Energy storage and power transmission line collaborative planning method considering transmission flexibility

Abstract

The invention relates to an energy storage and power transmission line collaborative planning method considering transmission flexibility. The method comprises the following steps of S1, defining and calculating a line flexibility load rate; s2, based on a conditional value-at-risk theory, quantifying an operation risk of line flexibility load rate out-of-limit, and obtaining a line risk cost of insufficient line transmission flexibility; s3, constructing an energy storage and power transmission line collaborative double-layer multi-stage planning model with constraint conditions; and S4, solving the energy storage and power transmission line collaborative planning model to obtain an optimal operation scheduling scheme of energy storage and power transmission line collaboration. According to the method, the flexibility and the operation safety of the system can be remarkably improved while the economical efficiency of the power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and operation, and in particular to a double-layer multi-stage planning method for coordinating energy storage and transmission lines to cope with the problem of power system flexibility caused by the access of new energy. Background Art

[0002] With the large-scale access to renewable energy, the flexibility issues faced by the power system are becoming increasingly prominent. The volatility and uncertainty of renewable energy generation often lead to the lack of sufficient flexibility resources, such as energy storage systems and fast-adjusting power sources, when the system responds to load fluctuations. This lack of resources can easily lead to wind and solar power abandonment and load shedding, affecting the stable operation of the system. The transmission network also faces a bottleneck in transmission capacity, especially when the proportion of renewable energy access is high. Even if there are sufficient flexibility resources, the limitations of transmission lines may still lead to insufficient system flexibility.

[0003] Traditional transmission network planning mainly focuses on the maximum transmission capacity of the line, ignoring the dynamic adaptability of the line transmission capacity under high load and severe fluctuations, which increases the risk of the system under extreme conditions.

[0004] In the prior art, for example, the invention patent with publication number "CN 114725994 A" proposes a multi-element energy storage collaborative control system and method thereof, which combines photovoltaic power generation with thermal power generation and utilizes energy storage devices such as battery clusters and supercapacitors to achieve dynamic storage and optimized supply of electric energy, so as to improve the stability and diversity of power supply of the power grid and meet the needs of different power consumption periods; the invention patent with publication number "CN 114884103 A" proposes a collaborative planning modeling method for improving the resilience of the power grid, which constructs a two-stage three-layer robust optimization model, comprehensively considers the expansion of transmission lines and the configuration of energy storage systems, maximizes the resilience of the power system under extreme natural disasters, and uses a dual column constraint generation algorithm to effectively solve the problem.

[0005] However, the above two solutions still have the following problems: lack of consideration for the overall flexibility of the power grid, especially when facing line transmission bottlenecks, effective adjustments may not be made in a timely manner. Summary of the invention

[0006] The purpose of the present invention is to provide a method for collaborative planning of energy storage and transmission lines that optimizes the planning of energy storage and transmission networks, takes into account the flexibility and risk management of power transmission lines, and more stably considers transmission flexibility when dealing with the volatility and uncertainty of new energy access.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A method for coordinated planning of energy storage and transmission lines considering transmission flexibility comprises the following steps:

[0009] Step S1: defining and calculating a line flexibility load rate, where the line flexibility load rate is used to characterize the ability of a transmission line to flexibly transmit loads;

[0010] Step S2: based on the conditional risk value theory, quantify the operation risk of the line flexibility load rate exceeding the limit, and obtain the line risk cost of insufficient line transmission flexibility;

[0011] Step S3: construct a two-layer multi-stage planning model for coordinated energy storage and transmission lines with constraints, the two-layer multi-stage planning model for coordinated energy storage and transmission lines includes an upper-layer planning model and a lower-layer planning model; the upper-layer planning model takes the investment location, capacity and power of energy storage and the investment location and number of new loops of transmission lines as decision variables, with the goal of minimizing the sum of investment and construction costs and operating costs; the lower-layer planning model takes the unit output as a decision variable, with the goal of minimizing operating costs; the operating costs are obtained based on the line risk cost of insufficient line transmission flexibility;

[0012] Step S4: Solve the energy storage and transmission line coordinated planning model to obtain an optimal operation scheduling plan for the coordination of energy storage and transmission lines.

[0013] In the step S1, the line transmission flexibility demand is defined and calculated, and the line flexibility load rate is calculated based on the line transmission flexibility demand.

[0014] The line transmission flexibility requirement is:

[0015]

[0016] In the formula, F l,t is the line transmission flexibility requirement of line l at time t; N is the number of nodes; PTDF l,i is the power transfer distribution factor of line l at time t; is the flexibility requirement of node i at time t;

[0017] The line flexibility load rate is:

[0018]

[0019] where, LFLR l,t is the line flexibility load rate of line l at time t; P l max is the maximum permissible transmission capacity of line l; P l,t-1 is the transmission power of line l at time t-1.

[0020] Flexibility requirement of node i at time t for:

[0021]

[0022] In the formula, is the net load power of node i at time t, is the net load power of node i at time t+1.

