Dispatching method and device for distribution network containing distributed wind power and storage medium

Abstract

The application provides a power distribution network dispatching method and device containing distributed wind power and a storage medium. The method comprises the following steps: obtaining a day-ahead dispatching model according to day-ahead distributed wind power prediction power and day-ahead load prediction power; obtaining an intra-day dispatching model according to intra-day distributed wind power prediction power and intra-day load prediction power; and adjusting the day-ahead dispatching model according to the intra-day dispatching model to obtain an intra-day dispatching instruction of the adjusted power distribution network. The intra-day dispatching model of the rolling optimization is established on the basis of the day-ahead dispatching model of the power distribution network, the day-ahead dispatching model is optimized, the day-ahead dispatching model is more suitable for the actual situation of the power distribution network in the execution process, and better power distribution network dispatching effect is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network scheduling, in particular to a power distribution network scheduling method and device containing distributed wind power and a storage medium. BACKGROUND

[0002] With the continuous development and utilization of renewable energy, distributed wind power is a new way of new energy generation. However, the problem of power distribution network fluctuation and power quality caused by the grid connection of distributed wind power is becoming more and more serious. Due to the randomness of distributed wind power and load, the difficulty of voltage reactive power control of the power distribution network is increased, so the power distribution network needs to be scheduled to enable the power distribution network combined with distributed wind power to operate safely and efficiently.

[0003] In the research of power distribution network scheduling containing distributed wind power, the minimum system loss or the minimum operation cost is generally taken as the objective function, or a multi-objective optimization model of related technical indicators and economic indicators is constructed. At present, the scheduling of the power distribution network mainly adopts the method of formulating a day-ahead scheduling plan by using day-ahead load prediction data. The randomness and volatility of distributed wind power after grid connection make the day-ahead scheduling plan have a large deviation from the actual situation in the execution process, and cannot obtain good power distribution network scheduling effect. SUMMARY

[0004] Therefore, the present application provides a power distribution network scheduling method and device containing distributed wind power and a storage medium, which modifies the day-ahead scheduling model by comparing the prediction results of the day-ahead scheduling model and the intra-day scheduling model, and obtains a power distribution network scheduling plan close to the actual execution situation.

[0005] In the first aspect, the present application provides a power distribution network scheduling method containing distributed wind power, comprising:

[0006] obtaining a day-ahead scheduling model according to the day-ahead distributed wind power prediction and the day-ahead load prediction;

[0007] obtaining an intra-day scheduling model according to the intra-day distributed wind power prediction and the intra-day load prediction;

[0008] adjusting the day-ahead scheduling model according to the intra-day scheduling model to obtain the intra-day scheduling instruction of the adjusted power distribution network.

[0009] In the second aspect, the present application provides a power distribution network scheduling device containing distributed wind power, comprising:

[0010] a day-ahead scheduling model acquisition module, configured to obtain a day-ahead scheduling model according to the day-ahead distributed wind power prediction and the day-ahead load prediction;

[0011] an intra-day scheduling model acquisition module, configured to obtain an intra-day scheduling model according to the intra-day distributed wind power prediction and the intra-day load prediction;

[0012] The dispatching instruction correction module is configured to adjust the day-ahead dispatching model according to the day-ahead dispatching model to obtain day-in dispatching instructions of the power distribution network.

[0013] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the power distribution network dispatching method with distributed wind power of any one of the first aspect.

[0014] The beneficial effects of the above technical solution are that: the rolling optimization day-in dispatching model is established on the basis of the day-ahead dispatching model of the power distribution network, the day-ahead dispatching model is optimized, the day-ahead dispatching model is more suitable for the real-time situation of the power distribution network during the execution process, and better power distribution network dispatching effect is obtained. Moreover, the reconstruction of the power distribution network is considered when the day-ahead dispatching model of the power distribution network is established, the network structure is optimized by changing the state of the switch of the power distribution network, the potential of the power distribution network is exerted without increasing additional equipment of the power distribution network, and the economy and safety of the power grid operation are realized at a low cost. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below.

[0016] Figure 1 The power distribution network dispatching method with distributed wind power in one embodiment of the present application is shown in the schematic diagram.

[0017] Figure 2 The power distribution network dispatching method with distributed wind power in one embodiment of the present application is shown in the flowchart.

[0018] Figure 3 The power distribution network dispatching device with distributed wind power in one embodiment of the present application is shown in the schematic diagram. DETAILED DESCRIPTION

[0019] 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. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application. In order to more specifically describe the present application, the power distribution network dispatching method, device and storage medium with distributed wind power provided by the present application are described in detail below with reference to the drawings.

