A power distribution network and microgrid distributed collaborative planning method and system based on an improved target cascade analysis method

Abstract

The application discloses a kind of power distribution network and microgrid distributed collaborative planning method and system based on improved target cascade analysis method.It includes: considering power distribution network topology and safety constraints, with the minimum total cost of power distribution network planning as the goal, establish upper power distribution network planning model;Considering the aggregation ability of microgrid to internal various resources, with the minimum total planning cost of microgrid itself as the goal, establish lower multi-microgrid planning model;Improved target cascade analysis method is used to solve microgrid collaborative planning model.The application aims at the power grid planning problem under the background of future multi-planning subject, plays the role of microgrid integration various distributed energy, coordinates the planning resources of microgrid and power distribution network, reduces the planning cost of each planning subject.Can reasonably plan power distribution network and microgrid internal power generation resources, realize collaborative planning only by relying on a small amount of information under the premise of not needing global equipment information, protect the privacy of each planning subject information while improving the economy of each planning subject scheme.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, and relates to a method and system for collaborative planning of distribution networks and microgrids, particularly a method and system for distributed collaborative planning of distribution networks and microgrids based on an improved target cascade analysis method. Background Technology

[0002] Microgrids, as effective units for integrating various distributed resources, energy storage devices, and flexible loads, can effectively promote the large-scale integration of renewable energy into the distribution network. With the continuous increase in the penetration rate of new energy sources in the distribution system, the large-scale development and extensive integration of microgrids into the distribution network has become a trend. Distribution networks and microgrids belong to different entities; therefore, how to coordinate planning resources between distribution networks and microgrids in power system planning to reduce planning costs for each entity will become an important focus of future power grid planning.

[0003] Currently, traditional power grid planning employs a centralized planning method, requiring comprehensive global information. However, with the diversification of planning stakeholders, the centralized planning method cannot obtain complete global planning information due to the protection of internal information by different stakeholders. Furthermore, the centralized planning method becomes overly complex as the system scale increases. Most importantly, the centralized planning method fails to fully reflect the autonomy and independence of the various stakeholders involved in the planning process. To address these issues, this 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 address the aforementioned problems, this 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 in this invention is as follows:

[0006] A distributed collaborative planning method for distribution networks and microgrids based on an improved objective cascade analysis method includes the following steps:

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

[0008] 2) Considering the ability of microgrids to aggregate various internal resources, and 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 collaborative planning model for the distribution network and microgrids;

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

[0010] Furthermore, considering the distribution network topology and security constraints, and aiming to minimize the total cost of distribution network planning, an upper-level distribution network planning model is established, including:

[0011] A distribution network planning model is constructed with the objective function of minimizing the sum of the annual energy storage construction cost and the annual operating cost of the distribution network. This distribution network planning model is a second-order cone programming model, and its objective function can be expressed as:

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

[0013]

[0014] In the formula, F dn For the total planned cost of the distribution network, F dn,inv For the annual investment cost of energy storage in the distribution network, F dn,opt Annual operating cost of the power distribution network; These are the costs of purchasing electricity from the main grid, the cost of load shedding in the distribution network, the network loss cost, and the cost of purchasing and selling electricity to microgrids; Ω ess Ω G Ω B Ω L Ω M These represent the sets of nodes to be built for energy storage, the main grid nodes, all distribution network nodes, all distribution network lines, and the microgrid nodes, respectively; r is the discount rate; T ess For the lifespan of energy storage; c ess Cost per unit capacity of energy storage; c represents the configured energy storage capacity of the distribution network at node i; grid c cut c loss c buy c sell These are the unit costs of purchasing electricity from the main grid, the unit costs of load shedding, the unit costs of grid losses, the unit costs of purchasing electricity from the distribution network to the microgrid, and the unit costs of selling electricity from the distribution network to the microgrid. These are the electricity purchased by the distribution network from the main grid, the amount of load shedding, and the electricity traded with the same microgrid. It can be positive or negative. A positive value indicates that the distribution network is purchasing electricity from the microgrid, while a negative value indicates that the distribution network is selling electricity to the microgrid. + To select non-negative numbers, i.e. I l and r l These represent the magnitude of the current flowing through line l and the resistance of line l, respectively.

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

[0016] (1) Node power balance constraints

[0017] For any node i, i.e. All satisfied

[0018]

[0019] In the formula, and These are the subsets of routes starting from node i and the subsets of routes ending at node i, respectively; P l and Q l These represent the active power and reactive power emitted from the beginning of line l, respectively; r l and x l These represent the resistance and reactance of line l, respectively; and These represent the active load and reactive load at node i, respectively. The reactive power that the distribution network obtains from the main grid; The active power flowing into node i from various distributed power sources; and These represent the discharge power and charging power of the energy stored at node i, respectively.

