Distribution network distributed energy optimization configuration method, electronic equipment and storage medium

Abstract

The invention discloses a distribution network distributed energy optimization configuration method, an electronic device and a storage medium, and the method comprises the steps: firstly providing a water-light distributed energy joint optimization strategy based on the water-light energy output complementary characteristic, and building an active and reactive collaborative optimization scheduling model of a distributed photovoltaic and water storage type small hydropower station; secondly, considering the active or reactive power regulation capability of flexible resources such as an energy storage battery, and establishing a multi-flexible-resource active and reactive power collaborative optimization model of the power distribution network by taking the minimum active loss and voltage deviation of the power distribution network as an optimization target; adopting a preset linearization method to convert each model from a mixed integer nonlinear model into a mixed integer linear model; and calling a Cplex solver in the MATLAB to solve the converted distributed energy optimization model to obtain a distributed energy optimization configuration scheme. Through active and reactive collaborative optimization of multi-flexibility resources of the power distribution network, optimization of power flow of the power distribution network is facilitated, line loss is reduced, electric energy quality is improved, and economic and safe operation of the power distribution network is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization and dispatching, and in particular to a distribution network distributed energy optimization configuration method, electronic equipment and storage medium. Background Art

[0002] In response to the national dual-carbon strategy, promoting the large-scale and decentralized development of clean energy such as wind power, solar energy, and hydropower has become the main direction of the current transformation of the distribution network. In many remote mountainous areas of my country, water resources are abundant, and a large number of small hydropower stations have been built. These small hydropower stations have played an important role in alleviating the power shortage in remote areas. However, the output of small hydropower stations is affected by seasonality and river basin characteristics, and they are usually connected to the end of the power grid, facing a series of problems such as long power supply radius, small transmission line cross-section, and aging power equipment. In the flood season or dry season, active power backflow, excessive or insufficient reactive power, etc. are prone to occur, which seriously affects the power quality and safety of the distribution network. With the country's support for the electrification process in remote areas, the distributed photovoltaic and small hydropower industries have developed rapidly. With the free access of large-scale small hydropower and distributed photovoltaics, the power quality of the distribution network faces great challenges. Therefore, it is particularly important to fully tap the regulation potential of distributed photovoltaics and small hydropower, combine other types of flexibility resources, enhance the synergy between the "source-grid-load-storage" links, and study the coordinated optimization strategy of multiple types of flexibility resources in the distribution network.

[0003] Storage hydropower stations have a certain regulation capability and can adjust the output appropriately by adjusting the water flow. However, due to limitations such as ramp rate, minimum startup / shutdown time and output fluctuations, the hydropower units lack the ability to adjust flexibly. In addition, distributed photovoltaic power generation has strong volatility and randomness. It is difficult to effectively cope with power and voltage fluctuations in the power grid by relying solely on the active scheduling of small hydropower.

[0004] Therefore, it is urgent to combine other flexibility resources to carry out more comprehensive and in-depth research on the coordinated optimization of active and reactive power in distribution networks to ensure the economic and safe operation of the power grid. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing the configuration of distributed energy in a distribution network.

[0006] In a first aspect, the present invention provides a method for optimizing the configuration of distributed energy in a distribution network, comprising:

[0007] Establish active and reactive output models of distributed photovoltaic and storage hydropower stations;

[0008] According to the active power regulation capability and / or reactive power regulation capability of the flexible resources, an active power output model and / or reactive power output model of the flexible resources is established; the flexible resources include energy storage batteries, static VAR compensators, shunt capacitors and on-load tap-changing transformers;

[0009] Under the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimum active power loss and voltage deviation of the distribution network as the optimization goal;

[0010] For the active and reactive output models of distributed photovoltaic and storage hydropower stations, the active output models of flexible resources, and / or the nonlinear constraints in the reactive output models, a preset linearization method is used to convert each model from a mixed integer nonlinear model to a mixed integer linear model;

[0011] The Cplex solver is called in MATLAB to solve the converted distributed energy optimization model and obtain the distributed energy optimization configuration plan.

[0012] In a second aspect, the present invention provides a device for optimizing the configuration of distributed energy in a distribution network, comprising:

[0013] The first modular building block is used to establish the active and reactive output model of distributed photovoltaic and storage hydropower stations;

[0014] The second modular construction module is used to establish an active output model and / or a reactive output model of the flexibility resources according to the active regulation capability and / or reactive regulation capability of the flexibility resources; the flexibility resources include energy storage batteries, static VAR compensators, shunt capacitors and on-load tap-changing transformers;

[0015] The third modular construction module is used to establish a collaborative optimization model with the optimization objectives of minimizing the active power loss and voltage deviation of the distribution network under the premise of satisfying the power balance of the distribution network;

[0016] A model linear conversion module, for converting the active and reactive output models of distributed photovoltaic and storage hydropower stations, the active output model of flexible resources, and / or the nonlinear constraints in the reactive output model from mixed integer nonlinear models to mixed integer linear models using a preset linearization method;

[0017] A solution module is used to call the Cplex solver in MATLAB to solve the converted distributed energy optimization model and obtain the distributed energy optimization configuration plan;

[0018] The distribution network distributed energy optimization configuration device is used to execute the distribution network distributed energy optimization configuration method as described in the first aspect.

