Transmission and storage multi-stage collaborative planning method considering power transmission network structure optimization

Abstract

The invention relates to a transmission and storage multi-stage collaborative planning method considering power transmission network structure optimization, belongs to the technical field of power system planning, and solves the problem that an existing planning scheme is not high in feasibility and reliability. The method comprises the following steps: predicting a wind-light output sequence and a load sequence of each time period in a planning period and putting the sequences into a data set; a clustering algorithm is used for clustering the data set to obtain a plurality of operation scenes, and the occurrence probability of each operation scene is calculated; constructing a first target function and a first constraint condition for minimizing the sum of the total input and storage investment construction cost and the total annual operation cost as an upper layer model; constructing a second objective function and a second constraint condition for minimizing the total annual operation cost according to the wind and light output sequence, the load sequence and the occurrence probability of each operation scene, and taking the second objective function and the second constraint condition as a lower-layer model; the upper-layer model and the lower-layer model are combined into a transmission and storage double-layer model; and iteratively solving the transmission and storage double-layer model to obtain a transmission and storage multi-stage collaborative planning scheme. The feasibility and reliability of the planning scheme are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a transmission and storage multi-stage collaborative planning method considering transmission network structure optimization. Background Art

[0002] Renewable energy sources are gradually being widely used in power systems due to their flexibility, economy, and environmental protection to address energy shortages, environmental protection, and other issues. However, the volatility, randomness, and intermittency of the output of renewable energy sources pose risks to the safety and stability of the power system. In addition, wind and solar power are often abandoned in places where renewable energy is abundant, resulting in serious waste of energy. Therefore, smoothing the output curve of renewable energy and increasing the absorption of renewable energy have become the focus of attention of academic and industrial circles at home and abroad. It is of great significance to study the synergy between transmission network planning and energy storage optimization configuration and the transmission network and energy storage collaborative planning method for the purpose of improving the absorption of renewable energy.

[0003] The existing multi-stage planning method divides the period from the starting year to the planning year into several stages, but it is necessary to ensure that the sum of the economic efficiency of all stages is optimal, rather than the optimality of each stage. The multi-stage planning method is a planning method that responds to the actual requirements of the project.

[0004] The existing single-layer planning model used in the multi-stage planning method only considers long-term planning and ignores the impact of short-term operation of new energy. It cannot reflect the dynamic characteristics and uncertainty of the new power system in actual operation. In addition, the planning cost of the power system is relatively high, and the actual feasibility and reliability of the obtained planning scheme are not high, which cannot improve the new energy absorption capacity. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a multi-stage collaborative planning method for transmission and storage that takes into account the optimization of the transmission network structure, so as to solve the problem that the existing planning scheme has low feasibility and reliability due to the neglect of the wind and solar power generation conditions of new energy nodes and the uncertainty of load nodes.

[0006] The embodiment of the present invention provides a multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure, comprising the following steps:

[0007] According to historical meteorological data and power system operation data, the wind and solar power output sequence of each renewable energy access node and the load sequence of each load node in each period of the planning period are predicted and put into the data set; the clustering algorithm is used to cluster the data set into multiple operation scenarios, and the occurrence probability of each operation scenario is calculated;

[0008] Based on the grid structure of the transmission network, the first objective function and the first constraint condition of minimizing the sum of the total investment and construction cost of transmission and storage and the total annual operating cost are constructed as the upper model; according to the wind and solar power output sequence, load sequence and the probability of occurrence of each operating scenario, the second objective function and the second constraint condition of minimizing the total annual operating cost are constructed as the lower model; the upper model and the lower model are combined into a transmission and storage double-layer model;

[0009] By iteratively solving the double-layer model of transportation and storage, a multi-stage coordinated planning scheme for transportation and storage is obtained.

[0010] Based on the further improvement of the above method, a clustering algorithm is used to cluster the data set into multiple operation scenarios, including: taking the scheduling periods of preset new energy access nodes and load nodes as time slices, dividing the wind and solar power output sequences and load sequences in the data set according to the time slices, and taking the wind and solar power output subsequences and load subsequences of each period belonging to the same time slice as a sample; using the K-means algorithm for the multiple samples obtained, each clustered class corresponds to an operation scenario.