[0020] (2) Branch power constraints

[0021] For any branch l, i.e. All satisfied

[0022]

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

[0024] (3) Node voltage constraints

[0025]

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

[0027] (4) Branch current constraint

[0028]

[0029] In the formula, This represents the maximum current that can flow through branch l.

[0030] (5) Constraints of energy storage devices

[0031]

[0032] 0≤P ch ≤P ch,max (17)

[0033]

[0034] In the formula, P dis,max and P ch,max These are the upper limits for charging and discharging energy storage devices, respectively. γ represents the energy storage capacity of the energy storage device; γ represents the charging efficiency of the energy storage device.

[0035] (6) Power interaction constraints with microgrids

[0036]

[0037] In the formula, This represents the upper limit for the interaction of electrical energy between the distribution network and the microgrid; and These represent the electricity purchased by the distribution network from the microgrid and the electricity sold to the microgrid, respectively. The 0-1 variables represent the state of the power distribution network purchasing and selling electricity to the microgrid. Constraints (21)-(24) achieve linearization of equation (7).

[0038] (7) Load shedding constraint

[0039]

[0040] Furthermore, considering the microgrid's ability to aggregate various internal resources, and aiming to minimize the total planning cost of the microgrid itself, a lower-level multi-microgrid planning model is established, 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 planning problem. The microgrid planning model is constructed with the objective function of minimizing the sum of the annual construction cost and annual operating cost of the microgrid. The objective function of the microgrid planning model can be expressed as:

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

[0043]

[0044] In the formula, F mg For the total planned cost of the microgrid, F mg,inv The cost of microgrid equipment includes the cost of energy storage devices and gas turbines. mg,opt The annual operating cost of a microgrid; These are the costs of load shedding in a microgrid, the cost of transferable electrical load, and the cost of purchasing and selling electricity within the same distribution network; Ω Dev This refers to the collection of equipment deployed within a microgrid, including gas turbines and energy storage devices, i.e., Ω. D ev = {mt, ess}; T i For the lifespan of the equipment i; c i Configuration cost per unit capacity of device i; E i,max The configured capacity of device i; c tr c' buy c' sell These are the unit compensation cost for transferable electrical loads, the unit cost of the microgrid purchasing electricity from the distribution network, and the unit cost of the microgrid selling electricity to the distribution network; These represent the load shedding, load transfer, and electricity traded between the microgrid and the distribution network at time t, respectively. A positive value indicates that the microgrid sells electricity to the distribution network, while a negative value indicates 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] In the formula, P mt The output of the gas turbine. The constraints for the energy storage device can be found in constraints (16)-(20).

[0048] (2) Power interaction constraints with distribution network

[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 )P dn,max (36)

[0053] In the formula, P dn,max This represents the upper limit of energy interaction between the microgrid and the distribution network. dn,buy With P dn,sellThese represent the electricity purchased by the microgrid from the distribution network and the electricity sold to the distribution network, respectively; s dn Let be the 0-1 variables representing the state of the microgrid purchasing and selling electricity to the distribution network. Constraints (33)-(36) linearize equation (31).

[0054] (3) Power balance constraints within microgrids

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

[0056] In the formula, P load For the internal load of the microgrid; P der For the output of renewable energy within the microgrid; P dis and P ch These represent the discharge power and charging power of the energy storage devices within the microgrid, respectively.

[0057] (4) Load shedding constraint

[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 method of using an improved objective cascade analysis to solve the collaborative planning model for distribution networks and microgrids includes:

[0063] The collaborative planning models of the distribution network and microgrid are decoupled by introducing Lagrange augmented penalty functions with respect to consistency constraints into the distribution network planning model and the microgrid planning model, respectively; then, the decoupled collaborative planning models of the distribution network and microgrid are solved using the improved objective cascade analysis method.

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

[0065]

[0066] In the formula, ΔP i This represents the difference between the target value of the interactive power 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 bi-level planning model cannot be solved directly under constraint (41). In order to take into account the interests of each planning subject and reflect their independence, this invention is based on a target cascade solution algorithm. By introducing Lagrange augmented penalty functions with respect to consistency constraints into the distribution network layer model and the microgrid layer model respectively, the bi-level model is decoupled. The distributed solution of the original planning model is achieved by updating the multipliers in the penalty function.