[0019] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for optimizing the configuration of distributed energy in the distribution network described in the first aspect of the present invention.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for optimizing the configuration of distributed energy in a distribution network as described in the first aspect of the present invention when executed.

[0024] The distributed energy optimization configuration method for a distribution network provided by an embodiment of the present invention establishes active and reactive output models of distributed photovoltaic and water storage hydropower stations; establishes active output models and / or reactive output models of flexible resources according to the active regulation capability and / or reactive regulation capability of flexible resources; flexible resources include energy storage batteries, static VAR compensators, shunt capacitors and on-load tap-changing transformers; on the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimization of active loss and voltage deviation of the distribution network as the optimization objectives; for the active and reactive output models of distributed photovoltaic and water storage hydropower stations, the active output models of flexible resources, and / or the reactive output models, a preset linearization method is used to convert each model from a mixed integer nonlinear model to a mixed integer linear model; and the Cplex solver is called in MATLAB to solve the converted distributed energy optimization model to obtain a distributed energy optimization configuration plan. Firstly, based on the complementary characteristics of hydropower and photovoltaic power output, a joint optimization strategy for distributed photovoltaic and water-storage hydropower was proposed, and a coordinated optimization dispatch model for active and reactive power of distributed photovoltaic and water-storage small hydropower stations was established; secondly, the active or reactive power regulation capability of flexible resources such as energy storage batteries was considered, and the active power loss and voltage deviation of the distribution network were minimized as the optimization objectives, and a coordinated optimization model for active and reactive power of multiple flexible resources in the distribution network was established. The coordinated optimization of active and reactive power of multiple flexible resources in the distribution network is conducive to optimizing the network flow of the distribution network, reducing line losses and improving power quality, and realizing the economical and safe operation of the distribution network.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 It is a flow chart of a method for optimizing configuration of distributed energy in a distribution network provided by an embodiment of the present invention;

[0028] Figure 2 It is a strategy diagram of a method for evaluating the access capability of distributed photovoltaic power distribution networks based on static voltage stability analysis provided by an embodiment of the present invention;

[0029] Figure 3 It is a schematic diagram of IEEE 33-node system simulation provided by an embodiment of the present invention;

[0030] Figure 4 A distributed photovoltaic and load day-ahead power prediction curve provided by an embodiment of the present invention;

[0031] Figure 5 It is a voltage amplitude situation of each node of a distribution network provided by an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram of active power balance of a distribution network after ignoring line loss provided by an embodiment of the present invention;

[0033] Figure 7 This is a schematic diagram of reactive power balance in a distribution network provided by an embodiment of the present invention;

[0034] Figure 8 This is a schematic diagram of active power output of three hydropower generating units provided in an embodiment of the present invention;

[0035] Fig. 9 It is a schematic diagram of the change of the number of switching groups of parallel capacitors and the transformation ratio of the on-load tap-changing transformer provided by an embodiment of the present invention;

[0036] Fig.10 It is a schematic diagram of line loss and average node voltage deviation of a distribution network provided by an embodiment of the present invention;

[0037] Fig.11 It is a structural schematic diagram of a distributed energy optimization configuration device for a distribution network provided by an embodiment of the present invention;

[0038] Fig.12 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0040] Figure 1 The present invention provides a flowchart of a method for optimizing the configuration of distributed energy in a distribution network. This embodiment is applicable to the case of optimizing the configuration of distributed energy in a distribution network. The method can be executed by a device for optimizing the configuration of distributed energy in a distribution network. The device for optimizing the configuration of distributed energy in a distribution network can be implemented in the form of hardware and / or software. The device for optimizing the configuration of distributed energy in a distribution network can be configured in an electronic device. Figure 1 As shown, the distribution network distributed energy optimization configuration method includes:

[0041] S101. Establish active and reactive output models of distributed photovoltaic and water storage hydropower stations.

[0042] (1) Distributed photovoltaic

[0043] Based on the constant power factor control method, the photovoltaic inverter has a certain reactive power regulation capability, but the output active power is coupled with the reactive power. Affected by the rated capacity of the inverter, the active and reactive output model of distributed photovoltaic can be expressed as follows:

[0044]

[0045]

[0046] In order to ensure the absorption rate of distributed photovoltaic power in the distribution network and avoid waste of resources, it is necessary to constrain the absorption rate of distributed photovoltaic power. The formula for constraining the absorption rate of distributed photovoltaic power is as follows:

[0047]

[0048] Where n is the number of the distributed photovoltaic unit; are the actual power consumption and predicted power of the nth distributed photovoltaic unit at time t, They are respectively the active power and reactive power output by the distributed photovoltaic units. is the power factor angle of the distributed photovoltaic unit, is the rated power of the distributed photovoltaic unit; N pvis the total number of distributed photovoltaic units; T is the scheduling period; β is the preset distributed photovoltaic absorption rate.

