Micro-distribution double-layer collaborative optimization scheduling method and system based on active power aggregation domain

Abstract

The invention discloses a distribution network and micro network double-layer collaborative optimization scheduling method and system based on an active power aggregation domain, and the method comprises the steps: obtaining the operation parameters of a distribution network, and constructing the operation constraint of the distribution network; obtaining operation parameters of the micro-grid, and constructing operation constraints of the micro-grid; constructing a mathematical model of a micro-grid active power aggregation domain based on micro-grid operation constraints; solving the mathematical model by using a rank generation algorithm to obtain an operation boundary of the active power aggregation domain of the micro-grid; and submitting the active power aggregation domain of the micro-grid to the distribution network for scheduling, judging whether the active power aggregation domain of the micro-grid meets a distribution network loss minimization criterion or not in combination with the operation constraint of the distribution network, if so, controlling power output at a micro-gateway port by using the operation boundary, otherwise, adjusting the operation boundary of the active power aggregation domain of the micro-grid. And updating the mathematical model of the aggregation domain and solving until the operation boundary meets the target of minimum distribution network loss. According to the method, the scheduling model of the medium-voltage distribution network can be simplified and the scheduling efficiency can be improved under the condition that information interaction is limited.

Description

Technical Field

[0001] The present invention relates to a coordinated dispatching strategy for distribution network - microgrid cooperation of flexible and schedulable resources in a microgrid, specifically to the construction of an aggregated adjustable region of active power in a microgrid, the solution of the aggregated adjustable region of active power in a microgrid, and the coordinated optimal dispatching of multiple strategies such as active distribution network constraints. In particular, it relates to a method and system for two - layer coordinated optimal dispatching of distribution network - microgrid based on the aggregated active power region. Background Art

[0002] In recent years, with the increasing depletion of global fossil energy and the need to transition to a renewable energy system, developing and utilizing new energy sources and building a low - carbon and flexible new power system have gradually become a consensus. This has led to a continuous increase in the penetration rate of distributed resources including energy storage, photovoltaic, and thermostatic loads in the distribution network, and has forced the traditional distribution network to gradually evolve into an active distribution network containing flexible and controllable microgrids. The coordinated dispatching of distribution network - microgrid around a large number of distributed resources has gradually become the focus of attention.

[0003] A microgrid refers to a small - scale power generation, distribution, and consumption system composed of distributed power sources, electrical loads, distribution facilities, monitoring, and protection devices. Through flexible technologies on the energy user side, it interacts with the power system in the way of "load following the source". In addition, the power aggregation domain of a microgrid is the set of operating points of all state variables that satisfy the operating constraints and safety constraints of flexible and controllable resources within the system, which is a high - dimensional complex space and is difficult to directly observe. Therefore, it is of great significance to obtain the power aggregation domain of a microgrid.

[0004] During the process of coordinated dispatching of distribution network - microgrid, considering the flexible resources in the microgrid, a dispatching domain model is constructed to obtain the aggregated active power region of the microgrid at multiple time sections, and the entire process does not require the participation of the medium - voltage distribution network; secondly, the medium - voltage distribution network conducts dispatching based on the given dispatching domain of the microgrid, without directly dispatching numerous controllable devices under the microgrid, protecting the operation privacy of the microgrid. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for two - layer coordinated optimal dispatching of distribution network - microgrid based on the aggregated active power region, which can simplify the dispatching model of the medium - voltage distribution network and improve the dispatching efficiency under the condition of limited information interaction.

[0006] To achieve the above object, the solution of the present invention is as follows:

[0007] A method for two - layer coordinated optimal dispatching of distribution network - microgrid based on the aggregated active power region includes:

[0008] Obtaining the operating parameters of the distribution network and constructing the operating constraints of the distribution network; obtaining the operating parameters of the microgrid and constructing the operating constraints of the microgrid;

[0009] Based on the operating constraints of the microgrid, a mathematical model of the active power aggregation domain of the microgrid is constructed; the row and column generation algorithm is used to solve the mathematical model of the aggregation domain to obtain the operating boundary of the active power aggregation domain of the microgrid;

[0010] The active power aggregation domain of the microgrid is submitted to the distribution network for scheduling. Combining the operating constraints of the distribution network, it is judged whether the active power aggregation domain of the microgrid meets the criterion of minimizing the distribution network loss. If it meets, the power output at the microgrid gateway is controlled by the operating boundary. Otherwise, the operating boundary of the active power aggregation domain of the microgrid is adjusted, the mathematical model of the aggregation domain is updated and solved until the obtained operating boundary meets the goal of minimizing the distribution network loss.

