Distribution-microgrid collaborative optimization scheduling method based on flexible operation domain

Abstract

The invention discloses a distribution-microgrid collaborative optimization scheduling method based on a flexible operation domain, and the method comprises the steps: describing the flexible adjustment capability of a microgrid through a flexible operation domain model of the microgrid, constructing a distribution-microgrid coordinated economic scheduling model based on the flexible adjustment capability, and achieving the collaborative optimization scheduling of the distribution-microgrid. Defining a flexible operation domain concept of the micro-grid, and constructing a flexible operation domain model of the micro-grid by considering a distribution micro-grid power balance constraint, a safe operation constraint of each flexible resource device and a distribution-micro-grid boundary coupling constraint; a convex hull piecewise linear fitting formula is adopted to describe the flexible adjustment capability of the micro-grid, the flexible adjustment capability is incorporated into the optimization decision process of the power distribution network, and a power distribution-micro-grid coordinated economic dispatching model based on the flexible operation domain is constructed. According to the method, the adjustable potential of massive flexible resources in the micro-grid is fully excavated and participates in the optimization regulation and control of the power distribution network, and through the flexible aggregation-decomposition of the micro-grid, the collaborative optimization scheduling of the power distribution network and the micro-grid is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution-microgrid collaborative optimization, and in particular relates to a distribution-microgrid collaborative optimization scheduling method based on a flexible operation domain. Background Art

[0002] In recent years, distributed energy represented by distributed photovoltaics has developed rapidly in microgrids, and the flexibility resources in microgrids have continued to increase. Exploring the adjustable potential of massive flexibility resources in microgrids can provide strong support for the economic, safe, stable and low-carbon operation of distribution systems, thereby improving the flexibility of distribution systems. Therefore, it is necessary to coordinate and optimize the distribution microgrid to achieve better resource allocation and operation management.

[0003] In terms of quantifying the regulation capacity of the grid with massive flexible resources, most existing studies focus on analyzing the flexibility of the distribution network, and the quantification of the adjustable potential of the microgrid needs further research; in terms of coordinated grid regulation, most of the coordinated operations between distribution and microgrids are based on distributed algorithms, which require repeated iterations to ensure the consistency of the optimal strategy, which is not conducive to simplifying the coordination process of distribution and microgrids and improving the dispatching efficiency. Therefore, it is urgent to develop a coordinated optimization dispatching method of distribution and microgrids based on the flexible operation domain to fully tap the flexible regulation potential of microgrids and enable them to participate in the optimization and regulation of distribution networks. Summary of the invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to quantify the flexible adjustment capability of a microgrid under the access of massive flexible resources, so as to provide a distribution-microgrid collaborative optimization scheduling method based on a flexible operation domain. The present invention is oriented to the collaborative operation of distribution networks and microgrids, introduces a microgrid flexible operation domain model to quantify the flexible operation capability of the microgrid, uses a convex hull fitting formula to mathematically describe the microgrid's flexible operation domain, and adds it to the optimization decision model of the distribution network, thereby establishing a distribution-microgrid coordinated economic scheduling model, thereby realizing the collaborative optimization scheduling of distribution-microgrids. The present invention can effectively characterize the flexible adjustment capability of a microgrid, while ensuring the economy of the collaborative scheduling of distribution-microgrids.

[0005] Technical solution: In order to solve the above technical problems, the present invention provides a distribution-microgrid coordinated optimization scheduling method based on a flexible operation domain, comprising the following steps:

[0006] Step 1: Obtain network coefficients and equipment operating parameters of the distribution-microgrid model, wherein the network coefficients include grid topology, line impedance, and line transmission capacity, and the equipment operating parameters include parameters related to the distribution network energy storage device, photovoltaic power station, and reactive power compensation device, and parameters related to the microgrid photovoltaic inverter and energy storage device;

[0007] Step 2: Obtain grid load demand and photovoltaic output data;

[0008] Step 3: Construct a microgrid flexible operation domain model based on the grid network coefficient and equipment operation parameters obtained in step 1 and the grid load demand and photovoltaic output data obtained in step 2;

[0009] Step 4: According to the microgrid flexible operation domain model in step 3, a two-dimensional convex hull fitting function is used to obtain an observable microgrid flexible operation domain image, and a convex hull piecewise linear fitting formula is used to describe the flexible adjustment capability of the microgrid;

[0010] Step 5. According to the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid in step 4, a distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is established. At the distribution network layer, the distribution network operation constraints and the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid are considered. With the minimum operation cost of the distribution network as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the distribution network and the transmission power of the distribution microgrid. At the microgrid layer, the microgrid performs flexibility decomposition based on the transmission power given by the distribution network. With the minimum operation cost of the microgrid as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the microgrid.