[0020] With the increasingly serious environmental problems and the depletion of fossil energy, society has reached a consensus to strive for the vigorous development and utilization of renewable energy. Wind power has developed rapidly and occupies a higher and higher proportion in the distribution network due to its own potential and advantages. However, wind power is different from conventional power sources, and its power output is determined by the wind conditions, so wind power has the characteristics of intermittency, volatility and randomness. After the distributed wind power is connected to the grid, the voltage fluctuation and power quality problem of the distribution network becomes more and more serious, and the randomness of the output and load of the distributed wind power increases the difficulty of reactive power control of the distribution network.

[0021] The embodiment of the application provides a specific application scenario of the distribution network dispatching method containing distributed wind power. The application scenario includes a terminal device provided by the embodiment, and the terminal device includes but is not limited to a smart phone and a computer device, wherein the computer device can be at least one of a desktop computer, a portable computer, a laptop computer, a tablet computer and the like. A user operates the terminal device, and the terminal device executes the distribution network dispatching method containing distributed wind power of the application, combined with the description of the application, the user can understand the application better. Figure 1 , and the specific process is described in the distribution network dispatching method containing distributed wind power.

[0022] Step S101: obtaining a day-ahead dispatching model according to day-ahead distributed wind power prediction power and day-ahead load prediction power.

[0023] The day-ahead dispatching model is a distribution network dispatching model obtained according to day-ahead state information of each device in the distribution network, and the day-ahead dispatching model includes a day-ahead dispatching objective function and a day-ahead dispatching constraint condition.

[0024] Step S102: obtaining an intra-day dispatching model according to intra-day distributed wind power prediction power and intra-day load prediction power.

[0025] The intra-day dispatching model is a distribution network dispatching model obtained according to intra-day short-term state information of each device in the distribution network, and the intra-day dispatching model includes an intra-day dispatching objective function and an intra-day dispatching constraint condition.

[0026] Step S103: adjusting the day-ahead dispatching model according to the intra-day dispatching model to obtain an intra-day dispatching instruction of the adjusted distribution network.

[0027] Specifically, if the difference between the day-ahead distributed wind power prediction power and the intra-day distributed wind power prediction power at any time is greater than a set threshold, the day-ahead dispatching model is adjusted according to the intra-day dispatching model.

[0028] The embodiment of the application proposes a multi-time scale distribution network scheduling method for a distribution network containing distributed power, which divides scheduling into two stages of day-ahead optimal scheduling and intra-day rolling optimal scheduling. The target of the intra-day rolling optimal scheduling is to minimize the operation cost, and the output plan of the next day's schedulable resources is obtained through the day-ahead optimal scheduling, and the fine control scheduling is realized through the intra-day rolling optimization under the intra-day ultra-short term prediction information.

[0029] The accompanying drawings are incorporated Figure 2 In the embodiment of the application, the specific definitions of the day-ahead scheduling model and the intra-day scheduling model are as follows:

[0030] First, regarding the day-ahead scheduling model, the day-ahead scheduling model includes a day-ahead scheduling objective function and a day-ahead scheduling constraint condition, which are described as follows:

[0031] (1) The day-ahead scheduling objective function comprehensively considers the distribution network operation cost, network reconstruction cost and distribution network loss. In order to improve the scheduling efficiency and economy of the distribution network, the day-ahead scheduling objective function needs to reduce the above-mentioned operation cost, network reconstruction cost and loss as much as possible. That is, in the day-ahead optimization stage, the day-ahead scheduling objective function of the distribution network containing distributed wind power mainly takes reducing the distribution network operation cost and the distribution network loss as the optimization target. The expression of the day-ahead scheduling objective function is as follows:

[0032]

[0033] Wherein, f DA is the day-ahead scheduling objective function, is the distribution network operation cost of the day-ahead scheduling, is the network reconstruction cost of the day-ahead scheduling, is the distribution network loss of the day-ahead scheduling, λ1 is the first weight coefficient of the day-ahead scheduling, λ2 is the second weight coefficient of the day-ahead scheduling, and λ3 is the third weight coefficient of the day-ahead scheduling. Since the magnitudes of the voltage regulation amount, the current regulation amount and the power regulation amount may be different, the above-mentioned weight coefficients need to be adjusted to keep the magnitude of the parameters of the objective function consistent.