[0011] Among them, the operating constraints of the microgrid include energy storage system constraints, photovoltaic system constraints, temperature-controlled load operating constraints, and active power balance constraints.

[0012] Among them, the operating constraints of the distribution network include safe operation constraints and distribution network root node injection power constraints.

[0013] Among them, constructing the mathematical model of the active power aggregation domain of the microgrid includes,

[0014] Constructing the operating domain Fitting the operating domain To obtain the aggregation domain;

[0015] Among them, the operating domain The optimization objective function and constraint conditions are,

[0016]

[0017] s.t.P MG =Ax(P MG )+b

[0018] Cx(P MG )≤d

[0019] ||E r x(P MG )|| 2 ≤f r

[0020]

[0021] Among them, obj represents the optimization objective function of the operating domain , is the feasible operating range of the operating domain ; P MG =(P t MG ) t∈ΥIndicates the active power output at multiple moments. Υ represents all time periods, i.e., t = 1, 2, …, T; vector x: (P t χ ) χ∈{ESS,PV,TCL},t∈Υ Indicates the active power output of the energy storage system ESS, photovoltaic system PV, and temperature control load TCL at each moment; matrices A, C, E r And vectors b, d, f r Are parameters related to flexible distributed resources in the microgrid respectively.

[0022] Among them, the goal of minimizing the distribution network loss includes,

[0023] Constructing an objective function with the goal of minimizing the network loss,

[0024]

[0025] Among them, Δt is the time interval; N is the total number of nodes in the power grid; set n(i) is the set of end nodes of the branch with i as the head node in the power grid; P loss Is the total line loss.

[0026] A distribution-microgrid two-layer collaborative optimization scheduling system based on the active power aggregation domain includes,

[0027] A distribution network operation constraint construction module, configured to obtain the operation parameters of the distribution network and construct the distribution network operation constraints;

[0028] A microgrid operation constraint construction module, configured to obtain the operation parameters of the microgrid and construct the microgrid operation constraints;

[0029] A microgrid active power aggregation domain mathematical model construction module, configured to construct a mathematical model of the microgrid active power aggregation domain based on the microgrid operation constraints;

[0030] A microgrid active power aggregation domain operation boundary acquisition module, configured to use the column generation algorithm to solve the mathematical model of the aggregation domain to obtain the operation boundary of the microgrid active power aggregation domain; and,

[0031] A microgrid active power aggregation domain operation boundary optimization module, configured to submit the microgrid active power aggregation domain to the distribution network for scheduling, combine the distribution network operation constraints, and determine whether the microgrid active power aggregation domain meets the criterion of minimizing the distribution network loss. If it meets, control the power output at the microgrid gateway with the operation boundary. Otherwise, adjust the operation boundary of the microgrid active power aggregation domain, update the mathematical model of the aggregation domain and solve it until the obtained operation boundary meets the goal of minimizing the distribution network loss.

[0032] Among them, the microgrid operation constraints include energy storage system constraints, photovoltaic system constraints, temperature control load operation constraints, and active power balance constraints.

[0033] Among them, the distribution network operation constraints include safe operation constraints and injection power constraints of the distribution network root node.

[0034] Among them, constructing the mathematical model of the active power aggregation domain of the microgrid includes

[0035] constructing the operation domain fitting the operation domain to obtain the aggregation domain;

[0036] Among them, the operation domain has the following optimization objective function and constraint conditions

[0037]

[0038] s.t.P MG = Ax(P MG ) + b

[0039] Cx(P MG ) ≤ d

[0040] ||E r x(P MG )|| 2 ≤ f r

[0041]

[0042] Among them, obj represents the optimization objective function of the operation domain and is the feasible operation range of the operation domain; P is the operation domain of the feasible operation range; P MG = (P t MG ) t∈Υ represents the active power output at multiple moments, Υ represents all time periods, that is, t = 1, 2,..., T; the vector x:(P t χ ) χ∈{ESS,PV,TCL},t∈Υ represents the active power output of the energy storage system ESS, photovoltaic system PV, and temperature control load TCL at each moment; the matrices A, C, E r and the vectors b, d, f r are parameters related to the flexible distributed resources in the microgrid respectively.

