Active power distribution network scheduling method and system considering gateway scheduling boundary probability distribution

Abstract

The invention relates to the technical field of power distribution networks, and provides an active power distribution network scheduling method and system considering gateway scheduling boundary probability distribution, and the method comprises the steps: initializing the generation power of each generator set in an active power distribution network and the power on a transmission and distribution gateway connecting line; with the operation cost minimization as a target function, under the power flow constraint, the node power balance constraint, the variable upper and lower bound constraint and the opportunity constraint, the generation power of a generator set and the power on a transmission and distribution gateway tie line are optimized so as to dispatch the active power distribution network; wherein the opportunity constraint is used for constraining the dispatching boundary of the active power distribution network at the transmission and distribution gateway, and the cumulative distribution function of the exchange power boundary of the transmission and distribution gateway of the active power distribution network is calculated according to a total probability formula by constructing a mapping relation between the new energy output and the exchange power boundary of the transmission and distribution gateway. The out-of-limit rate of gateway power can be effectively reduced, and safe operation of a power grid is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network, and particularly relates to an active power distribution network dispatching method and system considering boundary probability distribution of dispatching. BACKGROUND

[0002] The statements in this section merely provide background information related to the application and do not necessarily constitute prior art.

[0003] Unlike the traditional power distribution network which is regarded as a passive load, the active power distribution network has certain upward support capacity due to the large access of distributed new energy. However, the distributed energy has small installed capacity and scattered layout, and cannot directly control its participation in grid dispatching, resulting in low utilization rate of new energy, and the uncertainty of distributed energy brings voltage fluctuation and harmonic pollution and other hazards to the power distribution network.

[0004] To solve the above challenges, power aggregation becomes an effective alternative. By aggregating the distributed photovoltaic, wind power and controllable load in the active power distribution network, the allowable power interaction range of the tie line is reported to the main grid, thereby solving the problem that the massive subjects are difficult to handle in the cross-layer mutual aid of transmission and distribution. It can be seen that determining the dispatching boundary of the transmission and distribution interface of the active power distribution network is the key to its participation in the main grid dispatching.

[0005] Although existing researches have carried out a lot of work on the dispatching boundary representation of the transmission and distribution interface of the active power distribution network, there are still some deficiencies in some aspects, mainly as follows: (1) The existing dispatching boundary representation of the active power distribution network considering uncertainty is a deterministic dispatching interval, which can obtain a relatively clear and understandable result, but ignores the influence of distributed energy fluctuation on the fluctuation of the dispatching boundary; (2) The uncertainty analysis method based on simulation method needs to solve the optimization problem under multiple scenarios, and the calculation burden is heavy. SUMMARY

[0006] In order to solve the technical problems existing in the background art, the application provides an active power distribution network dispatching method and system considering boundary probability distribution of dispatching, studies the mapping relationship between new energy output and transmission and distribution interface exchange power boundary, and analyzes the influence of new energy uncertainty on the fluctuation of the interface power boundary according to the total probability formula. On this basis, the transmission and distribution collaborative optimization dispatching is carried out, the influence of new energy fluctuation on the safe and reliable operation of the power grid is fully considered, the over-limit rate of the interface power can be effectively reduced, and the safe operation of the power grid is ensured.

[0007] In order to achieve the above purpose, the application adopts the following technical scheme: The first aspect of the application provides an active power distribution network dispatching method considering boundary probability distribution of dispatching, which comprises: Initialize the power generation of each generator set and the power on the tie-line of the power transmission and distribution interface in the active power distribution network; Optimize the power generation of each generator set and the power on the tie-line of the power transmission and distribution interface under the constraints of power flow, power balance, variable upper and lower limit, and opportunity constraint, with the minimum operation cost as the objective function, to schedule the active power distribution network. The opportunity constraint constrains the scheduling boundary of the active power distribution network at the power transmission and distribution interface, and the cumulative distribution function of the power exchange boundary of the active power distribution network at the power transmission and distribution interface is calculated according to the full probability formula by constructing the mapping relationship between the new energy output and the power exchange boundary of the power transmission and distribution interface.

