A wind-solar-storage collaborative planning method considering evolution of power transmission and distribution network morphology

Abstract

The application discloses a kind of wind light storage collaborative planning method considering the evolution of power transmission and distribution network, belong to the field of power transmission and distribution network optimization planning.The method includes: respectively with the optimal economy of power transmission and distribution network as target, considering system safety and stability operation constraint, constructs the wind light storage planning model of power transmission network based on direct current flow and the wind light storage planning model of distribution network based on alternating current flow;Considering the coupling interaction between power transmission network and distribution network, a wind light storage planning model for power transmission and distribution collaboration based on second-order cone relaxation is constructed;By analyzing the multiple driving factors of the evolution of power transmission and distribution network, the evolution path and typical stage characteristics of power transmission and distribution network are determined;A fast solution architecture based on heterogeneous decomposition is proposed;The wind light storage planning model for power transmission and distribution collaboration is solved, and the global optimal planning scheme of the system is obtained.The planning strategy proposed in the application can realize effective collaboration of power transmission and distribution network, and improve the foresight and application value of the system planning scheme.

Description

Technical Field

[0001] This invention belongs to the field of power transmission and distribution network optimization planning, and more specifically, relates to a wind-solar-storage coordinated planning method that takes into account the evolution of power transmission and distribution network morphology. Background Technology

[0002] New energy power generation is characterized by its environmental friendliness, low carbon emissions, and abundant resources, and has received widespread attention from academia and industry in recent years. However, considering the inherent physical properties and dynamic characteristics of new energy power generation, large-scale integration of new energy sources will lead to increased system randomness, volatility, and uncertainty, posing a severe challenge to the safe, stable, clean, and efficient operation of the power grid.

[0003] Existing research has provided a foundation for the coordinated optimization of transmission and distribution networks. However, current research mainly focuses on optimal power flow and day-ahead scheduling, failing to fully consider the impact of transmission and distribution network coupling and interaction on power grid planning results. Furthermore, current power grid planning research primarily focuses on the current power grid configuration, with little discussion on the impact of transmission and distribution network configuration evolution driven by multiple factors on system planning strategies, lacking specificity and forward-looking perspective. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a wind-solar-storage coordinated planning method that takes into account the evolution of the transmission and distribution network. Its purpose is to solve the problems that the existing technology does not fully take into account the impact of the evolution of the transmission and distribution network, lacks transmission and distribution coordination at the planning level, and lacks foresight in the planning scheme.

[0005] To achieve the above objectives, this invention provides a wind-solar-storage coordinated planning method that takes into account the evolution of power transmission and distribution network configurations, comprising the following steps:

[0006] S1. With the goals of optimizing the economic efficiency of the power transmission network and maximizing the absorption of new energy sources, and considering the constraints of DC power flow and the safe and stable operation of the system, a planning model for wind, solar and energy storage in the power transmission network is established.

[0007] S2. With the goal of optimizing the economic efficiency of the distribution network and maximizing the absorption of new energy sources, and considering the constraints of AC power flow and the safe and stable operation of the system, a wind-solar-storage planning model for the distribution network is established.

[0008] S3. Considering the coupling and interaction of the transmission and distribution networks, establish a transmission and distribution coordinated wind, solar and energy storage planning model based on second-order cone relaxation, based on the transmission network wind, solar and energy storage planning model and the distribution network wind, solar and energy storage planning model.

[0009] S4. Analyze the multiple driving factors of the evolution of the power transmission and distribution network morphology, and determine the evolution path of the power transmission and distribution network morphology and its typical stage characteristics;

[0010] S5. Determine the fast solution architecture for the model based on heterogeneous decomposition;

[0011] S6. Input the basic structural parameters and source-load characteristic parameters of the power transmission and distribution network. Use the typical stage characteristics of the power transmission and distribution network morphology evolution path determined in step S4 as the boundary condition input for solving the power transmission and distribution coordinated wind, solar and energy storage planning model. Solve the power transmission and distribution coordinated wind, solar and energy storage planning model according to the model fast solution architecture based on heterogeneous decomposition determined in step S5, and obtain the global optimal planning scheme of the system.

[0012] Furthermore, the objective function of the power grid wind-solar-storage planning model is expressed as follows:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power transmission network. These represent the power generation cost of the transmission network, the power flow return cost, the equipment installation cost, and the penalty for wind and solar curtailment, respectively. These represent the coefficients of the quadratic, primary, and constant terms of the power generation cost of thermal power units in the power transmission network, respectively. Let these represent the set of generator nodes in the transmission network, the set of root nodes connected to the distribution network, and the set of all nodes in the transmission network, respectively. These represent the output power of the thermal power unit and the power transmitted to the distribution network at time t at node i, respectively. This represents the electricity price sold by the power grid at node i at time t. These represent the equipment depreciation factor and the power grid curtailment penalty factor, respectively. These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the power transmission network, respectively. These represent the total amount of wind and solar power curtailed by the power grid, respectively.