[0011] Based on the further improvement of the above method, the occurrence probability of each operation scenario is the proportion of the number of each operation scenario in the total number of samples.

[0012] Based on the further improvement of the above method, the total annual operating cost in the second objective function includes: wind curtailment cost, solar curtailment cost, load shedding cost, carbon emission cost, unit start-up and shutdown cost, and power generation cost; the second constraint conditions include: node power balance constraint, branch flow constraint, branch power constraint, thermal power unit and new energy station output constraint, short-term energy storage operation constraint, and thermal power unit start-up and shutdown constraint.

[0013] Based on the further improvement of the above method, the expression of wind curtailment cost is:

[0014]

[0015] Among them, N Y represents the set of planning years, N s Indicates the running scene set, d s represents the probability of occurrence of running scenario s, R Y represents the annualized factor of investment cost in year Y, c wind Indicates the penalty fee for wind curtailment per unit of electricity, N wind represents the set of wind farms in the renewable energy access node, T represents the scheduling period, and P wind,w,t,s,Y They represent the predicted power and actual output power of wind farm w in time period t under operating scenario s in year Y, respectively.

[0016] Based on the further improvement of the above method, the expression of abandoned light cost is:

[0017]

[0018] Among them, N Y represents the set of planning years, N s Represents a set of running scenarios, N solar represents the set of photovoltaic power stations in the new energy access node, d s represents the probability of occurrence of running scenario s, R Y represents the annualized factor of investment cost in year Y, c solar represents the penalty fee for abandoned solar power per unit of electricity, T represents the scheduling period, and P solar,k,t,s,Y They respectively represent the predicted power and actual output power of PV power station k in time period t under operating scenario s in year Y.

[0019] Based on the further improvement of the above method, the expression of output constraint of thermal power units and new energy stations is:

[0020]

[0021] Among them, ω d,g and ω u,g represent the sliding rate and climbing rate of the g-th thermal power unit respectively; P gen,g,t,s,Y and P gen,g,t-1,s,Y They represent the active power output of the g-th thermal power unit in period t and period t-1 under the operation scenario s in year Y respectively; and They represent the upper and lower limits of the output of the g-th thermal power unit respectively; Δt represents the time interval between adjacent time periods; and P wind,w,t,s,Y They represent the predicted power and actual output power of wind farm w in time period t under operation scenario s in year Y respectively; and P solar,k,t,s,Y They respectively represent the predicted power and actual output power of PV power station k in time period t under operating scenario s in year Y.

[0022] Based on the further improvement of the above method, the predicted powers of wind farms and photovoltaic power stations are calculated according to the predicted wind and solar power output sequences, and the actual output powers of wind farms and photovoltaic power stations are obtained by calculating the per-unit value of the wind and solar power output sequence according to the wind and solar power output sequence in the corresponding operating scenario and then multiplying it by the rated power of the corresponding unit.

[0023] Based on the further improvement of the above method, the total investment and construction cost of transmission and storage in the first objective function includes: the total investment and construction cost of the transmission network line and the total investment and construction cost of energy storage. The annual total operating cost in the first objective function is the minimum annual total operating cost returned by the lower model; the first constraint condition includes: transmission line investment and construction constraint, energy storage investment and construction constraint and energy storage capacity constraint.

[0024] Based on the further improvement of the above method, the multi-stage coordinated planning scheme of transportation and storage is obtained by iteratively solving the two-layer model of transportation and storage, including:

[0025] The upper model in the transmission and storage double-layer model outputs the initial collaborative planning scheme to the lower model. The lower model calculates the minimum annual operating total cost based on the received collaborative planning scheme according to the second constraint and returns it to the upper model. The upper model optimizes the initial collaborative planning scheme according to the latest minimum annual operating total cost and the first constraint and outputs it to the lower model. The lower model updates the minimum annual operating total cost and returns it to the upper model, thereby iteratively optimizing the collaborative planning scheme until the convergence conditions are reached and the optimal collaborative planning scheme is output. The optimal collaborative planning scheme includes: the grid structure of the optimized transmission network and the site selection and sizing results of the energy storage.