[0049] The preferred value of β is 90%.

[0050] (2) Storage type hydropower station

[0051] The distributed small hydropower station studied in the present invention is a water storage type small hydropower station, which has a certain adjustable capacity and adjusts the output by controlling the power generation flow and the water head height.

[0052] The active and reactive output model of the storage hydropower station is constructed as follows:

[0053]

[0054] In the formula, A is the active power of the water storage hydropower station m at time t, i.e., the active output; m is the preset water energy conversion factor; H m,t is the net water head of the storage hydropower station m at time t; S m,t is the power generation flow of the storage hydropower station m at time t; is the outflow of the hydropower station, is the abandoned water flow of the hydropower station;

[0055] Among them, A m Generally, the value is between 6 and 8.

[0056] The water level in front of the dam and the water storage capacity of the reservoir, and the tailwater level and the outflow flow are all nonlinear. In general, the water level in front of the dam can be expressed as a cubic polynomial of the water storage capacity of the reservoir. The relationship between the water level in front of the dam and the water storage capacity of the reservoir is as follows:

[0057]

[0058] In the formula, is the water level before the dam, V m,t is the water storage capacity of the reservoir of a water storage hydropower station; m,0 、a m,1 、a m,2 、a m,3 is the correlation coefficient;

[0059] The constraints of a storage hydropower station include hydropower station constraints and hydropower unit constraints. The constraints of a hydropower station include water balance constraints, reservoir water storage constraints, reservoir water level constraints, outflow constraints, and abandoned water flow constraints.

[0060] The water balance constraint formula is: V m,t =V m,t-1 +3600(I m,t -S m,t )Δt;

[0061] In the formula, I m,t is the inflow of the storage hydropower station; V m,t-1 and S m,t Please refer to the above V m,t and S m,t meaning;

[0062] Reservoir water storage capacity V m,t The constraint formula is: V m,min ≤V m,t ≤V m,max ;

[0063] Where V m,max 、V m,min It is the upper and lower limits of the water storage capacity of the reservoir of a water storage hydropower station;

[0064] Reservoir water level The constraint formula is:

[0065]

[0066] In the formula, are the upper and lower limits of the reservoir water level; The initial dispatching water level and the final dispatching water level of the reservoir; is the deviation limit between the initial water level and the final water level of the reservoir in the dispatching cycle (used to avoid affecting the next dispatching cycle); T is the dispatching cycle;

[0067] Outbound flow S m,t The constraint formula is: S m,min ≤S m,t ≤S m,max ;

[0068] In the formula, S m,max , S m,min The upper and lower limits of the power generation flow of the hydropower station;

[0069] Discarded water flow The constraint formula is:

[0070] In the formula, is the maximum water discharge of a storage hydropower station;

[0071] The constraints of hydropower units include active power output constraints, output ramp constraints, start-stop duration constraints, output fluctuation constraints and reactive power output constraints.

[0072] Active output of hydropower units The constraint formula is:

[0073] In the formula, It is the upper and lower limits of the output power (active output) of the hydropower unit; U m,t is the start and stop status of the hydropower unit at time t, U m,t It is an integer variable of 0 or 1, 1 indicates the start-up state of the hydropower unit, and 0 indicates the shutdown state of the hydropower unit; the hydropower unit is a hydropower unit in a storage hydropower station;

[0074] The output ramp constraint formula of the hydropower unit is:

[0075] In the formula, is the maximum climbing power of the hydropower unit, is the climbing power of the hydropower unit at time t;

[0076] The constraint formula for the start and stop duration of the hydropower unit is:

[0077]

[0078] Where, T m,on , T m,off is the upper and lower limits of the start and stop time of the hydropower unit, U i,t , U i,t-1 is the voltage of the hydropower unit at time t and t-1;

[0079] The output fluctuation constraint formula of hydropower unit is:

[0080]

[0081] In the formula, α m,t , β m,t is the up-climbing state and down-climbing state of the hydropower unit output, which is a 0-1 integer variable, 1 means that the hydropower unit is in the up-climbing or down-climbing state at time t, and 0 means it is in a stable state; κ m The maximum number of power adjustments of the hydropower unit in a dispatching cycle;

[0082] Reactive power output of hydropower units The constraint formula is:

[0083]

[0084] In the formula, The reactive power output of the hydropower unit. is the power factor angle of the hydropower unit, is the rated power of the hydropower unit.

[0085] S102. Establish an active power output model and / or a reactive power output model of the flexible resources according to the active power regulation capability and / or the reactive power regulation capability of the flexible resources.

