Distribution network and micro-grid distributed collaborative planning method and system based on improved target cascade analysis method

Abstract

The invention discloses a power distribution network and micro-grid distributed collaborative planning method and system based on an improved target cascade analysis method. Comprising the following steps: considering power distribution network topology and security constraints, and establishing an upper-layer power distribution network planning model by taking the minimum total cost of power distribution network planning as a target; establishing a lower-layer multi-microgrid planning model by taking the minimum total planning cost of the microgrid as a target in consideration of the aggregation capability of the microgrid to various internal resources; and solving the power distribution and micro-grid collaborative planning model by adopting an improved target cascade analysis method. Aiming at the power grid planning problem under the background of multiple planning subjects in the future, the effect of integrating various distributed energy sources by the micro-grid is exerted, the planning resources of the micro-grid and the power distribution network are coordinated, and the planning cost of each planning subject is reduced. According to the method, internal power generation resources of the power distribution network and the micro-grid can be reasonably planned, collaborative planning is realized only by means of a small amount of information on the premise that global equipment information is not needed, information privacy of each planning main body is protected, and economical efficiency of each planning main body scheme is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and relates to a distribution network and microgrid collaborative planning method and system, and in particular to a distribution network and microgrid distributed collaborative planning method and system based on an improved target cascade analysis method. Background Art

[0002] As an effective unit integrating various distributed resources, energy storage devices and flexible loads, microgrids can effectively promote the access of large-scale renewable energy to the distribution network. With the continuous growth of the penetration rate of new energy in the distribution system, the large-scale development of microgrids and their large-scale access to the distribution network has become a trend. The distribution network and microgrid belong to different entities. Therefore, how to coordinate the planning resources between the distribution network and microgrid in the power system planning to reduce the planning costs of each entity will become an important focus of future power grid planning.

[0003] At present, traditional power grid planning adopts a centralized planning method, which requires global information. However, with the diversification of the subjects involved in the planning, the centralized planning method is unable to obtain complete global planning information due to the protection of some of the internal information of different subjects. At the same time, when the scale of the system increases, the use of a centralized planning method will be too complicated. Most importantly, the centralized planning method cannot fully reflect the autonomy and independence of the various stakeholders involved in the planning. In order to solve the above problems, the present invention discloses a distributed collaborative planning method for distribution networks and microgrids based on an improved target cascade analysis method. Summary of the invention

[0004] To solve the above problems, the present invention proposes a distributed collaborative planning method for distribution networks and microgrids based on an improved target cascade analysis method.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method comprises the following steps:

[0007] 1) Considering the distribution network topology and security constraints, the upper-level distribution network planning model is established with the goal of minimizing the total cost of distribution network planning;

[0008] 2) Considering the aggregation capability of microgrids for various internal resources, with the goal of minimizing the total planning cost of the microgrid itself, a lower-level multi-microgrid planning model is established to obtain a coordinated planning model of distribution networks and microgrids;

[0009] 3) The improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model and obtain the optimal collaborative planning scheme.

[0010] Furthermore, the upper-level distribution network planning model is established by taking distribution network topology and safety constraints into consideration and minimizing the total cost of distribution network planning, including:

[0011] The distribution network planning model is constructed with the minimum sum of the annual energy storage investment cost and the annual operation cost of the distribution network as the objective function. The distribution network planning model is a second-order cone programming model. The objective function of the distribution network planning model can be expressed as:

[0012] f dn =F dn,inv +F dn,opt (1)

[0013]

[0014] In the formula, F dn is the total planning cost of the distribution network, F dn,inv is the annual investment and construction cost of energy storage in the distribution network, F dn,opt is the annual operating cost of the distribution network; They are the cost of purchasing electricity from the main grid, the cost of load shedding, the network loss cost and the cost of purchasing and selling electricity to the microgrid; Ω ess ,Ω G ,Ω B ,Ω L ,Ω M are the set of energy storage nodes to be built, the set of main network nodes, the set of all distribution network nodes, the set of all distribution network lines and the set of microgrid nodes; r is the discount rate; T ess is the service life of energy storage; c ess Allocate cost per unit capacity of energy storage; is the configured capacity of energy storage in the distribution network at node i; c grid 、c cut 、c loss 、c buy 、c sell They are the unit cost of purchasing electricity from the main grid, the unit cost of load shedding, the unit cost of grid loss, the unit cost of purchasing electricity from the distribution grid to the microgrid, and the unit cost of selling electricity from the distribution grid to the microgrid; They are the amount of electricity purchased from the main grid, the amount of load shedding, and the amount of electricity traded with the microgrid. It can be positive or negative. When it is positive, it means that the distribution network purchases electricity from the microgrid. When it is negative, it means that the distribution network sells electricity to the microgrid. + is an operator that takes non-negative numbers, that is I l and r l are the current flowing through line l and the resistance of line l respectively.

[0015] Furthermore, the distribution network planning model constraints include:

[0016] (1) Node power balance constraints

[0017] For any node i, All satisfied

[0018]

[0019] In the formula, and are the subset of routes starting from node i and the subset of routes ending at node i respectively; P l and Q l are respectively the active power and reactive power emitted from the beginning of line l; r l and x l They represent the resistance and reactance of line l respectively; and are the active load and reactive load at node i respectively; It is the reactive power obtained by the distribution network from the main grid; is the active power of various distributed power sources flowing into node i; and are the discharge power and charging power of the energy storage at node i respectively.