[0011] Furthermore, in step 1, the parameters related to the energy storage device of the distribution network include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency; the parameters related to the photovoltaic power station of the distribution network include its rated capacity and the minimum power factor; the parameters related to the reactive compensation device of the distribution network include the capacity of the reactive compensation device; the parameters related to the photovoltaic inverter of the microgrid include its rated capacity and the minimum power factor; the parameters related to the energy storage device of the microgrid include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency.

[0012] Furthermore, the method of step 3 is as follows:

[0013] The flexible operation domain of the microgrid is defined as: a set of stable operation points of the microgrid that take into account the multi-type flexibility resources of the microgrid and satisfy the power balance constraints of the distribution microgrid, the safe operation constraints of each flexible resource equipment, and the boundary coupling constraints of the distribution microgrid within the dispatching period; among them, the microgrid operation point W adopts the active power P transmitted from the distribution network to the microgrid. D-MG and reactive power Q D-MG The composed vector representation is:

[0014] W=(P D-MG ,Q D-MG )(A-1)

[0015] A radial search algorithm is used to find the boundary points of the microgrid flexible operation domain. The compact form of the microgrid flexible operation domain model is expressed as:

[0016]

[0017] Where α and β are given parameters; x is the decision vector of the grid node within the dispatching period, including the active power and reactive power transmitted from the distribution network to the microgrid; y is the vector composed of the remaining variables in the grid, including the active power and reactive power obtained by the distribution network from the main grid, the reactive compensation power of the reactive compensation device of the distribution network, the charging and discharging power of the energy storage device of the distribution microgrid, and the active power and reactive power generated by the photovoltaic power generation of the distribution microgrid; g(x,y)=0 represents the power balance equation constraint of the distribution microgrid; h(x,y)≤0 represents the inequality constraint of the safe operation of the flexible resource equipment of the distribution microgrid; x MV With x MG are the boundary coupling vectors of the distribution network and microgrid, including the voltage amplitude and boundary power of the corresponding node; x MV =x MG represents the boundary coupling equality constraint of the distribution microgrid, and are vector spaces of different dimensions.

[0018] Furthermore, in step 3, the relevant constraints satisfied by the microgrid flexible operation domain model are as follows:

[0019] (1) Power balance constraints of distribution network

[0020]

[0021]

[0022]

[0023] Where i and j are node numbers; F(i) is the set of branch end nodes with node i as the head node; T(i) is the set of branch head node with node i as the end node; and are the net active power and net reactive power injected into node i in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch ij in the tth period; r ij and x ij is the resistance and reactance of line ij; is the voltage amplitude of node i in the tth period, and is a set of distribution network nodes and branches;

[0024]

[0025]

[0026] Where P t sub and The active power and reactive power obtained by the root node of the distribution network from the upper power grid in the tth period; and is the charging power and discharging power of the energy storage at node i in the tth period; and is the active output and reactive compensation power of the photovoltaic power station connected to node i in the tth period; is the reactive compensation power of the reactive compensation device at node i in the tth period; and is the load active and reactive demand of node i in the tth period; and is the active power and reactive power transmitted from the distribution network node i to the sth microgrid in the tth period; D MG Assemble for microgrids;

[0027] (2) Operation and safety constraints of distribution network flexibility resources

[0028] PV power plant operation constraints

[0029]

[0030]

[0031] In the formula, is the maximum photovoltaic power factor angle at node i; is the rated capacity of the photovoltaic power station at node i; energy storage operation constraints

[0032]

[0033]

[0034]

[0035] E i,min ≤E i,t ≤E i,max (A-13)

[0036] In the formula, and is the charging and discharging state variable of the energy storage at node i in the tth period; and is the maximum charging and discharging power of energy storage at node i; E i,t is the amount of energy stored at node i in the tth period; η i,ch and η i,disis the charging and discharging efficiency of energy storage at node i; Δt is the time interval; E i,max and E i,min are the upper and lower limits of the energy storage at node i;

[0037] Reactive power compensation device operation constraints

[0038]

[0039] In the formula, and are the upper and lower limits of the compensation power of the reactive power compensation device at node i;

[0040] Security constraints

[0041]

[0042]

[0043] In the formula, and are the upper and lower limits of the voltage amplitude of node i respectively; is the transmission capacity of branch ij; ΔU i is the voltage relaxation variable;