[0019] Furthermore, the objective function of the power distribution network wind-solar-storage planning model is expressed as follows:

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power distribution network. These represent the power purchase cost of the distribution network, the equipment installation cost, and the penalty for wind and solar curtailment, respectively. The power purchased at time t represents the power of the node connected to the distribution network and the transmission network. For ease of analysis, this invention only considers the scenario where only one node in the distribution network is connected to the transmission network. This indicates the node where the distribution network connects to the upstream transmission network; the depreciation factor for distribution network equipment is consistent with that of the transmission network. This represents the wind and solar power curtailment coefficient of the power distribution network; These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the distribution network, respectively. These represent the total amount of wind and solar power curtailed in the power distribution network, respectively.

[0025] Furthermore, step S3 includes the following steps:

[0026] S31. For the non-convex terms of the model introduced by the AC power flow constraints of the distribution network, the model non-convex constraint transformation is performed by second-order cone relaxation to obtain the convex optimization model of wind, solar and storage of the distribution network.

[0027] S32. Considering the coupling and interaction of the transmission and distribution networks, and aiming at optimizing the overall system economy and clean energy consumption, a wind-solar-storage collaborative planning model for the transmission and distribution networks is established, taking into account both the optimal operation constraints and tie-line constraints of the transmission and distribution networks. The optimization objective of the wind-solar-storage collaborative planning model is the sum of the optimization objectives of the transmission network wind-solar-storage planning model and the distribution network wind-solar-storage planning model. The constraints are the union of the constraints of the transmission network wind-solar-storage planning model and the distribution network wind-solar-storage planning model. The expression form of the wind-solar-storage collaborative planning model is as follows:

[0028] .

[0029] Furthermore, the wind-solar-storage collaborative planning model for the power transmission and distribution network satisfies constraints on system construction capacity, active power balance, new energy units, energy storage systems, and system safe and stable operation.

[0030] Furthermore, the constraints satisfied by the wind-solar-storage coordinated planning model for the power transmission and distribution network are as follows:

[0031] (1) System capacity constraints

[0032] Due to limitations imposed by natural conditions and the physical characteristics of the generating units, the installed capacity of wind power, photovoltaic, and energy storage units must meet the following constraints:

[0033]

[0034] In the formula, These represent the upper limits of the installed capacity of wind power, photovoltaic, and energy storage units at node i, respectively.

[0035] (2) Active power balance constraint

[0036] Considering the structural characteristics of the transmission network, the transmission network optimization model takes into account the active power balance constraints of the system, and its expression is as follows:

[0037]

[0038] In the formula, Let i and t represent the output of the wind turbine, photovoltaic unit, energy storage unit, and load of the system at time t. Considering that the energy storage power station may have two states, charging and discharging, it is assumed that the output of the energy storage system is negative when it is charging and positive when it is discharging. The energy storage system can only be in one of the charging and discharging states at any time. Represents the set of power transmission network branches; This represents the network loss of branch l of the transmission network at time t;

[0039] (3) Constraints of new energy units

[0040] The output of wind power and photovoltaic units in the power transmission network must meet the following constraints:

[0041]

[0042] In the formula, These represent the maximum available power of wind power and photovoltaic units at node i of the transmission network at time t, respectively.

[0043] (4) Constraints of energy storage systems

[0044] The constraints on energy storage units mainly include charge / discharge power constraints, capacity constraints, and state of charge constraints:

[0045]

[0046] In the formula, , These represent the active power and installed power of the energy storage system at time t, respectively, at node i. This indicates the installed capacity of the energy storage system at node i of the power transmission network; This represents the State of Charge (SOC) of the energy storage system at time t at node i. This represents the charging / discharging efficiency of the energy storage system, where the charging and discharging efficiencies are reciprocals of each other. Indicates the upper / lower limits of the SOC of the energy storage system;

[0047] (5) Constraints on safe and stable operation of the system

[0048] The power transmission network optimization model must meet the constraints for safe and stable system operation, mainly including node voltage constraints, branch current carrying constraints, and minimum start-up and shutdown time constraints, namely:

[0049]

[0050]

[0051]

[0052] In the formula, This represents the voltage magnitude of the transmission network node at the i-th node within time period t. These represent the upper and lower limits of the voltage at the transmission network nodes, respectively. This indicates the current carrying capacity of branch l in the transmission network. This indicates the maximum current carrying capacity of branch line l; These represent the start-up time and shutdown time of the i-th generating unit in the power transmission network, respectively. These represent the minimum start / stop time for thermal power units in the power transmission network.

[0053] Furthermore, the evolution path of the power transmission and distribution network is as follows: the power transmission and distribution network is divided into three stages according to the differences in the forms of source-grid-load-storage: the nascent stage, the development stage, and the mature stage. The typical characteristics of each stage are used as the boundary conditions input for solving the power transmission and distribution coordinated wind, solar and storage planning model.