[0020] (2) Branch power constraints

[0021] For any branch l, that is, All satisfied

[0022]

[0023] In the formula, and are the starting node subset and the ending node subset of line l respectively; V i is the voltage amplitude at node i.

[0024] (3) Node voltage constraints

[0025]

[0026] In the formula, and They represent the lower and upper limits of the voltage amplitude at node i respectively.

[0027] (4) Branch current constraints

[0028]

[0029] In the formula, It indicates the maximum value of the current that can flow through branch l.

[0030] (5) Energy storage device constraints

[0031]

[0032] 0≤P ch ≤P ch,max (17)

[0033]

[0034] Where P dis,max and P ch,max are the upper limits of charging and discharging of the energy storage device, respectively; is the storage capacity of the energy storage device; γ is the charging efficiency of the energy storage device.

[0035] (6) Interaction constraints with microgrid power

[0036]

[0037] In the formula, is the upper limit of the power interaction between the distribution grid and the microgrid; and They are the amount of electricity purchased by the distribution network from the microgrid and the amount of electricity sold to the microgrid; A 0-1 variable representing the state of power purchase and sale from the distribution network to the microgrid. Constraints (21)-(24) realize the linearization of equation (7).

[0038] (7) Load shedding constraints

[0039]

[0040] Furthermore, the microgrid's aggregation capability for various internal resources is considered, and the overall planning cost of the microgrid itself is minimized to establish a lower-level multi-microgrid planning model, including:

[0041] Taking microgrid i as an example, the microgrid layer model under the collaborative planning framework generally does not consider the specific internal grid topology and can be regarded as a power supply planning problem. The microgrid planning model is constructed with the minimum sum of the annual investment and construction cost and the annual operation cost of the microgrid as the objective function. The objective function of the microgrid planning model can be expressed as:

[0042] f mg =F mg,inv +F mg,opt (26)

[0043]

[0044] In the formula, F mg is the total planning cost of the microgrid, F mg,inv is the construction cost of microgrid equipment, including the construction cost of energy storage device and gas turbine, F mg,opt is the annual operating cost of the microgrid; They are the load shedding cost of the microgrid, the transferable load cost and the power purchase and sales cost of the same distribution network; Ω Dev It is a collection of equipment invested and built in the microgrid, including gas turbines and energy storage devices, namely Ω D ev={mt,ess};T i is the life of the invested equipment i; c i is the unit capacity configuration cost of device i; E i,max is the configuration capacity of device i; c tr 、c' buy 、c' sell They are the unit compensation cost for transferable electric load, the unit cost of microgrid purchasing electricity from distribution network and the unit cost of microgrid selling electricity to distribution network; are the load shedding amount, load transfer amount and the amount of electricity traded between the microgrid and the distribution network at time t, respectively. When it is positive, it means that the microgrid sells electricity to the distribution network, and when it is negative, it means that it purchases electricity from the distribution network.

[0045] Furthermore, the constraints of the microgrid planning model include: (1) gas turbine output constraints

[0046] 0≤P mt ≤E mt,max (32)

[0047] Where P mt is the output of the gas turbine. The constraints of the energy storage device can refer to constraints (16)-(20).

[0048] (2) Interaction constraints with distribution network power

[0049] [-P dn ] + =P dn,buy (33)

[0050] [P dn ] + =P dn,sell (34)

[0051] P dn,buy ≤s dn P dn,max (35)

[0052] P dn,sell ≤(1-s dn ) dn,max (36)

[0053] Where P dn,max is the upper limit of the energy interaction between the microgrid and the distribution network. dn,buy With P dn,sellare the amount of electricity purchased by the microgrid from the distribution network and the amount of electricity sold to the distribution network; dn is a 0-1 variable indicating the state of the microgrid purchasing and selling electricity to the distribution network. Constraints (33)-(36) realize the linearization of equation (31).

[0054] (3) Power balance constraints within the microgrid

[0055] P load =P cut +P tr +P mt +P der +P dis -P ch -P dn (37)

[0056] Where P load is the internal load of the microgrid; P der is the output of renewable energy within the microgrid; P dis and P ch are the discharge power and charging power of the energy storage device inside the microgrid respectively.

[0057] (4) Load shedding constraints

[0058] 0≤P cut ≤0.2P load (38)

[0059] (5) Transferable load constraints

[0060] -0.15P load ≤P tr ≤0.15P load (39)

[0061]

[0062] Furthermore, the improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model, including:

[0063] The distribution network and microgrid collaborative planning model is decoupled by introducing Lagrangian augmented penalty functions about consistency constraints into the distribution network planning model and the microgrid planning model respectively; then the decoupled distribution network and microgrid collaborative planning model is solved using an improved target cascade analysis method.

[0064] The established distribution network and microgrid collaborative planning model is a two-layer planning model, the upper layer is the distribution network planning model, and the lower layer is the microgrid planning model. Taking microgrid i as an example, it interacts with the upper distribution network through power and When the interaction power satisfies the consistency constraint (41) for any microgrid i, the original collaborative planning problem has a solution:

[0065]

[0066] Where ΔP i It represents the difference between the interactive power target value on the distribution network side and the interactive power response value on the microgrid i side, reflecting the degree of inconsistency.

