Active power distribution network distributed resource collaborative optimization method and device, medium and equipment

Abstract

The invention discloses an active power distribution network distributed resource collaborative optimization method and device, a medium and equipment. The method comprises the following steps: constructing a probability distribution uncertainty set in a distributed photovoltaic output feature scene; determining feed line load regulation and control cost; constructing a power distribution network cooperative interaction multi-objective optimization model based on feeder line load regulation and control cost, line network loss cost, light abandoning cost, operation cost of each power regulation device, superior electricity purchase cost and node voltage deviation ratio; and solving the power distribution network collaborative interaction multi-objective optimization model to obtain the optimal scheduling cost under the probability distribution uncertainty set of the distributed photovoltaic output feature scene, and realizing active power distribution network distributed resource collaborative optimization. According to the method, collaborative optimization of the distributed resources of the active power distribution network can be effectively realized, so that the energy consumption is reduced, and the safety of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and control, and particularly to a method, device, medium, and equipment for collaborative optimization of distributed resources in an active distribution network. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, distributed photovoltaic power, as an important clean and renewable energy source, has been widely applied to the power system. The active distribution network, as an important part of the power system, is a key hub for coordinating the power generation side and the user side.

[0003] However, due to the uncertainties and fluctuations often existing in the power supply and demand patterns of distributed energy sources, and for the distribution network, high-penetration distributed energy sources convert the power flow from a radial waterfall pattern to a random bidirectional power flow, which easily leads to safety problems such as node voltage over-limit. How to balance the supply and demand relationships of each energy unit in the active distribution network through optimized scheduling and coordination strategies, reduce energy consumption, and improve the security of the system has important research significance.

[0004] Typical power adjustable resources in the distribution network include energy storage, diesel engines, and micro gas turbines. However, the economy of energy storage is often poor. Although diesel engines and micro gas turbines can participate in peak shaving optimization, they need to operate at a low power level for a long time to ensure sufficient regulation capacity, which seriously reduces the equipment efficiency. Some studies have also shown that the regulation potential on the load side can be exploited, but the control right of this method does not belong to the grid side, and there are limitations such as uncontrollable response quantity and uncertain response speed, making it difficult to efficiently participate in the real-time regulation of the distribution network. Therefore, it is necessary to provide a method for collaborative optimization of distributed resources in an active distribution network to balance the supply and demand relationships of each energy unit in the active distribution network, reduce energy consumption, and improve the security of the system. Summary of the Invention

[0005] The present invention aims to solve at least some of the technical problems in the related technologies to some extent. For this purpose, the first object of the present invention is to provide a method for collaborative optimization of distributed resources in an active distribution network, which can effectively achieve the collaborative optimization of distributed resources in the active distribution network, and thus facilitate reducing energy consumption and improving the security of the system.

[0006] The second object of the present invention is to provide a device for collaborative optimization of distributed resources in an active distribution network.

[0007] The third object of the present invention is to provide a computer-readable storage medium.

[0008] The fourth object of the present invention is to provide an electronic device.

[0009] To achieve the above objects, the present invention is realized through the following technical solutions:

[0010] A collaborative optimization method for distributed resources in an active distribution network, comprising:

[0011] Constructing a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output;

[0012] Determining the feeder load regulation cost;

[0013] Based on the feeder load regulation cost, line network loss cost, curtailment cost, operation cost of each power regulation device, superior power purchase cost, and node voltage deviation rate, constructing a collaborative interaction multi-objective optimization model for the distribution network;

[0014] Solving the collaborative interaction multi-objective optimization model of the distribution network to obtain the optimal scheduling cost under the probability distribution uncertainty set of the distributed photovoltaic output characteristic scenario, and realizing the collaborative optimization of distributed resources in the active distribution network.

[0015] Preferably, constructing a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output, comprising:

[0016] Performing random processing on the photovoltaic power generation to obtain distributed photovoltaic output data;

[0017] Using the K-means clustering algorithm to cluster the distributed photovoltaic output data for the distributed photovoltaic output scenario, and constructing a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output in combination with KL divergence constraints.