[0034] The distribution network operation cost of the day-ahead scheduling mainly consists of the power purchase cost of the generation side of the distribution network, the operation cost of the distributed wind power, the regulation cost of each device and the demand response subsidy cost of the system, which is specifically as follows:

[0035]

[0036] is the distribution network operation cost of the day-ahead scheduling, is the distribution network operation cost function of the day-ahead scheduling; C Gis the exchange power cost between distribution network and main network, T is the time period of day-ahead dispatch, ρ G is the unit price of purchasing power from the upper grid, P G.t is the power purchased from the upper grid at time t; C WTG is the regulation cost of wind turbine, H is the number of wind turbines in the distribution network, ρ W is the unit regulation cost of wind turbine power, P M.i.t is the active power of the ith wind turbine at time t, ρ W.CF is the unit penalty cost of wind turbine curtailment, ΔP W.i.t is the wind turbine curtailment of the ith wind turbine at time t; C SVG is the regulation cost of high-voltage static var generator, K is the number of high-voltage static var generators in the distribution network, ρ W is the unit regulation cost of high-voltage static var generator, Q M.i.t is the absolute value of the active power or reactive power output by the ith high-voltage static var generator at time t; C L is the compensation cost of adjustable load participating in demand response, M is the number of nodes participating in demand response, l c.i.t is the power adjustment of the ith node at time t, λ e is the unit compensation price; C ES is the regulation cost of energy storage, ρ ES is the unit regulation cost of energy storage, P ES.t is the charging and discharging power of energy storage at time t.

[0037] Further, in the dispatch process of the distribution network, the network flow can be managed through network reconstruction, only the state of the network switch needs to be changed, the network structure of the distribution network is optimized, no additional equipment is needed to play the potential of the distribution network itself, and the purpose of improving the economic efficiency of the distribution network operation is achieved. The network reconstruction cost of day-ahead dispatch is to take the switch operation cost of network reconstruction as the optimization target:

[0038]

[0039] Among them, is the network reconstruction cost of day-ahead dispatch, C S is the cost of single switch action, m is the mth node in the distribution network, n is the nth node in the distribution network, M is the number of nodes in the distribution network, ΔK mn indicates whether the switch state of branch m-n changes, which is a binary variable, when the switch state of the branch changes, ΔK mn = 1, when the switch state of the branch does not change, ΔK mn= 0. It should be noted that, in order to maintain the stability of the power distribution network, the switch state of the network after the power distribution network reconstruction needs to be determined in the day-ahead optimization, and the switch state of the network is no longer changed in the intra-day rolling optimization process.

[0040] The power distribution network loss refers to the power loss in the form of heat energy during power transmission, i.e., the active power consumed by resistance and conductance; in order to improve the scheduling efficiency of the power distribution network, it is necessary to reduce the power distribution network loss, so that the power distribution network loss is minimized, and the specific expression is:

[0041]

[0042] wherein, is the power distribution network loss of day-ahead scheduling, G ij is the conductance of branch i-j, V i,t is the voltage amplitude of the i-th node at time t, V j,t is the voltage amplitude of the j-th node at time t, cosθ ij,t is the voltage phase angle difference between the i-th node and the j-th node at time t.

[0043] (2) The day-ahead scheduling constraint conditions include power flow constraints, distributed wind power constraints, controllable load constraints, energy storage constraints, high-voltage static var generator constraints, power distribution network operation safety constraints, and network topology structure constraints.

[0044] The power flow constraint is a limitation on the output of the distributed wind turbine, the reactive power of the reactive power compensation device, the output of the energy storage, and the power of the demand response load, and specifically:

[0045]

[0046] wherein, is the active power injected into the i-th node at time t, is the active power of the wind turbine injected into the i-th node at time t, is the active power of the energy storage injected into the i-th node at time t, is the active power adjustment amount of the adjustable load injected into the i-th node at time t; is the reactive power injected into the i-th node at time t, is the reactive power of the wind turbine injected into the i-th node at time t, is the reactive power of the energy storage injected into the i-th node at time t, is the high-voltage static var generator power injected into the i-th node at time t.

[0047] The calculation of the above-mentioned injected fan active power, injected energy storage active power, injected adjustable load active adjustment amount, injected fan reactive power, injected energy storage reactive power and injected high-voltage static reactive power generator power is further expanded to obtain:

[0048]

[0049] Wherein, G ii is the conductance of the branch where node i is located, G ij is the conductance of branch i-j, B ij is the magnetic induction intensity of branch i-j, B ii is the magnetic induction intensity of the branch where node i is located, V i is the voltage amplitude of the i-th node, V j is the voltage amplitude of the j-th node, θ ij is the voltage phase angle difference between the i-th node and the j-th node.

[0050] Due to the limitation of the regulation capacity of the distributed wind power, the reduction of the fan output cannot exceed the existing active power, and the active reduction of each node is positive. Each distributed wind turbine has reactive power regulation capacity and can control the voltage. Each node can both absorb and emit reactive power, and the emitted reactive power is positive and the absorbed reactive power is negative. Thus, the constraint conditions of the distributed wind power on active reduction and reactive output can be obtained, which are specifically:

[0051]

[0052] Wherein, ΔP i is the active power reduction of the i-th fan, ΔP imax is the maximum active power reduction of the i-th fan; Q i is the reactive power of the i-th fan, Q imax_a is the maximum reactive power absorbed by the i-th fan, Q imax_o is the maximum reactive power emitted by the i-th fan.