[0086] Flexibility resources include energy storage batteries, static var compensators (SVCs), shunt capacitors (SCs) and on-load tap changers (OLTCs).

[0087] (1) Energy storage battery

[0088] Energy storage batteries have the advantages of flexible control and fast charging and discharging. They can alleviate problems such as power fluctuations, frequency changes, and load peak-valley differences in distribution networks. In particular, the reactive voltage regulation capability of energy storage batteries can effectively address voltage fluctuations in distribution networks caused by the connection of a high proportion of distributed energy sources to the grid.

[0089] The active output model of the energy storage battery is:

[0090]

[0091] In the formula, They are respectively the active power absorbed and released by the energy storage battery at time t; They are the charging state and discharging state of the energy storage battery, both of which are integer variables of 0 or 1; They are the maximum active power absorbed and released by the energy storage battery per hour respectively;

[0092] The remaining capacity of the energy storage battery can be expressed as follows:

[0093]

[0094] In the formula, E t 、E t+1 are the capacities of the energy storage batteries at time t and t+1 respectively; T is the scheduling period, E max 、E min They are the upper and lower limits of the remaining capacity of the energy storage battery respectively; They are the charging efficiency and discharging efficiency of the energy storage battery respectively;

[0095] The reactive output model of the energy storage battery is:

[0096]

[0097] In the formula, is the reactive power absorbed and released by the energy storage battery at time t, θ ES is the power factor angle of the energy storage inverter; It is the maximum charging capacity and maximum discharging capacity of the energy storage battery.

[0098] (2) Static VAR Compensator

[0099] Static VAR compensator (SVC) has the ability to regulate reactive power in both directions. When the voltage fluctuates, it can achieve reactive power balance at the current voltage level. For the distribution network with centralized access to small hydropower, the SVC can effectively reduce reactive power and thus achieve voltage regulation.

[0100] The reactive output model of the static VAR compensator is:

[0101]

[0102] In the formula, They are respectively the upper and lower limits of reactive power regulation of the static VAR compensator; It is the reactive power output by the static VAR compensator.

[0103] (3) Parallel capacitors

[0104] After being connected to the grid, the parallel capacitor can provide reactive power to the grid. The output reactive power can be adjusted by changing the number of grid-connected capacitor groups. The reactive output model of the parallel capacitor is:

[0105]

[0106] In the formula, is the reactive power output by the parallel capacitor; m t is the number of shunt capacitor groups switched, m t is an integer variable; is the reactive power of the minimum unit capacitor; M is the maximum number of parallel capacitor groups.

[0107] (4) On-load tap-changing transformer

[0108] The on-load tap-changing transformer can adjust the node voltage by changing the tap position, but it cannot achieve continuous adjustment of the voltage amplitude. The tap adjustment position of the on-load tap-changing transformer is adjusted according to a certain step length. The reactive output model of the on-load tap-changing transformer is expressed as follows:

[0109] k min ≤k t ≤k max ;k t =k min +Δτh t ;h t ≤H;

[0110] In the formula, k t k is the transformation ratio of the on-load tap-changing transformer; max , k min are the upper and lower limits of the on-load tap-changing transformer ratio respectively; Δτ is the tap adjustment step of the on-load tap-changing transformer winding; h tis the current position of the on-load tap-changing transformer winding tap; H is the total number of gears of the on-load tap-changing transformer winding tap.

[0111] S103. On the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimization of active power loss and voltage deviation of the distribution network as the optimization objectives.

[0112] The purpose of the active and reactive collaborative optimization of multiple types of flexible resources in the distribution network is to reasonably arrange the active or reactive output of each flexible resource under the premise of satisfying the power balance of the distribution network, so as to optimize the economy and safety of the distribution network. Therefore, the present invention takes the minimization of the active power loss and total voltage deviation of the distribution network as the optimization goal, and establishes a collaborative optimization model to ensure the network flow safety of the distribution network.

[0113] Specifically, under the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimum active power loss and voltage deviation of the distribution network as the optimization goal, including:

[0114] The function for constructing the minimum active power loss min F1 of the distribution network is:

[0115]

[0116] Where N is the total number of distribution network nodes; ij,t is the square value of the current in branch ij; r ij is the resistance value of branch ij; Ω i is the set of nodes connected to the i-node as the head end;

[0117] The function for constructing the minimum total voltage deviation of the distribution network min F2 is:

[0118]

[0119] The collaborative optimization model is obtained according to the functions of min F1 and min F2;

[0120]

[0121] Where T is the scheduling period, λ1 and λ2 are the weight coefficients of each function, which represent the importance of each function; C0 and ΔV0 are the active power loss and voltage deviation before optimization, respectively.

[0122] Preferably, the collaborative optimization model is a DistFlow AC power flow model.