[0018] Preferably, the collaborative interaction multi-objective optimization model of the distribution network includes two objective functions. Among them, a first objective function is constructed with the comprehensive minimum of all costs including the feeder load regulation cost, line network loss cost, curtailment cost, operation cost of each power regulation device, and superior power purchase cost as the objective; a second objective function is constructed with the minimum node voltage deviation rate as the objective.

[0019] Preferably, the constraint conditions of the collaborative interaction multi-objective optimization model of the distribution network include distributed photovoltaic operation constraint conditions, OLTC model constraint conditions, micro gas turbine operation constraint conditions, feeder transmission capacity constraint conditions, reactive power compensation device model constraint conditions, and power flow constraint conditions.

[0020] To achieve the above object, the second aspect of the present invention provides a collaborative optimization device for distributed resources in an active distribution network, comprising:

[0021] A first construction module for constructing a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output;

[0022] A determination module for determining the feeder load regulation cost;

[0023] A second construction module for constructing a collaborative interaction multi-objective optimization model of a distribution network based on feeder load regulation cost, line network loss cost, light curtailment cost, operating costs of various power regulation devices, superior power purchase cost, and node voltage deviation rate;

[0024] A solution module for solving the collaborative interaction multi-objective optimization model of the distribution network to obtain the optimal scheduling cost under the probability distribution uncertainty set of distributed photovoltaic output characteristics scenarios, and realizing the collaborative optimization of distributed resources in the active distribution network.

[0025] Preferably, when constructing the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenarios, the first construction module is specifically used for:

[0026] Performing random processing on the photovoltaic power generation to obtain distributed photovoltaic output data;

[0027] Using the K-means clustering algorithm to cluster the distributed photovoltaic output data for distributed photovoltaic output scenarios, and constructing the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenarios in combination with KL divergence constraints.

[0028] Preferably, the collaborative interaction multi-objective optimization model of the distribution network constructed by the second construction module includes two objective functions. Among them, the first objective function is constructed with the goal of minimizing the comprehensive sum of all costs including feeder load regulation cost, line network loss cost, light curtailment cost, operating costs of various power regulation devices, and superior power purchase cost; the second objective function is constructed with the goal of minimizing the node voltage deviation rate.

[0029] Preferably, the constraint conditions of the collaborative interaction multi-objective optimization model of the distribution network constructed by the second construction module include distributed photovoltaic operation constraint conditions, OLTC model constraint conditions, micro gas turbine operation constraint conditions, feeder transmission capacity constraint conditions, reactive power compensation device model constraint conditions, and power flow constraint conditions.

[0030] To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned collaborative optimization method of distributed resources in the active distribution network is realized.

[0031] To achieve the above object, a fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned collaborative optimization method of distributed resources in the active distribution network is realized.

[0032] The present invention has at least the following technical effects:

[0033] In view of the defect that it is difficult to control the user-side load in real time, the present invention introduces a voltage reduction and energy-saving technology, namely the CVR technology, and directly controls the feeder load power through common direct control devices such as on-load tap changers (OLTCs). This operation is simple and easy to implement, has a positive impact on energy conservation and safety and stability, and can effectively cooperate with the coordinated interaction of the active distribution network. In addition, the present invention constructs a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output, determines the feeder load regulation cost, and then constructs a multi-objective optimization model for the coordinated interaction of the distribution network based on the feeder load regulation cost, line network loss cost, light curtailment cost, operating costs of various power regulation devices, superior power purchase cost, and node voltage deviation rate. By solving the multi-objective optimization model for the coordinated interaction of the distribution network, the optimal scheduling cost under the probability distribution uncertainty set of the characteristic scenario of distributed photovoltaic output can be obtained, so as to realize the coordinated optimization of distributed resources in the active distribution network, and further balance the supply and demand relationship of each energy unit in the active distribution network, reduce energy consumption, and improve the safety of the system.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of the method for coordinated optimization of distributed resources in the active distribution network according to an embodiment of the present invention.