[0044] (3) Microgrid power balance constraints

[0045]

[0046]

[0047]

[0048] Where m and n are the node numbers of the sth microgrid; E(m) is the set of branch end nodes with node m as the head node; R(m) is the set of branch head node with node m as the end node; and are the net active power and net reactive power injected into node m in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch mn in the tth period; r mn and x mn is the resistance and reactance of line mn; is the voltage amplitude of node m in the tth time period; and is the node set and branch set of the sth microgrid;

[0049] (4) Microgrid flexibility resource operation constraints and safety constraints

[0050] Rooftop PV Operation Constraints

[0051]

[0052]

[0053] In the formula, and are the active output and reactive compensation power of the lth photovoltaic inverter in the sth microgrid in the tth time period respectively; is the maximum power factor angle of the lth PV inverter in the sth microgrid; is the rated capacity of the lth PV inverter in the sth microgrid;

[0054] Energy storage operation constraints

[0055]

[0056]

[0057]

[0058] E g,min ≤E g,t ≤E g,max (A-25)

[0059] In the formula, and are the charging and discharging state variables of the g-th energy storage in the s-th microgrid in the t-th time period; and are the charging and discharging power of the g-th energy storage in the s-th microgrid in the t-th time period; and is the maximum charging and discharging power of the g-th energy storage in the s-th microgrid; E g,t is the amount of energy stored in the g-th energy storage in the s-th microgrid in the t-th period; η g,ch and η g,dis is the charging and discharging efficiency of the g-th energy storage in the s-th microgrid; E g,max and E g,min are the upper and lower limits of the g-th energy storage capacity in the s-th microgrid;

[0060] Security constraints

[0061]

[0062]

[0063] In the formula, and are the upper and lower limits of the voltage amplitude of node m respectively; is the transmission capacity of branch mn;

[0064] (5) Distribution-microgrid boundary coupling constraints

[0065]

[0066]

[0067]

[0068] In the formula, is the voltage amplitude of the distribution network node i connected to the sth microgrid; is the root node voltage amplitude of the sth microgrid; and is the active power and reactive power obtained by the root node of the sth microgrid from the distribution network node i in the tth period.

[0069] Furthermore, in step 4, the convexhull function is used to process a series of microgrid flexible operation domain boundary points to obtain the microgrid flexible operation domain image. At the same time, the convex hull piecewise linear fitting formula can be used to describe the flexible adjustment capability of the microgrid:

[0070]

[0071] In the formula, k i is the i-th convex hull vertex; k i,P and k i,Q are the active power component and reactive power component of the i-th convex hull vertex, τ i is the corresponding i-th segment coefficient; r is the number of convex hull vertices, P(W) and Q(W) are the active power and reactive power obtained by the root node of the microgrid, respectively.

[0072] Furthermore, in step 5, the distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is:

[0073] (1) Distribution network optimization model

[0074] The distribution network dispatching goal is to minimize the electricity purchase cost from the main grid and user-side energy storage, so the objective function of the distribution network layer is:

[0075]

[0076] In the formula, is the electricity purchase price of the distribution network in the tth period; and are the energy storage dispatching costs of the distribution network and microgrid user side in the tth period respectively; is the difference between the net active load demand of the sth microgrid and the active power transmitted from the distribution network to the microgrid;

[0077] The charging cost of energy storage during off-peak electricity prices and the discharging income of energy storage during peak electricity prices are collectively referred to as energy storage dispatching costs, which are calculated as follows:

[0078]

[0079] In the formula, C e,t The current electricity purchase price of the network where the energy storage device is located; η ch and η dis are the charging and discharging efficiency of energy storage; C ESS Energy consumption cost for energy storage action;

[0080] The distribution network layer optimization model must satisfy constraints (A-3)-(A-16) and convex hull fitting constraint (A-31).

[0081] (2) Microgrid layer optimization model

[0082] The objective function is to minimize the cost of purchasing electricity from the distribution network and the energy storage on the user side of the microgrid:

[0083]

[0084] In the formula, is the electricity purchase price of the microgrid in the tth period;

[0085] In addition, the microgrid layer optimization model must satisfy constraints (A-17)-(A-27).