[0026] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0027] 1. On the basis of the synergy between the transmission network and energy storage, the wind and solar power output of the new energy access nodes and the volatility of the load nodes are considered, a variety of wind and solar power output and load operation scenarios are generated, and a transmission and storage double-layer model is constructed to more accurately reflect the dynamic characteristics and uncertainties of the power system in actual operation, thereby improving the practical feasibility and reliability of the planning scheme.

[0028] 2. The transmission and storage two-layer model conducts overall planning at the upper layer and operation simulation at the lower layer. It better coordinates resource allocation and operation constraints according to the probability of occurrence of operation scenarios, dynamically adjusts planning strategies, optimizes transmission network design based on real-time data and operation feedback, improves resource utilization efficiency and system stability, and provides a more comprehensive and accurate solution to meet the increasingly complex needs of modern new power systems.

[0029] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0031] Figure 1 This is a flow chart of a multi-stage collaborative planning method for transmission and storage that takes into account the optimization of the transmission network structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0033] A specific embodiment of the present invention discloses a multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure, such as Figure 1 As shown, the following steps are included:

[0034] S1. Based on historical meteorological data and power system operation data, the wind and solar power output sequence of each renewable energy access node and the load sequence of each load node in each period of the planning period are predicted and put into the data set; the clustering algorithm is used to cluster the data set into multiple operation scenarios, and the occurrence probability of each operation scenario is calculated.

[0035] It should be noted that when conducting coordinated planning of transmission network planning and energy storage optimization configuration for a new power system containing a large amount of renewable energy power generation and thermal power generation, this embodiment, in order to accurately reflect the actual situation of wind power and photovoltaic power generation at the renewable energy access node, and at the same time consider the volatility and uncertainty of the load, generates operating scenarios that take into account different wind power and photovoltaic outputs and different load conditions. According to real-time data and time series simulation of different operating scenarios, the planning scheme is dynamically adjusted to optimize the transmission network design, thereby more accurately reflecting the dynamic characteristics and uncertainty of the system in actual operation, improving resource utilization efficiency and system stability, and improving the practical feasibility and reliability of the planning scheme.

[0036] Specifically, the output of wind farms is affected by meteorological conditions such as wind speed and wind direction, the output of photovoltaic power stations is affected by light intensity and weather changes, and the load is also affected by meteorological conditions such as temperature, humidity, wind speed, and sunshine hours. Therefore, based on historical meteorological data and power system operation data, the meteorological data features corresponding to wind power output, photovoltaic output, and load are extracted from the meteorological data, and statistical methods or deep learning methods are used to predict the wind and solar output sequence of each new energy access node and the load sequence of each load node in each period of the planning period.

[0037] Exemplarily, three long short-term memory network models are constructed to predict wind power output, photovoltaic output and load respectively; the meteorological data in the historical period are matched with the wind power output value, photovoltaic output value and load value in the historical operation data of the power system at the same time point; the three long short-term memory network models take the meteorological data of the time series as input, and take the wind power output value, photovoltaic output value and load value corresponding to the meteorological data as the actual value respectively, and take the mean square error between the predicted value output by the long short-term memory network model and the corresponding actual value as the loss function, and continuously update the network parameters by minimizing the loss function until the training is completed. The three trained long short-term memory network models learn the relationship between meteorological data and wind power output value, photovoltaic output value and load value respectively. When implemented, the weather forecast data of each period in the planning period are input into the three long short-term memory network models respectively, and the wind power output prediction value, photovoltaic output prediction value and load prediction value of the corresponding period are output respectively.

[0038] Furthermore, a clustering algorithm is used to cluster the data set into multiple operating scenarios, including: taking the scheduling periods of preset new energy access nodes and load nodes as time slices, dividing each wind and solar power output sequence and each load sequence in the data set according to the time slices, and taking the wind and solar power output subsequences and load subsequences of each period belonging to the same time slice as a sample; using the K-means algorithm for the multiple samples obtained, each clustered class corresponds to an operating scenario, and is put into the operating scenario set.