[0036] Figure 2 It is a structural block diagram of the device for coordinated optimization of distributed resources in the active distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following describes the embodiments in detail. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0038] The following describes the method, device, medium, and equipment for coordinated optimization of distributed resources in the active distribution network according to an embodiment of the present invention with reference to the drawings.

[0039] Figure 1 It is a flowchart of the method for coordinated optimization of distributed resources in the active distribution network according to an embodiment of the present invention. As Figure 1 shown, the method includes:

[0040] Step S101: Construct a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output.

[0041] Among them, constructing a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output includes:

[0042] Perform stochastic processing on the photovoltaic power generation to obtain distributed photovoltaic output data;

[0043] Use the K-means (K-means clustering algorithm) clustering algorithm to cluster the distributed photovoltaic output data, and construct a probability distribution uncertainty set under the distributed photovoltaic output characteristic scenario by combining the KL divergence (an index used to measure the difference between two probability distributions).

[0044] Step S102: Determine the feeder load regulation cost.

[0045] Step S103: Based on the feeder load regulation cost, line network loss cost, curtailment cost, operating costs of various power regulation devices, upstream power purchase cost, and node voltage deviation rate, construct a multi-objective optimization model for coordinated interaction of the distribution network.

[0046] The multi-objective optimization model for coordinated interaction of the distribution network includes two objective functions. Among them, the first objective function is constructed with the goal of minimizing the comprehensive sum of all costs, including the feeder load regulation cost, line network loss cost, curtailment cost, operating costs of various power regulation devices, and upstream power purchase cost; the second objective function is constructed with the goal of minimizing the node voltage deviation rate.

[0047] The constraint conditions of the multi-objective optimization model for coordinated interaction of the distribution network include distributed photovoltaic operation constraint conditions, OLTC (on-load tap changer) model constraint conditions, micro gas turbine operation constraint conditions, feeder transmission capacity constraint conditions, reactive power compensation device model constraint conditions, and power flow constraint conditions.

[0048] Step S104: Solve the multi-objective optimization model for coordinated interaction of the distribution network to obtain the optimal scheduling cost under the probability distribution uncertainty set of the distributed photovoltaic output characteristic scenario, and realize the coordinated optimization of distributed resources in the active distribution network.

[0049] The method for coordinated optimization of distributed resources in the active distribution network is as follows: First, perform stochastic processing on the photovoltaic power generation, use the K-means clustering algorithm to cluster the distributed photovoltaic output data, and construct a probability distribution uncertainty set under the distributed photovoltaic output characteristic scenario by combining the KL divergence constraint. Then, based on the load modeling method, construct the CVR (voltage reduction energy-saving technology) coefficient to identify the power regulation characteristics of the feeder load, and at the same time construct a feeder load regulation cost model to determine the feeder load regulation cost. Finally, considering the active management measures of various regulating devices on power and voltage, taking the line network loss cost, curtailment cost, operating costs of various power regulation devices, upstream power purchase cost, and node voltage deviation rate as indicators, establish and solve a multi-objective optimization model for coordinated interaction of the distribution network, and finally realize the coordinated optimization of distributed resources in the active distribution network.

[0050] The specific steps of this active distribution network distributed resource collaborative optimization method are as follows:

[0051] Step 1: Perform random processing on the photovoltaic power generation. Use the K-means clustering algorithm to cluster the distributed photovoltaic output data, and construct a probability distribution uncertainty set under the characteristic scenario by combining the KL divergence constraint.