[0008] Further, the objective function is: ; In the formula: C represents the operation cost; P pcc,t represents the power on the tie-line of the power transmission and distribution interface in the t period; N T is the number of time steps for optimization; N ng is the number of generator sets; a G i , b G i and c G i are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the cost-power function of the i generator set, respectively, a pcc , b pcc and c pcc are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the tie-line power-cost function of the active power distribution network, respectively.

[0009] Further, the power flow constraint requires that the active power flow of the power transmission network in each period does not exceed the upper and lower limits of the line.

[0010] Further, the power balance constraint is: ; ; In the formula: start ( l )、 end ( l ) respectively represent the start and end point sets of the line, , Pij,t 、 and Q ij,t are the active and reactive power flow of the line at the maximum and minimum gate power, respectively, N G j , N pv j , N w j , N pcc j and N svc j denote the generator, photovoltaic, wind power, gate node and static var compensator connected at node j, , P G,t , and Q G,t denote the active and reactive power output of the generator at time t when the gate power is at the maximum and minimum, , P pv,t , and Q pv,t denote the active and reactive power output of the photovoltaic at time t when the gate power is at the maximum and minimum, , P w,t , and Q w,t denote the active and reactive power output of the wind power at time t when the gate power is at the maximum and minimum, and P pcc t denote the maximum and minimum output power of the gate node at time t , and Q svc,t denote the reactive power compensation of the static var compensator at time t when the gate power is at the maximum and minimum; P i,t and Q i,t denote the active and reactive load of the node at time t , i,t and ΔQ i,t denote the active and reactive load fluctuation of the node at time t .

[0011] Further, the upper and lower bounds of the variables are: ; ; ; ; In the formula: and Nodal amplitude value The upper and lower limits, and The node voltage phase angle The upper and lower limits, and Output power to conventional units Upper and lower limits, For conventional unit capacity, P G,t , and Q G,t These represent the generator sets under the conditions of maximum and minimum power at the gate node, respectively. t The active and reactive power generated at all times.

[0012] A second aspect of the present invention provides an active distribution network dispatching system that considers the probability distribution of the dispatch boundary at the gateway, comprising: The initialization module is configured to initialize the generating power of each generator set in the active distribution network and the power on the transmission and distribution junction lines. The optimization scheduling module is configured to optimize the generating power of generator units and the power on the transmission and distribution junction lines under the constraints of power flow constraints, node power balance constraints, variable upper and lower bound constraints and opportunity constraints, with the objective function of minimizing operating costs, so as to schedule the active distribution network. Among them, the opportunity constraint constrains the active distribution network dispatch boundary at the transmission and distribution point, and the cumulative distribution function of the exchange power boundary at the active distribution network transmission and distribution point is calculated based on the full probability formula by constructing the mapping relationship between the power output of new energy sources and the exchange power boundary at the transmission and distribution point.

[0013] Furthermore, the objective function is: ; In the formula: C Indicates operating costs; P represents the power output of the i-th generator unit during time period t. pcc,t This represents the power on the transmission and distribution junction line during time period t; N T To optimize the number of time steps; N ng Number of generator sets; a G i , b G i and c G i Let be the coefficients of the quadratic term, the linear term, and the constant term of the cost-power function for the i-th generator unit, respectively. apcc 、 b pcc and c pcc are quadratic, linear and constant coefficients of the power-cost function of the tie-line of the active distribution network, respectively.

[0014] Further, the power flow constraint requires that the active power flow of the transmission network in each time period does not exceed the upper and lower limits of the line.

[0015] A third aspect of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the active distribution network scheduling considering the probability distribution of the scheduling boundary of the tie-in point as described above.

[0016] A fourth aspect of the present application provides a computer device comprising a computer readable storage medium, a processor and a computer program stored on the computer readable storage medium and executable on the processor, wherein the processor implements the steps in the active distribution network scheduling considering the probability distribution of the scheduling boundary of the tie-in point as described above.