[0053] Controllable load is mainly the power load in industrial production which has low requirements on power quality and is less important. The user signs a demand side response agreement with the power grid, and in the peak period of power distribution network electricity consumption, according to the received corresponding power-off notice, on the premise of meeting the minimum electricity demand, the demand side response is carried out in the form of reducing the load electricity demand, and the corresponding power-off economic compensation fee is obtained, and the specific expression is:

[0054] P load，min，t ≤P load，t ≤P load，max，t

[0055] P load,t P load,min,t P load,max,t P

[0056] For the constraints of the active power and the reactive power of the energy storage in the distribution network, specifically:

[0057]

[0058] wherein, is the total energy of the energy storage connected to the i-th node at time t, is the minimum value of the energy storage connected to the i-th node, is the maximum value of the energy storage connected to the i-th node.

[0059] The total energy of the energy storage connected to the i-th node at time t is limited, specifically:

[0060]

[0061] wherein, is the charging power of the energy storage connected to the i-th node at time t, is the maximum charging power of the energy storage connected to the i-th node at time t, is the charging state of the energy storage connected to the i-th node at time t in the day-ahead scheduling model, when the energy storage connected to the node is in the charging state, when the energy storage connected to the node is not in the charging state, is the discharging power of the energy storage connected to the i-th node at time t, is the maximum discharging power of the energy storage connected to the i-th node at time t, is the discharging state of the energy storage connected to the i-th node at time t in the day-ahead scheduling model, when the energy storage connected to the node is in the discharging state, when the energy storage connected to the node is not in the discharging state, is the maximum apparent power of the PCS system of the energy storage system, η ch is the charging efficiency of the energy storage, η dis is the discharging efficiency of the energy storage, ΔT is a set time period, is the total energy of the energy storage connected to the i-th node at time t+1.

[0062] The high-voltage static var generator refers to a device for dynamic reactive power compensation by a self-commutated power semiconductor bridge converter, which is the most superior static reactive power compensation equipment. The constraints of the high-voltage static var generator are specifically:

[0063]

[0064] wherein, is the adjustable power of the high-voltage static var generator of the ith node at the t time point, is the minimum value of the adjustable power of the high-voltage static var generator connected to the ith node, is the maximum value of the adjustable power of the high-voltage static var generator connected to the ith node.

[0065] Since the distribution network has a set standard range of current and voltage, the voltage and current adjusted through the day-ahead scheduling model still need to be controlled within the standard range, and there is a no-line-crossing constraint for the voltage of the distribution network, which is specifically:

[0066]

[0067] wherein, U i is the voltage amplitude of the ith node, is the minimum value of the voltage amplitude of the ith node, is the maximum value of the voltage amplitude of the ith node, and the minimum value and the maximum value of the voltage amplitude are set according to the actual requirements of the distribution network.

[0068] The distribution network topology structure requirement needs to be met during the network reconfiguration process and after the network reconfiguration, that is, the operation structure of the distribution network should be the network structure of the distribution network, and the network structure of the distribution network can be radial, which is specifically:

[0069] g∈G

[0070] wherein, g is the network topology structure of the current distribution network, and G is a set of all radial topology structures of the distribution network.

[0071] According to the day-ahead state of each device in the distribution network, combined with the above day-ahead scheduling model, the next day's distribution network scheduling can be optimized to obtain the next day's distribution network scheduling instruction; and the optimization scheduling period of the day-ahead scheduling model can be set according to the actual situation, in order to improve the accuracy of the scheduling, the optimization scheduling period of the day-ahead scheduling model is generally taken as 1 hour.

[0072] The above objective function and constraint condition limit make the day-ahead scheduling model take into account the economic operation of the distribution network and the reactive voltage regulation, and the reactive power output of the distributed wind power, the reactive power output of the energy storage, and the reactive power output of the high-voltage static var generator can all adjust the voltage of the distribution network system, wherein the reactive power output of the distributed wind power and the energy storage is not counted as cost.

[0073] Because the prediction step of the distributed wind power, controllable load, high-voltage static var generator and energy storage device is relatively long in the day-ahead optimal scheduling, the prediction accuracy is greatly reduced when the uncertain factors affecting the prediction of these devices increase, and the optimal result of the long time scale lacks actual guiding significance, so it is necessary to make further correction in the intra-day stage.