[0067] However, the original two-layer planning model cannot be solved directly under constraint (41). At the same time, in order to take into account the interests of each planning subject and reflect its independence, the present invention is based on a target cascade solution algorithm. By introducing Lagrangian augmented penalty functions about consistency constraints in the distribution network layer model and the microgrid layer model respectively, the two-layer model is decoupled, and the distributed solution of the original planning model is achieved by iteratively updating the multiplier in the penalty function.

[0068] The decoupled distribution network planning objective function and the i-th microgrid objective function are shown in equations (42) and (43):

[0069]

[0070] Where, ⊙ is the Hamada product; λ i,t and ω i,t They are the linear and quadratic multipliers of the penalty function respectively; It represents the target value of the interactive power in the distribution network planning model, and its value is provided by each microgrid. It represents the target value of the interactive power in the microgrid planning model, and its value is provided by the distribution network.

[0071] In summary, the decoupled distribution network planning model consists of the objective function (42) and constraints (1)-(25); the decoupled i-th microgrid planning model consists of the objective function (43) and constraints (26)-(40).

[0072] Furthermore, the improved target cascade analysis method is used to solve the decoupled distribution network and microgrid collaborative planning model, including the following steps:

[0073] 1) Initialization: number of iterations k = 1; given the penalty function multiplier value for the first iteration and Given the initial value of the expected value of interaction power and Modify the constraints and relax the 0-1 variables in the model into continuous variables between the interval [0-1]. That is, relax the decision variables that can only take the value 0 or 1 into decision variables that can take any value in the interval [0,1] (set the decision variable form to a continuous variable form, and set the range to be greater than or equal to 0 and less than or equal to 1), temporarily expanding the value range;

[0074] 2) Parallel solution of each planning subject: the distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning solutions and optimal interaction power; the distribution network takes the optimal interaction power as the expected value of the interaction power Sent to each microgrid, each microgrid takes the optimal interaction power as the expected value of interaction power Sent to the distribution network to obtain the distribution network cost value of the kth iteration Microgrid cost value Distribution network interactive power value and microgrid power interaction value

[0075] 3) Convergence judgment: If the convergence conditions (44) and (45) are met, go to step 4); otherwise, update the penalty function multiplier according to (46) and (47), set k = k + 1 and return to step 2).

[0076]

[0077] Where ε1 and ε2 are the convergence thresholds.

[0078]

[0079] Where η is the penalty function multiplier update coefficient, which is generally not less than 1.

[0080] 4) Integer judgment: judge whether all relaxed variables are 1 or 0. If satisfied, the iteration ends and the optimal collaborative planning scheme is output. Otherwise, all decision variables relaxed to the interval [0,1] are restored to variables that can only take the value of 0 or 1 in the constraints and return to step 2).

[0081] Furthermore, MATLAB's YALMIP is used to build the model, and the GUROBI solver is called to solve the problem.

[0082] A distributed collaborative planning system for distribution network and microgrid based on improved target cascade analysis method, comprising:

[0083] The distribution network model building module is configured to consider the distribution network topology and safety constraints, and to establish an upper-level distribution network planning model with the goal of minimizing the total cost of distribution network planning;

[0084] The microgrid model building module is configured to consider the microgrid's ability to aggregate various internal resources, take the microgrid's own total planning cost as the minimum, establish a lower-level multi-microgrid planning model, and obtain a distribution network and microgrid collaborative planning model;

[0085] The model coupling solution module is configured to solve the distribution network and microgrid collaborative planning model using an improved target cascade analysis method to obtain an optimal collaborative planning solution;

[0086] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned distributed collaborative planning method for distribution networks and microgrids based on an improved target cascade analysis method.

[0087] A terminal device includes a processor and a computer-readable storage medium, the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the above-mentioned distribution network and microgrid distributed collaborative planning method based on the improved target cascade analysis method.

[0088] The beneficial effects of the present invention are:

[0089] The present invention aims to address the problem of grid planning under the background of multiple planning entities in the future, give full play to the role of microgrids in integrating various types of distributed energy, coordinate the planning resources of microgrids and distribution networks, and reduce the planning costs of various planning entities. The method of the present invention can reasonably plan the internal power generation resources of the distribution network and the microgrid, and realize collaborative planning by relying on only a small amount of information without the need for global equipment information, while protecting the privacy of information of each planning entity and improving the economy of each planning entity's scheme. The improved target cascade analysis method can speed up the early iteration speed when processing large-scale collaborative planning models. This planning method can serve as an effective reference for the collaborative planning of distribution networks and multiple microgrids under the background of large-scale access of microgrids to distribution networks in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 This is an improved IEEE 33-node distribution network topology diagram in an embodiment of the present invention.

[0091] Figure 2 1 is a graph showing the wind and solar output and load level on a typical day in an embodiment of the present invention.

[0092] Figure 3 This is the collaborative planning and solving process of the micro-distribution network in an embodiment of the present invention.

[0093] Figure 4 It is an 18-hour interactive variable iteration curve between the microgrid 3 and the distribution network in an embodiment of the present invention.

[0094] Figure 5 It is the total cost iteration curve of micro-distribution collaborative planning in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0096] The present invention provides a method and system for distributed collaborative planning of distribution networks and microgrids based on an improved target cascade analysis method, the method comprising the following steps:

[0097] 1) Considering the distribution network topology and security constraints, the upper-level distribution network planning model is established with the goal of minimizing the total cost of distribution network planning;

[0098] 2) Considering the aggregation capability of microgrids for various internal resources, with the goal of minimizing the total planning cost of the microgrid itself, a lower-level multi-microgrid planning model is established to obtain a coordinated planning model of distribution networks and microgrids;

[0099] 3) The improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model and obtain the optimal collaborative planning scheme.