[0017] Compared with the prior art, the present application has the following beneficial effects: The present application studies the mapping relationship between the new energy output and the tie-in point power boundary, analyzes the influence of new energy uncertainty on the fluctuation of the tie-in point power boundary according to the total probability formula, and performs the transmission and distribution collaborative optimization scheduling, fully considers the influence of new energy fluctuation on the safe and reliable operation of the power grid, and can effectively reduce the over-limit rate of the tie-in point power and ensure the safe operation of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0018] The drawings accompanying the specification of the present application form a part of the present application and serve to provide a further understanding of the present application, the illustrative embodiments thereof and the description thereof serve to explain the present application and do not limit the present application in any way.

[0019] Figure 1 is an analytic function calculation schematic diagram of the active distribution network scheduling boundary and the new energy output of the first embodiment of the present application; Figure 2 is an IEEE-30 node system and an IEEE-33 node system schematic diagram of the first embodiment of the present application; Figure 3 is a 33-node ADN of the first embodiment of the present application P pccmax CDF curve diagram; Figure 4 is a CDF curve diagram of the 33-node ADN of the first embodiment of the present application P pccmin ; Figure 5 is a structural schematic diagram of a computer device according to Embodiment Four of the present application. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0021] It should be pointed out that the following detailed description is exemplary and is intended to provide further illustration of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those of ordinary skill in the art to which the present application belongs.

[0022] Embodiment One The embodiment provides an active power distribution network scheduling method considering a scheduling boundary probability distribution of a gateway.

[0023] Unlike a traditional power distribution network, an active power distribution network can participate in main network scheduling by using distributed new energy, which provides flexible support for the main network while increasing the difficulty of regulating the active power distribution network. Accurate characterization of the scheduling boundary of the active power distribution network at the transmission and distribution junction point is the key to ensuring the safe operation thereof. The embodiment provides an aggregated scheduling boundary probability representation method of the transmission and distribution junction point of the active power distribution network. First, the mapping relationship between new energy output and the boundary of the transmission and distribution gateway exchange power is studied, and based on the total probability criterion, the influence of new energy uncertainty on the fluctuation of the gateway power boundary is analyzed. Second, a transmission and distribution collaborative optimization scheduling method based on the probability representation of the scheduling boundary is established. Finally, an example analysis is carried out based on an IEEE-30 node system, and the effectiveness of the method is verified.

[0024] The active power distribution network scheduling method considering the scheduling boundary probability distribution of the gateway provided by the embodiment carries out research on the scheduling boundary probability representation method of the transmission and distribution junction point of the active power distribution network, realizes efficient and accurate solution of the transmission and distribution gateway exchange power boundary of the active power distribution network, and the main contributions are as follows: (1) Based on the multi-parameter programming theory, the mapping relationship between new energy power and the feasible region of the transmission and distribution gateway power exchange of the active power distribution network is studied, and according to the total probability criterion, the influence of new energy uncertainty on the fluctuation of the feasible region is represented; (2) A transmission and distribution collaborative optimization scheduling based on the probability representation of the gateway scheduling boundary is proposed to improve the safety of power grid operation.

[0025] The active power distribution network scheduling method considering the scheduling boundary probability distribution of the gateway provided by the embodiment includes the following steps: Step 1, characterization of the aggregated regulation boundary of the transmission and distribution junction point of the active power distribution network considering new energy uncertainty, as shown in Figure 1 .

[0026] The calculation model of the dispatching boundary of the transmission and distribution interface in the active distribution network can be written in the following compact form: (1); (2); In the formula, P P pcc represents the power on the transmission and distribution interface tie line; Formula (2) represents the operation constraints of the active distribution network, including node power balance, power flow constraints and variable upper and lower limit constraints; wherein, and P pcc is an optimization variable, P G , Q G is the output of the adjustable unit in the active distribution network, V is the node voltage, θ is the phase angle of the node voltage; is a random variable, P R , Q R represents the new energy output, P D , Q D represents the load demand; A , B , C , D is a constraint coefficient matrix.

