Micro-grid cooperative optimization scheduling method and system based on active power aggregation domain

Abstract

The application discloses a microgrid and distribution network collaborative optimization scheduling method and system based on an active power aggregation domain. The method comprises the following steps: obtaining operation parameters of a distribution network and constructing operation constraints of the distribution network; obtaining operation parameters of a microgrid and constructing operation constraints of the microgrid; constructing a mathematical model of the active power aggregation domain of the microgrid based on the operation constraints of the microgrid; solving the mathematical model by using a row-column generation algorithm to obtain an operation boundary of the active power aggregation domain of the microgrid; submitting the active power aggregation domain of the microgrid to the distribution network for scheduling; combining the operation constraints of the distribution network to determine whether the active power aggregation domain of the microgrid satisfies a distribution network loss minimization criterion; if yes, controlling power output at a gateway of the microgrid by using the operation boundary; otherwise, adjusting the operation boundary of the active power aggregation domain of the microgrid, updating the mathematical model of the aggregation domain and solving the mathematical model until the operation boundary satisfies the target of the minimum distribution network loss. The application can simplify a scheduling model of a medium-voltage distribution network and improve scheduling efficiency under the condition that information interaction is limited.

Description

TECHNICAL FIELD

[0001] The present application relates to the micro-grid flexible schedulable resource of micro-micro-grid coordination scheduling strategy, specifically relates to the construction of the active power aggregation adjustable domain of micro-grid, the solving of the active power aggregation adjustable domain of micro-grid, the coordination optimization scheduling of multiple strategies such as active distribution network constraint, particularly relates to a kind of based on active power aggregation domain Micro double-layer collaborative optimization scheduling method and system. BACKGROUND

[0002] In recent years, with the global fossil energy increasingly exhausted and the demand for transition to renewable energy system, developing and utilizing new energy and building low-carbon flexible new power system gradually become consensus. This leads to the penetration rate of distributed resources including energy storage, photovoltaic, temperature-controlled load in distribution network growing, and forces the traditional distribution network to gradually evolve into active distribution network containing flexible controllable micro-grid. The micro-grid coordination scheduling around massive distributed resources gradually becomes the focus of attention.

[0003] Micro-grid refers to a small-scale power generation and distribution system composed of distributed power supply, power load, distribution facilities, monitoring and protection devices, etc., which interacts with the power system in a "load following source" manner through energy user side flexible technology. In addition, the power aggregation domain of micro-grid is the operating point set of all state variables to meet the operating constraints and safety constraints of flexible controllable resources in the system, which is a high-dimensional complex space and difficult to observe directly. Therefore, it is of great significance to obtain the power aggregation domain of micro-grid.

[0004] In the process of micro-grid coordination scheduling, considering the flexible resources in micro-grid, a scheduling domain model is constructed to obtain the active power aggregation domain of micro-grid in multiple time sections, without the participation of medium-voltage distribution network; secondly, the medium-voltage distribution network is scheduled based on the scheduling domain given by micro-grid, without directly scheduling numerous controllable devices under micro-grid, protecting the operation privacy of micro-grid. SUMMARY

[0005] The purpose of the present application is to provide a micro double-layer collaborative optimization scheduling method and system based on active power aggregation domain, which can simplify the scheduling model of medium-voltage distribution network and improve scheduling efficiency under limited information interaction.

[0006] To achieve the above purpose, the solution of the present application is:

[0007] A micro double-layer collaborative optimization scheduling method based on active power aggregation domain, comprising,

[0008] Obtaining the operating parameters of distribution network and constructing distribution network operating constraints; obtaining the operating parameters of micro-grid and constructing micro-grid operating constraints;

[0009] construct a mathematical model of the microgrid active power aggregation domain based on the microgrid operation constraints; solve the mathematical model of the aggregation domain using a row-column generation algorithm to obtain an operation boundary of the microgrid active power aggregation domain;

[0010] 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 yes, 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 target of minimizing the distribution network loss.

[0011] The microgrid operation constraints include energy storage system constraints, photovoltaic system constraints, temperature-controlled load operation constraints, and active power balance constraints.

[0012] The distribution network operation constraints include safe operation constraints and distribution network root node injection power constraints.