[0123] In the DistFlow AC power flow model, the active power and reactive power balance of each node can be expressed as follows:

[0124] P j,t =∑ i:i→j (Pij,t -r ij l ij,t )-∑ k:j→k P jk,t

[0125] Q j,t =∑ i:i→j (Q ij,t -x ij l ij,t )-∑ k:j→k Q jk,t

[0126] in,

[0127]

[0128] Where P j,t , Q j,t are the active power and reactive power flowing out of distribution network node j respectively; P ij,t , P jk,t are the active power of the line with node j as the end node and the head node of the line respectively; Q ij,t , Q jk,t are the reactive power of the line with node j as the end node and the head node of the line respectively; r ij 、x ij are the resistance and reactance values ​​of line ij respectively; Purchase power from the upper grid for the distribution network; are the active load and reactive load of node j respectively.

[0129] When the ij branch does not contain an on-load tap-changing transformer:

[0130]

[0131] When the ij branch contains an on-load tap-changing transformer:

[0132]

[0133] Where V i,t 、V j,t are the squares of the voltage values ​​at nodes i and j respectively; U i,max , U i,min are the maximum and minimum values ​​of the node voltage, I i,max ,I i,min Maximum and minimum values ​​of line current.

[0134] S104. For the active and reactive output models of distributed photovoltaic and water storage hydropower stations, the active output model of flexible resources, and / or the nonlinear constraints in the reactive output model, a preset linearization method is used to convert each model from a mixed integer nonlinear model to a mixed integer linear model.

[0135] The preset linearization methods include second-order cone relaxation technique, piecewise linearization, big M method, introduction of 0-1 variables, variable discretization and other linearization methods. In the following, mixed integer nonlinearity is MINLP, and mixed integer linear model is MILP.

[0136] The present invention adopts a piecewise linearization method to linearize the equality constraint of the water level in front of the dam. The specific method is as follows:

[0137] 1) Set the interval [V m,min ,V m,max ] is divided into K equally spaced subintervals, and we get:

[0138]

[0139] 2) Introduce binary auxiliary variable d m,t,k

[0140]

[0141] 3) Introduce auxiliary variable x m,t,k

[0142]

[0143] 4) Linear fitting

[0144]

[0145] (2) The present invention uses the 0-1 variable method and the large M method to linearize the branch voltage equation constraint model including the on-load tap-changing transformer.

[0146]

[0147] Since Δτ is generally 0.01, the last term in the above formula is small and can be ignored, so we can get:

[0148]

[0149] Introducing 0-1 variables Then we get:

[0150]

[0151] Wherein, NT is an integer constant, which is determined by the number of binary digits of the total number of gears of the on-load tap-changing transformer; s is an integer variable.

[0152] A new nonlinear term appears in the above equation The big M method can be used for linearization.

[0153]

[0154] Wherein: M represents a sufficiently large constant.

[0155] (3) The present invention uses the second-order cone relaxation technique to relax the branch current equation constraint into a second-order cone constraint:

[0156] ‖[2P ij，t ;2Q ij,t ;(l ij,t -V i,t )]‖2≤l ij,t +V i,t ;

[0157] S105. Invoke the Cplex solver in MATLAB to solve the transformed distributed energy optimization model and obtain a distributed energy optimization configuration plan.

[0158] After the above method is used to transform the original MINLP model into a MILP model, the Cplex solver can be directly called in MATLAB to solve it. The decision variables include node voltage, line current, active / reactive power output by hydropower units, active / reactive power output by photovoltaic units, charging and discharging power of energy storage batteries, and reactive compensation power of static VAR compensators and shunt capacitors.

[0159] Figure 2 is a strategy diagram of the method for evaluating the access capability of distributed photovoltaic power distribution network based on static voltage stability analysis of the present invention, such as Figure 2 As shown in the figure, the active and reactive coordinated optimization dispatch model of distributed photovoltaic and small hydropower stations is first constructed; secondly, the reactive voltage regulation capability of energy storage batteries, SVC (static VAR compensator), SC (shunt capacitor), and OLTC (on-load tap changer) is considered, and the active loss and voltage deviation of the distribution network are minimized as the optimization objectives, and the active and reactive coordinated optimization model of the distribution network with multiple flexible resources is established; finally, the linearization method is adopted for the nonlinear constraints in the model, and the Cplex solver is called in MATLAB to solve it. The active and reactive coordinated optimization of multiple flexible resources in the distribution network is conducive to optimizing the network flow of the distribution network, reducing line losses and improving power quality, and realizing the economical and safe operation of the distribution network.

[0160] The following are specific examples and Figure 3-Figure 10 To further illustrate the technical solution of the present invention.