[0027] The optimal solution of the design calculation model is , then the constraints can be divided into active constraints and inactive constraints, and the linear mapping relationship between the optimal solution and the random variable can be obtained by Gaussian elimination as follows: (3); (4); (5); In the formula, P a , b respectively represent the active constraints and the inactive constraints; A a , B a , C a , D a is the active constraint coefficient matrix; A b , B b , C b , Db inactive constraint matrix. G w,i denotes the optimal solution corresponding to the i critical region CR i denotes the random variable coefficient matrix corresponding to the optimal solution in the F w,i denotes the optimal solution corresponding to the i critical region CR i denotes the constant term vector corresponding to the optimal solution in the

[0028] The inactive constraint can be obtained by substituting equation (5) into equation (6): (6); wherein, denotes the definition of; denotes the equivalence of. J w,i denotes the random variable coefficient matrix corresponding to the i critical region, K w,i denotes the constant term vector corresponding to the i critical region.

[0029] At the same time, the mapping relationship between the active distribution network gateway power boundary and the random variable under this critical region can be obtained: (7); wherein, G w , F w denote the linear coefficient matrix and the constant term vector of the mapping relationship between the active distribution network optimization variable and the new energy random variable w, M i , m i denote the linear coefficient vector and the constant term vector of the mapping relationship between the active distribution network scheduling boundary and the new energy variable i under the w critical region.

[0030] Based on the multi-parameter programming theory, enumerating all possible active constraints and inactive constraints can obtain the piecewise linear mapping relationship between the active distribution network gateway power boundary and the random variable: (8); wherein: n CR denotes the number of critical regions.

[0031] Since the random variable wOnly fall in one critical domain, and no overlap between critical domain, so you can according to the total probability formula to calculate the active distribution network transmission and distribution of the cumulative distribution function (CDF) of the scheduling boundary (i.e., transmission and distribution of the exchange power boundary): (9); In the formula, The random variable w Fall in the ith critical domain, z * ≤ Z The probability of; The random variable w Fall in the ith critical domain, z* represents the random variable of the active distribution network scheduling boundary, and Z is the given threshold value of the scheduling boundary random variable.

[0032] Step 2, active distribution network optimization scheduling considering transmission and distribution of the scheduling boundary probability distribution.

[0033] Objective function: (10); In the formula, C The operating cost; The power of the ith generator in the t period; P pcc,t The power on the tie line between transmission and distribution in the t period; N T The number of optimized time steps; N ng The number of generator units in the active distribution network; a G i , b G i , c G i The quadratic term coefficient, the linear term coefficient and the constant term coefficient of the cost-power function of the ith generator are respectively, a pcc , b pcc , c pcc The quadratic term coefficient, the linear term coefficient and the constant term coefficient of the tie line power-cost function of the active distribution network are respectively The constraint conditions include power flow constraint, node power balance constraint, variable upper and lower bound constraint and opportunity constraint.

[0034] (1) The power flow constraint requires that the active power flow of the transmission network in each period does not exceed the upper and lower limits of the line capacity: (11); (12); (13); (14); wherein: g ij , b ij , r ij , x ij represent the line conductance, susceptance, resistance, reactance between node i and node j , P ijmax and P ijmin represent the upper and lower limit values of the line active power between node i and j . , P ij,t , and Q ij,t are the line active and reactive power between node t and node i at the moment of maximum and minimum gate power, j , , , , are the node voltages at the moment of maximum and minimum gate power, , are the phase angle differences between node i and j at the moment of maximum and minimum gate power, and the above constraints ensure that the power flow constraints within the distribution network are met at both the maximum and minimum gate power.