[0039] Exemplarily, the power system includes three wind farms, two photovoltaic power stations and three load nodes. The plan is planned in units of one hour and one year as a stage. In each predicted stage, each wind and solar output sequence and each load sequence contains 8760 values. The scheduling period is set to 24 hours, that is, each wind and solar output subsequence and each load subsequence of each day are used as multidimensional data features to form a sample, and 365 samples in one stage are clustered, and each clustered class corresponds to an operating scenario.

[0040] By calculating the proportion of the number of each operating scenario in the total number of samples, the occurrence probability of each operating scenario is obtained.

[0041] S2. Based on the grid structure of the transmission network, the first objective function and the first constraint condition of minimizing the sum of the total investment and construction cost of transmission and storage and the annual total operating cost are constructed as the upper model; according to the wind and solar power output sequence, load sequence and the probability of occurrence of each operating scenario, the second objective function and the second constraint condition of minimizing the annual total operating cost are constructed as the lower model; the upper model and the lower model are combined into a transmission and storage double-layer model.

[0042] It should be noted that the grid structure of the initial transmission network to be planned is obtained based on the abstraction of the actual power system, and then the set of candidate lines of the transmission network is obtained according to the grid structure of the transmission network; according to the unit combination plan in the power system, the initial energy storage device location is obtained, that is, the set of candidate installation nodes for energy storage.

[0043] In this embodiment, the upper model minimizes the total investment and construction cost of the transmission network line. line , the total investment and construction cost of energy storage ess and the minimum annual operating cost f returned by the lower model ope The sum is the first objective function f, and the expression is as follows:

[0044] minf=f line +f ess +minf ope (1)

[0045] Furthermore, the total investment and construction cost of the transmission network line is line The expression is as follows:

[0046]

[0047] Among them, N Y represents the set of planning years, N p represents the set of candidate routes, R Y represents the annualized factor of the investment cost in year Y, that is, the one-time investment cost is spread over each year, which is convenient for the analysis of income, cost and expenditure; c ij Indicates the unit investment cost of the line; L ij Indicates the line length; A 0-1 decision variable representing the construction of the pth line on branch (i, j) in year Y. If the line is constructed, the value is 1, otherwise the value is 0.

[0048] The total investment and construction cost of energy storage ess The total investment and construction cost, including long-term and short-term energy storage, is calculated using the following formula:

[0049]

[0050] Among them, c p and c e They represent the unit power and unit capacity investment costs of energy storage at node i respectively; ess,i,Y represents the 0-1 decision variable for configuring energy storage at node i in year Y. If energy storage is built, the value is 1, otherwise the value is 0; P ess,i,Y and E ess,i,Y They represent the power and capacity of energy storage built at node i in year Y respectively; N essA collection of nodes to be installed for energy storage.

[0051] It should be noted that the first constraint condition corresponding to the first objective function established at the upper layer includes: transmission line construction constraint, energy storage construction constraint and energy storage capacity constraint.

[0052] Specifically, the expression of the transmission line construction constraint is as follows:

[0053]

[0054] in, The maximum number of new lines between nodes representing line (i, j) is determined based on the geographical conditions and requirements of the nodes.

[0055] The expression of energy storage investment and construction constraint is as follows:

[0056]

[0057] in, Indicates the maximum number of energy storage nodes allowed to be installed in the power system, which is determined based on the geographical conditions and requirements of the nodes.

[0058] The expression of energy storage capacity constraint is as follows:

[0059] δ min P ess,i,g ≤E ess,i,Y ≤δ max P ess,i,g (6)

[0060] Among them, δ min and δ max They respectively represent the minimum and maximum capacity-power ratios of the energy storage power station, and are preferably set to 10 / 1 and 1000 / 1 respectively.

[0061] It should be noted that the minimum annual operating cost f returned by the lower model in the first objective function is ope Various operating costs are taken into account, including: the corresponding wind power generation cost f wind 、The corresponding cost of abandoned photovoltaic power generation f solar , load shedding cost corresponding to the load node f nse , the carbon emission cost corresponding to the thermal power unit f co2 , the start-up and shutdown cost of the thermal power unit corresponding to the unit f start and the power generation cost f gen , with minimizing the sum of these operating costs as the second objective function.