[0043] Among them, the goal of minimizing the distribution network power loss includes

[0044] constructing an objective function with the goal of minimizing power loss

[0045]

[0046] where Δt is the time interval; N is the total number of nodes in the power grid; the set n(i) is the set of end nodes of the branches in the power grid with node i as the head node; P loss is the total line loss.

[0047] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of the above-mentioned distribution and micro double-layer collaborative optimization scheduling method based on the active power aggregation domain are implemented.

[0048] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the steps of the above-mentioned distribution and micro double-layer collaborative optimization scheduling method based on the active power aggregation domain are implemented.

[0049] After adopting the above solution, the present invention first constructs constraints based on the operating range of microgrid active power for quantifying the flexible schedulable capabilities of a large number of distributed resources, based on the collected basic operating parameters of the microgrid; secondly, by mapping the original high-dimensional operating domain of the microgrid to the two-dimensional state space of active power and time at the microgrid outlet, a mathematical model of the microgrid active power aggregation domain is constructed to approximate the actual schedulable range of the microgrid; then, the CCG algorithm is used to solve the mathematical model of the microgrid active power aggregation domain; finally, based on the collected basic operating parameters of the distribution network, distribution network operating constraints are constructed and embedded into the obtained microgrid active power aggregation domain, aiming to minimize the distribution network power loss.

[0050] Considering from two aspects of the construction of the multi-time scale microgrid active power aggregation adjustable domain and taking into account the active distribution network constraints to achieve distributed distribution and micro collaborative scheduling, the present invention can first achieve better distribution and micro collaboration under the condition of limited information interaction, simplify the scheduling model of the medium-voltage distribution network, and improve the scheduling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is the flowchart of the method of the present invention;

[0052] Figure 2 is the diagram of the distribution network and microgrid hierarchical collaborative scheduling example;

[0053] Figure 3 is the aggregation of the operating range of Microgrid 1 and the actual operating point of Microgrid 1

[0054] Figure 4 is the decomposition of the aggregated power of Microgrid 1;

[0055] Among them, (a) is the energy storage system of Microgrid 1, (b) is the photovoltaic of Microgrid 1, and (c) is the temperature control load of Microgrid 1. DETAILED DESCRIPTION OF THE INVENTION

[0056] The technical solutions and beneficial effects of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0057] As Figure 1 shown, the present invention proposes a coordinated optimization scheduling method for distribution and microgrid at the active power aggregation domain, and the method includes the following steps:

[0058] Step 1: Collect the operation parameters of the distribution network and the microgrid. The operation parameters include information such as energy storage systems, photovoltaic stations, temperature-controlled loads, lines, and conventional load demands. Based on the above information, construct the operation constraints of the microgrid;

[0059] Step 2: On the basis of Step 1, by mapping the original high-dimensional operation domain to the two-dimensional state space of active power and time at the microgrid outlet, a mathematical model of the active power aggregation domain of the microgrid is obtained;

[0060] Step 3: On the basis of Step 2, use the CCG algorithm to solve the aggregation domain mathematical model, and a series of operation boundaries of the active power aggregation domain of the microgrid are obtained;

[0061] Step 4: Based on the operation parameters collected in Step 1, construct the operation constraints of the distribution network, and embed the boundary of the active power aggregation domain of the microgrid obtained in Step 3 into it. Specifically, the power flow constraint of the distribution network introduces the active power aggregation domain of the microgrid obtained in Step 3 and aims to minimize the distribution network loss.

[0062] Furthermore, in Step 1, the operation constraints of the microgrid are constructed, and the specific method is as follows:

[0063] The constraints of the distributed resource model are as follows:

[0064] 1) Energy storage system constraint

[0065]

[0066] In the formula: P t ch and P t dis are the charging and discharging powers of the energy storage at time t, respectively; is the maximum output and input power of the energy storage; β ch,t and β dis,t are the charging and discharging states of the energy storage at time t, respectively. When charging or discharging, it is 1, otherwise it is 0; is the energy storage power at time t; η ch and η dis are the charging and discharging efficiencies of the energy storage, respectively; δ is the self-consumption rate of the energy storage; Δt is the time interval between adjacent time periods; is the rated capacity of the energy storage access; and They are the maximum and minimum values of the ratio of the energy stored in the energy storage system to its rated capacity respectively; T represents a period; S ESS is the capacity of the energy storage system.