[0035] (2) The power balance constraint is: (15); (16); wherein: start ( l ), end ( l ) represent the start and end point sets of the line, N G j , N pv j , N w j , N pcc j , N svc jThese represent the generator set, photovoltaic power, wind power, gateway node, and static var compensator connected to node j, respectively. P G,t , and Q G,t This indicates the generator set under the conditions of maximum and minimum power at the gate node. t The active and reactive power generated at any given time (a superscript horizontal bar indicates the minimum, and a subscript horizontal bar indicates the maximum). P pv,t , and Q pv,t This indicates the photovoltaic unit under the conditions of maximum and minimum power at the gate. t The active and reactive power generated at all times P w,t , and Q w,t This indicates the wind turbine under the conditions of maximum and minimum power at the gate. t Active and reactive power at any given time and P pcc t express t The maximum and minimum output power of the time-sensitive node. and Q svc,t express t The reactive power compensation amount of the static var compensator when the power at the time of maximum and minimum; P i,t Q i,t express t Active and reactive loads at time node, ΔP i,t ΔQ i,t yes t The fluctuation of active and reactive loads at any given moment.

[0036] (3) Upper and lower bound constraints for variables: (17); (18); (19); (20); In the formula: , These are the upper and lower limits of the nodal voltage amplitude. , These are the upper and lower limits of the phase angle difference between node voltages. , These are the upper and lower limits of the output of conventional generating units. This is the capacity of a conventional generating unit.

[0037] (4) In addition to the above-mentioned tidal constraints, node power balance constraints and variable upper and lower bound constraints, in order to deal with the uncertainty of the active distribution network, the dispatching boundary of the active distribution network at the transmission-distribution interface is constrained by the opportunity constraint: (21); (22); In the formula: P pccmaxand P pccminare the upper and lower boundaries of the dispatching of the active distribution network considering the uncertainty of new energy obtained above, the piecewise linear relationship between the dispatching boundary and the random variable of new energy is established by the multi-parameter programming theory, the cumulative distribution function of the dispatching boundary is obtained by combining the total probability criterion, and the upper and lower boundaries acting on the opportunity constraint are obtained by the quantile transformation technology, P pccis the interaction power of the active distribution network and the transmission network; α max , α min are the confidence levels that meet the upper and lower dispatching boundaries of the active distribution network.

[0038] The following is an example analysis.

[0039] (1) Characterization of the dispatching boundary of the transmission-distribution interface of the active distribution network.

[0040] This embodiment is based on the improved IEEE-30 node transmission system.

[0041] Based on the improved IEEE-30 node system, the 33 node ADN (active distribution network) is set at node 3, as shown in Figure 2 . The dispatching period is 24 hours, and there is no distributed renewable energy access to the transmission network. Node 1 of the ADN is set as the transmission-distribution interface node. The load and renewable energy output fluctuation obeys the normal distribution, wherein the renewable energy output at node 30 obeys N(2, 0.22), and there are 6 conventional units in the transmission network connected to nodes 1, 2, 13, 22, 23 and 27; Two conventional units in the distribution network are connected to nodes 12 and 26. Based on the proposed method and Monte Carlo simulation, the transmission-distribution interface dispatching boundary of the 33 node ADN is characterized, and the CDF curves obtained are shown in Figure 3 and Figure 4 .

[0042] When P pccmax =2.81MW, the CDF (distribution function) curve value obtained by the method of this embodiment is 0.05, and the CDF curve value obtained by the Monte Carlo simulation is 0.053, with a deviation of only 0.003. When Ppccmin =0.67 MW, the corresponding value of the CDF curve obtained by the proposed method is 0.95, while the corresponding value of the CDF curve obtained by the Monte Carlo simulation is 0.948, with a deviation of only 0.002. To verify the overall accuracy of the probability representation of the dispatch boundary of the 33-node active distribution network, the calculation results are compared with those obtained by the Monte Carlo simulation method (5000 sampling samples), and the average root mean square error (ARMS) is used to measure the error of the probability representation of the dispatch boundary of the ADN.

[0043] (23); wherein: N f is the value of the curve abscissa, f PM and f MC are the probability curve values obtained by the proposed method and the Monte Carlo simulation method, respectively, corresponding to the abscissa.