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

[0069]

[0070] In the formula, ⊙ represents the Hamada product; λ i,t and ω i,t These are the multipliers of the linear and quadratic terms in the penalty function, respectively; This represents the target value of the interactive power in the distribution network planning model, which is provided by each microgrid. This represents the target value of the interactive power in the microgrid planning model, which is provided by the distribution network.

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

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

[0073] 1) Initialization: Iteration count k = 1; given the penalty function multiplier value for the first iteration. and Given the initial value of the expected interaction power and Modify the constraints to relax the 0-1 variables in the model into continuous variables in the interval [0-1]. That is, relax the decision variables that 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 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 range of values.

[0074] 2) Parallel solution by each planning entity: The distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning schemes and optimal interaction power; the distribution network uses the optimal interaction power as the expected value of the interaction power. The data is sent to each microgrid, and each microgrid uses the optimal interactive power as the expected interactive power value. Send the data to the distribution network to obtain the distribution network cost value for the k-th iteration. Microgrid cost Distribution network interactive power value Interaction value with microgrid power

[0075] 3) Convergence check: If convergence conditions (44) and (45) are met, proceed 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] In the formula, η is the penalty function multiplier update coefficient, which is generally not less than 1.

[0080] 4) Integer judgment: Determine whether all relaxed variables can take the value 1 or 0. If so, end the iteration and output the optimal collaborative programming scheme; otherwise, restore all decision variables relaxed to the interval [0,1] in the constraints to variables that can only take the value 0 or 1 and return to step 2).

[0081] Furthermore, MATLAB's YALMIP was used for modeling, and the GUROBI solver was called to solve the problem.

[0082] A distributed collaborative planning system for distribution networks and microgrids based on an improved objective cascade analysis method includes:

[0083] The distribution network model building module is configured to consider the distribution network topology and security 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, and with the goal of minimizing the total planning cost of the microgrid itself, establish a lower-level multi-microgrid planning model to obtain a collaborative planning model for the distribution network and the microgrid.

[0085] The model coupling solution module is configured to use the improved objective cascade analysis method to solve the collaborative planning model of distribution network and microgrid to obtain the optimal collaborative planning scheme.

[0086] A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described 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, wherein the processor implements various instructions; and the computer-readable storage medium stores multiple instructions adapted to be loaded and executed by the processor using the aforementioned distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method.

[0088] The beneficial effects of this invention are:

[0089] This invention aims to address the power grid planning problem in the context of multiple planning entities in the future. It leverages the role of microgrids in integrating various distributed energy sources, coordinating planning resources between microgrids and distribution networks, and reducing planning costs for each planning entity. The method of this invention can rationally plan the power generation resources within the distribution network and microgrid. It achieves collaborative planning using only limited information, without requiring global equipment information, protecting the privacy of each planning entity's information while improving the economics of each entity's plan. The improved target cascade analysis method can accelerate the initial iteration speed when dealing with large-scale collaborative planning models. This planning method can serve as an effective reference for the collaborative planning of distribution networks and multiple microgrids in the context of large-scale microgrid integration into the distribution network in the future. Attached Figure Description

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

[0091] Figure 2 This is a typical daily wind and solar power output and load level curve in an embodiment of the present invention.

[0092] Figure 3 This is the solution process for collaborative planning of micro-distribution networks in this embodiment of the invention.

[0093] Figure 4 The figure shows the interaction variable iteration curves of microgrid 3 and distribution network in an embodiment of the present invention over 18 hours.

[0094] Figure 5 This is the iterative curve of the total cost of micro-allocation collaborative planning in an embodiment of the present invention. Detailed Implementation

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

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

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

[0098] 2) Considering the ability of microgrids to aggregate various internal resources, and 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 collaborative planning model for the distribution network and microgrids;

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

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

[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 construction cost of the distribution network and the annual simulated operation cost of the distribution network. The annual construction cost is equal to the annual value of the cost of building distributed energy storage in the distribution network. The annual simulated operation cost includes the cost of the distribution network 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.

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

[0103] Specifically:

[0104] A distribution network planning model is constructed with the objective function of minimizing the sum of the annual energy storage construction cost and the annual operating cost of the distribution network. This distribution network planning model is a second-order cone programming model, and its objective function can be expressed as:

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

[0106]

[0107]

[0108] In the formula, F dnFor the total planned cost of the distribution network, F dn,inv For the annual investment cost of energy storage in the distribution network, F dn,opt Annual operating cost of the power distribution network; These are the costs of purchasing electricity from the main grid, the cost of load shedding in the distribution network, 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 These represent the set of nodes to be built for energy storage, 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, respectively; r is the discount rate; T ess For the lifespan of energy storage; c ess Cost per unit capacity of energy storage; c represents the configured energy storage capacity of the distribution network at node i; grid c cut c loss c buy c sell These are the unit costs of purchasing electricity from the main grid, the unit costs of load shedding, the unit costs of grid losses, the unit costs of purchasing electricity from the distribution network to the microgrid, and the unit costs of selling electricity from the distribution network to the microgrid. These are the electricity purchased by the distribution network from the main grid, the amount of load shedding, and the electricity traded with the same microgrid. It can be positive or negative. A positive value indicates that the distribution network is purchasing electricity from the microgrid, while a negative value indicates that the distribution network is selling electricity to the microgrid. + To select non-negative numbers, i.e. I l and r l These represent the magnitude of the current flowing through line l and the resistance of line l, respectively.