[0074] Specifically, whether the day-ahead scheduling model is corrected is determined according to that the day-ahead scheduling model can obtain the output plan of the schedulable resources for the whole day of the next day, the intra-day scheduling model is rolling optimization for the next day, and if the difference between the distributed wind power predicted power at t time point in the day-ahead and the distributed wind power predicted power in the intra-day exceeds a set threshold, it means that the guiding significance of the day-ahead scheduling model is weakened, and if the power distribution network continues to be scheduled according to the day-ahead scheduling model, the accuracy will be greatly reduced, so the intra-day scheduling model needs to be used to adjust the day-ahead scheduling model.

[0075] Secondly, regarding the intra-day scheduling model, the intra-day scheduling model is constructed on the basis of the day-ahead scheduling model, and the optimization scheduling period of the intra-day scheduling model is less than that of the day-ahead scheduling model. In the case that the optimization scheduling period of the day-ahead scheduling model is 1 hour, the optimization scheduling period of the intra-day scheduling model can be set to 15 minutes, the intra-day scheduling model optimizes the day-ahead scheduling model once every 4 time periods, and the power plan of the τ time period in the intra-day is the adjustment reference of the four time periods of (4τ-3)-4τ. When the time period ends and enters the τ+1 time period, the optimization time period moves forward, the prediction information is updated, and the optimization solving is re-performed. The rolling optimization is continuously performed until 24 hours end, that is, the adjustment of the intra-day scheduling model to the day-ahead scheduling model is completed.

[0076] The intra-day scheduling model includes an intra-day scheduling objective function and an intra-day scheduling constraint condition, which will be described below:

[0077] (1) In order to further reflect the coupling of the day-ahead scheduling model and the intra-day scheduling model, that is, to show the effectiveness of the day-ahead optimal scheduling result, the gap between the intra-day optimal scheduling result and the day-ahead optimal scheduling result should be minimized, and a penalty function part is added to the objective function of the intra-day scheduling model for intra-day rolling optimization, which is specifically:

[0078]

[0079] Wherein, f R is the intra-day scheduling objective function, the operation cost of the intra-day scheduling power distribution network, the power distribution network loss of the intra-day scheduling, f DAμ1 is a first weight coefficient of the intraday scheduling, μ2 is a second weight coefficient of the intraday scheduling, and η is a unit penalty cost of the distribution network operation.

[0080] The operation cost of the intraday distribution network mainly includes a power purchase cost of a generation side of the distribution network, an operation cost of a distributed wind power, an adjustment cost of each device, and a demand response subsidy cost of the system, and is specifically as follows:

[0081]

[0082] wherein, is the operation cost of the intraday distribution network, is an operation cost function of the intraday distribution network; is an exchange power cost of the intraday distribution network and the main network, T Δ is a time period of the intraday scheduling, l0 is an optimization starting point of a current scheduling period, a rolling optimization is started at the l0 time point with an interval of 15 minutes in the period, ρ G is a unit price of power purchased from a superior grid, P G.t is an amount of power purchased from the superior grid at the t time point; is an adjustment cost of an intraday wind turbine, H is a number of wind turbines in the distribution network, ρ W is a unit adjustment cost of wind turbine power, is an actual scheduling power of the i th wind turbine at the t time point, is a power correction value of the i th wind turbine at the t time point, ρ W.CF is a unit penalty cost of wind turbine curtailment, ΔP W.i.t is a wind turbine curtailment amount of the i th wind turbine at the t time point; is an adjustment cost of an intraday high-voltage static var generator, K is a number of high-voltage static var generators in the distribution network, ρ M is a unit adjustment cost of the high-voltage static var generator, Q M.i.t is an absolute value of the reactive power output or the reactive power absorption of the i th high-voltage static var generator at the t time point; is a compensation cost of an intraday adjustable load participating in demand response, M is a number of nodes participating in the demand response, l c.i.t is a power adjustment amount of the i th node at the t time point, λ e is a unit compensation price of adjustment; C ES is an adjustment cost of energy storage, ρ ES is a unit adjustment cost of the energy storage, P ES.t is a charging and discharging power of the energy storage at the t time point.

[0083] The distribution network loss refers to a power loss in the form of heat performance in the power transmission process. In order to improve the scheduling efficiency of the distribution network, it is necessary to reduce the distribution network loss, so that the distribution network loss reaches a minimum value, and a specific expression is as follows:

[0084]

[0085] wherein, is the power loss of the distribution network in the day-ahead scheduling, G ij is the conductance of branch i-j, V i,t is the voltage amplitude of the i-th node at time t, V j,t is the voltage amplitude of the j-th node at time t, cosθ ij,t is the phase angle difference between the i-th node and the j-th node at time t.

[0086] (2) The constraints of the day-ahead scheduling include the energy storage constraints and the correction value constraints.

[0087] In the day-ahead scheduling, in order to better track the day-ahead scheduling model, the state of charge of the energy storage system at the end of each hour should be the same as the state of charge at the end of each hour in the day-ahead scheduling, i.e.:

[0088]

[0089] wherein, is the state of charge of the energy storage at the end of the time period of the day-ahead scheduling, is the state of charge of the energy storage at the end of the time period of the day-ahead scheduling, which is 1 hour.