[0052] (1) Photovoltaic output prediction model based on stochastic fuzzy theory

[0053] The photovoltaic power generation is affected by multiple uncertain factors such as weather and temperature. The stochastic fuzzy theory in the stochastic uncertainty theory can be used for processing to obtain the following simplified model for calculating the photovoltaic output under the predicted light intensity and battery temperature for the day-ahead:

[0054]

[0055] In the formula, H ref , S ref are the reference values of light intensity and battery temperature, with the values being 25°C and 1 kW / m 2 respectively; P m is the maximum output power of the photovoltaic array under standard conditions; P pv is the photovoltaic predicted power; a, b, c are the first to third compensation coefficients, with the values being: 0.0025 / °C, 0.5 m 2 / kW, 0.00288 / °C; e represents the natural constant, H represents the day-ahead predicted temperature of the battery, S represents the light intensity, ΔH represents the difference between the day-ahead predicted temperature and the reference value of the battery temperature, and ΔS represents the difference in light intensity.

[0056] (2) Construct the probability distribution uncertainty set of the distributed photovoltaic output characteristic scenario

[0057] Using the historical light intensity and battery temperature data, convert them into distributed photovoltaic output data through the above simplified model. Use the K-means algorithm to cluster a large amount of generated distributed photovoltaic output data to obtain representative distributed photovoltaic output characteristic scenarios and their probability distributions. Since there may be a certain error between the probability distribution of the distributed photovoltaic output characteristic scenario after scenario clustering and the true probability distribution, in this embodiment, the KL divergence is used to constrain the error between the true scenario probability distribution and the initial scenario probability distribution obtained by clustering, and construct the probability distribution uncertainty set Ω of the distributed photovoltaic output characteristic scenario:

[0058]

[0059] In the formula, K is the number of distributed photovoltaic output characteristic scenarios after K-means clustering; p kis the true probability value under scenario k; is the initial probability under scenario k after K-means clustering; ζ is the KL divergence value between the initial probability distribution and the true scenario probability distribution.

[0060] Step 2: Identify the feeder load power regulation characteristics based on the load modeling method, provide economic compensation for load users with poor power quality caused by voltage regulation according to the time-of-use electricity price, and use this as the cost of feeder load regulation.

[0061] (1) Feeder load regulation characteristics based on the ZIP (constant impedance - constant current - constant power) load model

[0062] The essence of feeder load power control is the process of load power responding to voltage changes. By establishing a feeder-level static load model, the coupling relationship between feeder load power and voltage is analyzed from a theoretical perspective. Its steady-state characteristics can be represented by the constant impedance (Z) - constant current (I) - constant power (P) load model as follows:

[0063]

[0064] In the formula, P i and Q i and U i are the active power and reactive power after the change of node i respectively; P i,0 and Q i,0 and U i,0 are the initial active power, initial reactive power and initial voltage of node i respectively; a Z and a I and a P are the active component proportion coefficients of the three static loads respectively; b Z and b I and b P are the reactive component proportion coefficients of the three static loads respectively, where node i is a system node, including photovoltaic nodes and feeder load nodes.

[0065] Define the sensitivity of feeder load power responding to voltage changes as the voltage - active power coupling coefficient, that is, the CVR coefficient f i CVR :

[0066]

[0067] In the formula, ΔP i feeder and ΔU i feeder are the change amount of feeder load power and the change amount of voltage of node i respectively.

[0068] (2) Feeder load regulation cost model based on power quality compensation

[0069] In this embodiment, a feeder load regulation cost model is established to determine the feeder load regulation cost. Based on the control principle of the feeder load, the power quality of the load on the user side will fluctuate. Therefore, from the perspective of power quality, this embodiment provides economic compensation for the load with power decline, which is used as the feeder load regulation cost. The voltage deviation rate D v is used as an index to judge the power quality level L, and its segmentation is as follows:

[0070]

[0071] Based on this, the feeder load regulation cost model under different power qualities at different times is obtained:

[0072]

[0073] In the formula, is the feeder load regulation cost of the feeder load at node i at time t; is the compensation coefficient under different power quality levels L at different times; is the change in active power of the feeder load at node i at time t, which can be quickly obtained based on voltage sensitivity and CVR coefficient.

[0074] Step 3: Considering the active management measures of various regulating devices for power and voltage, taking the line network loss cost, light abandonment cost, operating costs of various power regulating devices, superior power purchase cost, and node voltage deviation rate as indicators, establish a coordinated interaction multi-objective optimization model for the distribution network and solve it.