[0054] Furthermore, step S5, based on the heterogeneous decomposition model for rapid solution, specifically involves decomposing the transmission and distribution coordinated planning problem into a transmission network optimization sub-problem and a distribution network optimization sub-problem, and using the iterative interaction of grid boundary quantities to obtain the global optimal solution of the system.

[0055] Furthermore, the optimization process for quickly solving the architecture based on heterogeneous decomposition is as follows:

[0056] Step 1: Program initialization, load data import, initialization of transmission and distribution network structure and basic variables, iteration number ite=1;

[0057] Step 2: Determine the typical stages and quantitative characteristics of the power transmission and distribution network under study, and obtain the boundary conditions of the optimization program;

[0058] Step 3: Optimize the planning layer model solution to obtain the site selection and capacity determination scheme for wind, solar and energy storage in the power transmission and distribution network;

[0059] Step 4: Run the scheduling layer model to solve the problem, obtain the optimal power output scheme of multiple types of units in the power transmission and distribution network, and determine the coupling power matrix of the power transmission and distribution network throughout the time period;

[0060] Step 5: Solve for the electricity sales price of the transmission network throughout the day. This price is represented by the marginal electricity price of the transmission network nodes based on Lagrange multipliers, including the marginal generation cost, loss cost, and network congestion cost. Then, construct the convergence judgment condition of the model and iterate the program until the algorithm converges.

[0061] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0062] 1. Compared with traditional transmission and distribution cooperative optimization algorithms based on DC power flow, the cooperative planning model established in this invention can fully consider the structural characteristics of the transmission and distribution network, ensuring good computational efficiency and convergence ability of the model while ensuring solution accuracy;

[0063] 2. The architecture proposed in this invention can fully take into account the coordinated interaction of transmission and distribution networks, and promote the complementary advantages of clean energy and flexible and controllable resources in the face of system uncertainties;

[0064] 3. Compared to traditional power grid planning models, the collaborative planning strategy proposed in this invention can effectively characterize the impact of the evolution of the transmission and distribution network morphology and formulate differentiated optimal planning schemes for different stages of the network morphology evolution. It has broad application prospects for future power grid morphologies where information and data coupling in transmission and distribution networks is becoming increasingly tight and the proportion of clean energy and energy storage in the system is continuously increasing. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the process of solving the model based on the heterogeneous decomposition model fast solution architecture of the present invention;

[0066] Figure 2 This is a structural diagram of the T6D7D9 system according to an embodiment of the present invention;

[0067] Figure 3 This refers to the power interaction quantity of one node in the distribution network at each typical stage of the embodiments of the present invention.

[0068] Figure 4 This invention relates to the power output of various types of generating units during the development phase of the distribution network 1 in this embodiment.

[0069] Figure 5 This is the output power of multiple types of generating units in the mature stage of the distribution network 1 according to the embodiments of the present invention;

[0070] Figure 6 This refers to the output of the energy storage unit at each typical stage of the system in the embodiments of the present invention;

[0071] Figure 7 This refers to the wind turbine output at each typical stage of the system in the embodiments of the present invention;

[0072] Figure 8 This refers to the output of the photovoltaic unit at each typical stage of the system in the embodiments of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0074] To achieve the above objectives, this invention provides a wind-solar-storage coordinated planning method that takes into account the evolution of power transmission and distribution network configurations, comprising the following steps:

[0075] S1. To achieve optimal economic efficiency of the power transmission network and maximum renewable energy absorption, and considering DC power flow constraints and system safety and stability operation constraints, a wind-solar-storage planning model for the power transmission network is established.

[0076] Specifically, the objective function of the power transmission network wind-solar-storage planning model is expressed as follows:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power transmission network. These represent the power generation cost of the transmission network, the power flow return cost, the equipment installation cost, and the penalty for wind and solar curtailment, respectively. These represent the coefficients of the quadratic, primary, and constant terms of the power generation cost of thermal power units in the power transmission network, respectively. Let these represent the set of generator nodes in the transmission network, the set of root nodes connected to the distribution network, and the set of all nodes in the transmission network, respectively. These represent the output power of the thermal power unit and the power transmitted to the distribution network at time t at node i, respectively. This represents the electricity price sold by the power grid at node i at time t. These represent the equipment depreciation factor and the power grid curtailment penalty factor, respectively. These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the power transmission network, respectively. These represent the total amount of wind and solar power curtailed by the power grid, respectively.

[0083] The power transmission network wind-solar-storage coordinated planning model is divided into optimization planning layer constraints (system construction capacity constraints) and operation and scheduling layer constraints (active power balance constraints, new energy unit constraints, energy storage system constraints, and system safe and stable operation constraints), namely:

[0084] (1) System capacity constraints

[0085] Due to limitations imposed by natural conditions and the physical characteristics of the generating units, the installed capacity of wind power, photovoltaic, and energy storage units must meet the following constraints:

[0086]

[0087] In the formula, These represent the upper limits of the installed capacity of wind power, photovoltaic, and energy storage units at node i, respectively.