[0086] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0087] Compared with the traditional technical solution of coordinated optimization of distribution and microgrid, the technical solution of the present invention introduces a microgrid flexible operation domain model to characterize the flexible adjustment capability of the microgrid, and mathematically describes it through a convex hull fitting formula, and then adds it to the optimization decision-making process of the distribution network, thereby realizing the coordinated economic dispatch of the distribution and microgrid. The results of the example test show that the method proposed by the present invention can accurately characterize the flexible adjustment capability of the microgrid compared with the existing method, and ensure the economy of the coordinated dispatch of the distribution and microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is the optimization flow chart of the method of the present invention;

[0089] Figure 2 This is a diagram of a calculation example of a distribution-microgrid model;

[0090] Figure 3 It is a diagram of the flexible operation domain of the microgrid during a typical period;

[0091] Figure 4It is an optimized scheduling strategy diagram obtained by applying the method of the present invention. DETAILED DESCRIPTION

[0092] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0093] like Figure 1 As shown, the present invention provides a distribution-microgrid collaborative optimization scheduling method based on a flexible operation domain, comprising the following steps:

[0094] Step 1: Obtain network coefficients and equipment operating parameters of the distribution-microgrid model, wherein the network coefficients include grid topology, line impedance, and line transmission capacity, and the equipment operating parameters include parameters related to the distribution network energy storage device, photovoltaic power station, and reactive power compensation device, and parameters related to the microgrid photovoltaic inverter and energy storage device;

[0095] Step 2: Obtain grid load demand and photovoltaic output data;

[0096] Step 3: Construct a microgrid flexible operation domain model based on the grid network coefficient and equipment operation parameters obtained in step 1 and the grid load demand and photovoltaic output data obtained in step 2;

[0097] Step 4: According to the microgrid flexible operation domain model in step 3, a two-dimensional convex hull fitting function is used to obtain an observable microgrid flexible operation domain image, and a convex hull piecewise linear fitting formula is used to describe the flexible adjustment capability of the microgrid;

[0098] Step 5. According to the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid in step 4, a distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is established. At the distribution network layer, the distribution network operation constraints and the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid are considered. With the minimum operation cost of the distribution network as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the distribution network and the transmission power of the distribution microgrid. At the microgrid layer, the microgrid performs flexibility decomposition based on the transmission power given by the distribution network. With the minimum operation cost of the microgrid as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the microgrid.

[0099] Furthermore, in step 1, the parameters related to the energy storage device of the distribution network include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency; the parameters related to the photovoltaic power station of the distribution network include its rated capacity and the minimum power factor; the parameters related to the reactive compensation device of the distribution network include the capacity of the reactive compensation device; the parameters related to the photovoltaic inverter of the microgrid include its rated capacity and the minimum power factor; the parameters related to the energy storage device of the microgrid include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency.

[0100] Furthermore, the method of step 3 is as follows:

[0101] The flexible operation domain of the microgrid is defined as: a set of stable operation points of the microgrid that take into account the multi-type flexibility resources of the microgrid and satisfy the power balance constraints of the distribution microgrid, the safe operation constraints of each flexible resource equipment, and the boundary coupling constraints of the distribution microgrid within the dispatching period; among them, the microgrid operation point W adopts the active power P transmitted from the distribution network to the microgrid. D-MG and reactive power Q D-MG The composed vector representation is:

[0102] W=(P D-MG ,Q D-MG )(A-1)

[0103] A radial search algorithm is used to find the boundary points of the microgrid flexible operation domain. The compact form of the microgrid flexible operation domain model is expressed as:

[0104]

[0105] Where α and β are given parameters; x is the decision vector of the grid node within the dispatching period, including the active power and reactive power transmitted from the distribution network to the microgrid; y is the vector composed of the remaining variables in the grid, including the active power and reactive power obtained by the distribution network from the main grid, the reactive compensation power of the reactive compensation device of the distribution network, the charging and discharging power of the energy storage device of the distribution microgrid, and the active power and reactive power generated by the photovoltaic power generation of the distribution microgrid; g(x,y)=0 represents the power balance equation constraint of the distribution microgrid; h(x,y)≤0 represents the inequality constraint of the safe operation of the flexible resource equipment of the distribution microgrid; x MV With x MG are the boundary coupling vectors of the distribution network and microgrid, including the voltage amplitude and boundary power of the corresponding node; x MV =x MG represents the boundary coupling equality constraint of the distribution microgrid, and are vector spaces of different dimensions.