[0100] In step 1), considering the distribution network topology and safety constraints, the upper-level distribution network planning model is established with the goal of minimizing the total cost of distribution network planning:

[0101] The goal of the distribution network planning model is to minimize the annual comprehensive cost within the planning period. The annual comprehensive cost includes the annual investment and construction cost of the distribution network and the annual simulated operation cost of the distribution network. The annual investment and construction cost is equal to the annual value of the cost of investing in distributed energy storage in the distribution network. The annual simulated operation cost includes the cost of purchasing electricity from the main grid, the load shedding cost, the network loss cost, and the cost of purchasing and selling electricity from each microgrid.

[0102] The constraints of the distribution network planning model include node power balance constraints, node voltage constraints, branch flow constraints, branch current constraints and energy storage construction and operation constraints.

[0103] Specifically:

[0104] The distribution network planning model is constructed with the minimum sum of the annual energy storage investment cost and the annual operation cost of the distribution network as the objective function. The distribution network planning model is a second-order cone programming model. The objective function of the distribution network planning model can be expressed as:

[0105] f dn =F dn,inv +F dn,opt (48)

[0106]

[0107]

[0108] In the formula, F dnis the total planning cost of the distribution network, F dn,inv is the annual investment and construction cost of energy storage in the distribution network, F dn,opt is the annual operating cost of the distribution network; They are the cost of purchasing electricity from the main grid, the cost of load shedding, the network loss cost and the cost of purchasing and selling electricity with each microgrid; Ω ess ,Ω G ,Ω B ,Ω L ,Ω M are the set of energy storage nodes to be built, the main grid nodes, the set of all nodes in the distribution network, the set of all lines in the distribution network and the set of nodes where the microgrid is located; r is the discount rate; T ess is the service life of energy storage; c ess Allocate cost per unit capacity of energy storage; is the configured capacity of energy storage in the distribution network at node i; c grid 、c cut 、c loss 、c buy 、c sell They are the unit cost of purchasing electricity from the main grid, the unit cost of load shedding, the unit cost of grid loss, the unit cost of purchasing electricity from the distribution grid to the microgrid, and the unit cost of selling electricity from the distribution grid to the microgrid; They are the amount of electricity purchased from the main grid, the amount of load shedding, and the amount of electricity traded with the microgrid. It can be positive or negative. When it is positive, it means that the distribution network purchases electricity from the microgrid. When it is negative, it means that the distribution network sells electricity to the microgrid. + is an operator that takes non-negative numbers, that is I l and r l are the current flowing through line l and the resistance of line l respectively.

[0109] The distribution network planning model constraints include:

[0110] (1) Node power balance constraints

[0111] For any node i, All satisfied

[0112]

[0113] In the formula, and are the subset of routes starting from node i and the subset of routes ending at node i respectively; P l and Q l are respectively the active power and reactive power emitted from the beginning of line l; r l and x l They represent the resistance and reactance of line l respectively; and are the active load and reactive load at node i respectively; It is the reactive power obtained by the distribution network from the main grid; is the active power of various distributed power sources flowing into node i; and are the discharge power and charging power of the energy storage at node i respectively.

[0114] (2) Branch power constraints

[0115] For any branch l, that is, All satisfied

[0116]

[0117] In the formula, and are the starting node subset and the ending node subset of line l respectively; V i is the voltage amplitude at node i.

[0118] (3) Node voltage constraints

[0119]

[0120] In the formula, and They represent the lower and upper limits of the voltage amplitude at node i respectively.

[0121] (4) Branch current constraints

[0122]

[0123] In the formula, It indicates the maximum value of the current that can flow through branch l.

[0124] (5) Energy storage device constraints

[0125]

[0126] 0≤P ch ≤P ch,max (64)

[0127]

[0128] Where P dis,max and P ch,max are the upper limits of charging and discharging of the energy storage device, respectively; is the storage capacity of the energy storage device; γ is the charging efficiency of the energy storage device.

[0129] (6) Interaction constraints with microgrid power

[0130]

[0131] In the formula, is the upper limit of the power interaction between the distribution grid and the microgrid; and They are the amount of electricity purchased by the distribution network from the microgrid and the amount of electricity sold to the microgrid; A 0-1 variable representing the state of power purchase and sale from the distribution network to the microgrid. Constraints (21)-(24) realize the linearization of equation (7).

[0132] (7) Load shedding constraints

[0133]

[0134] In step 2), the aggregation capability of the microgrid to various internal resources is considered, and the overall planning cost of the microgrid itself is minimized to establish a lower-level multi-microgrid planning model:

[0135] The goal of the microgrid planning model is to minimize the annual comprehensive cost within the microgrid planning cycle, including the annual investment and construction cost of the microgrid and the annual simulated operation cost of the microgrid; the annual investment and construction cost is equal to the annual value of the cost of configuring a gas turbine and energy storage device inside the microgrid; the annual simulated operation cost of the microgrid includes the load shedding cost, the cost of flexible load participating in demand response, and the cost of purchasing and selling electricity from the same distribution network.