[0013] The mathematical model of the microgrid active power aggregation domain is constructed, including,

[0014] constructing an operation domain fitting the operation domain to obtain an aggregation domain;

[0015] The optimization objective function and constraint conditions of the operation domain 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] The optimization objective function of the operation domain is denoted as obj, and the feasible operation range of the operation domain is denoted as P MG = (P t MG ) t∈Υ denotes the active power output at multiple times, Y represents all time periods, i.e., t = 1, 2, …, T; and the vector x: (Pt χ ) χ∈{ESS,PV,TCL},t∈Υ P represents the active power output of the energy storage system ESS, the photovoltaic system PV and the temperature-controlled load TCL at each time; matrices A, C, E r and vectors b, d, f r are respectively parameters related to the flexible distributed resources in the microgrid.

[0022] The target of minimizing the network loss of the distribution network comprises,

[0023] A target function is constructed with the target of minimizing the network loss,

[0024]

[0025] Wherein, 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 branches with i as the first end node in the power grid; P loss is the total line loss.

[0026] A distribution-microgrid dual-layer collaborative optimization scheduling system based on an active power aggregation domain comprises,

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

[0028] A microgrid operation constraint construction module configured to obtain operation parameters of the microgrid and construct 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 a row-column generation algorithm to solve the mathematical model of the aggregation domain and 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 judge whether the microgrid active power aggregation domain meets the criterion of minimizing the network loss of the distribution network, if it meets, control the power output at the gateway of the microgrid 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 target of minimizing the network loss of the distribution network.

[0032] The microgrid operation constraints comprise energy storage system constraints, photovoltaic system constraints, temperature-controlled load operation constraints and active power balance constraints.

[0033] The operation constraint of the power distribution network includes a safe operation constraint and a power distribution network root node injection power constraint.

[0034] The mathematical model of the microgrid active power aggregation domain is constructed, including,

[0035] The operation domain is constructed The operation domain is fitted to obtain the aggregation domain.

[0036] The optimization objective function and the constraint condition of the operation domain are,

[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] Wherein, obj represents the optimization objective function of the operation domain , is the feasible operation range of the operation domain ; P MG =(P t MG ) t∈Υ represents the active power output at multiple times, and Y represents all time periods, i.e., 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, the photovoltaic system PV, and the temperature-controlled load TCL at each time; the matrices A, C, E r and the vectors b, d, f r are parameters related to the flexible distributed resources in the microgrid.

[0043] The target of minimizing the power distribution network loss includes,

[0044] The objective function is constructed with the target of minimizing the network loss,

[0045]

[0046] Wherein, Δt is a time interval; N is the total number of nodes in the power grid; the set n(i) is a set of end nodes of branches in the power grid with i as the first end node; Ploss is the total line loss.

[0047] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor; the processor implements the steps of the microgrid and distribution network collaborative optimization scheduling method based on the active power aggregation domain as described above when executing the computer program.

[0048] A computer readable storage medium stores a computer program; the computer program implements the steps of the microgrid and distribution network collaborative optimization scheduling method based on the active power aggregation domain as described above when executed by a processor.

[0049] After adopting the above scheme, the present application firstly quantifies the flexible schedulable capacity of a large number of distributed resources, constructs a constraint condition based on the microgrid basic operation parameters collected based on the active power operation range of the microgrid; secondly, by mapping the original high-dimensional operation domain of the microgrid to the two-dimensional state space of the active power and time at the outlet of the microgrid, a mathematical model of the active power aggregation domain of the microgrid is constructed to approximate the actual schedulable range of the microgrid; then, the CCG algorithm is used to solve the mathematical model of the active power aggregation domain of the microgrid; finally, based on the basic operation parameters of the distribution network collected, the operation constraints of the distribution network are constructed, and the active power aggregation domain of the microgrid solved is embedded, aiming to minimize the distribution network loss.

[0050] The present application considers the construction of the multi-time scale microgrid active power aggregation schedulable domain and the consideration of the active distribution network constraints, realizes the distributed microgrid and distribution network collaborative scheduling, and under the condition of limited information interaction, firstly, the microgrid and distribution network are well coordinated, the scheduling model of the medium voltage distribution network is simplified, and the scheduling efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is the flow chart of the method of the present application;

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

[0053] Figure 3 is the aggregation of the operation 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 controlled load of microgrid 1. DETAILED DESCRIPTION

[0056] The technical solutions and beneficial effects of the present application will be described in detail below with reference to the drawings.