[0023] In step S2, the line risk cost of insufficient line transmission flexibility is:

[0024]

[0025] In the formula, is the line risk cost of insufficient transmission flexibility of the line at time t; δ is the auxiliary decision variable for calculating the conditional risk value; α is the confidence level; s and S are the dimension and scenario set of the scenario respectively; ρ s is the probability of the sth scenario; line is the penalty cost for exceeding the line flexibility load rate limit by 1%; Ω l For transmission line collection; LFLR s,l,t is the line flexibility load rate of line l at time t in the sth scenario; LFLR m The maximum line flexibility load rate allowed regardless of operational risk (LFLR s,l,t -LFLR m ) + represents max{LFLR s,l,t -LFLR m ,0}, express

[0026] In step S3, the calculation formula of the investment and construction cost is:

[0027]

[0028] Where k and K represent the kth stage and the number of planning stages respectively; y and Y k denote the yth year of the kth stage and the number of years of the kth stage respectively; r is the discount rate; They represent the investment and construction costs of the k-th stage transmission line and energy storage that have not yet been converted to present value; R L , R E Respectively represent the depreciation coefficients of transmission lines and energy storage; z k,l , L l are the number of expansions of the transmission line l in the kth stage, the expansion cost per unit length of the transmission line in the kth stage, and the length of the transmission line l; x k,i is a 01 variable, indicating whether energy storage is built at node i in stage k; Ek,i and P k,i They represent the capacity and power of the energy storage configuration of node i in the kth stage respectively; and They represent the construction cost of energy storage per unit capacity and power at the kth stage respectively; T A Indicates the age of the equipment;

[0029] The calculation formula of the operating cost is:

[0030]

[0031] Where, T is the total operation period; σ is the operation cost conversion coefficient; P k,s,g,t is the output of thermal power unit g at time t under scenario s in stage k; N g is the number of thermal power units; a g 、b g 、c g is the fuel coefficient cost of thermal power unit g; wt and χ load They are the penalty costs for wind curtailment and load shedding per unit power; and are respectively the wind abandonment and load shedding of node i at time t under scenario s in the kth stage.

[0032] The value of σ is 365.

[0033] The constraints of the upper-level planning model include the number of transmission lines and the existence of energy storage site selection constraints;

[0034] The line number constraint of the transmission channel is:

[0035]

[0036] In the formula, is the maximum number of expansions of the transmission line l;

[0037] The energy storage site selection has the following constraints:

[0038]

[0039] Where N E The number of nodes that allow energy storage to be configured;

[0040] The constraints of the lower-level planning model include power balance constraints, thermal power unit operation constraints, energy storage operation constraints, node voltage phase angle constraints, line flow constraints, node wind abandonment and load shedding constraints;

[0041] The power balance constraint is:

[0042]

[0043] In the formula, and They are respectively the wind power output, energy storage charging and discharging power and load power at time t on node i in the kth stage scenario s;

[0044] The operating constraints of the thermal power unit are:

[0045]

[0046] Where P g,min and P g,max are the upper and lower limits of the output of thermal power unit g; r g,down and r g,up are the downward and upward climbing rates of thermal power unit g respectively; Δt is the time scale of operation;

[0047] The energy storage operation constraints are:

[0048]

[0049] In the formula, E k,s,i,t is the energy storage capacity of node i at time t in the kth stage scenario s; μ min and μ max are the minimum and maximum states of charge of energy storage respectively;

[0050] The node voltage phase angle constraint is:

[0051] θ i,min ≤θ k,s,i,t ≤θ i,max ,

[0052] In the formula, θ k,s,i,t is the voltage phase angle of node i at time t in the kth stage scenario s; θ i,min ,θ i,max are the upper and lower limits of the voltage phase angle of node i respectively;

[0053] The line power flow constraint is:

[0054] -P l max ≤P k,s,l,t ≤P l max ,

[0055] Where P k,s,l,t is the power passing through the transmission line l at time t in the kth stage scenario s; P l max is the maximum power allowed to pass through the transmission line l;

[0056] The node wind abandonment and load shedding constraints are:

[0057]

[0058] In the formula, r wt and r load are the maximum allowable proportions of wind curtailment and load shedding, respectively.

[0059] In step S4, a particle swarm algorithm is used to solve the upper-level planning model to determine the energy storage configuration and the transmission network expansion plan; and a GUROBI solver is used to solve the lower-level planning model to obtain the optimal operation scheduling plan.

[0060] The step S4 comprises the following steps:

[0061] Step 1: Input the parameters of the energy storage and transmission line coordinated planning model and the particle swarm algorithm parameters;

[0062] Step 2: Generate an initial particle swarm population of the upper-level planning model, wherein the initial particle swarm population includes energy storage site selection and capacity determination and new transmission lines;

[0063] Step 3: inputting the upper-level planning scheme obtained by using the upper-level planning model into the lower-level planning model, and inputting typical wind power and load scenarios of each stage;

[0064] Step 4: Call the GUROBI solver to solve the optimal operation of the lower-level planning model, and return the result to the upper-level planning model;

[0065] Step 5: Calculate the investment and construction cost and the operating cost;

[0066] Step 6: Iterate and optimize, compare and update the optimal individual and global optimal until the optimal solution is found.