[0062] Among them, the cost of wind abandonment and solar abandonment reduces the waste of resources that cannot be used due to excess wind power and photovoltaic power generation; the cost of unit start-up and shutdown optimizes the start-up and shutdown frequency of the generator set and reduces unnecessary costs; the cost of load shedding effectively manages load reduction and reduces the impact on users. This comprehensive consideration not only optimizes economic benefits, but also takes into account environmental impact and system stability, thereby improving the overall efficiency and sustainability of the power system.

[0063] Specifically, the expression of the second objective function is as follows:

[0064] minf ope =f wind +f solar +f nse +f co2 +f start +f gen (7)

[0065] It should be noted that the wind curtailment cost f wind The expression is as follows:

[0066]

[0067] Among them, N s Indicates the running scene set, d s represents the probability of occurrence of running scenario s, c wind Indicates the penalty fee for wind curtailment per unit of electricity, N wind represents the set of wind farms in the renewable energy access node, T represents the scheduling period, and P wind,w,t,s,Y They represent the predicted power and actual output power of wind farm w in time period t under operating scenario s in year Y, respectively.

[0068] Abandoned light cost solar The expression is as follows:

[0069]

[0070] Among them, N solar represents the set of photovoltaic power stations in the new energy access node, c solar Indicates the penalty fee for abandoned solar power per unit of electricity. and P solar,k,t,s,Y They respectively represent the predicted power and actual output power of PV power station k in time period t under operating scenario s in year Y.

[0071] It should be noted that the predicted powers of wind farms and photovoltaic power stations are calculated based on the predicted wind and solar power output sequences. The actual output powers of wind farms and photovoltaic power stations are calculated based on the wind and solar power output sequence in the corresponding operating scenarios, and then the per-unit value of the wind and solar power output sequence (that is, the output prediction value is converted into a proportional value relative to the maximum prediction value) is multiplied by the rated power of the corresponding unit.

[0072] Load shedding cost f nse The expression is as follows:

[0073]

[0074] Among them, N l represents the load node set, c Voll represents the unit load shedding cost, P nse,l,t,s,Y It represents the load shedding amount of load node l in time period t under operating scenario s in year Y.

[0075] Carbon emission cost co2 The expression is as follows:

[0076]

[0077] Among them, c co2 P represents the unit emission cost of carbon dioxide per unit of electricity; gen,g,t,s,Y It represents the active power output of the gth thermal power unit in the period t under the operating scenario s in the Yth year.

[0078] Unit start-up and shutdown cost start The expression is as follows:

[0079]

[0080] Among them, N G represents the set of thermal power units, c SU,g,Y and c SD,g,Y They represent the startup and shutdown costs of the g-th thermal power unit in year Y, and They represent the 0-1 state variables of the start and stop of the g-th thermal power unit in the Y-th year period t, 1 indicates that the thermal power unit is in the start state, and 0 indicates that the thermal power unit is in the stop state.

[0081] Unit operating cost gen The expression is as follows:

[0082]

[0083] Among them, c gen It represents the unit carbon dioxide emission cost per unit of electricity and the unit power generation cost per unit of electricity.

[0084] Furthermore, the second constraint conditions corresponding to the second objective function established by the lower model include: node power balance constraints, branch flow constraints, branch power constraints, thermal power unit and new energy station output constraints, short-term energy storage operation constraints and thermal power unit start and stop constraints.

[0085] Specifically, the node power balance constraint ensures the power balance of any node in the transmission network in each period in each operation scenario. The expression is as follows:

[0086]

[0087] Among them, A y A represents the node-branch association matrix of the newly built line in year y; o and A p The node-branch correlation matrices of the initial line and the line to be selected of the power system are represented respectively; represents the active power vector of the bth branch of the newly built line in year y in time period t under the operation scenario s in year Y; and They represent the active power vector of the bth branch of the power system initial line and the candidate line in the operation scenario s in the Yth year in time period t; P gen,t,t,Y , P wind,t,s,Y and P solar,t,t,Y They represent the active output vectors of all thermal power units, wind farms and photovoltaic power stations in time period t under the operation scenario s in year Y; ess,t,s,Y is the charging and discharging power vector of the newly built energy storage in time period t under the operation scenario s in year Y; P is the charging and discharging power vector of the newly built energy storage in year y during time period t under the operation scenario s in year Y; l,t,s,Y and P nse,r,s,T They are respectively the load vector and load shedding vector of each node in period t under the operating scenario s in the Tth year.