[0057] As Figure 1 shown, the application proposes a microgrid collaborative optimization scheduling method based on active power aggregation domain, which includes the following steps:

[0058] Step 1, collect the operating parameters of the distribution network and the microgrid, including the information of energy storage system, photovoltaic station, temperature-controlled load, line, and conventional load demand. Based on the above information, the microgrid operation constraints are constructed;

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

[0060] Step 3, on the basis of step 2, the CCG algorithm is used to solve the mathematical model of the aggregation domain, and a series of operating boundaries of the microgrid active power aggregation domain are obtained;

[0061] Step 4, based on the operating parameters collected in step 1, the distribution network operation constraints are constructed, and the boundaries of the microgrid active power aggregation domain obtained in step 3 are embedded. Specifically, the power flow constraints of the distribution network introduce the microgrid active power aggregation domain obtained in step 3 and aim to minimize the distribution network loss.

[0062] Further, in step 1, the microgrid operation constraints are constructed, and the specific method is as follows:

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

[0064] 1) Energy storage system constraints

[0065]

[0066] In the formula: P t ch and P t dis are the charging and discharging power of the energy storage at t time period; 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 t time, 1 when charging or discharging, otherwise 0; is the energy storage capacity at t time; η ch and η dis are the charging and discharging efficiencies of the energy storage; δ is the self-consumption rate of the energy storage; Δt is the time interval of adjacent time periods; is the rated capacity of the energy storage; and are the maximum and minimum values of the ratio of the energy storage system storage energy to its rated capacity; T represents a period; S ESSCapacity of the energy storage system.

[0067] 2) Photovoltaic system constraints

[0068]

[0069] where: Pmaxis the maximum power output of the photovoltaic at time t; P t PV Pis the actual power output of the photovoltaic at time t; P t PV,cut is the curtailed power at time t; Sis the upper limit of the curtailment rate; PV Cis the capacity of the photovoltaic.

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

[0071] 3) Thermostat load operation constraints

[0072]

[0073] where: t TCL Ftis the active power of the thermostat load at time t; Fis the upper limit of the active power of the thermostat load; t in Ttis the indoor temperature of the building at time t; Tmaxand Tminare the maximum and minimum temperature of the indoor temperature, respectively; T ∈(0, 1) and β T βis the thermal parameter that specifies the building and environmental characteristics; T can be positive or negative, positive for heating (in winter) and negative for cooling (in summer).

[0074] Equation (B-12) is the upper limit constraint of the load power; Equation (B-13) ensures that the indoor temperature is within an interval; Equation (B-14) represents a linear relationship between the indoor temperature at the next time step and the current indoor temperature, outdoor temperature, and load power; and Equation (B-15) requires that the indoor temperature returns to the initial value at the end of a period.

[0075] 4) Active power balance

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

[0077] P t MG is the active power at the microgrid gateway; P t load is the other uncontrollable load.

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

[0079] Further, in step 2, based on step 1, by mapping the original high-dimensional operating domain to the two-dimensional state space of active power at the microgrid gateway and time, the mathematical model of the microgrid active power aggregation domain is obtained, and the specific method is as follows:

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

[0081] To facilitate the expression of the controllable power of the flexible resource inside the microgrid, the vector x: (P t χ ) χ∈{ESS,PV,TCL},t∈Υ is defined, and x uniformly expresses the active output or demand of the energy storage, photovoltaic, and temperature-controlled load at each time. In addition, the active power P t MG at the microgrid gateway at multiple times is combined into the vector P MG = (P t MG ) t∈Υ . Wherein Y represents all time periods, i.e. t = 1, 2, …, T. The models (B-1)-(B-16) of the distributed resources in the microgrid at each period can be re-expressed as follows:

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

[0083] Cx≤d (B-18)

[0084] ||E r x||2≤f r (B-19)

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

[0086] Equation (B-17) re-expresses Equation (B-16) with the uncontrollable load P t load denoted by b alone; Equation (B-18) expresses the linear inequality related to x; Equation (B-19) represents the capacity constraints expressed by Equations (B-7) and (B-11), with the subscript r e {ESS, PV}. In the energy storage constraints (B-4)-(B-6) and the temperature-controlled load constraints (B-13)-(B-15), there are variables coupled over time t and F t in and thus need to be decoupled, rewritten as linear constraints related to x, and included in Equation (B-17). For Equations (B-4)-(B-6), we inductively deduce the elimination of from the recursive relation and its boundary conditions Equation (B-20) can be obtained. Similarly for Equations (B-13)-(B-15), the elimination of F t in Equation (B-21) can be obtained.