[0075] (1) Taking the line network loss cost, light abandonment cost, operating costs of various power regulating devices, superior power purchase cost, and node voltage deviation rate as indicators, establish a coordinated interaction multi-objective optimization model for the distribution network.

[0076] First, the first objective function F1 with the minimum line network loss cost, light abandonment cost, total operating cost, and feeder load regulation cost is as follows:

[0077]

[0078]

[0079]

[0080]

[0081] In the above formula, f1 is the line network loss cost, f2 is the light abandonment cost, f3 is the operating cost of the micro gas turbine unit and the superior power purchase cost, T is the system operation period, N bus is the total number of nodes in the system, C lossis the line loss cost coefficient of the line network, ij is the line between nodes i and j, Ω L is the set of system lines, l ij,t is the square of the current value of line ij at time t, r ij is the resistance on line ij, C pva is the curtailment cost coefficient, Ω p is the set of photovoltaic nodes, is the active power generated by the photovoltaic at node i at time t, is the active power input of the photovoltaic at node i at time t, N G is the number of micro gas turbines, P G,s,t is the active power output value of micro gas turbine s at time t, b s 、c s are the first and second cost consumption parameters of micro gas turbine s, u G,s,t is the micro gas turbine switch state at time t; C sup,t 、P sup,t are the power purchase cost coefficient and power purchase power of the active distribution network from the superior power grid under time-of-use electricity price.

[0082] Secondly, construct the second objective function F2 with the minimum node voltage deviation rate as the goal as follows:

[0083]

[0084] In the formula, U0 is the rated voltage of the node; U i,t is the actual voltage of node i at time t.

[0085] (2) Considering the impact of active management measures on the economic and safe operation of the distribution network, establish the constraint conditions of the distribution network collaborative interaction multi-objective optimization model.

[0086] 1) Distributed photovoltaic operation constraints

[0087] This embodiment considers the PV (constant voltage and constant frequency) control mode and PQ (constant power) control mode of distributed photovoltaic in the distribution network. Working in different operation modes according to the current state of the system can play a role in adjusting the voltage level and regulating the power flow. The constraints are as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] In the formula, is the maximum PV active power input at node i at time t, i.e., the predicted power; is the PV reactive power input at node i at time t; is the PV installed capacity at node i; is the square of the PV voltage at node i at time t; is the square of the expected PV voltage value at node i, i.e., when the PV is in the PV control mode, it can be flexibly selected according to the state of the distribution network to maintain voltage stability; are the introduced first and second state variables, U min and U max are the minimum and maximum values of the node voltage respectively; are the introduced third and fourth state variables. When it is in the PV control mode at this time, is given; are the upper and lower limits of the PV reactive power input at node i respectively; when or it is in the PQ control mode at this time, and the PV reactive power at the node is fixed at the upper and lower limits.

[0096] 2) OLTC model constraints

[0097] OLTC is a relatively traditional voltage and reactive power control device, usually installed between the distribution network and the superior transmission network, playing a role in regulating voltage and maintaining system stability. Its model constraints are:

[0098] U o,t = U i,t - 2(r ij P ij,t + x ij Q ij,t ) + [(r ij ) 2 + (x ij ) 2 l ij ,t (19)

[0099]

[0100]

[0101] In the formula, OLTC is incorporated into the power grid in the form of injected power. o is the auxiliary node added on the line ij where OLTC is located; U o,tThe square value of the voltage of the auxiliary node o at time t; x ij The reactance value on line ij; P ij,t 、Q ij,t The active and reactive powers on line ij at time t; N ij,t The total number of tap positions of the OLTC on line ij; n is the tap position of the OLTC on line ij, λ ij,n,t Is a binary variable; K ij The maximum tap position of the OLTC on line ij; m ij,t 、x ij,n,t 、y ij,n,t Are the first to third intermediate variables; The minimum transformation ratio of the OLTC on line ij; U j,t The actual voltage of node j at time t, Δk ij,t The unit increment of the OLTC of line ij at time t; M is the constant of the Big-M method (penalty factor method).