[0109] The constraints of the power distribution network planning model include:

[0110] (1) Node power balance constraints

[0111] For any node i, i.e. All satisfied

[0112]

[0113] In the formula, and These are the subsets of routes starting from node i and the subsets of routes ending at node i, respectively; P l and Q l These represent the active power and reactive power emitted from the beginning of line l, respectively; r l and x l These represent the resistance and reactance of line l, respectively; and These represent the active load and reactive load at node i, respectively. The reactive power that the distribution network obtains from the main grid; The active power flowing into node i from various distributed power sources; and These represent the discharge power and charging power of the energy stored at node i, respectively.

[0114] (2) Branch power constraints

[0115] For any branch l, i.e. All satisfied

[0116]

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

[0118] (3) Node voltage constraints

[0119]

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

[0121] (4) Branch current constraint

[0122]

[0123] In the formula, This represents the maximum current that can flow through branch l.

[0124] (5) Constraints of energy storage devices

[0125]

[0126] 0≤P ch ≤P ch,max (64)

[0127]

[0128] In the formula, P dis,max and P ch,max These are the upper limits for charging and discharging energy storage devices, respectively. γ represents the energy storage capacity of the energy storage device; γ represents the charging efficiency of the energy storage device.

[0129] (6) Power interaction constraints with microgrids

[0130]

[0131] In the formula, This represents the upper limit for the interaction of electrical energy between the distribution network and the microgrid; and These represent the electricity purchased by the distribution network from the microgrid and the electricity sold to the microgrid, respectively. The 0-1 variables represent the state of the power distribution network purchasing and selling electricity to the microgrid. Constraints (21)-(24) achieve linearization of equation (7).

[0132] (7) Load shedding constraint

[0133]

[0134] In step 2), considering the microgrid's ability to aggregate various internal resources, and with the goal of minimizing the total planning cost of the microgrid itself, a lower-level multi-microgrid planning model is established:

[0135] The goal of the microgrid planning model is to minimize the annual comprehensive cost within the microgrid planning period, including the annual construction cost and the annual simulated operation cost of the microgrid. The annual construction cost is equal to the annual value of the cost of configuring gas turbines and energy storage devices within the microgrid. The annual simulated operation cost of the microgrid includes load shedding costs, flexible load participation in demand response costs, and electricity purchase and sale costs within 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 aforementioned approach considers the microgrid's ability to aggregate various internal resources and aims to minimize the total planning cost of the microgrid itself. A lower-level multi-microgrid planning model is established, 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 planning problem. The microgrid planning model is constructed with the objective function of minimizing the sum of the annual construction cost and annual operating cost of the microgrid. The objective function of the microgrid planning model can be expressed as:

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

[0141]

[0142] In the formula, F mg For the total planned cost of the microgrid, F mg,inv The cost of microgrid equipment includes the cost of energy storage devices and gas turbines. mg,optThe annual operating cost of a microgrid; These are the costs of load shedding in a microgrid, the cost of transferable electrical load, and the cost of purchasing and selling electricity within the same distribution network; Ω Dev This refers to the collection of equipment deployed within a microgrid, including gas turbines and energy storage devices, i.e., Ω. Dev ={mt,ess};T i For the lifespan of the equipment i; c i Configuration cost per unit capacity of device i; E i,max The configured capacity of device i; c tr c' buy c' sell These are the unit compensation cost for transferable electrical loads, the unit cost of the microgrid purchasing electricity from the distribution network, and the unit cost of the microgrid selling electricity to the distribution network; These represent the load shedding, load transfer, and electricity traded between the microgrid and the distribution network at time t, respectively. A positive value indicates that the microgrid sells electricity to the distribution network, while a negative value indicates 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) In formula P mt The output of the gas turbine. The constraints for the energy storage device can be found in constraints (16)-(20).