[0088] Specifically, branch flow constraints refer to the fact that the current or power flow of each line (branch) in the power system must be within certain restrictions to ensure the safe operation of the power grid. It is often determined by the physical characteristics and safety standards of the line, such as the maximum current carrying capacity, thermal capacity, and stability requirements of the line. Violation of these constraints may cause line overload, equipment damage or system instability, and even cause large-scale power outages. Therefore, branch flow constraints play a key role in the operation and dispatch of power systems to ensure the safety and reliability of power transmission.

[0089] In this embodiment, the expression for the branch power flow constraint of the initial existing transmission line of the power system is as follows:

[0090]

[0091] The expression of branch power flow constraint for the newly built transmission line of the power system is as follows:

[0092]

[0093] Among them, b ij , and They represent the single line susceptance of branch (i, j), the initial number of lines in the power system and the maximum number of newly added lines respectively; represents the number of new lines built on branch (i, j) in year y; represents the total active power flowing through the initial branch (i, j) of the power system in time period t under the operation scenario s in year Y; represents the active power flowing through all newly built lines on the branch (i, j) in year y during period t under the operating scenario s in year y; represents the active power flowing through the newly built p-th line of branch (i, j) in time period t under the operation scenario s in year Y; θ i,t,s,Y ,θ j,t,s,Y and θ ref,t,s,Y They represent the voltage phase angles of nodes i, j and the balance node ref in time period t of year y respectively; Represents the initial line set of the power system; M is a sufficiently large number, such as 10 8 ;v ij,t,s,Y represents the operating status of branch (i, j) in time period t under operating scenario s in year Y. ij,t,s,Y =1 indicates that it is in operation state. ij,t,s,Y =0 means it is in non-operating state; A 0-1 decision variable representing the construction of the pth line on branch (i, j) in year Y. If the line is constructed, the value is 1, otherwise the value is 0.

[0094] Specifically, the branch power constraint ensures that the power of any branch at any time is strictly within the limit, and the expression is as follows:

[0095]

[0096] in, is the maximum transmission power of a single line of branch (i, j).

[0097] Specifically, the output constraints of thermal power units and new energy stations take into account the maximum and minimum constraints of thermal power plant technical output and the output forecast constraints of wind farms and photovoltaic power stations, ensuring that the output limits of thermal power units, wind farms and photovoltaic power stations are within the allowable range. The expression is as follows:

[0098]

[0099] Among them, ω d,g and ω u,g represent the sliding rate and climbing rate of the g-th thermal power unit respectively; P gen,g,t,s,Y and P gen,g,t-1,s,Y They represent the active power output of the g-th thermal power unit in period t and period t-1 under the operation scenario s in year Y respectively; and They represent the upper and lower limits of the output of the g-th thermal power unit respectively; Δt represents the time interval between adjacent time periods; the following two expressions indicate that the actual output power of the wind farm and photovoltaic power station in the output constraint of the new energy station is less than or equal to the corresponding predicted power.

[0100] Specifically, the short-term energy storage operation constraint is used to optimize the energy storage capacity, and the expression is as follows:

[0101]

[0102] Among them, p c,i,t,s,Y and p d,i,t,s,Y They represent the charging power and discharging power of the energy storage device at branch node i in time period t under the operation scenario s in year Y; e i,t+1,s,Y and e i,t,s,Y They represent the capacity of the energy storage device of branch node i in period t+1 and period t under the operation scenario s in year Y respectively; c,i,t,s,Y and x d,i,t,s,Y They represent the charging and discharging states of the energy storage device of node i in time period t under the operation scenario s in year Y, respectively. The charging and discharging states are 0-1 variables, 1 represents charging / discharging, and 0 represents that the energy storage device is not operating; and Respectively represent the charging efficiency and discharging efficiency of the energy storage device; SOC max and SOC min They represent the upper and lower limits of the state of charge of the energy storage device respectively; the last constraint represents the energy balance constraint of the energy storage, T 24 =24, that is, the energy balance constraint of energy storage is the daily balance constraint.