[0102] 3) Micro gas turbine operation constraints

[0103] The operation of the micro gas turbine needs to consider the upper and lower limits of the output power and the ramp rate constraints:

[0104]

[0105]

[0106] In the formula, P G,s,max 、P G,s,min Are the upper and lower limits of the active power output of the micro gas turbine s; Q G,s,t Is the reactive power output value of the micro gas turbine s at time t, Q G,s,max 、Q G,s,min Are the upper and lower limits of the reactive power output of the micro gas turbine s; P G,s,t-1 Is the active power output value of the micro gas turbine s at time t - 1, Are the upper and lower ramp rate limit values of the micro gas turbine s respectively.

[0107] 4) Feeder transmission capacity constraints

[0108]

[0109] In the formula, Is the transmission capacity on line ij; Is the maximum transmission capacity limit value on line ij.

[0110] 5) Reactive power compensation device model constraints

[0111] The reactive power compensation device adjusts the reactive power distribution of the distribution network system, enabling distributed resources to generate more active power and improving energy utilization efficiency.

[0112]

[0113]

[0114]

[0115] In the formula, is the reactive power regulation amount of the static var compensator (SVC) at node j at time t; are the maximum and minimum values of the adjustable reactive power of the SVC at node j, respectively; is the reactive power regulation amount of the switched capacitor (CB) at node j at time t; is the number of groups of CBs put into operation at node j at time t; is the reactive power compensation power of the CB at node j; are the increase and decrease amounts of the number of groups of CBs in operation at node j at time t, respectively; B CB,max is the maximum number of groups of CBs, is the number of groups of CBs put into operation at node j at time t-1, N CB，max is the maximum value of the change amount of the number of groups of CBs in operation.

[0116] 6) Power flow constraint

[0117] An optimal power flow model based on Distflow (a mathematical model used to describe the power flow in a distribution network) is adopted to establish a power flow constraint model that is converted into a mixed-integer second-order cone programming through a second-order cone convex relaxation:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, P j,t , Q j,t are the active and reactive power injection powers at node j at time t; P jk,t , Q jk,t are the active and reactive powers on line jk at time t; is the active power input of the photovoltaic at node j at time t; is the reactive power input of the photovoltaic at node j at time t; are the active and reactive powers input from the superior power grid at node j at time t, respectively; are the active power input and reactive power input of the load at node j at time t, respectively, and U j,t,0 is the initial voltage at node j at time t; the amount of electricity adjusted at node j at time t; is the maximum value of the square of the current value on line ij, are the maximum and minimum values of the active power purchased from the superior power grid, respectively, are the maximum and minimum values of the reactive power purchased from the superior power grid, respectively.

[0124] Step 4: According to Step 1, the characteristic scenarios and the uncertainty set of the probability distribution of the distributed photovoltaic output can be obtained. Then, the optimal scheduling cost under the characteristic scenario set of the distributed photovoltaic output can be solved as follows:

[0125]

[0126] In the formula, ω1 and ω2 are the weight coefficients of the two objective functions, respectively.

[0127] Thus, the optimal scheduling cost under the uncertainty set of the probability distribution of the distributed photovoltaic output characteristic scenarios can be obtained by solving the collaborative interaction multi-objective optimization model of the distribution network, and then the collaborative optimization of the distributed resources in the active distribution network can be realized.

[0128] In the present invention, the power flow constraint has been transformed into a convex optimization model based on mixed-integer second-order cone programming. The collaborative interaction multi-objective optimization model and constraints of the distribution network can be established on the Yalmip (optimization solver) toolkit based on the MATLAB (simulation software) platform, and then completed by calling algorithm packages such as Cplex (solver) and Gurobi (planning optimizer). The example can adopt an actual system example or an IEEE (a model for studying distribution networks) distribution network standard example.