[0145] (2) Power interaction constraints with distribution network

[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 )P dn,max (83)

[0150] In the formula, P dn,max This represents the upper limit of energy interaction between the microgrid and the distribution network. dn,buy With P dn,sell These represent the electricity purchased by the microgrid from the distribution network and the electricity sold to the distribution network, respectively; s dnLet be the 0-1 variables representing the state of the microgrid purchasing and selling electricity to the distribution network. Constraints (33)-(36) linearize equation (31).

[0151] (3) Power balance constraints within microgrids

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

[0153] In the formula, P load For the internal load of the microgrid; P der For the output of renewable energy within the microgrid; P dis and P ch These represent the discharge power and charging power of the energy storage devices within the microgrid, respectively.

[0154] (4) Load shedding constraint

[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 objective cascade analysis method is used to solve the collaborative planning model of the distribution network and microgrid:

[0160] The original collaborative planning model was a two-layer model: the upper layer was the distribution network planning model, and the lower layer was the planning model for each microgrid. The upper and lower layers were coupled based on consistency constraints, where the consistency constraints stipulated that the interaction power of the distribution network and microgrids on the tie lines must be consistent. First, the consistency constraints were introduced into the objective function of each planning model in the form of a penalty function, thus decoupling the two-layer model. Then, the decoupled sub-problems of each layer were solved in parallel. Once the convergence condition was met, the optimal planning scheme was obtained. Because the collaborative planning model for the distribution network and microgrids is relatively complex, an improvement was made to the traditional objective cascade analysis method. First, the 0-1 variables in the model were relaxed to continuous variables between 0 and 1 to accelerate the iteration speed in the early stages. Once the convergence condition was met, 0-1 variable constraints were added for iterative solving.

[0161] Specifically:

[0162] The collaborative planning models of the distribution network and microgrid are decoupled by introducing Lagrange augmented penalty functions with respect to consistency constraints into the distribution network planning model and the microgrid planning model, respectively; then, the decoupled collaborative planning models of the distribution network and microgrid are solved using the improved objective cascade analysis method.

[0163] The consistency constraint is:

[0164]

[0165] In the formula, ΔP i This represents the difference between the target value of the interactive power 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] In the formula, ⊙ represents the Hamada product; λ i,t and ω i,t These are the multipliers of the linear and quadratic terms in the penalty function, respectively; This represents the target value of the interactive power in the distribution network planning model. This represents the target value of interactive power in the microgrid planning model.

[0171] The method of solving the decoupled distribution network and microgrid collaborative planning model using the improved objective cascade analysis method includes the following steps:

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

[0173] 2) Parallel solution by each planning entity: The distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning schemes and optimal interaction power; the distribution network uses the optimal interaction power as the expected value of the interaction power. The data is sent to each microgrid, and each microgrid uses the optimal interactive power as the expected interactive power value. Send the data to the distribution network to obtain the distribution network cost value for the k-th iteration. Microgrid cost Distribution network interactive power value Interaction value with microgrid power

[0174] 3) Convergence check: If the convergence condition is met, proceed 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] In the formula, ε1 and ε2 are the convergence thresholds;

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

[0179]

[0180] In the formula, η is the update coefficient of the penalty function multiplier;

[0181] 4) Integer judgment: Determine whether the relaxed 0-1 variable takes the form of 0 or 1. If so, end the iteration and output the optimal collaborative programming solution; otherwise, replace the relaxed continuous variable type with the original 0-1 variable type in the constraints and return to step 2).