[0044] The ARMS of the dispatch boundary cumulative distribution function of the transmission and distribution interface of the active distribution network obtained is shown in Table 1, and it can be seen that the proposed method greatly improves the efficiency of boundary solving under the premise of ensuring calculation accuracy Table 1 ARMS value of the dispatch boundary of the transmission and distribution interface of the 3-node active distribution network

[0045] (2) Transmission and distribution collaborative optimization scheduling considering the probability distribution of the dispatch boundary of the active distribution network interface.

[0046] The transmission and distribution collaborative optimization scheduling method based on the probability representation of the dispatch boundary of the active distribution network interface is simulated and analyzed, and the following examples are used for comparison and verification: Case 1: Transmission and distribution collaborative optimization scheduling based on the deterministic boundary of the interface power without considering the influence of new energy uncertainty; Case 2: Transmission and distribution collaborative optimization scheduling based on the probability representation of the dispatch boundary of the active distribution network interface.

[0047] The confidence level of the up and down dispatch boundaries of the active distribution network is set to 0.95, and the dispatch boundary interval of the active distribution network is obtained as [0.67, 2.81] MW. If the uncertainty is not considered, the dispatch boundary interval of the active distribution network is obtained as [-0.21, 3] MW.

[0048] Table 2 Transmission and distribution collaborative scheduling results under different algorithms

[0049] Table 2 is the optimization result, and from the table, it can be obtained that the interconnection exchange power of case 2 is 2.81 MW, which is less than 3.00 MW of case 1, which shows that, without considering the uncertainty, the active distribution network can provide more scheduling capability for the power transmission network, and reduce the total cost of power grid operation. The total operation cost of case 1 in the scheduling period is 6053.71$, which is less than 6428.13$ of case 2.

[0050] However, example 1 needs to face the operation risk caused by the uncertainty of new energy, and it is difficult to guarantee the safety of power grid operation. Table 3 gives the number of times that the interconnection power does not meet the chance constraint in 5000 times of sampling. It can be seen that, without considering the uncertainty of new energy, there is a 12.42% probability that the interconnection power does not meet the chance constraints of formula (21) and formula (22), and the constraint exceeds the limit rate is high. However, the method only increases the operation cost by about 5% under the condition that the interconnection power exceeding limit rate is reduced to 0. After weighing, the scheduling method based on the probability representation of the interconnection limit exchange power of case 2 can avoid the risk caused by the uncertainty of new energy, and guarantee the safe and reliable operation of the power grid.

[0051] Table 3, interconnection power exceeding limit under different algorithms

[0052] The method for solving the probability distribution of the scheduling boundary of the distribution and transmission junction point considering the uncertainty of distributed new energy provided in the embodiment improves the model solving rate on the premise of guaranteeing the accuracy of solving the boundary of the interconnection power.

[0053] The active distribution network scheduling method considering the probability distribution of the interconnection scheduling boundary provided in the embodiment fully considers the influence of new energy fluctuation on the safe and reliable operation of the power grid, and the method can effectively reduce the exceeding limit rate of the interconnection power and guarantee the safe operation of the power grid.

[0054] Embodiment two The embodiment provides an active distribution network scheduling system considering the probability distribution of the interconnection scheduling boundary, and specifically comprises: an initialization module configured to initialize the power generation of each generator in the active distribution network and the power on the distribution and transmission interconnection tie line; An optimization scheduling module configured to optimize the power generation of the generator and the power on the distribution and transmission interconnection tie line to schedule the active distribution network, with the minimum operation cost as the objective function, under the constraints of power flow, node power balance, variable upper and lower limit, and opportunity. The opportunity constraint limits the scheduling boundary of the active distribution network at the distribution and transmission interconnection, and the cumulative distribution function of the distribution and transmission interconnection exchange power boundary of the active distribution network is calculated by constructing the mapping relationship between the new energy output and the distribution and transmission interconnection exchange power boundary and according to the total probability formula.