[0129] Furthermore, the present invention also provides an active distribution network distributed resource collaborative optimization device. As Figure 2 shown, the active distribution network distributed resource collaborative optimization device 100 includes a first construction module 10, a determination module 20, a second construction module 30, and a solution module 40 that are connected in sequence.

[0130] Among them, the first construction module 10 is used to construct a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output; the determination module 20 is used to determine the feeder load regulation cost; the second construction module 30 is used to construct a coordinated interaction multi-objective optimization model of the distribution network based on the feeder load regulation cost, line network loss cost, light curtailment cost, operation cost of each power regulation device, superior power purchase cost, and node voltage deviation rate; the solution module 40 is used to solve the coordinated interaction multi-objective optimization model of the distribution network to obtain the optimal scheduling cost under the probability distribution uncertainty set of the distributed photovoltaic output characteristic scenario, so as to realize the coordinated optimization of distributed resources in the active distribution network.

[0131] In an embodiment of the present invention, when constructing the probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output, the first construction module 10 is specifically used for: performing random processing on the photovoltaic power generation to obtain distributed photovoltaic output data; using the K-means clustering algorithm to cluster the distributed photovoltaic output data for distributed photovoltaic output scenarios, and constructing the probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output in combination with the KL divergence constraint.

[0132] In an embodiment of the present invention, the coordinated interaction multi-objective optimization model of the distribution network constructed by the second construction module 30 includes two objective functions. Among them, the first objective function is constructed with the comprehensive minimum of all costs including the feeder load regulation cost, line network loss cost, light curtailment cost, operation cost of each power regulation device, and superior power purchase cost as the goal; the second objective function is constructed with the minimum node voltage deviation rate as the goal.

[0133] In an embodiment of the present invention, the constraint conditions of the coordinated interaction multi-objective optimization model of the distribution network constructed by the second construction module 30 include distributed photovoltaic operation constraint conditions, OLTC model constraint conditions, micro gas turbine operation constraint conditions, feeder transmission capacity constraint conditions, reactive power compensation device model constraint conditions, and power flow constraint conditions.

[0134] It should be noted that the specific implementation manner of the active distribution network distributed resource coordinated optimization device in this embodiment can refer to the specific implementation manner of the above-mentioned active distribution network distributed resource coordinated optimization method. To avoid redundancy, it will not be described in detail here.

[0135] Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned active distribution network distributed resource coordinated optimization method is realized.

[0136] Furthermore, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned active distribution network distributed resource coordinated optimization method is realized.

[0137] In summary, in view of the defect that the user-side load is difficult to control in real time, the present invention introduces a voltage reduction and energy saving technology, namely the CVR technology, to directly control the feeder load power through common direct control devices such as on-load tap changers (OLTCs). This operation is simple and easy to implement, has a positive impact on energy conservation, safety and stability, and can effectively cooperate with the coordinated interaction of the active distribution network. In addition, the present invention constructs a probability distribution uncertainty set under the characteristic scenario of distributed photovoltaic output, determines the feeder load regulation cost, and then constructs a multi-objective optimization model for the coordinated interaction of the distribution network based on the feeder load regulation cost, line network loss cost, light curtailment cost, operating costs of various power regulation devices, superior power purchase cost, and node voltage deviation rate. By solving the multi-objective optimization model for the coordinated interaction of the distribution network, the optimal scheduling cost under the probability distribution uncertainty set of the characteristic scenario of distributed photovoltaic output can be obtained, thereby realizing the coordinated optimization of distributed resources in the active distribution network, further balancing the supply-demand relationship of each energy unit in the active distribution network, reducing energy consumption, and improving the safety of the system.

[0138] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0139] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as a limitation of the present invention. After those skilled in the art have read the above content, various modifications and substitutions to the present invention will be obvious. Therefore, the protection scope of the present invention should be defined by the appended claims.