[0088] (2) Active power balance constraint

[0089] Considering the structural characteristics of the transmission network, the transmission network optimization model takes into account the active power balance constraints of the system, and its expression is as follows:

[0090]

[0091] In the formula, Let i and t represent the output of the wind turbine, photovoltaic unit, energy storage unit, and load of the system at time t. Considering that the energy storage power station may have two states, charging and discharging, it is assumed that the output of the energy storage system is negative when it is charging and positive when it is discharging. The energy storage system can only be in one of the charging and discharging states at any time. Represents the set of power transmission network branches; This represents the network loss at time t in the l-branch of the transmission network.

[0092] (3) Constraints of new energy units

[0093] The output of wind power and photovoltaic units in the power transmission network must meet the following constraints:

[0094]

[0095] In the formula, These represent the maximum available power of wind power and photovoltaic units at time t, respectively, at node i of the transmission network. This invention characterizes the output characteristics of new energy units using a typical day-based approach; therefore, the maximum available power of these units satisfies the following constraints:

[0096]

[0097] In the formula, These represent the typical per-unit output values ​​for wind power and photovoltaic power at time t, respectively.

[0098] (4) Constraints of energy storage systems

[0099] Energy storage unit constraints mainly include charge / discharge power constraints, capacity constraints, and state of charge (SOC) constraints.

[0100]

[0101] In the formula, , These represent the active power and installed power of the energy storage system at time t, respectively, at node i. This indicates the installed capacity of the energy storage system at node i of the power transmission network; This represents the State of Charge (SOC) of the energy storage system at time t at node i. This represents the charging / discharging efficiency of the energy storage system, where the charging and discharging efficiencies are reciprocals of each other. This indicates the upper / lower limit of the SOC of the energy storage system.

[0102] (5) Constraints on safe and stable operation of the system

[0103] To ensure the safe and stable operation of the power grid, considering factors such as system thermal stability, dynamic stability, and transformer capacity, the transmission network optimization model needs to further satisfy constraints on system safety and stability operation. These mainly include node voltage constraints, branch current carrying constraints, and minimum start-up and shutdown time constraints, namely:

[0104]

[0105]

[0106]

[0107] In the formula, This represents the voltage magnitude of the transmission network node at the i-th node within time period t. These represent the upper and lower limits of the voltage at the transmission network nodes, respectively. This indicates the current carrying capacity of branch l in the transmission network. This indicates the maximum current carrying capacity of branch line l; These represent the start-up time and shutdown time of the i-th generating unit in the power transmission network, respectively. These represent the minimum start / stop time for thermal power units in the power transmission network.

[0108] S2. With the goal of optimizing the economic efficiency of the distribution network and maximizing the absorption of new energy sources, and considering the constraints of AC power flow and the safe and stable operation of the system, a wind-solar-storage planning model for the distribution network is established.

[0109] Specifically, the objective function of the wind-solar-storage planning model for the power distribution network is expressed as follows:

[0110]

[0111]

[0112]

[0113]

[0114] In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power distribution network. These represent the power purchase cost of the distribution network, equipment installation cost, and penalties for wind and solar power curtailment, respectively. The power purchased at time t represents the power of the node connected to the distribution network and the transmission network. For ease of analysis, this invention only considers the scenario where only one node in the distribution network is connected to the transmission network. This indicates the node where the distribution network connects to the upstream transmission network; the depreciation factor for distribution network equipment is consistent with that of the transmission network. This represents the wind and solar power curtailment coefficient of the power distribution network; These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the distribution network, respectively. These represent the total amount of wind and solar power curtailed in the power distribution network, respectively.

[0115] For the optimization planning layer, the constraints of the distribution network and the transmission network are expressed in the same way. For the operation and dispatching layer, the constraints of the distribution network mainly consider power balance constraints, AC power flow constraints, new energy unit constraints, energy storage system constraints, and system safety and stability operation constraints, and the specific expressions are as follows:

[0116] (1) Power balance constraint

[0117]

[0118] In the formula, These represent the output power of wind turbines, photovoltaic units, energy storage system charging and discharging power, active power purchased from the upstream power grid, and active load power of the distribution network at time t at node i, respectively. Indicates the active power of branch l; Let i represent the reactive power of the distribution network SVC and the reactive power of the load at time t, respectively. These represent the active power and reactive power of branch l, respectively. This represents the set of all branches in the distribution network that include node i; This represents the set of all distribution network nodes.

[0119] (2) Constraints of the flow of communication

[0120]

[0121] In the formula, i and j represent the first and last nodes of branch l, respectively; These represent the conductance and susceptance values ​​of branch l in the distribution network, respectively. These represent the voltage amplitude at node i at time t, respectively. This represents the phase angle of branch l at time t in the distribution network.

[0122] For the distribution network, the constraints of new energy generating units, energy storage systems, and system safety and stability operation are expressed in the same way as those of the transmission network, and therefore will not be repeated here. Since this invention does not consider thermal power units in the distribution network, the safety and stability operation constraints of the distribution network do not involve the start-up and shutdown time constraints of thermal power units.