[0067] 2) Photovoltaic system constraints

[0068]

[0069] In the formula: is the maximum output of the photovoltaic at time t; P t PV is the actual output of the photovoltaic at time t; P t PV,cut is the amount of curtailment at time t; is the upper limit of the curtailment rate; S PV is the capacity of the photovoltaic.

[0070] Equation (B-8) is the upper and lower limit constraint of the photovoltaic output; Equation (B-9) calculates the amount of curtailment at time t; Equation (B-10) is the upper limit constraint of the curtailment rate; Equation (B-11) is the capacity constraint.

[0071] 3) Thermostatic load operation constraints

[0072]

[0073] In the formula: P t TCL is the active power of the thermostatic load at time t; is the upper limit of the active power of the thermostatic load; F t in is the indoor temperature of the building at time t; are the highest and lowest temperatures of the indoor temperature respectively; α T ∈(0,1) and β T are the thermal parameters of the specified building and environmental characteristics; β T can be positive or negative, taking positive for heating (in winter) and negative for cooling (in summer).

[0074] Equation (B-12) is an upper limit constraint on the load power; Equation (B-13) ensures that the indoor temperature is within a certain range; Equation (B-14) represents a linear relationship between the indoor temperature at the next moment, the current indoor temperature, the outdoor temperature and the load power; Equation (B-15) requires the room temperature to return to the initial value at the end of one cycle.

[0075] 4) Active power balance

[0076] P t MG =-P t ESS +P t TCL -Pt PV +P t load (B-16)

[0077] Where: P t MG is the active power at the microgrid connection point; P t load is other uncontrollable loads.

[0078] Equation (B-16) represents the active power conservation.

[0079] Furthermore, in Step 2, based on Step 1, by mapping the original high-dimensional operation domain to the two-dimensional state space of the active power and time at the microgrid outlet, a mathematical model of the microgrid active power aggregation domain is obtained. The specific method is as follows:

[0080] In this embodiment, the active power aggregation domain is projected from the high-dimensional operation domain at the microgrid outlet. This high-dimensional operation domain has a large number of dimensions, and each dimension corresponds to an operation parameter of the flexible distributed resources in the microgrid, such as the current power of the energy storage, the actual output of the photovoltaic, the indoor temperature controlled by the temperature control load, and so on. All the operation points in the high-dimensional operation domain satisfy all the safe operation constraints of the microgrid.

[0081] To conveniently represent the controllable power of the flexible resources inside the microgrid, define the vector x: (P t χ ) χ∈{ESS,PV,TCL},t∈Υ , where x uniformly represents the active power output or demand of the energy storage, photovoltaic, and temperature control load at each moment. In addition, the active power P t MG at the microgrid connection point at multiple moments is combined into the vector P MG =(P t MG ) t∈Υ . Where Υ represents all time periods, that is, t = 1, 2,..., T. The models (B-1)-(B-16) of the distributed resources in the microgrid at each time period can be reformulated as follows:

[0082] P MG =Ax + b (B-17)

[0083] Cx ≤ d (B-18)

[0084] ||E r x|| 2 ≤ f r (B-19)

[0085] Where: matrices A, C, E r and vectors b, d, f rThey are parameters related to flexible distributed resources in the microgrid. They are integrated according to (B-1)-(B-16) and used as parameters for equality constraints, inequality constraints, and second-order cone constraints.

[0086] Equation (B-17) reformulates equation (B-16), where the uncontrollable load P t load is represented by b alone; equation (B-18) represents a linear inequality related to x; equation (B-19) represents the capacity constraints expressed by equations (B-7) and (B-11), with the subscript r ∈ {ESS, PV}. In the energy storage constraints (B-4)-(B-6) and the thermostatic load constraints (B-13)-(B-15), there are variables coupled in time t and F t in , so they need to be decoupled, rewritten as linear constraints related to x, and included in equation (B-17). For equations (B-4)-(B-6), based on the recursive relationship and its boundary conditions, through inductive derivation and elimination of , equation (B-20) can be obtained. Similarly, for equations (B-13)-(B-15), after eliminating F t in , equation (B-21) can be obtained.

[0087]

[0088] Due to the existence of a large number of flexible distributed resources, it is often difficult to obtain an absolutely accurate active power aggregation domain. However, it is feasible to approximate the absolutely accurate active power aggregation domain by fitting a relatively accurate operating domain. Define this relatively accurate operating domain as which needs to meet the following requirements:

[0089] 1) Since is obtained by approximating the internal optimization of the absolutely accurate active power aggregation domain, so it must first meet all the safe operating constraints within the microgrid.