[0087]

[0088] Due to the existence of massive flexible distributed resources, it is often difficult to obtain an absolutely accurate active power aggregation domain, but 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 The following requirements need to be met:

[0089] 1) Since is obtained by approximation from the interior optimization of the absolutely accurate active power aggregation domain, it is necessary to satisfy First, all the safe operating constraints within the microgrid need to be satisfied.

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

[0091] In view of the above requirements, the optimization objective function and constraint conditions are as follows:

[0092]

[0093] stP 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 Feasible operating range and The essence is the superposition of the operating range of all resources within the microgrid. Equation (B-23) defines x(P) MG ) and P MG The relationship is essentially a power balance equation. Equations (B-24)-(B-25) represent the relationship between x(P) and power balance. MG The second-order cone constraint and linear inequality constraint ensure the safety and stability of the system operation. Equation (B-26) represents 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 This ensures that a feasible scheduling scheme can be found for each aggregate power requirement.

[0098] To simplify the model and calculations, a time-discrete aggregated active power operating space is defined.

[0099]

[0100] In the formula: and P t MG P represents time t t MG The maximum and minimum values.

[0101] Obviously, P t MG Meet the conditions For ease of explanation, multiple moments will be used. and P t MG Merge them into vectors respectively and P MG( P t MG ) t∈Υ , then:

[0102]

[0103] P t MG can be further utilized P t MG The weighted sum is:

[0104]

[0105] The uncertain variable a t represents the weight and 0≤a t ≤1. When a t =0, i.e., P t MG takes the upper power limit. When a t =1, P t MG = P t MG i.e., P t MG takes the lower power limit. Originally described by the variable , the adaptive robust optimization (ARO) framework does not directly apply. However, after introducing a set of uncertainty variables (a t ) t∈Υ , the description of can be made feasible using the ARO framework that relies on a fixed set of uncertainties. The set

[0106]

[0107] By the equivalent change of variables, the condition can be replaced by “x(P MG )” can be rewritten as “x(a)”. Formulas (B-22)-(B-26) can be further rewritten as follows to obtain the maximum active power interval:

[0108]

[0109] Cx(a)≤d (B-34)

[0110] ||E r x(a)||2≤f r (B-35)

[0111]

[0112] The objective function (B-31) adopts a three-layer optimization (max-min-max) to handle the uncertainty and ensure flexibility and feasibility. The first layer of optimization (max) aims to determine the optimal upper and lower bounds of active power and P MG to maximize the total aggregated flexibility of the system. This is a "here and now" decision, focusing on the current optimal setting. The second layer of optimization (min) takes into account the impact of the uncertainty variable a. By making decisions under the worst-case scenario, this layer ensures that once the uncertainty is revealed, the system can adapt to the changes in time and make the corresponding power dispatch scheme x(a). The third layer of optimization (max) is to ensure that a feasible power dispatch scheme x(a) can still be found under the worst-case scenario. This layer guarantees that all constraints can be satisfied even under adverse conditions, thus ensuring the stability and reliability of the model.

[0113] Further, in step 3, based on step 2, the Column and constraint generation (CCG) algorithm is used to solve the aggregated domain mathematical model, obtaining a series of operating boundaries of the microgrid active power aggregated domain, the specific method is as follows:

[0114] Equations (B-32)-(B-36) describe a two-stage ARO model, which can be solved using the CCG algorithm. According to the CCG algorithm, the above problem can be decomposed into a master problem (MP) and a subproblem (SP) and iteratively solved. The MP is described 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 the MP aims to maximize the active power range, corresponding to the first layer of optimization of equation (B-31). In the MP, a m,Δ is given as a known parameter. That is, M enumerated scenarios a m,Δ are used to replace the uncertainty set This allows the model to simplify the problem while taking into account the uncertainty. Each scenario is associated with a corresponding power dispatch scheme x(a m,Δ ), which allows the model to adapt to different possible situations. Since only a finite number of scenarios are considered, the objective value J MP provides an upper bound for equation (B-30). But as new constraints are added, the optimal solution can be progressively approached.