[0182] Before the iteration begins, the improved target cascade analysis method in this invention relaxes the decision variables that originally could only take values ​​of 0 or 1 into variables that can take any value in the interval [0,1]. After relaxation, the decision variables can take values ​​of 0 or 1 as well as decimals between 0 and 1. After the convergence condition is met, it is judged whether all these relaxed variables take values ​​of 0 or 1. If they do, 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 values ​​of 0 or 1 in the constraint conditions and returned. The methods to replace the relaxed continuous variable type back to the original 0-1 variable type include: (1) modifying the decision variable type back to 0-1 variable; (2) adding constraints that only take values ​​of 0 or 1 in the original constraints. For example, if x is a relaxed decision variable in the interval [0,1], 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 collaborative planning model for distribution networks and microgrids is a multi-agent, hierarchical collaborative optimization model with a large overall scale. Both the distribution network layer planning model and each microgrid layer planning model contain a large number of 0-1 variables. As the number of 0-1 variables in the optimization problem increases, the complexity of the problem also increases, the solution space grows exponentially, and more combination possibilities need to be considered, leading to a slower solution speed. Traditional cascaded objective analysis is an iterative algorithm that decomposes the problem by introducing an augmented Lagrange penalty function into the objective function of each layer model and increasing the penalty function value in each iteration, making the interaction power between layers tend to be consistent. Based on this characteristic, in the early stages of iteration, the 0-1 integer variables can be relaxed to continuous variables between 0 and 1. Relaxing the 0-1 integer variables to continuous variables between 0 and 1 makes the problem more consistent with the properties of convex optimization. Convex optimization has many efficient solution algorithms, and relaxing the 0-1 variables to continuous variables simplifies the complexity of the problem, which speeds up the early iteration speed. After the convergence condition is met, the 0-1 variable constraints are considered, ensuring the consistency of the planning model with the one before relaxation. This invention addresses the collaborative planning problem of distribution networks and microgrids by constructing a two-layer planning model that balances the interests of all planning stakeholders. An improved objective cascade analysis method considering relaxed 0-1 integer variables accelerates the model's solution speed. This method also achieves consistent power interaction among stakeholders without requiring global device information, thus realizing collaboration and ensuring the global optimum of the total collaborative planning cost with high accuracy. As microgrids develop and the number of connected microgrids increases, the scale and complexity of the collaborative planning model will grow, and global device information will become increasingly difficult to obtain. Solving the problem using the improved objective cascade analysis method accelerates the model's solution speed, eliminates the need for stakeholders to share internal device parameters, lowers the requirements for data communication reliability, and protects the privacy of each planning stakeholder.

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

[0185] Taking a modified IEEE 33-node power distribution system as an example, its topology is as follows: Figure 1 As shown in the figure. Nodes 5, 8, and 9 are equipped with distributed photovoltaic power of 300kW, 150kW, and 450kW respectively; nodes 28, 29, and 30 are equipped with distributed wind turbines of 300kW, 300kW, and 150kW respectively; nodes 6, 9, 13, and 29 are locations awaiting installation of distributed energy storage devices; and nodes 3, 17, and 20 are microgrid access nodes. The wind and solar power output and load curves for each time period on the selected typical day are shown in the figure. Figure 2 As shown.

[0186] The model was built using MATLAB's YALMIP library, and the two-level collaborative planning model was solved using the GUROBI solver based on an improved objective cascade analysis method. The solution process for the micro-distribution network collaborative planning is shown below. Figure 3 .

[0187] Figure 4 The convergence of the interaction power between microgrid 3 and the distribution network at 18 hours on a typical day is presented. As can be seen from the figure, based on the improved objective cascade analysis method, the model solution converges after 19 iterations, and the interaction power between microgrid 3 and the distribution network tends to be consistent, proving the convergence of the proposed algorithm.

[0188] Figure 5 The iterative curves for the total cost of microgrid collaborative planning are presented. The figures show that the total cost of distributed planning based on both the objective cascade analysis method and the improved objective cascade analysis method is close to that of centralized planning. However, the distributed planning method only requires the two planning parties to exchange boundary interaction power information and multiplier information to achieve collaboration, without needing to obtain global planning information. The solution process based on the objective cascade analysis method involved 18 iterations, but its total time was nearly 100 seconds longer than that using the improved objective cascade analysis method. This is because the improved objective cascade analysis method relaxes 0-1 variables into continuous variables in the early stages of iteration, which speeds up the speed of each iteration. In the future, as a large amount of renewable energy is connected to the distribution network in the form of microgrids, the amount of equipment data will increase. Using a distributed method based on the improved objective cascade analysis method to solve the microgrid collaborative planning problem will better meet the needs of practical engineering.

[0189] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] The above specific embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A distributed collaborative planning method for distribution networks and microgrids based on an improved objective cascade analysis method, characterized in that... This includes the following steps: 1) Considering the distribution network topology and security constraints, and with the goal of minimizing the total cost of distribution network planning, establish an upper-level distribution network planning model; 2) Considering the microgrid's ability to aggregate various internal resources, and 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 collaborative planning model for the distribution network and the microgrid. 3) An improved objective cascade analysis method is used to solve the collaborative planning model of the distribution network and microgrid to obtain the optimal collaborative planning scheme; this includes: decoupling the collaborative planning models of the distribution network and microgrid by introducing Lagrange augmented penalty functions with respect to consistency constraints into the distribution network planning model and the microgrid planning model respectively; and then solving the decoupled collaborative planning model of the distribution network and microgrid using the improved objective cascade analysis method. The method of solving the decoupled distribution network and microgrid collaborative planning model using the improved objective cascade analysis method includes the following steps: 1) Initialization: Set the number of iterations. Given the penalty function multiplier value at the first iteration. and Given an initial value for the expected interaction power. and Modify the constraints to relax the 0-1 variables in the model into continuous variables in the interval [0-1]. 2) Parallel solution by each planning entity: The distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning schemes and optimal interaction power; the distribution network uses the optimal interaction power as the expected value of the interaction power. The data is sent to each microgrid, and each microgrid uses the optimal interactive power as the expected interactive power value. Send to the distribution network, and get the first Distribution network cost value of the next iteration Microgrid cost value Distribution network interactive power value Interaction value with microgrid power ; 3) Convergence check: If the convergence condition is met, proceed to step 4); otherwise, update the penalty function multiplier and set... And return to step 2); The convergence condition is: ， ， In the formula, and This is the convergence threshold; The method for updating the penalty function multiplier is as follows: ， ， In the formula, The penalty function multiplier is used to update the coefficients; 4) Integer judgment: Determine whether all relaxed variables have taken the values ​​1 or 0. If so, end the iteration and output the optimal collaborative programming scheme; otherwise, restore all decision variables relaxed to the [0,1] interval in the constraints to variables that can only take the values ​​0 or 1 and return to step 2).