[0123] S3. Considering the coupling and interaction of the transmission and distribution networks, a transmission and distribution coordinated wind, solar and energy storage planning model based on second-order cone relaxation is established based on the transmission network wind, solar and energy storage planning model and the distribution network wind, solar and energy storage planning model.

[0124] For the power transmission and distribution network, in addition to the operational constraints of each subsystem, it must also satisfy the power interaction constraints of the power transmission and distribution network, namely:

[0125]

[0126] In the formula, This represents the power transmission matrix of the power transmission channels in the power transmission and distribution network at time t, and represents the power transmission network optimization variables. and distribution network optimization variables ; This indicates the upper limit of power transmission in the power transmission and distribution network.

[0127] Meanwhile, given that the distribution network wind-solar-storage planning model involves non-convex constraints such as AC power flow constraints, in order to ensure the solvability of the model of this invention, SOCR is further introduced. Through variable definition, equivalent deformation of constraints, and relaxation, the AC power flow constraints of the distribution network are transformed into relaxed second-order cone constraints that can be directly solved by mature commercial software.

[0128] In summary, this invention aims to optimize the overall economic efficiency of the power transmission and distribution network. Considering the operational constraints of the transmission network, the operational constraints of the distribution network, and the transmission-distribution coupling constraints, it constructs a collaborative planning model for wind, solar, and energy storage in the power transmission and distribution network. Its specific expression is as follows:

[0129]

[0130] In the formula, Let Trans represent the total set of transmission network constraints, and Let Dis represent the total set of distribution network constraints. const(Trans), const(Dis), and const(Con) represent the set of constraints for transmission network constraints, distribution network constraints, and tie line constraints, respectively.

[0131] S4. Analyze the multiple driving factors of the evolution of the power transmission and distribution network morphology, and determine the evolution path of the power transmission and distribution network morphology and its typical stage characteristics;

[0132] Compared to traditional power systems, future power systems will undergo significant changes at the source, grid, and load sides, and the roles of various key players in traditional power systems will also change further. Current research on transmission and distribution network planning primarily focuses on the current form, failing to consider changes in the proportion of source and load resources with different types and characteristics, nor analyzing the impact of the changing roles of the transmission and distribution network on planning schemes, thus exhibiting significant limitations. Therefore, it is urgent to conduct research on the typical characteristics of transmission and distribution network morphological evolution driven by multiple factors, in order to determine the optimal power system planning scheme that takes into account the evolution of transmission and distribution network morphology.

[0133] To facilitate the analysis of implementation examples, this invention mainly considers three typical stages of the evolution of power transmission and distribution network morphology: the nascent stage, the development stage, and the mature stage. The multi-faceted driving forces and quantitative characteristics of the typical stages of power grid morphology evolution are analyzed from three dimensions: source, grid, and storage. First, the evolution of the source side is mainly reflected in the transformation of the energy system structure, from a power system dominated by thermal power generation to a power system dominated by new energy sources. Its main characteristic is the continuous increase in the proportion of new energy sources in the system at each typical stage. Simultaneously, the evolution of the grid side is mainly reflected in the gradual transition of the distribution network from a traditional distribution network to an active distribution network. Its role and behavioral characteristics will undergo fundamental changes: the traditional distribution network (nascent stage) can be simply regarded as a load node of the transmission network, while the active distribution network (development and mature stages) may have large-scale distributed new energy sources, and the power system gradually becomes more flattened. Furthermore, unlike the traditional distribution network, the energy data coupling between the active distribution network and the transmission network is closer, thus further highlighting the necessity of carrying out transmission and distribution coordinated planning. Finally, the evolution of energy storage is mainly reflected in the following aspects: compared with the early stage of the power grid, the development and maturity stages need to further consider the impact of flexible regulation resources such as energy storage on the grid; at the same time, considering the development of energy storage technology, as the evolution of the transmission and distribution network continues, the way energy storage is connected to the system will gradually change from the traditional large-scale centralized connection to a simultaneous centralized and distributed connection.

[0134] In summary, this invention summarizes the main quantitative characteristics of each typical stage of the evolution of power transmission and distribution network morphology as follows:

[0135] Table 1. Comparison of the characteristics of the three typical evolution stages of power transmission and distribution networks

[0136]

[0137] It is worth noting that the power flow back cost of the transmission network only exists in the development and nascent stages. Since power flow back is not allowed in the traditional distribution network during the nascent stage, this item is 0.

[0138] S5. To reduce the computational complexity of the model and ensure the global optimality of the model solution, a fast model solution architecture based on heterogeneous decomposition is proposed.