[0120] MP solution and P MG will be passed to SP. SP is formulated as follows:

[0121]

[0122] Cx(a) < d (B-46)

[0123] ||E r x(a)||2< f r (B-47)

[0124]

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

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

[0127]

[0128] ||E r x(a M+1,Δ )||2< f (B-50)

[0129] Cx(a M+1,Δ ) < d (B-51)

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

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

[0132] 1) Power flow equation

[0133]

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

[0135] 2) Safe operation constraints of the distribution network

[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 upper and lower limit values of j node voltage respectively; I ij,t is a current upper limit value of line ij.

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

[0140] 3) Injection power constraints of the root node of the distribution network

[0141]

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

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

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

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

[0146]

[0147] Let and equations (B-52), (B-53), (B-55), (B-60) are further rewritten 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. In order to minimize the network loss, further define the objective function P loss :

[0150]

[0151] In the formula: 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 in the power grid with i as the first end node; P loss is the total loss of the line.

[0152] 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 simulation system. The system consists of 32 branches, connecting microgrid 1 and microgrid 2 at nodes 18 and 33 respectively. Each microgrid is equipped with flexible resources such as photovoltaics, energy storage systems, and temperature-controlled loads. The specific parameters of the equipment are shown in Table 1. The initial state of charge of the energy storage systems 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 system node voltage are set to 1.05 pu and 0.95 pu respectively, and the root node voltage is fixed at 1 p.u. The simulation example uses T equal to 24 to discretize the time. Scheduling optimization is implemented using the GAMS platform, and the model is solved using the GUROBI solver. The simulation environment for the simulation example is an Intel(R) Core(TM) i9-13900HX 2.20GHz with 32GB of memory.

[0153] Table 1 Parameters of Distributed Resources in Micronets

[0154]

[0155] like Figure 3 As shown, the upper and lower bounds of the aggregated power enclose the active power aggregation domain of the microgrid. The rhombus represents the actual operating point calculated after minimizing network losses, i.e., the interaction power between the distribution network and the microgrid. Furthermore, the area of ​​the active power aggregation domain represents the microgrid's flexible dispatchability; the larger the area of ​​the active power aggregation domain, the stronger the microgrid's flexible dispatchability. Since microgrid 1 has a higher capacity for energy storage and photovoltaic configurations, and these distributed resources can bring greater flexible dispatchability, the area of ​​the active power aggregation domain of microgrid 1 is significantly larger than that of microgrid 2.

[0156] The aggregation domain of the obtained micronet 1 is decomposed. Figure 4 The data provides the power output or demand of controllable resources within Microgrid 1, including energy storage, photovoltaic (PV), and temperature-controlled loads, at various times. During peak load periods of 10:00, 18:00, and 19:00, Microgrid 1's energy storage discharges to assist the grid in meeting load demands. During off-peak periods and peak PV output periods of 13:00 and 14:00, the energy storage charges to promote the absorption of PV renewable energy, and the stored energy is released when the next peak load period arrives.

[0157] This invention also provides another computer device, including a processor and a memory configured to store a computer program capable of running on the processor; wherein, when the processor is configured to run the computer program, it performs the method steps described in the foregoing embodiments.

[0158] In practical applications, the 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 device used to implement the functions of the processor can also be other electronic devices, and the embodiments of the present application are not limited in this regard.

[0159] The memory can be a volatile memory (such as a Random-Access Memory (RAM)), 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 exemplary embodiments, the embodiments of the present application also provide a computer-readable storage medium for storing a computer program.

[0161] Optionally, the computer-readable storage medium can be applied to any of the methods of the embodiments of the present application, and the computer program causes the computer to execute the corresponding procedures implemented by the processor in each method of the embodiments of the present application. For brevity, details are not repeated here.

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

[0163] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0164] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the one or more functions specified in the flowchart illustrations and / or block diagrams.

[0165] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the one or more functions specified in the flowchart illustrations and / or block diagrams.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the one or more functions specified in the flowchart illustrations and / or block diagrams.

[0167] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Accordingly, the appended claims are intended to encompass all modifications and variations as falling within the scope of the application.