[0106] Furthermore, in step 3, the relevant constraints satisfied by the microgrid flexible operation domain model are as follows:

[0107] (1) Power balance constraints of distribution network

[0108]

[0109]

[0110]

[0111] Where i and j are node numbers; F(i) is the set of branch end nodes with node i as the head node; T(i) is the set of branch head node with node i as the end node; and are the net active power and net reactive power injected into node i in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch ij in the tth period; r ij and x ij is the resistance and reactance of line ij; is the voltage amplitude of node i in the tth period, and is a set of distribution network nodes and branches;

[0112]

[0113]

[0114] Where P t sub and The active power and reactive power obtained by the root node of the distribution network from the upper power grid in the tth period; and is the charging power and discharging power of the energy storage at node i in the tth period; and is the active output and reactive compensation power of the photovoltaic power station connected to node i in the tth period; is the reactive compensation power of the reactive compensation device at node i in the tth period; and is the load active and reactive demand of node i in the tth period; and is the active power and reactive power transmitted from the distribution network node i to the sth microgrid in the tth period; D MG Assemble for microgrids;

[0115] (2) Operational and safety constraints of distribution network flexibility resources

[0116] PV power plant operation constraints

[0117]

[0118]

[0119] In the formula, is the maximum photovoltaic power factor angle at node i; is the rated capacity of the photovoltaic power station at node i; energy storage operation constraints

[0120]

[0121]

[0122]

[0123] E i,min ≤E i,t ≤E i,max (A-13)

[0124] In the formula, and is the charging and discharging state variable of the energy storage at node i in the tth period; and is the maximum charging and discharging power of energy storage at node i; E i,t is the amount of energy stored at node i in the tth period; η i,ch and η i,dis is the charging and discharging efficiency of energy storage at node i; Δt is the time interval; E i,max and E i,min are the upper and lower limits of the energy storage at node i;

[0125] Reactive power compensation device operation constraints

[0126]

[0127] In the formula, and are the upper and lower limits of the compensation power of the reactive power compensation device at node i;

[0128] Security constraints

[0129]

[0130]

[0131] In the formula, and are the upper and lower limits of the voltage amplitude of node i respectively; is the transmission capacity of branch ij; ΔU i is the voltage relaxation variable;

[0132] (3) Microgrid power balance constraints

[0133]

[0134]

[0135]

[0136] Where m and n are the node numbers of the sth microgrid; E(m) is the set of branch end nodes with node m as the head node; R(m) is the set of branch head node with node m as the end node; and are the net active power and net reactive power injected into node m in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch mn in the tth period; r mn and x mn is the resistance and reactance of line mn; is the voltage amplitude of node m in the tth time period; and is the node set and branch set of the sth microgrid;

[0137] (4) Microgrid flexibility resource operation constraints and safety constraints

[0138] Rooftop PV Operation Constraints

[0139]

[0140]

[0141] In the formula, and are the active output and reactive compensation power of the lth photovoltaic inverter in the sth microgrid in the tth time period respectively; is the maximum power factor angle of the lth PV inverter in the sth microgrid; is the rated capacity of the lth PV inverter in the sth microgrid;

[0142] Energy storage operation constraints

[0143]

[0144]

[0145]

[0146] E g,min ≤E g,t ≤E g,max (A-25)

[0147] In the formula, and are the charging and discharging state variables of the g-th energy storage in the s-th microgrid in the t-th time period; and are the charging and discharging power of the g-th energy storage in the s-th microgrid in the t-th time period; and is the maximum charging and discharging power of the g-th energy storage in the s-th microgrid; E g,t is the amount of energy stored in the g-th energy storage in the s-th microgrid in the t-th period; η g,ch and η g,dis is the charging and discharging efficiency of the g-th energy storage in the s-th microgrid; E g,max and E g,min are the upper and lower limits of the g-th energy storage capacity in the s-th microgrid;

[0148] Security constraints

[0149]

[0150]

[0151] In the formula, and are the upper and lower limits of the voltage amplitude of node m respectively; is the transmission capacity of branch mn;

[0152] (5) Distribution-microgrid boundary coupling constraints

[0153]

[0154]

[0155]

[0156] In the formula, is the voltage amplitude of the distribution network node i connected to the sth microgrid; is the root node voltage amplitude of the sth microgrid; and is the active power and reactive power obtained by the root node of the sth microgrid from the distribution network node i in the tth period.

[0157] Furthermore, in step 4, the convexhull function is used to process a series of microgrid flexible operation domain boundary points to obtain the microgrid flexible operation domain image. At the same time, the convex hull piecewise linear fitting formula can be used to describe the flexible adjustment capability of the microgrid:

[0158]

[0159] In the formula, k i is the i-th convex hull vertex; k i,P and k i,Q are the active power component and reactive power component of the i-th convex hull vertex, τ i is the corresponding i-th segment coefficient; r is the number of convex hull vertices, P(W) and Q(W) are the active power and reactive power obtained by the root node of the microgrid, respectively.