2. The distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method according to claim 1, characterized in that, The above-mentioned upper-level distribution network planning model, considering distribution network topology and security constraints, aims to minimize the total cost of distribution network planning and includes: A distribution network planning model is constructed with the objective function of minimizing the sum of the annual energy storage construction cost and the annual operating cost of the distribution network. The objective function of the distribution network planning model can be expressed as: ， ， ， ， ， ， ， In the formula, For the total planned cost of the distribution network, The annual investment cost of energy storage in the power distribution network, Annual operating cost of the power distribution network; , , , These are the costs of purchasing electricity from the main grid, the costs of shedding loads from the distribution network, network losses, and the costs of purchasing and selling electricity to microgrids. , , , , These are the sets of energy storage nodes to be built, the main grid nodes, all distribution network nodes, all distribution network lines, and the microgrid nodes, respectively. The discount rate; The lifespan of the energy storage; Cost per unit capacity of energy storage; For the distribution network at the node The configuration capacity of the energy storage system; , , , , These are the unit costs of purchasing electricity from the main grid, the unit costs of load shedding, the unit costs of grid losses, the unit costs of purchasing electricity from the distribution network to the microgrid, and the unit costs of selling electricity from the distribution network to the microgrid. , , These are respectively the amount of electricity purchased by the distribution network from the main grid, the amount of load shedding, and the amount of electricity purchased and sold within the same microgrid; and The lines are respectively The magnitude of the current flowing through the circuit The resistance.

3. The distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method according to claim 1, characterized in that, The constraints of the power distribution network planning model include: (1) Node power balance constraints For any node ,Right now All are satisfied ， ， In the formula, and Each is based on a node A subset of routes starting from a node and a subset of routes starting from a node A subset of routes whose endpoint is [the destination]. and The lines are respectively The active and reactive power emitted at the origin; and They represent the lines respectively. Resistance and reactance; and They are nodes Active and reactive loads at the location; The reactive power that the distribution network obtains from the main grid; For various distributed power sources flowing into nodes The active power; and They are nodes The discharge power and charging power of the energy storage device; (2) Branch power constraints For any line ,Right now All are satisfied ， ， ， ， In the formula, and The lines are respectively The set of starting nodes and the set of ending nodes; For nodes Voltage amplitude at the location; (3) Node voltage constraints ， In the formula, and Representing nodes respectively The lower and upper limits of the voltage amplitude at the location; (4) Branch current constraint ， In the formula, Indicates the line The maximum value of the current that can flow; (5) Constraints of energy storage devices ， ， ， ， ， In the formula, and These are the upper limits for charging and discharging energy storage devices, respectively. The energy storage capacity of the energy storage device; The charging efficiency of energy storage devices; (6) Power interaction constraints with microgrids ， ， ， ， In the formula, This represents the upper limit for the interaction of electrical energy between the distribution network and the microgrid; and These represent the electricity purchased by the distribution network from the microgrid and the electricity sold to the microgrid, respectively. A 0-1 variable representing the power purchase and sale status of the distribution network to the microgrid; (7) Load shedding constraint 。 4. The distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method according to claim 1, characterized in that, The aforementioned approach considers the microgrid's ability to aggregate various internal resources and aims to minimize the total planning cost of the microgrid itself. A lower-level multi-microgrid planning model is established, including: A microgrid planning model is constructed with the objective function of minimizing the sum of the annual construction cost and the annual operating cost of the microgrid. The objective function of the microgrid planning model can be expressed as: ， ， ， ， ， ， In the formula, The total planning cost of the microgrid. The cost of microgrid equipment includes the cost of energy storage devices and gas turbines. Annual operating cost of microgrid; , , These are the costs of microgrid load shedding, transferable electrical load, and electricity purchase and sale costs within the same distribution network. This refers to the collection of equipment deployed within a microgrid, including gas turbines and energy storage devices, i.e. ; For the construction of equipment Lifespan; For equipment The unit capacity configuration cost; For equipment Configuration capacity; , , These are the unit compensation cost for transferable electrical loads, the unit cost of the microgrid purchasing electricity from the distribution network, and the unit cost of the microgrid selling electricity to the distribution network; , , For microgrids The amount of load shedding, the amount of load transfer, and the amount of electricity purchased and sold between the microgrid and the distribution network at any given time.