[0161] This embodiment takes the IEEE 33-node system as an example for simulation analysis. Figure 3 The simulation diagram of IEEE 33-node system is shown in Figure 1. Figure 3 As shown, the node rated voltage is 12.66kV, the allowable voltage deviation is -0.05 to 0.05, and the maximum line current is 400A. The total active load and reactive load of the system are 3.715MW and 2.300Mvar, respectively. 4×0.25MW distributed photovoltaics are connected at nodes 6, 16, 20, and 29. 3×0.5MW small hydropower stations are connected at node 8. The runoff of small hydropower stations is generally stable during the day. The present invention sets the average runoff of the small hydropower station to be 18.56m3 / s, the head to be 8m, the minimum outflow flow to be 5m3 / h, the maximum outflow flow to be 30m3 / h, and the output coefficient to be 6. The energy storage device is connected at node 9, with a maximum capacity / power of 1MWh / 0.4MW and a charge and discharge efficiency of 0.95. SVC (static VAR compensator) is connected at node 4, and the reactive compensation power adjustment range is [-0.5,0.5]Mvar. SC (shunt capacitor) is connected at nodes 13 and 26, with a reactive compensation capacity of 0.05Mvar per group, and 10 groups each. An OLTC (on-load tap-changing transformer) is connected to branch 1-2, with an adjustable ratio range of 0.95 to 1.05.

[0162] Distributed photovoltaic and load day-ahead power forecast curves are as follows: Figure 4 As shown. In order to verify the proposed research method for active and reactive collaborative optimization of multiple flexible resources in the distribution network containing distributed hydropower stations, this embodiment, based on the proposed active and reactive collaborative optimization model of multiple types of flexible resources in the distribution network, is solved by Cplex solver to obtain the optimization result of the distribution network, analyze the impact of distributed hydropower stations on the voltage quality of the distribution network, and further study the role of flexible voltage regulation devices such as energy storage batteries, SVC (static VAR compensator), SC (shunt capacitor), OLTC (on-load tap-changing transformer) in reactive optimization of the distribution network, as well as the effectiveness of the active and reactive collaborative optimization method of multiple flexible resources in the distribution network containing distributed hydropower stations on the economic and safe operation of the distribution network.

[0163] Figure 5 The voltage amplitude of each node in the distribution network is shown. It can be seen that, affected by the flow direction, the voltage at the node head end of the line is generally high, and the voltage at the node end of the line is low. In terms of load power, the node voltage is low during the peak load period and high during the valley load period. However, after the optimization of the distribution network's multi-flexibility resources, the overall voltage deviation of each node in the distribution network is small, maintained between -0.03 and 0.1.

[0164] Figure 6The active power balance of the distribution network after ignoring line losses is shown. It can be seen that the active power of the distribution network mainly comes from the upper power grid. Unlike the photovoltaic unit, which generates power during the day and stops at night, the output of the small hydropower station is relatively stable. The energy storage device adjusts its own charging and discharging power time, discharges during peak load periods, and charges during low load periods, which not only effectively reduces the peak-to-valley difference in load power, but also reduces the operating cost of the distribution network.

[0165] Figure 7 The reactive power balance of the distribution network is shown. It can be seen that during the peak load period, the reactive power demand of the distribution network increases, and the reactive power is mainly compensated by SC (shunt capacitor) and SVC (static VAR compensator). After 7:00, the upper power grid needs to transmit reactive power to meet the reactive power demand of the load. While the energy storage battery is charging and discharging, it will also output and absorb reactive power accordingly.

[0166] Figure 8 The active power output of the three hydropower units is shown. It can be seen that the output of the hydropower units changes smoothly and fluctuates slightly due to the restrictions of the ramp rate, the minimum startup / shutdown time, and the output fluctuation. The output change trends of the three hydropower units are similar. The output of the hydropower units is small during the low load period, and the water is stored. The output is increased during the peak load period to play a peak load regulation role. During the period of large photovoltaic output, the output of the hydropower units decreases, showing the complementary effect of water and light. Therefore, the photovoltaic output fluctuation can be effectively smoothed through the adjustment of the hydropower units.

[0167] Fig. 9 The changes in the number of SC (shunt capacitor) switching groups and OLTC (on-load tap-changing transformer) ratio are shown. It can be seen that during the two peak load periods, the reactive power demand of the distribution network increases, and the number of SC (shunt capacitor) switching groups increases to ensure sufficient reactive power reserves in the distribution network. During the period when photovoltaic output is large, photovoltaics also output a part of reactive power, the reactive power demand of the distribution network decreases, and the number of SC (shunt capacitor) switching groups decreases. After 7:00, the upper power grid transmits reactive power to the distribution network, resulting in an overall low node voltage level. Therefore, the OLTC (on-load tap-changing transformer) reduces the ratio to increase the node voltage amplitude.

[0168] Fig.10 The distribution network line loss and average node voltage deviation are shown. It can be seen that the overall voltage amplitude of the distribution network nodes is low, but after coordinated optimization of diversified flexible resources such as energy storage, SC (shunt capacitor), SVC (static VAR compensator), the average node voltage deviation is maintained between -0.01 and 0.01. During peak load periods, the line transmission power increases and the line current increases, so the active power loss also increases.

