power grid. If so, output the quotation curves of each level of grid.

5. The distributed new energy efficient aggregation method according to claim 4 is characterized in that: The horizontal axis of the cost curve is the equivalent maximum capacity of the grid, and the vertical axis is the optimization result of the third objective function.

6. The distributed new energy efficient aggregation method according to claim 4 is characterized in that: The second constraint condition includes: power flow constraint and head-end injection constraint.

7. The distributed new energy efficient aggregation method according to claim 1 is characterized in that: The step of splitting the output required in the dispatching instruction into equivalent outputs of each low-voltage power grid and obtaining the output of the power generation units of all grids in each level of the power grid includes: Input the active output and reactive output of the power generation unit of the last grid of the highest voltage power grid; Based on the active output and the reactive output, a fourth objective function is optimized using a fourth optimization algorithm and constrained using a third constraint condition to obtain an optimal value of the fourth objective function; Determine whether the grid is the first grid of the layer grid at this time. If not, input the active output and reactive output of the next grid. If so, determine whether the grid is the smallest layer grid. If not, perform the above operation on the next layer grid. If so, output the output of all power generation units.

8. The distributed new energy efficient aggregation method according to claim 7 is characterized in that: The third constraint condition includes: power flow constraint, output constraint, and head-end injection constraint.

9. The distributed new energy efficient aggregation method according to any one of claims 1 to 8, characterized in that: Each layer of the power grid includes a plurality of grid frames, and each grid frame includes a plurality of power generation units.

10. A distributed new energy efficient aggregation system, characterized in that: include: The maximum capacity acquisition module is used to obtain the equivalent maximum capacity of all grids at all levels of power grids from low-voltage power grid to high-voltage power grid; A quotation curve acquisition module, used to obtain quotation curves of all grids in the power grids at each level; The dispatch instruction execution module splits the output required in the dispatch instruction into the output of each low-voltage power grid according to the equivalent maximum capacity and the quotation curve, and obtains the output allocation matrix of the power generation units of all grids in each level of the power grid.

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