[0136] The constraints of the microgrid planning model include equipment output constraints, power interaction constraints with the distribution network, and power balance constraints within the microgrid.

[0137] Specifically:

[0138] The above-mentioned consideration of the microgrid's ability to aggregate various internal resources and the goal of minimizing the total planning cost of the microgrid itself establishes a lower-level multi-microgrid planning model, including:

[0139] Taking microgrid i as an example, the microgrid layer model under the collaborative planning framework generally does not consider the specific internal grid topology and can be regarded as a power supply planning problem. The microgrid planning model is constructed with the minimum sum of the annual investment and construction cost and the annual operation cost of the microgrid as the objective function. The objective function of the microgrid planning model can be expressed as:

[0140] f mg =F mg,inv +F mg,opt (73)

[0141]

[0142] In the formula, F mg is the total planning cost of the microgrid, F mg,inv is the construction cost of microgrid equipment, including the construction cost of energy storage device and gas turbine, F mg,optis the annual operating cost of the microgrid; They are the load shedding cost of the microgrid, the transferable load cost and the power purchase and sales cost of the same distribution network; Ω Dev It is a collection of equipment invested and built in the microgrid, including gas turbines and energy storage devices, namely Ω Dev ={mt,ess};T i is the life of the invested equipment i; c i is the unit capacity configuration cost of device i; E i,max is the configuration capacity of device i; c tr 、c' buy 、c' sell They are the unit compensation cost for transferable electric load, the unit cost of microgrid purchasing electricity from distribution network and the unit cost of microgrid selling electricity to distribution network; are the load shedding amount, load transfer amount and the amount of electricity traded between the microgrid and the distribution network at time t, respectively. When it is positive, it means that the microgrid sells electricity to the distribution network, and when it is negative, it means that it purchases electricity from the distribution network.

[0143] The constraints of the microgrid planning model include: (1) gas turbine output constraints

[0144] 0≤P mt ≤E mt,max (79) Where P mt is the output of the gas turbine. The constraints of the energy storage device can refer to constraints (16)-(20).

[0145] (2) Interaction constraints with distribution network power

[0146] [-P dn ] + =P dn,buy (80)

[0147] [P dn ] + =P dn,sell (81)

[0148] P dn,buy ≤s dn P dn,max (82)

[0149] P dn,sell ≤(1-s dn ) dn,max (83)

[0150] Where P dn,max is the upper limit of the energy interaction between the microgrid and the distribution network. dn,buy With P dn,sellare the amount of electricity purchased by the microgrid from the distribution network and the amount of electricity sold to the distribution network; dn is a 0-1 variable indicating the state of the microgrid purchasing and selling electricity to the distribution network. Constraints (33)-(36) realize the linearization of equation (31).

[0151] (3) Power balance constraints within the microgrid

[0152] P load =P cut +P tr +P mt +P der +P dis -P ch -P dn (84)

[0153] Where P load is the internal load of the microgrid; P der is the output of renewable energy within the microgrid; P dis and P ch are the discharge power and charging power of the energy storage device inside the microgrid respectively.

[0154] (4) Load shedding constraints

[0155] 0≤P cut ≤0.2P load (85)

[0156] (5) Transferable load constraints

[0157] -0.15P load ≤P tr ≤0.15P load (86)

[0158]

[0159] In step 3), the improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model:

[0160] The original collaborative planning model is a two-layer model, the upper layer is the distribution network planning model, and the lower layer is the microgrid planning model. The upper and lower layers are coupled based on consistency constraints, where the consistency constraint is that the interactive power of the distribution network and the microgrid on the interconnection line must be consistent. First, the consistency constraint is introduced into the objective function of each layer of the planning model in the form of a penalty function to decouple the two-layer model. Then, the decoupled sub-problems of each layer are solved in parallel. When the convergence conditions are met, the optimal planning scheme can be obtained. Since the distribution network and microgrid collaborative planning model is relatively complex, an improvement is made on the basis of the traditional target cascade analysis method. First, the 0-1 variables in the model are relaxed to continuous variables between 0 and 1, and the iteration speed is accelerated in the early stage. When the convergence conditions are met, the 0-1 variable constraints are added for iterative solution.

[0161] Specifically:

[0162] The distribution network and microgrid collaborative planning model is decoupled by introducing Lagrangian augmented penalty functions about consistency constraints into the distribution network planning model and the microgrid planning model respectively; then the decoupled distribution network and microgrid collaborative planning model is solved using an improved target cascade analysis method.

[0163] The consistency constraints are:

[0164]

[0165] Where ΔP i It represents the difference between the interactive power target value on the distribution network side and the interactive power response value on the microgrid i side, reflecting the degree of inconsistency.

[0166] The objective function of the decoupled distribution network planning model is:

[0167]

[0168] The objective function of the decoupled microgrid planning model is:

[0169]

[0170] Where, ⊙ is the Hamada product; λ i,t and ω i,t They are the linear and quadratic multipliers of the penalty function respectively; represents the target value of the interactive power in the distribution network planning model, Represents the target value of the interactive power in the microgrid planning model.