[0169] The present invention also provides a distributed energy optimization configuration device for a power distribution network. Fig.11 The present invention provides a schematic diagram of a structure of a distributed energy optimization configuration device for a distribution network. Fig.11 As shown, the distribution network distributed energy optimization configuration device includes:

[0170] The first modular building block 100 is used to establish an active and reactive output model of distributed photovoltaic and storage hydropower stations;

[0171] A second modular building block 200 is used to establish an active output model and / or a reactive output model of the flexibility resources according to the active regulation capability and / or reactive regulation capability of the flexibility resources; the flexibility resources include energy storage batteries, static VAR compensators, shunt capacitors and on-load tap-changing transformers;

[0172] The third modular construction module 300 is used to establish a collaborative optimization model with the optimization objectives of minimizing the active power loss and voltage deviation of the distribution network under the premise of satisfying the power balance of the distribution network;

[0173] A model linear conversion module 400 is used to convert the active and reactive output models of distributed photovoltaic and storage hydropower stations, the active output model of flexible resources, and / or the nonlinear constraints in the reactive output model from mixed integer nonlinear models to mixed integer linear models using a preset linearization method;

[0174] A solution module 500 is used to call a Cplex solver in MATLAB to solve the converted distributed energy optimization model to obtain a distributed energy optimization configuration solution;

[0175] The distribution network distributed energy optimization configuration device is used to execute the distribution network distributed energy optimization configuration method as described in the first aspect.

[0176] The distribution network distributed energy optimization configuration device provided in the embodiment of the present invention can execute the distribution network distributed energy optimization configuration method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0177] Fig.12A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0178] like Fig.12 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0179] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0180] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as a method for optimizing the configuration of distributed energy resources in a distribution network.

[0181] In some embodiments, the method for optimizing the configuration of distributed energy in a distribution network may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for optimizing the configuration of distributed energy in a distribution network described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the method for optimizing the configuration of distributed energy in a distribution network by any other appropriate means (e.g., by means of firmware).

[0182] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0183] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0184] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0185] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0186] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0187] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0188] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0189] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of distributed energy in a distribution network, characterized in that: include: Establish active and reactive output models of distributed photovoltaic and storage hydropower stations; According to the active power regulation capability and / or reactive power regulation capability of the flexible resources, an active power output model and / or reactive power output model of the flexible resources is established; the flexible resources include energy storage batteries, static VAR compensators, shunt capacitors and on-load tap-changing transformers; Under the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimum active power loss and voltage deviation of the distribution network as the optimization goal; For the active and reactive output models of distributed photovoltaic and storage hydropower stations, the active output models of flexible resources, and / or the nonlinear constraints in the reactive output models, a preset linearization method is used to convert each model from a mixed integer nonlinear model to a mixed integer linear model; The Cplex solver is called in MATLAB to solve the converted distributed energy optimization model and obtain the distributed energy optimization configuration plan.

2. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The active and reactive output model of distributed photovoltaics is expressed as follows: The formula for constraining the absorption rate of distributed photovoltaics is as follows: Where n is the number of the distributed photovoltaic unit; are the actual power consumption and predicted power of the nth distributed photovoltaic unit at time t, They are respectively the active power and reactive power output by the distributed photovoltaic units. is the power factor angle of the distributed photovoltaic unit, is the rated power of the distributed photovoltaic unit; N pv is the total number of distributed photovoltaic units; T is the scheduling period; β is the preset distributed photovoltaic absorption rate.

3. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The active and reactive output model of the storage hydropower station is constructed as follows: In the formula, A is the active power output of the water storage hydropower station m at time t; m is the preset water energy conversion factor; H m,t is the net water head of the storage hydropower station m at time t; S m,t is the power generation flow of the storage hydropower station m at time t; is the outflow of the hydropower station, is the abandoned water flow of the hydropower station; The relationship between the water level in front of the dam and the water storage capacity of the reservoir is as follows: In the formula, is the water level before the dam, V m,t is the water storage capacity of the reservoir of a water storage hydropower station; m,0 、a m,1 、a m,2 、a m,3 is the correlation coefficient; The constraints of a storage hydropower station include hydropower station constraints and hydropower unit constraints. The constraints of a hydropower station include water balance constraints, reservoir water storage constraints, reservoir water level constraints, outflow constraints, and abandoned water flow constraints. The water balance constraint formula is: V m,t =V m,t-1 +3600(I m,t -S m,t )Δt; In the formula, I m,t is the inflow of the storage hydropower station; Reservoir water storage capacity V m,t The constraint formula is: V m,min ≤V m,t ≤V m,max ; Where V m,max 、V m,min The upper and lower limits of the water storage capacity of the reservoir of a water storage hydropower station; Reservoir water level The constraint formula is: In the formula, are the upper and lower limits of the reservoir water level; The initial dispatching water level and the final dispatching water level of the reservoir; is the deviation limit between the initial water level and the final water level of the reservoir in the dispatching period; T is the dispatching period; Outbound flow S m,t The constraint formula is: S m,min ≤S m,t ≤S m,max ; In the formula, S m,max , S m,min The upper and lower limits of the power generation flow of the hydropower station; Discarded water flow The constraint formula is: In the formula, is the maximum water discharge of a storage hydropower station; The constraints of hydropower units include active power output constraints, output ramp constraints, start-stop duration constraints, output fluctuation constraints and reactive power output constraints. Active output of hydropower units The constraint formula is: In the formula, The upper and lower limits of the output power of the hydropower unit; U m,t is the start and stop status of the hydropower unit at time t, U m,t It is an integer variable of 0 or 1, 1 indicates the hydropower unit is in the start state, and 0 indicates the hydropower unit is in the shutdown state; The output climbing constraint formula of the hydropower unit is: In the formula, is the maximum climbing power of the hydropower unit, is the climbing power of the hydropower unit at time t; The constraint formula for the start and stop duration of the hydropower unit is: Where, T m,on , T m,off is the upper and lower limits of the start and stop time of the hydropower unit, U i,t , U i,t-1 is the voltage of the hydropower unit at time t and t-1; The output fluctuation constraint formula of hydropower unit is: In the formula, α m,t , β m,t is the up-climbing state and down-climbing state of the hydropower unit output, which is a 0-1 integer variable, 1 means that the hydropower unit is in the up-climbing or down-climbing state at time t, and 0 means it is in a stable state; κ m is the maximum number of power adjustments of the hydropower unit in a dispatching cycle T; Reactive power output of hydropower units The constraint formula is: In the formula, The reactive power output of the hydropower unit. is the power factor angle of the hydropower unit, is the rated power of the hydropower unit.

4. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The active output model of the energy storage battery is: In the formula, They are respectively the active power absorbed and released by the energy storage battery at time t; They are the charging state and discharging state of the energy storage battery, both of which are integer variables of 0 or 1; They are the maximum active power absorbed and released by the energy storage battery per hour respectively; The remaining capacity of the energy storage battery can be expressed as follows: In the formula, E t 、E t+1 are the capacities of the energy storage battery at time t and t+1 respectively; T is the scheduling period, E max 、E min They are the upper and lower limits of the remaining capacity of the energy storage battery respectively; They are the charging efficiency and discharging efficiency of the energy storage battery respectively; The reactive output model of the energy storage battery is: In the formula, is the reactive power absorbed and released by the energy storage battery at time t, θ ES is the power factor angle of the energy storage inverter; It is the maximum charging capacity and maximum discharging capacity of the energy storage battery.

5. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The reactive output model of the static VAR compensator is: In the formula, They are respectively the upper and lower limits of reactive power regulation of the static VAR compensator; It is the reactive power output by the static VAR compensator.

6. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The reactive output model of parallel capacitors is: In the formula, is the reactive power output by the parallel capacitor; m t is the number of parallel capacitor groups switched, m t is an integer variable; is the reactive power of the minimum unit capacitor; M is the maximum number of parallel capacitor groups.

7. The method for optimizing the configuration of distributed energy in a distribution network according to claim 1, characterized in that: The tap adjustment position of the on-load tap-changing transformer is adjusted according to a certain step length. The reactive output model of the on-load tap-changing transformer is expressed as follows: k min ≤k t ≤k max ;k t =k min +Δτh t ;h t ≤H; In the formula, k t k is the transformation ratio of the on-load tap-changing transformer; max , k min are the upper and lower limits of the on-load tap-changing transformer ratio respectively; Δτ is the tap adjustment step of the on-load tap-changing transformer winding; h t is the current position of the on-load tap-changing transformer winding tap; H is the total number of gears of the on-load tap-changing transformer winding tap.

8. The method for optimizing the configuration of distributed energy in a distribution network according to any one of claims 1 to 7, characterized in that: Under the premise of satisfying the power balance of the distribution network, a collaborative optimization model is established with the minimum active power loss and voltage deviation of the distribution network as the optimization goal, including: The function for constructing the minimum active power loss min F1 of the distribution network is: Where N is the total number of distribution network nodes; ij,t is the square value of the current in branch ij; r ij is the resistance value of branch ij; Ω i is the set of nodes connected to the i-node as the head end; The function for constructing the minimum total voltage deviation of the distribution network min F2 is: The collaborative optimization model is obtained according to the functions of min F1 and min F2; Where T is the scheduling period, λ1 and λ2 are the weight coefficients of each function, which represent the importance of each function; C0 and ΔV0 are the active power loss and voltage deviation before optimization, respectively.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network distributed energy optimization configuration method described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for optimizing the configuration of distributed energy in a distribution network according to any one of claims 1 to 8 when executed.

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