[0055] where the objective function is: ; where: C represents the operation cost; Pit is the power output of the ith generator in the tth time period; pcc,t Pgt represents the power on the tie-line between transmission and distribution networks in the tth time period; N T N is the number of time steps for optimization; N ng N is the number of generators; a G i , b G i and c G i ai, bi and ci are the quadratic, linear and constant coefficients of the cost-power function of the ith generator, respectively, a pcc , b pcc and c pcc ai, bi and ci are the quadratic, linear and constant coefficients of the cost-power function of the ith generator, respectively.

[0056] where the power flow constraint requires the active power flow of the transmission network in each time period to be within the upper and lower limits of the line.

[0057] where the power balance constraint is: ; ; where: start ( l ), end ( l ) represent the start and end point sets of the line, respectively, , P ij,t , and Q ij,t are the active and reactive power flows of the line at the maximum and minimum tie-line power, respectively, G j , N pv j , N w j , N pcc j and N svc j represent the generators, photovoltaic, wind power, tie-line nodes and static var compensators connected to the jth node, , P G,t , and Q G,t These represent the generator sets under the conditions of maximum and minimum power at the gate node, respectively. t The active and reactive power emitted at all times. P pv,t , and Q pv,t These represent the photovoltaic units at their maximum and minimum power outputs, respectively. t The active and reactive power generated at all times P w,t , and Q w,t These represent the wind turbine units at their maximum and minimum power levels, respectively. t Active and reactive power at any given time and P pcc t They represent t The maximum and minimum output power of the time-sensitive node. and Q svc,t They represent t The reactive power compensation amount of the static var compensator when the power at the time of maximum and minimum; P i,t and Q i,t They represent t Active and reactive loads at time node, ΔP i,t and ΔQ i,t They are t The fluctuation of active and reactive loads at any given moment.

[0058] The upper and lower bound constraints for the variables are as follows: ; ; ; ; In the formula: and Nodal amplitude value The upper and lower limits, and The node voltage phase angle The upper and lower limits, and Output power to conventional units Upper and lower limits, For conventional unit capacity, P G,t , and Q G,t These represent the generator sets under the conditions of maximum and minimum power at the gate node, respectively. t The active and reactive power generated at all times.

[0059] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0060] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in active distribution network scheduling considering the boundary probability distribution of gate scheduling as described in Embodiment 1 above.

[0061] Example 4 This embodiment provides a computer device, such as... Figure 5 As shown, the system includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001. The processor 1001, communication interface 1002, and computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and transmit data. When the processor 1001 executes the program, it implements the steps in the active distribution network scheduling considering the boundary probability distribution of the gate scheduling as described in Embodiment 1 above.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An active distribution network dispatching method considering the probability distribution of the dispatching boundary, characterized in that, The method comprises: initializing the power generation of each generator set in the active power distribution network and the power on the tie-line of the power distribution interface; optimizing the power generation of the generator set and the power on the tie-line of the power distribution interface under the objective function of minimizing the operation cost, the power flow constraint, the node power balance constraint, the variable upper and lower bound constraint and the chance constraint, so as to schedule the active power distribution network; wherein the chance constraint constrains the scheduling boundary of the active power distribution network at the power distribution interface, and the cumulative distribution function of the power exchange boundary of the active power distribution interface is calculated by constructing the mapping relationship between the new energy output and the power exchange boundary of the power distribution interface according to the total probability formula.

2. The proactive distribution network scheduling method considering the probability distribution of the interconnection scheduling boundary of claim 1, wherein, The objective function is: ； In the formula: C represents the operation cost; is the power generated by the ith generator unit in the tth time period; P pcc,t represents the power on the transmission and distribution interconnection line in the tth time period; N T is the optimized time step number; N ng is the number of generator units; a G i , b G i and c G i are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the cost-power function of the ith generator unit, respectively, a pcc , b pcc and c pcc are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the transmission and distribution interconnection line power-cost function, respectively.