[0160] Furthermore, in step 5, the distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is:

[0161] (1) Distribution network optimization model

[0162] The distribution network dispatching goal is to minimize the electricity purchase cost from the main grid and user-side energy storage, so the objective function of the distribution network layer is:

[0163]

[0164] In the formula, is the electricity purchase price of the distribution network in the tth period; and are the energy storage dispatching costs of the distribution network and microgrid user side in the tth period respectively; is the difference between the net active load demand of the sth microgrid and the active power transmitted from the distribution network to the microgrid;

[0165] The charging cost of energy storage during off-peak electricity prices and the discharging income of energy storage during peak electricity prices are collectively referred to as energy storage dispatch costs, and the calculation method is as follows:

[0166]

[0167] In the formula, C e,t The current electricity purchase price of the network where the energy storage device is located; η ch and η dis are the charging and discharging efficiency of energy storage; C ESS Energy consumption cost for energy storage action;

[0168] The distribution network layer optimization model must satisfy constraints (A-3)-(A-16) and convex hull fitting constraint (A-31).

[0169] (2) Microgrid layer optimization model

[0170] The objective function is to minimize the cost of purchasing electricity from the distribution network and the energy storage on the user side of the microgrid:

[0171]

[0172] In the formula, is the electricity purchase price of the microgrid in the tth period;

[0173] In addition, the microgrid layer optimization model must satisfy constraints (A-17)-(A-27).

[0174] Case Analysis

[0175] The following example illustrates the superiority of the distribution-microgrid coordinated optimization scheduling method based on flexible operation domain described in the present invention. Figure 2 The modified IEEE 33-node power system shown in FIG. 1 is a modified IEEE 33-node power system. In order to compare the superiority of the method proposed in the present invention, the centralized optimization dispatching method, the isolated optimization dispatching method and the distribution-microgrid collaborative optimization dispatching method based on the flexible operation domain proposed in the present invention are respectively used for distribution-microgrid collaborative dispatching. The present invention is implemented through the GAMS optimization platform, and the Baron and Gurobi solvers are used to solve the nonlinear programming problem.

[0176] Based on this example, the calculation results of the centralized optimization dispatching method, the isolated optimization dispatching method and the distribution-microgrid collaborative optimization dispatching method based on the flexible operation domain proposed by the present invention are compared (see Table 1 for the results). It is shown that the calculation results of the distribution-microgrid collaborative optimization dispatching method based on the flexible operation domain proposed by the present invention have good reliability, and the error with the calculation results of the centralized method is only 0.08%; at the same time, compared with the calculation results of the isolated optimization dispatching method, the distribution network load shedding cost and the gateway voltage regulation cost are avoided, and the total operating cost is reduced by about 3.4%, which has good economy. Image of the flexible operation domain of the microgrid in a typical period ( Figure 3 ) shows that its distribution is affected by factors such as regional load characteristics, distributed power configuration and equipment operation mode. The dispatching results of the distribution-microgrid collaborative optimization dispatching method based on flexible operation domain proposed in this invention ( Figure 4 ) indicates that the method proposed in the present invention can realize the two-charge and two-discharge strategy of the energy storage device within one day, and the photovoltaic inverter and SVC can effectively provide reactive power compensation.

[0177] Table 1 Comparison of centralized, isolated and collaborative optimization results

[0178] Scheduling method Total operating cost / yuan MV operating cost / yuan MG1 operating cost / yuan MG2 operating cost / yuan Centralized 78290.2 74268.3 2264.5 1757.4 Isolated 81023.2 77002 2264 1757.2 Collaborative 78355.7 74317.2 2278.3 1760.2

[0179] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention are all covered by the protection scope of the present invention.