[0171] The improved target cascade analysis method is used to solve the decoupled distribution network and microgrid collaborative planning model, including the following steps:

[0172] 1) Initialization: Set the number of iterations k = 1; give the penalty function multiplier value for the first iteration and Given the initial value of the expected value of interaction power and Relax the 0-1 variables in the model into continuous variables between the interval [0,1];

[0173] 2) Parallel solution of each planning subject: the distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning solutions and optimal interaction power; the distribution network takes the optimal interaction power as the expected value of the interaction power Sent to each microgrid, each microgrid takes the optimal interaction power as the expected value of interaction power Sent to the distribution network to obtain the distribution network cost value of the kth iteration Microgrid cost value Distribution network interactive power value and microgrid power interaction value

[0174] 3) Convergence judgment: If the convergence condition is met, go to step 4); otherwise, update the penalty function multiplier, set k=k+1, and return to step 2);

[0175] The convergence condition is:

[0176]

[0177] Where ε1 and ε2 are convergence thresholds;

[0178] The method for updating the penalty function multiplier is:

[0179]

[0180] Where η is the penalty function multiplier update coefficient;

[0181] 4) Integer judgment: judge whether the relaxed 0-1 variable is 0 or 1. If it is satisfied, the iteration ends and the optimal collaborative planning solution is output. Otherwise, the relaxed continuous variable type is replaced back to the original 0-1 variable type in the constraint and return to step 2).

[0182] The improved target cascade analysis method of the present invention relaxes the decision variables that can only take values ​​of 0 or 1 into variables that can take any value in the interval [0,1] before the iteration starts. After the decision variables are relaxed, they can take values ​​of 0 or 1 and decimals between 0 and 1. After the convergence condition is met, it is determined whether all the relaxed variables take values ​​of 0 or 1. If so, the iteration is terminated and the optimal collaborative planning scheme is output. Otherwise, all the decision variables relaxed into the interval [0,1] are restored to variables that can only take values ​​of 0 or 1 in the constraint conditions and returned. The method of replacing the relaxed continuous variable type back to the original 0-1 variable type includes: (1) by modifying the decision variable type back to a 0-1 variable; (2) adding a constraint that can only take values ​​of 0 or 1 to the original constraint. For example, if x is a decision variable in the interval [0,1] after relaxation, the constraint x*(x-1)=0 can be added to force it to take values ​​of 0 or 1, which does not conflict with x being greater than or equal to 0 and less than or equal to 1.

[0183] The distribution network and microgrid collaborative planning model is a multi-agent, hierarchical collaborative optimization model with a large total scale. The distribution network layer planning model and each microgrid layer planning model contain a large number of 0-1 variables. When the number of 0-1 variables in the optimization problem increases, the complexity of the problem will also increase, and the size of the solution space will grow exponentially. More combination possibilities need to be considered, which will lead to a slower solution speed. The traditional target cascade analysis method is an iterative algorithm. The problem is decomposed by introducing an augmented Lagrangian penalty function in the objective function of each layer model, and the penalty function value is increased in each round of iteration, so that the interaction power between each layer tends to be consistent. Based on this feature, the 0-1 integer variables can be relaxed to continuous variables between 0 and 1 at the beginning of the iteration. Relaxing the 0-1 integer variables to continuous variables between 0 and 1 will make the problem more in line with the properties of convex optimization. Convex optimization has many efficient solution algorithms. At the same time, relaxing the 0-1 variables to continuous variables simplifies the complexity of the problem, which speeds up the early iteration speed. After meeting the convergence conditions, the 0-1 variable constraints are considered to ensure that the planning model is consistent with that before relaxation. The present invention aims at the problem of coordinated planning of distribution network and microgrid, constructs a two-layer planning model, takes into account the interests of each planning subject, and adopts an improved target cascade analysis method that considers the relaxation of 0-1 integer variables to speed up the solution of the model. The method can also achieve the consistency of the interactive power of each subject without the need for global device information, achieve coordination, and ensure the global optimality of the total coordinated planning cost of the system at a higher precision. With the development of microgrids, the number of microgrid accesses increases, the scale of the coordinated planning model will increase, the complexity will increase, and the global device information will be difficult to obtain. Solving based on the improved target cascade analysis method can speed up the model solution speed, and there is no need for each planning subject to share internal device parameters, and the reliability requirements for data communication are low, and the privacy of each planning subject is guaranteed.

[0184] A specific embodiment of the present invention is as follows:

[0185] Take a modified IEEE33 node power distribution system as an example, its topology is as follows Figure 1 As shown. Nodes 5, 8, and 9 are equipped with distributed photovoltaic 300kW, 150kW, and 450kW respectively; nodes 28, 29, and 30 are equipped with distributed wind turbines 300kW, 300kW, and 150kW respectively; nodes 6, 9, 13, and 29 are the points where distributed energy storage devices are to be installed; and nodes 3, 17, and 20 are microgrid access nodes. The wind and solar output and load level curves for each period of the selected typical day are shown in the figure below. Figure 2 shown.

[0186] The YALMIP of MATLAB is used to build the model, and the GUROBI solver is used to solve the two-layer collaborative planning model based on the improved target cascade analysis method. The collaborative planning solution process of the micro-distribution network is shown in Figure 3 .

[0187] Figure 4 The convergence of the interactive power between microgrid 3 and distribution network at 18h on a typical day is given. It can be seen from the figure that based on the improved target cascade analysis method, the model solution converges after 19 iterations, and the interactive power of microgrid 3 and distribution network tends to be consistent, which proves the convergence of the proposed algorithm.