[0090] 2) For each operating point within , that is, for P MG , the decomposability must be satisfied. That is to say, for a given P MG , then x can also be obtained. Considering the decomposability of P MG , x can be written as an expression x(P MG ) related to P MG .

[0091] Regarding the above requirements, The optimization objective function and constraints are as follows:

[0092]

[0093] s.t.P MG = Ax(P MG ) + b (B - 23)

[0094] Cx(P MG ) ≤ d (B - 24)

[0095] ||E r x(P MG )|| 2 ≤ f r (B - 25)

[0096]

[0097] Equation (B - 22) aims to maximize the feasible operating range of while is essentially the superposition of the operating ranges of all resources within the microgrid. Equation (B - 23) defines the relationship between x(P MG ) and P MG , and is essentially the active power balance equation. Equations (B - 24) - (B - 25) represent the second - order cone constraint and linear inequality constraint regarding x(P MG ), ensuring the safety and stability of the system operation. Equation (B - 26) means that for any P within MG , there must exist a corresponding distributed resource scheduling scheme x(P MG ): (P t χ ) χ∈{ESS,PV,TCL},t∈Υ , ensuring that a feasible scheduling scheme can be found for each demand of the aggregated power.

[0098] To simplify the model and calculation, a discretized aggregated active - power operating space in time is defined

[0099]

[0100] Wherein: and P t MG represent the maximum and minimum values of P t MG at time t.

[0101] Obviously, P t MG satisfies the condition For the sake of convenient expression, for multiple moments and P t MG are respectively combined into vectors and P MG = ( P t MG ) t∈Υ , then:

[0102]

[0103] P t MG can be further utilized P t MG to obtain through weighted summation:

[0104]

[0105] uncertain variable α t represents the weight and 0 ≤ α t ≤ 1. When α t = 0, i.e., P t MG reaches the power upper limit. When α t = 1, P t MG = P t MG , i.e., P t MG reaches the power lower limit. Originally described by the variable , the adaptive robust optimization (ARO) framework is not directly applicable. However, after introducing the set of uncertainty variables (α t ), it becomes feasible to describe using the ARO framework that depends on a fixed uncertainty set. Define the set t∈Υ as follows. Through the equivalent change of the variable, the condition can be replaced with

[0106]

[0107] . "x(P )" can be rewritten as "x(α)". Equations (B - 22)-(B - 26) can be further rewritten as follows to obtain the maximum active power interval: "x(P MG )" can be rewritten as "x(α)". Equations (B - 22)-(B - 26) can be further rewritten as follows to obtain the maximum active power interval:

[0108]

[0109] Cx(α) ≤ d (B - 34)

[0110] ||E r x(α)|| 2 ≤ f r (B - 35)

[0111]

[0112] The objective function (B - 31) adopts a three - layer optimization (max - min - max) to handle uncertainties and ensure flexibility and feasibility. The first - layer optimization (max) aims to determine the optimal upper and lower limits of active power and P MG , so as to maximize the total aggregated flexibility of the system. This is a "here - and - now" decision, focusing on the current optimal settings. The second - layer optimization (min) considers the influence of the uncertainty variable α. Through decision - making in the worst - case scenario, this layer ensures that once the uncertainty is revealed, it can adapt to changes in a timely manner and make corresponding power scheduling schemes x(α). The third - layer optimization (max) is to ensure that a feasible power scheduling scheme x(α) can still be found in the worst - case scenario. This layer guarantees that all constraints can be met even in adverse situations, thus ensuring the stability and reliability of the model.

[0113] Furthermore, in step 3, based on step 2, the Column and Constraint Generation (CCG) algorithm is used to solve the aggregated - domain mathematical model, and a series of operating boundaries of the micro - grid active - power aggregated domain are obtained. The specific method is as follows:

[0114] Equations (B - 32) - (B - 36) represent a two - stage ARO model, which can be solved using the CCG algorithm. According to the CCG algorithm, the above - mentioned problem can be decomposed into a master problem (MP) and a sub - problem (SP) and solved iteratively. MP is expressed as follows:

[0115]

[0116] Cx m ≤ d (B - 40)

[0117] ||E r x m || 2 ≤ f r (B - 41)

[0118]

[0119] The objective function of MP aims to maximize the active power range, corresponding to the first-layer optimization of Equation (B-31). In MP, α m,Δ is given as a known parameter. That is, using M enumerated scenarios α m,Δ to replace the uncertainty set This allows the model to simplify the problem while considering uncertainty. Each scenario is associated with a corresponding power scheduling scheme x(α m,Δ ), which enables the model to adaptively adjust to different possible situations. Since only a finite number of enumerated scenarios are considered, the objective value J MP provides an upper bound for Equation (B-30). However, as new constraints are added, the optimal solution can be gradually approximated.