[0090] And in each rolling correction of the day-ahead scheduling model, the new output of the distributed wind power is to meet the constraints of the power output of the distribution network; at the same time, the new output should be referenced to the day-ahead scheduling model, and the deviation value should not exceed the set range, which is:

[0091]

[0092] wherein, is the power correction value of the i-th wind turbine at time t, is the minimum power correction value of the i-th wind turbine at time t, is the minimum power of the i-th wind turbine at time t, is the actual scheduling power of the i-th wind turbine at time t in the day-ahead scheduling, is the predicted power of the i-th wind turbine at time t in the day-ahead scheduling, ΔP' i is the curtailed wind power of the i-th wind turbine at time t in the day-ahead optimization scheduling, is the correction plan power at the nearest time to time t, is the maximum power correction value of the i-th wind turbine at time t.

[0093] In addition, for the dispatching method of the distribution network containing the distributed wind power, the difficulty in solving is that the non-convexity of the alternating current flow model leads to the whole dispatching model being a non-convex programming problem, if the non-convex programming problem can be converted into a convex programming problem with high precision, the solving difficulty will be greatly reduced, so that the solving time is shortened and the occupation of the calculation resource is reduced.

[0094] The second-order cone method is to minimize or maximize a linear function on the intersection of a finite Cartesian product of affine subspaces of second-order cones, that is, the linear objective function under the linear equality and linear inequality constraints under the partial order introduced by the non-empty pointed convex cone. By converting the complex optimization model into a cone model, the complex relationship between variables can be represented by a special structure of the cone set, which greatly simplifies the solving of the original model and accelerates the convergence speed.

[0095] The embodiment converts the above problem into a mixed integer second-order cone programming problem by introducing intermediate variables, converts the distribution network flow constraint into a second-order cone form by second-order cone relaxation, and models by using a strict mathematical method, so that the search space is limited in a limited convex cone range, so that the original problem can be solved in an effective time, and the optimality and precision of the solution are guaranteed, wherein the introduced intermediate variables are:

[0096]

[0097] The non-linear objective function and the constraint condition in the above model are converted into a cone form, wherein the objective function in the day-ahead dispatching model is The objective function in the day-ahead dispatching model is The flow constraint and the operation safety constraint of the distribution network are non-linear functions, which are converted into linear functions.

[0098] The objective function in the day-ahead dispatching model is:

[0099]

[0100] The objective function in the day-ahead dispatching model is:

[0101]

[0102] The flow constraint of the distribution network is:

[0103]

[0104] The operation safety constraint of the distribution network is:

[0105]

[0106] When solving the mixed-integer second-order cone programming problem, relaxing the variables yields an approximate polyhedral description of the second-order cone. Therefore, the relaxation is transformed into cone constraints when solving the problem:

[0107]

[0108] At this point, the feasible region relaxes into a second-order cone, forming a convex feasible region. This problem can be solved quickly and efficiently using common commercial solvers. Because this model is a convex programming model, it exhibits better computational efficiency and convergence characteristics compared to non-convex programming models based on the original AC power flow equations. It improves solution efficiency while ensuring the optimality and accuracy of the solution.

[0109] It should be understood that, although attached Figure 1 The steps in the flowchart are shown sequentially according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders. Furthermore, [the following is a list of steps]. Figure 1 At least some of the steps in the process may include multiple sub-steps or sub-stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. 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 some of the sub-steps or stages of other steps.

[0110] The embodiments disclosed above describe in detail a distribution network dispatching method for distributed wind power. Since this method can be implemented using various types of equipment, this invention also discloses a distribution network dispatching device for distributed wind power corresponding to the above method, in conjunction with the appendix. Figure 3 The following are specific embodiments for detailed explanation.

[0111] The day-ahead scheduling model acquisition module 201 is used to obtain the day-ahead scheduling model based on the day-ahead distributed wind power forecast and the day-ahead load forecast, wherein the time scale of the day-ahead scheduling model is a first preset period.

[0112] The intraday scheduling model acquisition module 202 is used to obtain an intraday scheduling model based on the intraday distributed wind power forecast and the intraday load forecast. The time scale of the intraday scheduling model is a second preset period, and the first preset period is longer than the second preset period.

[0113] The dispatch instruction correction module 203 is used to adjust the day-ahead dispatch model according to the intraday dispatch model to obtain the adjusted intraday dispatch instruction of the distribution network.