[0139] The upper layer of the model-based fast solution architecture based on heterogeneous decomposition is the optimization planning layer, which determines the installation locations and capacities of wind power, photovoltaic, and energy storage power stations in the transmission and distribution network, and provides feedback on the site selection and capacity determination results to the lower layer. The lower layer is the operation and scheduling layer, which determines the optimal output schemes for various types of units in the transmission and distribution network and the interactive power of tie-line channels. Both the upper and lower layers achieve global optimization through the interaction of transmission and distribution network boundary information. The specific optimization process is as follows:

[0140] Step 1: Program initialization, load data import, initialization of transmission and distribution network structure and basic variables, iteration number ite=1;

[0141] Step 2: Determine the typical stages and quantitative characteristics of the power transmission and distribution network under study, and obtain the boundary conditions of the optimization program;

[0142] Step 3: Optimize the planning layer model solution to obtain the site selection and capacity determination scheme for wind, solar and energy storage in the power transmission and distribution network;

[0143] Step 4: Run the scheduling layer model to solve the problem, obtain the optimal power output scheme of multiple types of units in the power transmission and distribution network, and determine the coupling power matrix of the power transmission and distribution network throughout the time period;

[0144] Step 5: Solve for the electricity sales price of the transmission network throughout the day. This price can be represented by the local marginal price (LMP) of the transmission network nodes based on Lagrange multipliers, including the marginal generation cost, loss cost, and network congestion cost. Then, construct the convergence judgment condition of the model and iterate the program until the algorithm converges.

[0145] The specific flowchart of the algorithm is as follows: Figure 1 As shown.

[0146] S6. Input the basic structural parameters of the power transmission and distribution network and the source-load characteristic parameters, solve the power transmission and distribution coordinated wind, solar and energy storage planning model, and obtain the global optimal planning scheme of the system.

[0147] Example

[0148] use Figure 2 To highlight the advantages of the present invention, the T6D7D9 system shown is analyzed in two scenarios: a separate planning scenario for the distribution network under the consideration of power grid morphology evolution and a transmission and distribution coordination scenario.

[0149] To analyze the impact of different stages of power grid morphological evolution on the system's wind, solar, and energy storage planning schemes, this invention first takes distribution network 1 as the research object and solves the system planning schemes for the nascent, development, and mature stages as follows:

[0150] Table 2 Wind, Solar and Storage Planning Scheme for Distribution Network 1

[0151]

[0152] As shown in the table above, in a single distribution network planning scenario without considering transmission and distribution coordination, the total system cost exhibits a U-shaped distribution, meaning the total system cost is lowest during the development phase of the distribution network; Figures 3-5 It is known that in the nascent stage, the distribution network needs to purchase a large amount of electricity from the transmission network to meet the active power balance of the system because there are no distributed wind and solar power units. However, due to the fact that the grid structure and source-load distribution cannot fully support the high proportion of new energy access, the mature distribution network will experience a large amount of wind and solar curtailment, which will affect the system's economy and environmental protection.

[0153] At the same time, a mature power grid can make full use of transmission and distribution network interconnections to send excess electricity back to the transmission network, thereby improving the system's economy and promoting the safe consumption of clean energy in the power grid.

[0154] To further highlight the advantages of coordinated planning of power transmission and distribution networks, this invention further solves the coordinated planning model of wind, solar, and energy storage power transmission and distribution networks that takes into account morphological evolution. The specific solution results are shown below. It is worth mentioning that, since the power purchase cost of the distribution network has been reflected in the output cost of the thermal power units of the transmission network in the coordinated planning scenario, the costs of distribution networks 1 and 2 in the coordinated planning scenario only consider the equipment construction cost and the wind and solar curtailment penalty, and no longer consider the power purchase cost of the distribution network from the upstream.

[0155] Table 3 Wind, Solar and Energy Storage Planning Schemes for Transmission and Distribution Networks Taking Morphological Evolution into Consideration

[0156]

[0157] As shown in the table above, unlike the independent wind, solar and energy storage planning scenario of distribution network 1, the overall economic efficiency of the system continuously improves with the stage-by-stage evolution of the power grid form in the system planning scenario that considers transmission and distribution coordination. The transmission and distribution coordination planning scenario can better improve the economic efficiency of the system, rationally allocate redundant resources of the system, fully schedule the system's flexible adjustment resources, promote the safe consumption of clean energy, and achieve global optimization of the transmission and distribution network planning scheme.

[0158] At the same time, by Figures 6-8It is evident that, compared to nascent power grids, developing and mature power grids can better utilize the more economically advantageous distributed wind and solar power generation. Furthermore, through the large-scale construction of centralized and distributed energy storage and more efficient energy storage unit scheduling, they can promote the safe consumption of clean energy and mitigate the uncertainties of wind and solar power output, as well as its peak-shaving characteristics. Moreover, compared to the traditional distribution network configuration in its nascent stage, the active distribution network configuration considering power flow back can significantly alleviate the pressure on the distribution network to absorb new energy. While ensuring the active power balance of the distribution network during off-peak periods, power flow back can promote the absorption of redundant power in the system, reduce the output cost of thermal power units in the transmission network, improve the coordination efficiency of dispatchable resources under the heterogeneous decomposition architecture, and fully demonstrate the complementary and mutually beneficial benefits of the transmission and distribution networks throughout the entire time period.