[0120] The and P MG obtained by solving MP will be passed to SP. SP is expressed as follows:

[0121]

[0122] Cx(α) ≤ d (B-46)

[0123] ||E r x(α)|| 2 ≤ f r (B-47)

[0124]

[0125] The objective function of SP actually makes a feasibility judgment rather than an optimization in the traditional sense. Its core purpose is to check whether there exists an α such that the corresponding power scheduling x(α) can satisfy the constraints (B-44)-(B-48) when given and P MG . If there exists an extreme scenario α such that no feasible x(α) satisfies these constraints, then J SP will be assigned -∞. Conversely, if all constraints are satisfied, J SP will be assigned 0.

[0126] Let the solution obtained after optimizing the sub-problem be α M+1,Δ , and add α M+1,Δ and the associated new constraint conditions (B-49)-(B-51) to the solution process of MP.

[0127]

[0128] ||E r x(α M+1,Δ )|| 2 ≤ f (B-50)

[0129] Cx(α M+1,Δ )≤d (B - 51)

[0130] Furthermore, in step 4, based on the operating parameters collected in step 1, the distribution network operation constraints are constructed, and the microgrid active power aggregation domain obtained in step 3 is introduced into the power flow constraints of the distribution network, thus obtaining a coordinated distribution - microgrid scheduling framework under limited information interaction. The specific method is as follows:

[0131] The distribution network operation constraints are as follows:

[0132] 1) Power flow equation

[0133]

[0134] In the formula: The set m(j) is the set of the head - end nodes of the branches with j as the end - node in the power grid; the set n(j) is the set of the end - node of the branches with j as the head - end node in the power grid; the set u(j) is the set of nodes without microgrid access; the set v(j) is the set of nodes with microgrid access; U j,t is the voltage amplitude of node j at time t; P ij,t and Q ij,t are the active and reactive powers at the head - end of branch ij respectively; P j,t is the net active power input value; Q load,j,t is the reactive power of the load; r ij and x ij are the branch resistance and reactance respectively.

[0135] 2) Distribution network safe operation constraints

[0136] U j,min ≤U j,t ≤U j,max (B - 56)

[0137] I ij,t ≤I ij,max (B - 57)

[0138] In the formula: U j,max and U j,min are the upper and lower limits of the voltage of node j respectively; I ij,t is the upper limit of the current of line ij.

[0139] Formula (B - 56) is the node voltage upper and lower limit constraint; formula (B - 57) is the line current constraint.

[0140] 3) Distribution network root - node injection power constraint

[0141]

[0142] In the formula: and are the active power and reactive power flowing into the low-voltage distribution network from the root node, respectively; and are the upper and lower limits of the active power flowing into the low-voltage distribution network from the root node, respectively; and are the upper and lower limits of the reactive power flowing into the low-voltage distribution network from the root node, respectively.

[0143] Equation (B-58) is the constraint on the upper and lower limits of the active power injected at the node; Equation (B-59) is the constraint on the upper and lower limits of the reactive power injected at the node.

[0144] 4) Second-order cone relaxation of the power flow equation

[0145] As a convex programming, SOCP has excellent properties in terms of the optimality of its solution and computational efficiency. Therefore, performing SOCP relaxation on the power flow equation, for Equations (B-52)-(B-55), define I ij,t :

[0146]

[0147] Let and rewrite Equations (B-52), (B-53), (B-55), and (B-60) further as Equations (B-61)-(B-64):

[0148]

[0149] At this time, Equations (B-61)-(B-63) are linear equations, and Equation (B-64) satisfies the definition of the second-order cone. With the goal of minimizing network losses, further define the objective function P loss :

[0150]

[0151] where: Δt is the time interval; N is the total number of nodes in the power grid; the set n(i) is the set of end nodes of the branches in the power grid with i as the head node; P loss is the total line loss.