[0114] The specific limitations of the power distribution network dispatching device with distributed wind power can refer to the limitations of the method described above, and will not be repeated here. Each module in the device can be implemented by software, hardware, and a combination thereof, in whole or in part. The above modules can be embedded in the processor of the terminal device in hardware form or independent of the processor, or stored in the memory of the terminal device in software form, so that the processor invokes and executes the operations corresponding to each module.

[0115] In one embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the power distribution network dispatching method with distributed wind power of the first aspect.

[0116] The computer-readable storage medium can be an electronic storage such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM (erasable programmable read-only memory), a hard disk, or a ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has a storage space for program codes for executing any of the method steps described above. The program codes can be read from or written into one or more computer program products, and the program codes can be compressed in an appropriate form.

[0117] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A power distribution network dispatching method with distributed wind power, characterized in that, The method comprises the following steps: obtaining a day-ahead scheduling model according to day-ahead distributed wind power prediction and day-ahead load prediction; obtaining an intra-day scheduling model according to intra-day distributed wind power prediction and intra-day load prediction; adjusting the day-ahead scheduling model according to the intra-day scheduling model to obtain intra-day scheduling instructions of the power distribution network after adjustment; the step of adjusting the day-ahead scheduling model according to the intra-day scheduling model comprises: if the difference between the day-ahead distributed wind power prediction and the intra-day distributed wind power prediction at any time is greater than a set threshold, adjusting the day-ahead scheduling model according to the intra-day scheduling model; the day-ahead scheduling model comprises a day-ahead scheduling objective function and a day-ahead scheduling constraint condition; the day-ahead scheduling objective function comprises: ； wherein, is a day-ahead dispatch objective function, is a day-ahead dispatch distribution network operation cost, is a day-ahead dispatch network reconfiguration cost, is a day-ahead dispatch distribution network loss, is a first weight coefficient of day-ahead dispatch, is a second weight coefficient of day-ahead dispatch, is a third weight coefficient of day-ahead dispatch; the day-ahead scheduling constraint condition comprises a power flow constraint, a distributed wind power constraint, a controllable load constraint, an energy storage constraint, a high-voltage static var generator constraint, a power distribution network operation safety constraint and a network topology structure constraint; the expression of the power distribution network operation cost of the day-ahead scheduling is: ； wherein, is the operation cost of the distribution network for day-ahead scheduling, is the operation cost function of the distribution network for day-ahead scheduling; is the exchange power cost of the distribution network and the main network, is the time period for day-ahead scheduling, is the unit price of electricity purchased from the superior grid, is the amount of electricity purchased from the superior grid at time t; is the regulation cost of the wind turbine, is the number of wind turbines in the distribution network, is the unit regulation cost of the wind turbine power, is the active power of the i-th wind turbine at time t, is the unit penalty cost of wind turbine curtailment, is the amount of wind turbine curtailment at time t of the i-th wind turbine; is the regulation cost of the high-voltage static var generator, is the number of high-voltage static var generators in the distribution network, is the unit regulation cost of the high-voltage static var generator, is the absolute value of the reactive power generated or absorbed by the i-th high-voltage static var generator at time t; is the compensation cost of the adjustable load participating in demand response, is the number of nodes participating in demand response, is the power adjustment amount of the i-th node at time t, is the unit compensation price; is the regulation cost of the energy storage, is the unit regulation cost of the energy storage, is the charging and discharging power of the energy storage at time t; the expression of the network reconfiguration cost of the day-ahead scheduling is: ； wherein, is the network reconfiguration cost for day-ahead scheduling, is the cost of a single switch action, is the number of nodes in the distribution network, is the number of nodes in the distribution network, is the number of nodes in the distribution network, is the number of nodes in the distribution network, is the number of nodes in the distribution network, indicates whether the switch state of the branch is changed, when the switch state of the branch is changed, when the switch state of the branch is not changed, ; the expression of the power distribution network loss of the day-ahead scheduling is: ； wherein, is the day-ahead dispatch of the distribution network loss, is the conductance of the branch , is the voltage magnitude of the th node at the th time instant, is the voltage magnitude of the th node at the th time instant, is the voltage phase angle difference between the th node and the th node at the th time instant. the intra-day scheduling model comprises an intra-day scheduling objective function and an intra-day scheduling constraint condition; the intra-day scheduling objective function comprises: ； wherein, is an intra-day dispatch objective function, is an operation cost of the distribution network for intra-day dispatch, is a network loss of the distribution network for intra-day dispatch, is a day-ahead dispatch objective function, is a first weight coefficient for intra-day dispatch, is a second weight coefficient for intra-day dispatch, is a unit penalty cost of the distribution network operation; the intra-day scheduling constraint condition comprises an energy storage constraint condition and a correction value constraint condition; the expression of the power distribution network operation cost of the intra-day scheduling is: ； wherein, is the operation cost of the distribution network in the day, is the operation cost function of the distribution network in the day; is the exchange power cost of the distribution network and the main network in the day, is the time period of the day-ahead dispatch, is the optimization starting point of the current dispatch period, is the unit price of electricity purchased from the upper-level power grid, is the amount of electricity purchased from the upper-level power grid at the moment; is the regulation cost of the wind turbine in the day, is the number of wind turbines in the distribution network, is the unit regulation cost of the wind turbine power, is the actual dispatch power of the th wind turbine at the moment , is the power correction value of the th wind turbine at the moment , is the unit penalty cost of wind turbine curtailment, is the amount of wind turbine curtailment of the th wind turbine at the moment ; is the regulation cost of the high-voltage static var generator in the day, is the number of high-voltage static var generators in the distribution network, is the unit regulation cost of the high-voltage static var generator, is the absolute value of the reactive power output or absorption of the th high-voltage static var generator at the moment ; is the compensation cost of the adjustable load participating in demand response in the day, is the number of nodes participating in demand response, is the power adjustment amount of the th node at the moment t, is the unit compensation price of the adjustment; is the regulation cost of the energy storage, is the unit regulation cost of the energy storage, is the charge and discharge power of the energy storage at the moment t; the expression of the power distribution network loss of the intra-day scheduling is: ； wherein, is the net loss of the distribution network for the day-ahead dispatch, is the conductance of the branch , is the voltage magnitude of the th node at the th time instant, is the voltage magnitude of the th node at the th time instant, is the voltage phase angle difference between the th node and the th node at the th time instant.