Claims

1. A method for collaborative optimization of distributed resources in an active distribution network, characterized in that: include: Construct the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenario; Determine the feeder load regulation cost; A distribution network collaborative interactive multi-objective optimization model is constructed based on feeder load control costs, line network loss costs, abandoned light costs, operating costs of various power regulation equipment, upstream power purchase costs, and node voltage deviation rates. The coordinated interactive multi-objective optimization model of the distribution network is solved to obtain the optimal dispatching cost under the probability distribution uncertainty set of distributed photovoltaic output characteristic scenarios, and to achieve coordinated optimization of distributed resources in the active distribution network.

2. The method for collaborative optimization of distributed resources in an active power distribution network according to claim 1, characterized in that: Construct the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenario, including: Perform random processing on photovoltaic power generation to obtain distributed photovoltaic output data; The K-means clustering algorithm is used to cluster the distributed photovoltaic output scenarios of the distributed photovoltaic output data, and the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenarios is obtained by combining the KL divergence constraint.

3. The method for collaborative optimization of distributed resources in an active power distribution network according to claim 1, characterized in that: The coordinated interactive multi-objective optimization model of the distribution network includes two objective functions. The first objective function is constructed with the goal of minimizing the comprehensive cost of feeder load regulation cost, line network loss cost, abandoned light cost, operating cost of each power regulation equipment, and superior electricity purchase cost; the second objective function is constructed with the goal of minimizing the node voltage deviation rate.

4. The method for collaborative optimization of distributed resources in an active power distribution network according to claim 1, characterized in that: The constraints of the coordinated interactive multi-objective optimization model of the distribution network include distributed photovoltaic operation constraints, OLTC model constraints, micro-turbine operation constraints, feeder transmission capacity constraints, reactive compensation device model constraints and power flow constraints.

5. An active distribution network distributed resource collaborative optimization device, characterized in that: include: The first building module is used to build a probability distribution uncertainty set under a distributed photovoltaic output characteristic scenario; A determination module, used to determine the feeder load regulation cost; The second construction module is used to construct a distribution network collaborative interactive multi-objective optimization model based on feeder load control costs, line network loss costs, abandoned light costs, operating costs of various power regulation equipment, upper-level power purchase costs, and node voltage deviation rates; The solution module is used to solve the coordinated interactive multi-objective optimization model of the distribution network to obtain the optimal dispatching cost under the probability distribution uncertainty set of the distributed photovoltaic output characteristic scenario, and realize the coordinated optimization of distributed resources in the active distribution network.

6. The active power distribution network distributed resource collaborative optimization device according to claim 5, characterized in that: When constructing a probability distribution uncertainty set under a distributed photovoltaic output characteristic scenario, the first construction module is specifically used to: Perform random processing on photovoltaic power generation to obtain distributed photovoltaic output data; The K-means clustering algorithm is used to cluster the distributed photovoltaic output scenarios of the distributed photovoltaic output data, and the probability distribution uncertainty set under the distributed photovoltaic output characteristic scenarios is obtained by combining the KL divergence constraint.

7. The active power distribution network distributed resource collaborative optimization device according to claim 5, characterized in that: The distribution network collaborative interactive multi-objective optimization model constructed by the second construction module includes two objective functions. Among them, the first objective function is constructed with the goal of minimizing the comprehensive cost of feeder load control cost, line network loss cost, abandoned light cost, operating cost of each power regulation equipment, and superior electricity purchase cost; the second objective function is constructed with the goal of minimizing the node voltage deviation rate.

8. The active power distribution network distributed resource collaborative optimization device according to claim 5, characterized in that: The constraints of the distribution network collaborative interactive multi-objective optimization model constructed by the second building module include distributed photovoltaic operation constraints, OLTC model constraints, micro-turbine operation constraints, feeder transmission capacity constraints, reactive compensation device model constraints and power flow constraints.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for collaborative optimization of distributed resources in an active power distribution network as described in any one of claims 1 to 4 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for collaborative optimization of distributed resources in an active power distribution network according to any one of claims 1 to 8 is implemented.

Images