[0159] To address the planning problem of wind, solar, and energy storage in power transmission and distribution networks, this invention constructs a collaborative planning model for wind, solar, and energy storage based on SOCR (Solar-Optical Response Logic). It proposes a fast solution architecture based on heterogeneous decomposition and ultimately determines a collaborative planning strategy that considers the impact of power transmission and distribution network morphology evolution driven by multiple factors. Simulation results from implementation examples show that, compared to traditional power transmission and distribution collaborative optimization algorithms based on DC power flow, the collaborative planning model established in this invention can fully consider the structural characteristics of the power transmission and distribution network, ensuring both solution accuracy and good computational efficiency and convergence capability. Furthermore, the proposed architecture can fully consider the collaborative interaction of the power transmission and distribution network, promoting the complementary advantages of clean energy and flexible, controllable resources in the face of system uncertainties. Finally, compared to traditional power grid planning models, the collaborative planning strategy proposed in this invention can effectively characterize the impact of power transmission and distribution network morphology evolution and formulate differentiated optimal planning schemes for different stages of power transmission and distribution network morphology evolution. This approach has broad application prospects for future power grid morphologies where information and data coupling in power transmission and distribution networks is becoming increasingly close, and the proportion of clean energy and energy storage in the system is continuously increasing.

[0160] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology, characterized in that, Includes the following steps: S1. With the goals of optimizing the economic efficiency of the power transmission network and maximizing the absorption of new energy sources, and considering the constraints of DC power flow and the safe and stable operation of the system, a planning model for wind, solar and energy storage in the power transmission network is established. S2. With the goal of optimizing the economic efficiency of the distribution network and maximizing the absorption of new energy sources, and considering the constraints of AC power flow and the safe and stable operation of the system, a wind-solar-storage planning model for the distribution network is established. S3. Considering the coupling and interaction of the transmission and distribution networks, establish a transmission and distribution coordinated wind, solar and energy storage planning model based on second-order cone relaxation, based on the transmission network wind, solar and energy storage planning model and the distribution network wind, solar and energy storage planning model. S4. Analyze the multiple driving factors of the evolution of the power transmission and distribution network morphology, and determine the evolution path of the power transmission and distribution network morphology and its typical stage characteristics; S5. Determine the fast solution architecture for the model based on heterogeneous decomposition; S6. Input the basic structural parameters and source-load characteristic parameters of the power transmission and distribution network. Use the typical stage characteristics of the power transmission and distribution network morphology evolution path determined in step S4 as the boundary condition input for solving the power transmission and distribution coordinated wind, solar and energy storage planning model. Solve the power transmission and distribution coordinated wind, solar and energy storage planning model according to the model fast solution architecture based on heterogeneous decomposition determined in step S5, and obtain the global optimal planning scheme of the system. The specific evolution path of the power transmission and distribution network is as follows: the power transmission and distribution network is divided into three stages according to the differences in the forms of source-grid-load-storage: the nascent stage, the development stage, and the mature stage. The typical characteristics of each stage are used as the boundary conditions input for solving the power transmission and distribution coordinated wind, solar and storage planning model. Step S5, based on the heterogeneous decomposition model for rapid solution, specifically involves decomposing the transmission and distribution coordinated planning problem into a transmission network optimization sub-problem and a distribution network optimization sub-problem, and using the iterative interaction of grid boundary quantities to obtain the global optimal solution of the system.

2. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 1, characterized in that, The objective function of the power grid wind-solar-storage planning model is expressed as follows: ； ； ； ； ； In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power transmission network. These represent the power generation cost of the transmission network, the power flow return cost, the equipment installation cost, and the penalty for wind and solar curtailment, respectively. These represent the coefficients of the quadratic, primary, and constant terms of the power generation cost of thermal power units in the power transmission network, respectively. Let these represent the set of generator nodes in the transmission network, the set of root nodes connected to the distribution network, and the set of all nodes in the transmission network, respectively. These represent the output power of the thermal power unit and the power transmitted to the distribution network at time t at node i, respectively. This represents the electricity price sold by the power grid at node i at time t. These represent the equipment depreciation factor and the power grid curtailment penalty factor, respectively. These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the power transmission network, respectively. These represent the total amount of wind and solar power curtailed by the power grid, respectively.

3. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 2, characterized in that, The objective function of the power distribution network wind-solar-storage planning model is expressed as follows: ； ； ； ； In the formula, This represents the objective function of the wind-solar-storage collaborative planning model for the power distribution network. These represent the power purchase cost of the distribution network, equipment installation cost, and penalties for wind and solar power curtailment, respectively. Let t represent the power purchased at the node where the distribution network is connected to the transmission network at time t. For ease of analysis, we only consider the scenario where there is only one node in the distribution network connected to the transmission network. This indicates the node where the distribution network connects to the upstream transmission network; the depreciation factor for distribution network equipment is consistent with that of the transmission network. This represents the wind and solar power curtailment coefficient of the power distribution network; These represent the installed capacity of wind power, photovoltaic power, and energy storage at node i of the distribution network, respectively. These represent the total amount of wind and solar power curtailed in the distribution network, respectively. This represents the set of all nodes in the distribution network.

4. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 3, characterized in that, Step S3 includes the following steps: S31. For the non-convex terms of the model introduced by the AC power flow constraints of the distribution network, the model non-convex constraint transformation is performed by second-order cone relaxation to obtain the convex optimization model of wind, solar and storage of the distribution network. S32. Considering the coupling and interaction of the transmission and distribution networks, and aiming at optimizing the overall system economy and clean energy consumption, a wind-solar-storage collaborative planning model for the transmission and distribution networks is established, taking into account both the optimal operation constraints and tie-line constraints of the transmission and distribution networks. The optimization objective of the wind-solar-storage collaborative planning model is the sum of the optimization objectives of the transmission network wind-solar-storage planning model and the distribution network wind-solar-storage planning model. The constraints are the union of the constraints of the transmission network wind-solar-storage planning model and the distribution network wind-solar-storage planning model. The expression form of the wind-solar-storage collaborative planning model is as follows: ； , These represent the transmission network set and the distribution network set, respectively.

5. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 4, characterized in that, The proposed wind-solar-storage collaborative planning model for power transmission and distribution networks satisfies constraints on system construction capacity, active power balance, new energy generating units, energy storage systems, and system safe and stable operation.

6. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 5, characterized in that, The specific constraints satisfied by the wind-solar-storage collaborative planning model for the power transmission and distribution network are as follows: (1) System capacity constraints Due to limitations imposed by natural conditions and the physical characteristics of the generating units, the installed capacity of wind power, photovoltaic, and energy storage units must meet the following constraints: ； In the formula, These represent the upper limits of the installed capacity of wind power, photovoltaic, and energy storage units at node i, respectively. (2) Active power balance constraint Considering the structural characteristics of the transmission network, the transmission network optimization model takes into account the active power balance constraints of the system, and its expression is as follows: ； In the formula, Let i and t represent the output of the wind turbine, photovoltaic unit, energy storage unit, and load of the system at time t. Considering that the energy storage power station may have two states, charging and discharging, it is assumed that the output of the energy storage system is negative when it is charging and positive when it is discharging. The energy storage system can only be in one of the charging and discharging states at any time. Represents the set of power transmission network branches; This represents the network loss of branch l of the transmission network at time t; (3) Constraints of new energy units The output of wind power and photovoltaic units in the power transmission network must meet the following constraints: ； In the formula, These represent the maximum available power of wind power and photovoltaic units at node i of the transmission network at time t, respectively. (4) Constraints of energy storage systems Energy storage unit constraints include charge / discharge power constraints, capacity constraints, and state of charge constraints: ； In the formula, , These represent the active power and installed power of the energy storage system at time t, respectively, at node i of the transmission network. This indicates the installed capacity of the energy storage system at node i of the power transmission network; This represents the State of Charge (SOC) of the energy storage system at time t at node i. This represents the charging / discharging efficiency of the energy storage system, where the charging and discharging efficiencies are reciprocals of each other. Indicates the upper / lower limits of the SOC of the energy storage system; (5) Constraints on safe and stable operation of the system The power transmission network optimization model must meet the constraints for safe and stable system operation, including node voltage constraints, branch current carrying constraints, and minimum start-up and shutdown time constraints, namely: ； ； ； In the formula, This represents the voltage magnitude of the transmission network node at the i-th node within time period t. These represent the upper and lower limits of the voltage at the transmission network nodes, respectively. This indicates the current carrying capacity of branch l in the transmission network. This indicates the maximum current carrying capacity of branch line l; These represent the start-up time and shutdown time of the i-th generating unit in the power transmission network, respectively. These represent the minimum start / stop time for thermal power units in the power transmission network.

7. The wind-solar-storage coordinated planning method considering the evolution of power transmission and distribution network morphology as described in claim 1, characterized in that, The optimization process for the fast solution architecture based on heterogeneous decomposition is as follows: Step 1: Program initialization, load data import, initialization of transmission and distribution network structure and basic variables, iteration number ite=1; Step 2: Determine the typical stages and quantitative characteristics of the power transmission and distribution network under study, and obtain the boundary conditions of the optimization program; Step 3: Optimize the planning layer model solution to obtain the site selection and capacity determination scheme for wind, solar and energy storage in the power transmission and distribution network; Step 4: Run the scheduling layer model to solve the problem, obtain the optimal power output scheme of multiple types of units in the power transmission and distribution network, and determine the coupling power matrix of the power transmission and distribution network throughout the time period; Step 5: Solve for the electricity sales price of the transmission network throughout the day. This price is represented by the marginal electricity price of the transmission network nodes based on Lagrange multipliers, including the marginal generation cost, loss cost, and network congestion cost. Then, construct the convergence judgment condition of the model and iterate the program until the algorithm converges.

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