[0188] Figure 5 The total cost iteration curve of micro-distribution collaborative planning is given. It can be seen from the figure that the total cost of distributed planning based on the target cascade analysis method and the improved target cascade analysis method is close to the total cost of centralized planning, but the distributed planning method only requires the two planning parties to exchange boundary interaction power information and multiplier information to achieve collaboration, and does not need to obtain global planning information. The solution process based on the target cascade analysis method was iterated 18 times, but its total time consumption increased by nearly 100 seconds compared with the solution using the improved target cascade analysis method. This is because the improved target cascade analysis method relaxes 0-1 variables into continuous variables at the beginning of the iteration, which speeds up the single iteration speed. In the future, as a large number of renewable energy sources are connected to the distribution network in the form of microgrids, the amount of equipment data will increase. The use of a distributed method based on the improved target cascade analysis method to solve the micro-distribution collaborative planning problem can better meet the actual needs of the project.

[0189] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0193] The above specific implementation modes are used to explain the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method, characterized in that , including the following steps: 1) Considering the distribution network topology and security constraints, the upper-level distribution network planning model is established with the goal of minimizing the total cost of distribution network planning; 2) Considering the aggregation capability of microgrids for various internal resources, with the goal of minimizing the total planning cost of the microgrid itself, a lower-level multi-microgrid planning model is established to obtain a coordinated planning model of distribution networks and microgrids; 3) The improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model and obtain the optimal collaborative planning scheme.

2. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method according to claim 1, characterized in that: The upper-level distribution network planning model is established by considering the distribution network topology and safety constraints and taking the minimum total cost of distribution network planning as the goal, including: The distribution network planning model is constructed with the minimum sum of the annual energy storage investment and construction cost of the distribution network and the annual operation cost of the distribution network as the objective function. The objective function of the distribution network planning model can be expressed as: minF dn =F dn,inv +F dn,opt In the formula, F dn is the total planning cost of the distribution network, F dn,inv is the annual investment and construction cost of energy storage in the distribution network, F dn,opt is the annual operating cost of the distribution network; They are the cost of purchasing electricity from the main grid, the cost of load shedding, the network loss cost and the cost of purchasing and selling electricity to the microgrid; Ω ess ,Ω G ,Ω B ,Ω L ,Ω M are the set of energy storage nodes to be built, the set of main network nodes, the set of all distribution network nodes, the set of all distribution network lines and the set of microgrid nodes; r is the discount rate; T ess is the service life of energy storage; c ess Allocate cost per unit capacity of energy storage; is the configured capacity of energy storage in the distribution network at node i; c grid 、c cut 、c loss 、c buy 、c sell They are the unit cost of purchasing electricity from the main grid, the unit cost of load shedding, the unit cost of grid loss, the unit cost of purchasing electricity from the distribution grid to the microgrid, and the unit cost of selling electricity from the distribution grid to the microgrid; I is the amount of electricity purchased from the main grid, the amount of load shedding, and the amount of electricity purchased and sold from the microgrid. l and r l are the current flowing through line l and the resistance of line l respectively.

3. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method according to claim 1, characterized in that: The distribution network planning model constraints include: (1) Node power balance constraints For any node i, All satisfied In the formula, and are the subset of routes starting from node i and the subset of routes ending at node i respectively; P l and Q l are respectively the active power and reactive power emitted from the beginning of line l; r l and x l Respectively represent the resistance and reactance of line l; P i load and are the active load and reactive load at node i respectively; P is the reactive power obtained by the distribution network from the main grid; i der is the active power of various distributed power sources flowing into node i; P i dis and P i ch are the discharge power and charging power of the energy storage at node i respectively; (2) Branch power constraints For any branch l, that is, All satisfied -P l max ≤P l ≤P l max -Q l max ≤Q l ≤Q l max In the formula, and are the starting node subset and the ending node subset of line l respectively; V i is the voltage amplitude at node i; (3) Node voltage constraints In i min ≤V i ≤V i max Where V i min and V i max Respectively represent the lower and upper limits of the voltage amplitude at node i; (4) Branch current constraints In the formula, Indicates the maximum value of the current that can flow through branch l; (5) Energy storage device constraints 0≤P i dis ≤P dis,max 0≤P ch ≤P ch,max Where P dis,max and P ch,max are the upper limits of charging and discharging of the energy storage device, respectively; is the storage capacity of the energy storage device; γ is the charging efficiency of the energy storage device; (6) Interaction constraints with microgrid power [P i mg ] + =P i mg,buy [-P i mg ] + =P i mg,sell Where P i mg,max is the upper limit of the power interaction between the distribution network and the microgrid; P i mg,buy With P i mg,sell They are the amount of electricity purchased by the distribution network from the microgrid and the amount of electricity sold to the microgrid; A 0-1 variable indicating the state of power purchase and sale from the distribution network to the microgrid; (7) Load shedding constraints 0≤P i cut ≤0.2P i load 。 4. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method according to claim 1, characterized in that: The above-mentioned consideration of the microgrid's ability to aggregate various internal resources and the goal of minimizing the total planning cost of the microgrid itself establishes a lower-level multi-microgrid planning model, including: The microgrid planning model is constructed with the minimum sum of the annual investment and construction cost and the annual operation cost of the microgrid as the objective function. The objective function of the microgrid planning model can be expressed as: min F mg =F mg,inv +F mg,opt In the formula, F mg is the total planning cost of the microgrid, F mg,inv is the construction cost of microgrid equipment, including the construction cost of energy storage device and gas turbine, mg,opt is the annual operating cost of the microgrid; They are the load shedding cost of the microgrid, the transferable load cost and the power purchase and sales cost of the same distribution network; Ω Dev The equipment set to be built in the microgrid includes gas turbines and energy storage devices, namely Ω D ev={mt,ess};T i is the life of the invested equipment i; c i is the unit capacity configuration cost of device i; E i,max is the configuration capacity of device i; c tr 、c' buy 、c' sell are the unit compensation cost for transferable load, the unit cost of microgrid purchasing electricity from distribution network and the unit cost of microgrid selling electricity to distribution network respectively; P t cut , P t tr , P t dn They are respectively the load shedding amount, load transfer amount and the amount of electricity purchased and sold by the microgrid from the distribution network at time t.