2. The power distribution network scheduling method with distributed wind power according to claim 1, wherein the expression of the power flow constraint is: ； in, For the first Each node The active power injected at all times, For the first Each node The active power injected into the wind turbine at all times For the first Each node The constantly injected active power of energy storage For the first Each node Adjustable load active power adjustment injected at all times; For the first Each node The reactive power injected at all times For the first Each node The constantly injected reactive power of the wind turbine For the first Each node The constantly injected reactive power of energy storage For the first Each node The power of the high-voltage static var generator is constantly injected; the expression of the distributed wind power constraint is: ； wherein, is the active power reduction of the wind turbine, is the maximum active power reduction of the wind turbine; is the reactive power of the wind turbine, is the maximum reactive power absorbed by the wind turbine, is the maximum reactive power emitted by the wind turbine; the expression of the controllable load constraint is: ； wherein, is the controllable load capacity at the moment, is the minimum value of the controllable load capacity at the moment, is the maximum value of the controllable load capacity at the moment; the expression of the energy storage constraint is: ； in, For the first Each node Total energy stored at all times. For the first Minimum energy storage capacity connected to each node For the first The maximum energy storage capacity connected to each node; the expression of the high-voltage static var generator constraint is: ； wherein, is the adjustable power of the high voltage static var generator of the node at the time instant t, is the adjustable power of the high voltage static var generator of the node connected to the is the minimum adjustable power of the high voltage static var generator of the node connected to the is the maximum adjustable power of the high voltage static var generator of the the expression of the power distribution network operation safety constraint is: ； in, For the first The voltage amplitude at each node, For the first Minimum voltage amplitude at each node, For the first The maximum voltage amplitude at each node; the expression of the network topology structure constraint is: ； wherein, is the network topology of the current power distribution network, is the set of all radial topologies of the power distribution network.

3. The power distribution network scheduling method with distributed wind power according to claim 1, wherein the specific expression of the energy storage constraint condition is: ； wherein, SoCend is the state of charge of the energy storage at the end of the day-ahead dispatch time period, SoCend is the state of charge of the energy storage at the end of the day-ahead dispatch time period; the specific expression of the correction value constraint condition is: ； in, For the first Typhoon machine Power correction value at time, For the first Typhoon machine The minimum power correction value at time t. For the first Typhoon machine The minimum power at time [time]. The first in the recent dispatch Typhoon machine Actual scheduling power at any given time The first in the recent dispatch Typhoon machine Predicted power at time, In order to optimize the scheduling process in the past Typhoon machine The amount of wind curtailed at any given moment. The power of the most recent corrected plan at time t. For the first Typhoon machine The maximum power correction value at any given time.

4. A power distribution grid scheduling apparatus with distributed wind power, which performs the power distribution grid scheduling method with distributed wind power according to any one of claims 1 to 3, characterized by The method comprises the following steps: obtaining a day-ahead scheduling model according to day-ahead distributed wind power prediction and day-ahead load prediction; obtaining an intra-day scheduling model according to intra-day distributed wind power prediction and intra-day load prediction; adjusting the day-ahead scheduling model according to the intra-day scheduling model to obtain intra-day scheduling instructions of the power distribution network after adjustment.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the power distribution network scheduling method with distributed wind power according to any one of claims 1-3.

Images