Claims

1. A distribution-microgrid collaborative optimization scheduling method based on flexible operation domain, characterized in that: The following steps are involved: Step 1: Obtain network coefficients and equipment operating parameters of the distribution-microgrid model, wherein the network coefficients include grid topology, line impedance, and line transmission capacity, and the equipment operating parameters include parameters related to the distribution network energy storage device, photovoltaic power station, and reactive power compensation device, and parameters related to the microgrid photovoltaic inverter and energy storage device; Step 2: Obtain grid load demand and photovoltaic output data; Step 3: Construct a microgrid flexible operation domain model based on the grid network coefficient and equipment operation parameters obtained in step 1 and the grid load demand and photovoltaic output data obtained in step 2; Step 4: According to the microgrid flexible operation domain model in step 3, a two-dimensional convex hull fitting function is used to obtain an observable microgrid flexible operation domain image, and a convex hull piecewise linear fitting formula is used to describe the flexible adjustment capability of the microgrid; Step 5. According to the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid in step 4, a distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is established. At the distribution network layer, the distribution network operation constraints and the convex hull piecewise linear fitting formula used to describe the flexible adjustment capability of the microgrid are considered. With the minimum operation cost of the distribution network as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the distribution network and the transmission power of the distribution microgrid. At the microgrid layer, the microgrid performs flexibility decomposition based on the transmission power given by the distribution network. With the minimum operation cost of the microgrid as the objective function, a mixed integer optimization solution is used to obtain the specific dispatch strategy of each flexibility resource of the microgrid.

2. A distribution-microgrid coordinated optimization scheduling method based on flexible operation domain according to claim 1, characterized in that: In step 1, the parameters related to the energy storage device of the distribution network include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency; the parameters related to the photovoltaic power station of the distribution network include its rated capacity and the minimum power factor; the parameters related to the reactive power compensation device of the distribution network include the capacity of the reactive power compensation device; the parameters related to the photovoltaic inverter of the microgrid include its rated capacity and the minimum power factor; the parameters related to the energy storage device of the microgrid include the capacity of the energy storage device, the maximum charge and discharge power, and the charge and discharge efficiency.

3. A distribution-microgrid coordinated optimization scheduling method based on flexible operation domain according to claim 2, characterized in that: The method for step 3 is as follows: The flexible operation domain of the microgrid is defined as: a set of stable operation points of the microgrid that take into account the multi-type flexibility resources of the microgrid and satisfy the power balance constraints of the distribution microgrid, the safe operation constraints of each flexible resource equipment, and the boundary coupling constraints of the distribution microgrid within the dispatch cycle; Among them, the microgrid operation point W uses the active power P transmitted from the distribution network to the microgrid D-MG and reactive power Q D-MG The composed vector representation is: W=(P D-MG ,Q D-MG )(A-1) A radial search algorithm is used to find the boundary points of the microgrid flexible operation domain. The compact form of the microgrid flexible operation domain model is expressed as: Where α and β are given parameters; x is the decision vector of the grid node within the dispatching period, including the active power and reactive power transmitted from the distribution network to the microgrid; y is the vector composed of the remaining variables in the grid, including the active power and reactive power obtained by the distribution network from the main grid, the reactive compensation power of the reactive compensation device of the distribution network, the charging and discharging power of the energy storage device of the distribution microgrid, and the active power and reactive power generated by the photovoltaic power generation of the distribution microgrid; g(x,y)=0 represents the power balance equation constraint of the distribution microgrid; h(x,y)≤0 represents the inequality constraint of the safe operation of the flexible resource equipment of the distribution microgrid; x MV With x MG are the boundary coupling vectors of the distribution network and microgrid, including the voltage amplitude and boundary power of the corresponding node; x MV =x MG represents the boundary coupling equality constraint of the distribution microgrid, and are vector spaces of different dimensions.