3. The proactive distribution network scheduling method considering the probability distribution of interconnection scheduling boundaries of claim 1, wherein, The power flow constraint requires that the active power flow of the power transmission network in each period does not exceed the upper and lower limits of the line.

4. The proactive distribution network scheduling method considering the probability distribution of interconnection scheduling boundaries of claim 1, wherein, The power balance constraint is: ； ； In the formula: start ( l ), end ( l () represent the sets of the starting and ending points of the route, respectively. P ij,t , and Q ij,t These represent the active and reactive power flow of the line when the power at the junction is at its maximum and minimum, respectively, N. G j N pv j N w j N pcc j and N svc j These represent the generator set, photovoltaic power, wind power, gateway node, and static var compensator connected to node j, respectively. P G,t , and Q G,t These represent the generator sets under the conditions of maximum and minimum power at the gate node, respectively. t The active and reactive power emitted at all times. P pv,t , and Q pv,t These represent the photovoltaic units at their maximum and minimum power outputs, respectively. t The active and reactive power generated at all times P w,t , and Q w,t These represent the wind turbine units at their maximum and minimum power levels, respectively. t Active and reactive power at any given moment and P pcc t They represent t The maximum and minimum output power of the time-sensitive node. and Q svc,t They represent t The reactive power compensation amount of the static var compensator when the power at the time of maximum and minimum; P i,t and Q i,t They represent t Active and reactive loads at time node, ΔP i,t and ΔQ i,t They are t The fluctuation of active and reactive loads at any given moment.

5. The proactive distribution network scheduling method considering the probability distribution of interconnection scheduling boundaries of claim 1, wherein, The variable upper and lower bound constraint is: ； ； ； ； wherein: and are upper and lower limits of the nodal electric magnitude , and are upper and lower limits of the nodal voltage phase angle , and are upper and lower limits of the conventional generating unit output , is the conventional generating unit capacity, , P G,t , and Q G,t represent the active power and the reactive power generated by the generator unit t at the instant of time when the power at the gateway node is at its maximum and minimum.

6. An active distribution network scheduling system that takes into account the probability distribution of gateway scheduling boundaries, characterized in that, The method comprises: an initialization module configured to initialize the power generation of each generator set in the active power distribution network and the power on the tie-line of the power distribution interface; an optimization scheduling module configured to optimize the power generation of the generator set and the power on the tie-line of the power distribution interface under the objective function of minimizing the operation cost, the power flow constraint, the node power balance constraint, the variable upper and lower bound constraint and the chance constraint, so as to schedule the active power distribution network; wherein the chance constraint constrains the scheduling boundary of the active power distribution network at the power distribution interface, and the cumulative distribution function of the power exchange boundary of the active power distribution interface is calculated by constructing the mapping relationship between the new energy output and the power exchange boundary of the power distribution interface according to the total probability formula.

7. The proactive distribution network scheduling system considering the probability distribution of the interconnection scheduling boundary of claim 6, wherein, The objective function is: ； In the formula: C represents the operation cost; is the power generated by the ith generator unit in the tth time period; P pcc,t represents the power on the transmission and distribution interconnection line in the tth time period; N T is the optimized time step number; N ng is the number of generator units; a G i , b G i and c G i are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the cost-power function of the ith generator unit, respectively, a pcc , b pcc and c pcc are the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the transmission and distribution interconnection line power-cost function, respectively.

8. The proactive distribution network scheduling system considering the probability distribution of the interconnection scheduling boundary of claim 6, wherein, The power flow constraint requires that the active power flow of the power transmission network in each period does not exceed the upper and lower limits of the line.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps in the active power distribution network scheduling considering the probability distribution of the interface scheduling boundary as claimed in any one of claims 1-5.

10. A computer device, comprising a computer readable storage medium, a processor, and a computer program stored on the computer readable storage medium and executable on the processor, wherein, The processor executes the program to realize the steps in the active power distribution network scheduling considering the probability distribution of the interface scheduling boundary as claimed in any one of claims 1-5.

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