[0152] Such as Figure 2As shown, as a specific embodiment of the present invention, the effectiveness of the scheduling method of the present invention is tested on an improved IEEE 33-node test system. The system consists of 32 branches, and a microgrid 1 and a microgrid 2 are respectively connected to nodes 18 and 33. Flexible resources such as photovoltaic, energy storage system, and thermostatic load are configured in each microgrid. The specific parameters of the equipment are shown in Table 1. The initial state of charge of the energy storage system in the microgrid is set to 0.25, and the initial indoor temperature is set to 23 °C. The upper and lower limits of the node voltage of the system are set to 1.05 p.u. and 0.95 p.u. respectively, and the root node voltage is fixed at 1 p.u. In the example, T is taken as 24 and the time is discretized. The GAMS platform is used to implement the scheduling optimization, and the GUROBI solver is used to solve the model. The simulation environment of the example is Intel(R) Core(TM) i9-13900HX 2.20 GHz, 32 GB of memory.

[0153] Table 1 Parameters of Distributed Resources in the Microgrid

[0154]

[0155] As Figure 3 shown, the upper and lower bounds of the aggregated power enclose to form the active power aggregation domain of the microgrid. The rhombus is the actual operating point calculated after minimizing the network loss, that is, the interactive power between the distribution network and the microgrid. In addition, the area of the active power aggregation domain represents the flexible dispatchable ability of the microgrid. The larger the area of the active power aggregation domain, the stronger the flexible dispatchable ability of the microgrid. Since the energy storage system and photovoltaic configuration capacity in Microgrid 1 are higher, and these distributed resources can bring greater flexible dispatchable ability, the area of the active power aggregation domain of Microgrid 1 is significantly larger than that of Microgrid 2.

[0156] Decompose the aggregation domain of Microgrid 1 obtained by solving. Figure 4 The power output or power demand of the controllable resources inside Microgrid 1, including the energy storage system, photovoltaic, and thermostatic load, at each moment is given. During the peak load periods of 10:00, 18:00, and 19:00, the energy storage in Microgrid 1 discharges to assist the power grid to meet the load demand. During the low load period and the peak photovoltaic output periods of 13:00 and 14:00, the energy storage charges to promote the consumption of new photovoltaic energy, and the stored electric energy will be released externally when the next peak load period arrives.

[0157] The embodiment of the present invention also provides another computer device, including a processor and a memory configured to store a computer program that can run on the processor; wherein, when the processor is configured to run the computer program, it executes the method steps in the foregoing embodiment.

[0158] In practical applications, the above-mentioned processor includes a Field-Programmable Gate Array (FPGA), and the processor can be a Central Processing Unit (CPU) or a Digital Signal Processor (DSP). It can be understood that for different devices, the electronic devices used to implement the functions of the above-mentioned processor can also be others, and the embodiments of the present invention do not make specific limitations.

[0159] The above-mentioned memory can be a volatile memory, such as a Random-Access Memory (RAM); or a non-volatile memory, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD), or a Solid-State Drive (SSD); or a combination of the above types of memories, and provides instructions and data to the processor.

[0160] In an exemplary embodiment, the embodiments of the present invention also provide a computer-readable storage medium for storing a computer program.

[0161] Optionally, the computer-readable storage medium can be applied to any one of the methods in the embodiments of the present invention, and the computer program causes the computer to execute the corresponding processes implemented by the processor in each of the methods of the embodiments of the present invention. For the sake of brevity, it will not be elaborated here.

[0162] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the various components shown or discussed can be through some interfaces, and the indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0163] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0164] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0165] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0167] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A dual-layer coordinated optimization scheduling method for distribution and micro-distribution based on active power aggregation domain, characterized by: include, Obtain the operating parameters of the distribution network and construct the operating constraints of the distribution network; obtain the operating parameters of the microgrid and construct the operating constraints of the microgrid; Based on the microgrid operation constraints, a mathematical model of the microgrid active power aggregation domain is constructed; the mathematical model of the aggregation domain is solved using a row-column generation algorithm to obtain an operation boundary of the microgrid active power aggregation domain; The active power aggregation domain of the microgrid is submitted to the distribution network for scheduling. Combined with the distribution network operation constraints, it is determined whether the active power aggregation domain of the microgrid meets the criterion of minimizing the network loss of the distribution network. If so, the power output at the microgrid gateway is controlled by the operation boundary. Otherwise, the operation boundary of the active power aggregation domain of the microgrid is adjusted, and the mathematical model of the aggregation domain is updated and solved until the obtained operation boundary meets the goal of minimizing the network loss of the distribution network.