[0103] Specifically, the expression of the start-stop constraint of the thermal power unit is as follows:

[0104]

[0105] Among them, v g,t,Y SU represents the 0-1 operating state variable of the g-th thermal power unit in the period t of the Y-th year, 1 means the unit is in operation, and 0 means the unit is in shutdown; g,max and SD g,max They represent the maximum start-up time and maximum shutdown time of the g-th thermal power unit respectively.

[0106] S3. By iteratively solving the double-layer model of transportation and storage, a multi-stage coordinated planning scheme for transportation and storage is obtained.

[0107] It should be noted that the standard optimization model solver CPLEX or Gurobi is used to iteratively solve the transmission and storage double-layer model. During the solution, the initial grid structure of the transmission network and the location of the energy storage device are used as the initial collaborative planning scheme. The upper model outputs the initial collaborative planning scheme to the lower model. The lower model calculates the minimum annual operating total cost based on the received collaborative planning scheme according to the second constraint and returns it to the upper model. The upper model optimizes the initial collaborative planning scheme according to the latest minimum annual operating total cost and the first constraint and outputs it to the lower model. The lower model updates the minimum annual operating total cost and returns it to the upper model, thereby iteratively optimizing the collaborative planning scheme until the convergence condition is reached and the optimal collaborative planning scheme is output; the optimal collaborative planning scheme includes: the grid structure of the optimized transmission network and the site selection and capacity determination results of the energy storage, wherein the line expansion time and expansion location are added to the grid structure of the transmission network, and the site selection and capacity determination results of the energy storage include the construction time, optimal location and capacity of the energy storage device.

[0108] Compared with the prior art, this embodiment provides a multi-stage transmission and storage collaborative planning method that takes into account the optimization of the transmission network structure. On the basis of the synergy between the transmission network and energy storage, it adds consideration of the wind and solar output of the new energy access nodes and the volatility of the load nodes, generates a variety of wind and solar output and load operation scenarios, and constructs a transmission and storage double-layer model to more accurately reflect the dynamic characteristics and uncertainty of the power system in actual operation, thereby improving the practical feasibility and reliability of the planning scheme. The transmission and storage double-layer model performs overall planning at the upper layer and operation simulation at the lower layer, better coordinates resource allocation and operation constraints according to the probability of occurrence of the operation scenario, dynamically adjusts the planning strategy, optimizes the transmission network design according to real-time data and operation feedback, improves resource utilization efficiency and system stability, and provides a more comprehensive and accurate solution to meet the increasingly complex needs of modern new power systems.

[0109] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0110] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A multi-stage transmission and storage collaborative planning method considering the optimization of transmission network structure, characterized in that: The following steps are involved: According to the historical meteorological data and the operation data of the power system, the wind and solar power output sequence of each new energy access node and the load sequence of each load node in each period of the planning period are predicted and put into the data set; Use clustering algorithms to cluster the data set into multiple operating scenarios and calculate the occurrence probability of each operating scenario; Based on the grid structure of the transmission network, the first objective function and the first constraint condition of minimizing the sum of the total investment and construction cost of transmission and storage and the total annual operating cost are constructed as the upper model; According to the wind and solar power output sequence, load sequence and the occurrence probability of each operation scenario, a second objective function and a second constraint condition for minimizing the total annual operation cost are constructed as a lower model; the upper model and the lower model are combined into a transmission and storage double-layer model; By iteratively solving the transportation and storage double-layer model, a multi-stage transportation and storage collaborative planning scheme is obtained.

2. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 1 is characterized in that: A clustering algorithm is used to cluster the data set into multiple operating scenarios, including: taking the scheduling periods of preset new energy access nodes and load nodes as time slices, dividing the wind and solar power output sequences and load sequences in the data set according to the time slices, and taking the wind and solar power output subsequences and load subsequences of each period belonging to the same time slice as a sample; the K-means algorithm is used for the obtained multiple samples, and each clustered class corresponds to an operating scenario.

3. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 2 is characterized in that: The occurrence probability of each operation scenario is the proportion of the number of each operation scenario in the total number of samples.

4. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 1 is characterized in that: The total annual operating cost in the second objective function includes: wind curtailment cost, solar curtailment cost, load shedding cost, carbon emission cost, unit start-up and shutdown cost, and power generation cost; the second constraint conditions include: node power balance constraint, branch flow constraint, branch power constraint, thermal power unit and new energy station output constraint, short-term energy storage operation constraint, and thermal power unit start-up and shutdown constraint.

5. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 4 is characterized in that: The expression of the wind curtailment cost is: Among them, N Y represents the set of planning years, N s Indicates the running scene set, d s represents the probability of occurrence of running scenario s, R Y represents the annualized factor of investment cost in year Y, c wind Indicates the penalty fee for wind curtailment per unit of electricity, N wind represents the set of wind farms in the renewable energy access node, T represents the scheduling period, and P wind,w,t,s,Y They represent the predicted power and actual output power of wind farm w in time period t under operating scenario s in year Y, respectively.

6. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 4 is characterized in that: The expression of the abandoned light cost is: Among them, N Y represents the set of planning years, N s Represents a set of running scenarios, N solar represents the set of photovoltaic power stations in the new energy access node, d s represents the probability of occurrence of running scenario s, R Y represents the annualized factor of investment cost in year Y, c solar represents the penalty fee for abandoned solar power per unit of electricity, T represents the scheduling period, and P solar,k,t,s,Y They respectively represent the predicted power and actual output power of PV power station k in time period t under operating scenario s in year Y.

7. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 4 is characterized in that: The expression of the output constraint of the thermal power unit and the new energy station is: Among them, ω d,g and ω u,g represent the sliding rate and climbing rate of the g-th thermal power unit respectively; P gen,g,t,s,Y and P gen,g,t-1,s,Y They represent the active power output of the g-th thermal power unit in period t and period t-1 under the operation scenario s in year Y respectively; and They represent the upper and lower limits of the output of the g-th thermal power unit respectively; Δt represents the time interval between adjacent time periods; and P wind,w,t,s,Y They represent the predicted power and actual output power of wind farm w in time period t under operation scenario s in year Y respectively; and P solar,k,t,s,Y They respectively represent the predicted power and actual output power of PV power station k in time period t under operating scenario s in year Y.

8. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 7 is characterized in that: The predicted powers of the wind farm and the photovoltaic power station are calculated based on the predicted wind and solar power output sequence, and the actual output powers of the wind farm and the photovoltaic power station are calculated based on the wind and solar power output sequence in the corresponding operating scenario, and then multiplied by the rated power of the corresponding unit.

9. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 1 is characterized in that: The total investment and construction cost of transmission and storage in the first objective function includes: the total investment and construction cost of the transmission network line and the total investment and construction cost of energy storage. The annual total operating cost in the first objective function is the minimum annual total operating cost returned by the lower model; the first constraint condition includes: transmission line investment and construction constraint, energy storage investment and construction constraint and energy storage capacity constraint.

10. The multi-stage transmission and storage collaborative planning method considering the optimization of the transmission network structure according to claim 1 is characterized in that: The multi-stage coordinated planning scheme for transportation and storage is obtained by iteratively solving the transportation and storage double-layer model, including: The upper model in the transmission and storage double-layer model outputs the initial collaborative planning scheme to the lower model. The lower model calculates the minimum annual operating total cost based on the received collaborative planning scheme according to the second constraint and returns it to the upper model. The upper model optimizes the initial collaborative planning scheme according to the latest minimum annual operating total cost and the first constraint and outputs it to the lower model. The lower model updates the minimum annual operating total cost and returns it to the upper model, thereby iteratively optimizing the collaborative planning scheme until the convergence conditions are reached and the optimal collaborative planning scheme is output. The optimal collaborative planning scheme includes: the grid structure of the optimized transmission network and the site selection and capacity determination results of the energy storage.

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