5. The method for distributed collaborative planning of distribution network and microgrid based on improved target cascade analysis method according to claim 1, characterized in that: The constraints of the microgrid planning model include: (1) Gas turbine output constraints 0≤P mt ≤E mt,max Where P mt is the output of the gas turbine; (2) Interaction constraints with distribution network power [-P dn ] + =P dn,buy [P dn ] + =P dn,sell P dn,buy ≤s dn P dn,max P dn,sell ≤(1-s dn )P dn,max Where P dn,max is the upper limit of the power interaction between the microgrid and the distribution network; P dn,buy With P dn,sell are the amount of electricity purchased by the microgrid from the distribution network and the amount of electricity sold to the distribution network; dn A 0-1 variable indicating the state of the microgrid purchasing and selling electricity from the distribution network; (3) Power balance constraints within the microgrid P load =P cut +P tr +P mt +P der +P dis -P ch -P dn Where P load is the internal load of the microgrid; P der is the output of renewable energy within the microgrid; P dis and P ch are the discharge power and charging power of the energy storage device inside the microgrid respectively; (4) Load shedding constraints 0≤P cut ≤0.2P load (5) Transferable load constraints -0.15P load ≤P tr ≤0.15P load 6. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method according to claim 1, characterized in that: The improved target cascade analysis method is used to solve the distribution network and microgrid collaborative planning model, including: The distribution network and microgrid collaborative planning models are decoupled by introducing Lagrangian augmented penalty functions with respect to consistency constraints into the distribution network planning model and the microgrid planning model respectively; and then the decoupled distribution network and microgrid collaborative planning model is solved by using an improved target cascade analysis method; The consistency constraints are: ΔP i =P i mg -P i dn =0 In the formula, ΔP i It represents the difference between the interactive power target value on the distribution network side and the interactive power response value on the microgrid i side, indicating the degree of inconsistency in response; The objective function of the decoupled distribution network planning model is: The objective function of the decoupled microgrid planning model is: Where, ⊙ is the Hamada product; λ i,t and ω i,t They are the linear and quadratic multipliers of the penalty function respectively; represents the target value of the interactive power in the distribution network planning model, Represents the target value of the interactive power in the microgrid planning model.

7. A distributed collaborative planning method for distribution network and microgrid based on improved target cascade analysis method according to claim 6, characterized in that: The improved target cascade analysis method is used to solve the decoupled distribution network and microgrid collaborative planning model, including the following steps: 1) Initialization: Set the number of iterations k = 1; give the penalty function multiplier value for the first iteration and Given the initial value of the expected value of interaction power and Modify the constraints and relax the 0-1 variables in the model into continuous variables between the interval [0-1]; 2) Parallel solution of each planning subject: the distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning solutions and optimal interaction power; the distribution network takes the optimal interaction power as the expected value of the interaction power Sent to each microgrid, each microgrid takes the optimal interaction power as the expected value of interaction power Sent to the distribution network to obtain the distribution network cost value of the kth iteration Microgrid cost value Distribution network interactive power value and microgrid power interaction value 3) Convergence judgment: If the convergence condition is met, go to step 4); otherwise, update the penalty function multiplier, set k=k+1, and return to step 2); The convergence condition is: Where ε1 and ε2 are convergence thresholds; The method for updating the penalty function multiplier is: Where η is the penalty function multiplier update coefficient; 4) Integer judgment: judge whether all relaxed variables are 1 or 0. If satisfied, the iteration ends and the optimal collaborative planning scheme is output. Otherwise, all decision variables relaxed to the interval [0,1] are restored to variables that can only take the value of 0 or 1 in the constraints and return to step 2).

8. A distributed collaborative planning system for distribution network and microgrid based on improved target cascade analysis method, characterized in that: include: The distribution network model building module is configured to consider the distribution network topology and safety constraints, and to establish an upper-level distribution network planning model with the goal of minimizing the total cost of distribution network planning; The microgrid model building module is configured to consider the microgrid's ability to aggregate various internal resources, take the microgrid's own total planning cost as the minimum, establish a lower-level multi-microgrid planning model, and obtain a distribution network and microgrid collaborative planning model; The model coupling solution module is configured to solve the distribution network and microgrid collaborative planning model using an improved target cascade analysis method to obtain the optimal collaborative planning solution.

9. A computer-readable storage medium, characterized in that: A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the distributed collaborative planning method for distribution networks and microgrids based on an improved target cascade analysis method as described in any one of claims 1-7.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method as described in any one of claims 1-7.

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