4. A distribution-microgrid coordinated optimization scheduling method based on flexible operation domain according to claim 3, characterized in that: In step 3, the relevant constraints satisfied by the microgrid flexible operation domain model are as follows: (1) Power balance constraints of distribution network Where i and j are node numbers; F(i) is the set of branch end nodes with node i as the head node; T(i) is the set of branch headend nodes with node i as the terminal node; and are the net active power and net reactive power injected into node i in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch ij in the tth period; r ij and x ij is the resistance and reactance of line ij; is the voltage amplitude of node i in the tth period, and is a set of distribution network nodes and branches; Where P t sub and The active power and reactive power obtained by the root node of the distribution network from the upper power grid in the tth period; and is the charging power and discharging power of the energy storage at node i in the tth period; and is the active output and reactive compensation power of the photovoltaic power station connected to node i in the tth period; is the reactive compensation power of the reactive compensation device at node i in the tth period; and is the load active and reactive demand of node i in the tth period; and is the active power and reactive power transmitted from the distribution network node i to the sth microgrid in the tth period; D MG Assemble for microgrids; (2) Operation and safety constraints of distribution network flexibility resources PV power plant operation constraints In the formula, is the maximum photovoltaic power factor angle at node i; is the rated capacity of the PV power station at node i; Energy storage operation constraints AND i,min ≤E i,t ≤E i,max (A-13) In the formula, and is the charging and discharging state variable of the energy storage at node i in the tth period; and is the maximum charging and discharging power of energy storage at node i; E i,t is the amount of energy stored at node i in the tth period; η i,ch and η i,dis is the charging and discharging efficiency of energy storage at node i; Δt is the time interval; E i,max and E i,min are the upper and lower limits of the energy storage at node i; Reactive power compensation device operation constraints In the formula, and are the upper and lower limits of the compensation power of the reactive power compensation device at node i; Security constraints In the formula, and are the upper and lower limits of the voltage amplitude of node i respectively; is the transmission capacity of branch ij; ΔU i is the voltage relaxation variable; (3) Microgrid power balance constraints Where m and n are the node numbers of the sth microgrid; E(m) is the set of branch end nodes with node m as the head node; R(m) is the set of branch head node with node m as the end node; and are the net active power and net reactive power injected into node m in the tth period respectively; and are respectively the active power, reactive power and current amplitude through branch mn in the tth period; r mn and x mn is the resistance and reactance of line mn; is the voltage amplitude of node m in the tth time period; and is the node set and branch set of the sth microgrid; (4) Microgrid flexibility resource operation constraints and safety constraints Rooftop PV Operation Constraints In the formula, and are the active output and reactive compensation power of the lth photovoltaic inverter in the sth microgrid in the tth time period respectively; is the maximum power factor angle of the lth PV inverter in the sth microgrid; is the rated capacity of the lth PV inverter in the sth microgrid; Energy storage operation constraints AND g,min ≤E g,t ≤E g,max (A-25) In the formula, and are the charging and discharging state variables of the g-th energy storage in the s-th microgrid in the t-th time period; and are the charging and discharging power of the g-th energy storage in the s-th microgrid in the t-th time period; and is the maximum charging and discharging power of the g-th energy storage in the s-th microgrid; E g,t is the amount of energy stored in the g-th energy storage in the s-th microgrid in the t-th period; η g,ch and η g,dis is the charging and discharging efficiency of the g-th energy storage in the s-th microgrid; E g,max and E g,min are the upper and lower limits of the g-th energy storage capacity in the s-th microgrid; Security constraints In the formula, and are the upper and lower limits of the voltage amplitude of node m respectively; is the transmission capacity of branch mn; (5) Distribution-microgrid boundary coupling constraints In the formula, is the voltage amplitude of the distribution network node i connected to the sth microgrid; is the root node voltage amplitude of the sth microgrid; and is the active power and reactive power obtained by the root node of the sth microgrid from the distribution network node i in the tth period.

5. A distribution-microgrid coordinated optimization scheduling method based on flexible operation domain according to claim 4, characterized in that: In step 4, the convexhull function is used to process a series of microgrid flexible operation domain boundary points to obtain the microgrid flexible operation domain image. At the same time, the convex hull piecewise linear fitting formula can be used to describe the flexible adjustment capability of the microgrid: In the formula, k i is the i-th convex hull vertex; k i,P and k i,Q are the active power component and reactive power component of the i-th convex hull vertex, τ i is the corresponding i-th segment coefficient; r is the number of convex hull vertices, P(W) and Q(W) are the active power and reactive power obtained by the root node of the microgrid, respectively.

6. A distribution-microgrid coordinated optimization scheduling method based on flexible operation domain according to claim 5, characterized in that: In step 5, the distribution-microgrid coordinated economic dispatch model based on the flexible operation domain is: (1) Distribution network optimization model The distribution network dispatching goal is to minimize the electricity purchase cost from the main grid and user-side energy storage, so the objective function of the distribution network layer is: In the formula, is the electricity purchase price of the distribution network in the tth period; and are the energy storage dispatching costs of the distribution network and microgrid user side in the tth period respectively; is the difference between the net active load demand of the sth microgrid and the active power transmitted from the distribution network to the microgrid; The charging cost of energy storage during off-peak electricity prices and the discharging income of energy storage during peak electricity prices are collectively referred to as energy storage dispatching costs, which are calculated as follows: In the formula, C e,t The current electricity purchase price of the network where the energy storage device is located; η ch and η dis are the charging and discharging efficiency of energy storage; C ESS Energy consumption cost for energy storage action; The distribution network layer optimization model must satisfy constraints (A-3)-(A-16) and convex hull fitting constraint (A-31). (2) Microgrid layer optimization model The objective function is to minimize the cost of purchasing electricity from the distribution network and the energy storage on the user side of the microgrid: In the formula, is the electricity purchase price of the microgrid in the tth period; In addition, the microgrid layer optimization model must satisfy constraints (A-17)-(A-27).

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