2. The method according to claim 1, characterized in that: The microgrid operation constraints include energy storage system constraints, photovoltaic system constraints, temperature control load operation constraints and active power balance constraints.

3. The method according to claim 1, characterized in that: The distribution network operation constraints include safety operation constraints and distribution network root node injection power constraints.

4. The method according to claim 1, characterized in that: Construct a mathematical model of the microgrid active power aggregation domain, including, Build a runtime domain Fitting the operating domain To obtain the aggregation domain; The operation domain The optimization objective function and constraints are: s.t.P MG =Ax(P MG )+b Cx(P MG )≤d ||And r x(P MG )||2≤f r Among them, obj represents the running domain The optimization objective function is For running domain The feasible operating range; P MG =(P t MG ) t∈Υ Represents the active power output at multiple times, Υ represents all time periods, i.e. t=1,2,…,T; vector Represents the active output of the energy storage system ESS, photovoltaic system PV and temperature control load TCL at each moment; matrices A, C, E r and vectors b, d, f r They are parameters related to flexible distributed resources in microgrids.

5. The method according to claim 1, characterized in that: The goal of minimizing the distribution network loss includes: The objective function is constructed with the goal of minimizing network loss. Where Δt is the time interval; N is the total number of nodes in the power grid; set n(i) is the set of end nodes of the branch with i as the head node in the power grid; P loss is the total line loss.

6. A dual-layer coordinated optimization dispatching system based on active power aggregation domain, characterized by: include, A distribution network operation constraint building module is configured to obtain operation parameters of the distribution network and build distribution network operation constraints; A microgrid operation constraint building module is configured to obtain operation parameters of the microgrid and build microgrid operation constraints; A microgrid active power aggregation domain mathematical model construction module is configured to construct a mathematical model of the microgrid active power aggregation domain based on the microgrid operation constraints; A microgrid active power aggregation domain operation boundary acquisition module is configured to solve the mathematical model of the aggregation domain using a row-column generation algorithm to obtain the operation boundary of the microgrid active power aggregation domain; and The microgrid active power aggregation domain operation boundary optimization module is configured to submit the microgrid active power aggregation domain to the distribution network for scheduling, and determine whether the microgrid active power aggregation domain meets the criterion of minimizing the distribution network loss in combination with the distribution network operation constraints. If so, the power output at the microgrid gateway is controlled by the operation boundary. Otherwise, the operation boundary of the microgrid active power aggregation domain is adjusted, and the mathematical model of the aggregation domain is updated and solved until the obtained operation boundary meets the goal of minimizing the distribution network loss.

7. The system according to claim 6, characterized in that: The microgrid operation constraints include energy storage system constraints, photovoltaic system constraints, temperature control load operation constraints and active power balance constraints.

8. The system according to claim 6, characterized in that: The distribution network operation constraints include safety operation constraints and distribution network root node injection power constraints.

9. The system according to claim 6, characterized in that: Construct a mathematical model of the microgrid active power aggregation domain, including, Build a runtime domain Fitting the operating domain To obtain the aggregation domain; The operation domain The optimization objective function and constraints are: s.t.P MG =Ax(P MG )+b Cx(P MG )≤d ||And r x(P MG )||2≤f r Among them, obj represents the running domain The optimization objective function is For running domain The feasible operating range; P MG =(P t MG ) t∈Υ Represents the active power output at multiple times, Υ represents all time periods, that is, t=1,2,…,T; vector x:(P t χ ) χ∈{ESS,PV,TCL},t∈Υ Represents the active output of the energy storage system ESS, photovoltaic system PV and temperature control load TCL at each moment; matrices A, C, E r and vectors b, d, f r They are parameters related to flexible distributed resources in microgrids.

10. The system according to claim 6, characterized in that: The goal of minimizing the distribution network loss includes: The objective function is constructed with the goal of minimizing network loss. Where Δt is the time interval; N is the total number of nodes in the power grid; set n(i) is the set of end nodes of the branch with i as the head node in the power grid; P loss is the total line loss.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: When the processor executes the computer program, the steps of the distribution and micro-distribution dual-layer collaborative optimization scheduling method based on active power aggregation domain are implemented as described in any one of claims 1 to 5.

12. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by the processor, the steps of the distribution and micro-distribution dual-layer collaborative optimization scheduling method based on active power aggregation domain are implemented as described in any one of claims 1 to 5.

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