5. A distributed collaborative planning method for distribution networks and microgrids based on an 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 ， In the formula, For the output of the gas turbine; (2) Power interaction constraints with the distribution network ， ， ， ， In the formula, This represents the upper limit for energy interaction between the microgrid and the distribution network; and These represent the electricity purchased by the microgrid from the distribution network and the electricity sold to the distribution network, respectively. This represents a 0-1 variable indicating the state of the microgrid's power purchase and sale to the distribution network; (3) Power balance constraints within the microgrid ， In the formula, For the internal load of the microgrid; To power renewable energy sources within the microgrid; and These are the discharge power and charging power of the energy storage devices inside the microgrid, respectively. (4) Load shedding constraint ， (5) Transferable load constraints ， 。 6. The distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method according to claim 1, characterized in that, The consistency constraint is: ， In the formula, Indicates the target value of interactive power on the distribution network side and the microgrid. The difference in the side-to-side interactive power response values ​​indicates the degree of inconsistency in the response. The objective function of the decoupled distribution network planning model is: ， The objective function of the decoupled microgrid planning model is: ， In the formula, For Hamada; and These are the multipliers of the linear and quadratic terms in the penalty function, respectively; This represents the expected value of interactive power in the distribution network planning model. This represents the expected value of interactive power in the microgrid planning model.

7. A distributed collaborative planning system for distribution networks and microgrids based on an improved objective cascade analysis method, characterized in that, include: The distribution network model building module is configured to consider the distribution network topology and security 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, and with the goal of minimizing the total planning cost of the microgrid itself, establish a lower-level multi-microgrid planning model to obtain a collaborative planning model for the distribution network and the microgrid. The model coupling solution module is configured to solve the distribution network and microgrid collaborative planning model using an improved objective cascade analysis method to obtain the optimal collaborative planning scheme. This includes: decoupling the distribution network and microgrid collaborative planning models by introducing Lagrange augmented penalty functions related to consistency constraints into the distribution network planning model and the microgrid planning model respectively; and then solving the decoupled distribution network and microgrid collaborative planning model using the improved objective cascade analysis method. The method of solving the decoupled distribution network and microgrid collaborative planning model using the improved objective cascade analysis method includes the following steps: 1) Initialization: Set the number of iterations. Given the penalty function multiplier value at the first iteration. and Given an initial value for the expected interaction power. and Modify the constraints to relax the 0-1 variables in the model into continuous variables in the interval [0-1]. 2) Parallel solution by each planning entity: The distribution network and each microgrid independently solve their planning problems to obtain their respective optimal planning schemes and optimal interaction power; the distribution network uses the optimal interaction power as the expected value of the interaction power. The data is sent to each microgrid, and each microgrid uses the optimal interactive power as the expected interactive power value. Send to the distribution network, and get the first Distribution network cost value of the next iteration Microgrid cost value Distribution network interactive power value Interaction value with microgrid power ; 3) Convergence check: If the convergence condition is met, proceed to step 4); otherwise, update the penalty function multiplier and set... And return to step 2); The convergence condition is: ， ， In the formula, and This is the convergence threshold; The method for updating the penalty function multiplier is as follows: ， ， In the formula, The penalty function multiplier is used to update the coefficients; 4) Integer judgment: Determine whether all relaxed variables have taken the values ​​1 or 0. If so, end the iteration and output the optimal collaborative programming scheme; otherwise, restore all decision variables relaxed to the [0,1] interval in the constraints to variables that can only take the values ​​0 or 1 and return to step 2).

8. A computer-readable storage medium, characterized in that, It stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device according to any one of claims 1-6, the distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method.

9. A terminal device, characterized in that, It includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; and the computer-readable storage medium is used to store multiple instructions, which are adapted to be loaded by the processor and executed by the processor according to any one of claims 1-6, the distributed collaborative planning method for distribution networks and microgrids based on the improved target cascade analysis method.

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