[0168] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A two-layer collaborative optimization scheduling method based on active power aggregation domain, characterized in that: comprising, obtaining operation parameters of the distribution network, and constructing distribution network operation constraints; obtaining operation parameters of the microgrid, and constructing microgrid operation constraints; based on the microgrid operation constraints, constructing a mathematical model of a microgrid active power aggregation domain; using a row and column generation algorithm to solve the mathematical model of the aggregation domain, and obtaining an operation boundary of the microgrid active power aggregation domain; submitting the microgrid active power aggregation domain to the distribution network for scheduling, combining the distribution network operation constraints, and judging whether the microgrid active power aggregation domain meets a criterion of minimizing network loss of the distribution network; if yes, controlling power output at a gateway of the microgrid according to the operation boundary; otherwise, adjusting the operation boundary of the microgrid active power aggregation domain, updating the mathematical model of the aggregation domain, and solving the updated mathematical model until the obtained operation boundary meets the target of minimizing network loss of the distribution network; wherein the mathematical model of the microgrid active power aggregation domain comprises, constructing a run domain fitting the run domain to obtain a convergence domain; wherein the operating domain The optimization objective function and the constraint conditions are that, ， ， ， ， ， wherein obj denotes an optimization objective function of the operation domain , is a feasible operation range of the operation domain ; denotes active power of multiple time instants, represents all time periods, i.e. ; vector denotes active power of the energy storage system ESS, the photovoltaic system PV and the temperature-controlled load TCL at each time instant; matrix , , and vector , , are parameters related to the flexible distributed resources in the microgrid, respectively.

2. The method of claim 1, wherein: the microgrid operation constraints comprise energy storage system constraints, photovoltaic system constraints, temperature-controlled load operation constraints, and active power balance constraints.

3. The method of claim 1, wherein: the distribution network operation constraints comprise safe operation constraints and distribution network root node injected power constraints.

4. The method of claim 1, wherein: the target of minimizing network loss of the distribution network comprises, constructing an objective function with the target of minimizing network loss, ， wherein, is a time interval; is the total number of nodes in the grid; set is the set of end nodes of the branch with i as the head node in the grid; is the total line loss.

5. A micro double-layer collaborative optimization scheduling system based on active power aggregation domain, characterized in that: comprising, a distribution network operation constraint construction module configured to obtain operation parameters of the distribution network, and construct distribution network operation constraints; a microgrid operation constraint construction module configured to obtain operation parameters of the microgrid, and construct microgrid operation constraints; a microgrid active power aggregation domain mathematical model construction module configured to construct a mathematical model of a microgrid active power aggregation domain based on the microgrid operation constraints; a microgrid active power aggregation domain operation boundary obtaining module configured to use a row and column generation algorithm to solve the mathematical model of the aggregation domain, and obtain an operation boundary of the microgrid active power aggregation domain; and 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 judge whether the microgrid active power aggregation domain meets a criterion of minimizing network loss of the distribution network; if yes, control power output at a gateway of the microgrid according to 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 the updated mathematical model until the obtained operation boundary meets the target of minimizing network loss of the distribution network. wherein the mathematical model of the microgrid active power aggregation domain comprises, constructing a run domain fitting the run domain to obtain a convergence domain; wherein the operating domain The optimization objective function and the constraint conditions are that, ， ， ， ， ， wherein obj denotes an optimization objective function of the operation domain , is a feasible operation range of the operation domain ; denotes active power of multiple time instants, represents all time periods, i.e. ; vector denotes active power of the energy storage system ESS, the photovoltaic system PV and the temperature controlled load TCL at each time instant; matrix , , and vector , , are parameters related to flexible distributed resources in the microgrid, respectively.

6. The system of claim 5, wherein: the microgrid operation constraints comprise energy storage system constraints, photovoltaic system constraints, temperature-controlled load operation constraints, and active power balance constraints.

7. The system of claim 5, wherein: the distribution network operation constraints comprise safe operation constraints and distribution network root node injected power constraints.

8. The system of claim 5, wherein: the target of minimizing network loss of the distribution network comprises, constructing an objective function with the target of minimizing network loss, ， wherein, is a time interval; is the total number of nodes in the power grid; set is the set of end nodes of the branch with i as the head node in the power grid; is the total line loss.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: the processor implements the steps of the active power aggregation domain-based distribution-microgrid double-layer collaborative optimization scheduling method according to any one of claims 1 to 4 when executing the computer program. 10.A computer readable storage medium, storing a computer program; characterized in that: the computer program, when executed by the processor, implements the steps of the active power aggregation domain-based distribution-microgrid double-layer collaborative optimization scheduling method according to any one of claims 1 to 4.

Images