[0067] Due to the application of the above technical scheme, the present invention has the following advantages compared with the prior art: the present invention introduces the line flexibility load rate and conditional risk value theory, and optimizes the energy storage location, capacity and line expansion decisions, which not only optimizes the planning of energy storage and transmission networks, but also takes into account the flexibility and risk management of power transmission lines, making the system more stable when dealing with the volatility and uncertainty of new energy access, thereby achieving improved flexibility and economy of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic flow chart of the method of the present invention.

[0069] Figure 2 It is a double-layer multi-stage planning model framework for coordinated energy storage and transmission lines of the present invention.

[0070] Figure 3It is a flow chart for solving the energy storage and transmission line coordinated double-layer multi-stage planning model of the present invention.

[0071] Figure 4 This is the actual system topology diagram of a province in northwest China of the present invention.

[0072] Figure 5 It is the penalty cost of different planning stages of each scheme of the present invention.

[0073] Figure 6 The present invention is different from LFLR m Comparison between penalty costs and investment and construction costs under these circumstances. DETAILED DESCRIPTION

[0074] The present invention will be further described below in conjunction with the embodiments shown in the accompanying drawings.

[0075] Embodiment 1: As shown in the attached Figure 1 As shown, a method for coordinated planning of energy storage and transmission lines considering transmission flexibility includes the following steps:

[0076] Step S1: define and calculate the line flexibility load rate, which is used to characterize the ability of the transmission line to flexibly transmit loads.

[0077] The specific steps are: define and calculate the line transmission flexibility requirement:

[0078]

[0079] In formula (1), F l,t is the line transmission flexibility requirement of line l at time t; N is the number of nodes; PTDF l,i is the power transfer distribution factor of line l at time t; is the flexibility requirement of node i at time t.

[0080] Flexibility requirement of node i at time t for:

[0081]

[0082] In formula (2), is the net load power of node i at time t, is the net load power of node i at time t+1.

[0083] The line flexibility load rate is calculated based on the line transmission flexibility demand.

[0084]

[0085] In formula (3), LFLR l,tis the line flexibility load rate of line l at time t; P l max is the maximum permissible transmission capacity of line l; P l,t-1 is the transmission power of line l at time t-1.

[0086] When LFLR l,t >1, it means that the transmission line cannot support the transmission flexibility demand; when LFLR l,t When it is less than 1, it means that the transmission line can meet the transmission flexibility requirements, and the smaller its value is, the stronger the transmission flexibility capacity of the transmission line is and the lower the load rate of the line is.

[0087] Step S2: Based on the conditional risk value theory, the operation risk of the line flexibility load rate exceeding the limit is quantified to obtain the line risk cost of insufficient line transmission flexibility.

[0088] The specific steps are: using the Conditional Value at Risk (CVaR) theory to characterize LFLR l,t The operational risk caused by excessive transmission flexibility is:

[0089]

[0090] In formula (4), is the line risk cost of insufficient line transmission flexibility at time t; δ is the auxiliary decision variable for calculating conditional risk value; α is the confidence level; s and S are the dimension and scenario set of the scenario respectively; ρ s is the probability of the sth scenario; line is the penalty cost for exceeding the line flexibility load rate limit by 1%; Ω l For transmission line collection; LFLR s,l,t is the line flexibility load rate of line l at time t in the sth scenario; LFLR m The maximum line flexibility load rate allowed regardless of operational risk (LFLR s,l,t -LFLR m ) + represents max{LFLR s,l,t -LFLR m ,0}, express The smaller it is, the greater the transmission flexibility of the line.

[0091] Step S3: Construct a two-layer multi-stage planning model for coordinated energy storage and transmission lines with constraints. The two-layer multi-stage planning model for coordinated energy storage and transmission lines includes an upper-layer planning model and a lower-layer planning model, as shown in the attached figure. Figure 2As shown in the figure, the upper planning model takes the investment location, capacity and power of energy storage and the investment location and number of new circuits of transmission lines as decision variables, with the goal of minimizing the sum of investment and construction costs and operating costs; the lower planning model takes the unit output as the decision variable, with the goal of minimizing operating costs. Among them, the operating cost is obtained based on the line risk cost of insufficient line transmission flexibility.

[0092] The relationship between decision variables in the upper-level planning model:

[0093] ① In the upper model, the site selection and capacity configuration of energy storage complement each other. If the capacity configuration is too small after the site selection of the energy storage system is determined, it may not meet the flexibility requirements; if the capacity configuration is too large, it may lead to a waste of investment. Therefore, the site selection and capacity configuration need to be optimized in a coordinated manner;

[0094] ② When the energy storage system is located close to a high-load area, energy storage can balance load fluctuations locally and reduce the need for long-distance transmission. This may reduce the need for transmission line expansion. On the contrary, if the energy storage system is located farther away, more transmission line expansion may be required to ensure that the energy storage equipment can effectively participate in system scheduling. Therefore, the site selection of energy storage directly affects the planning of transmission line expansion. The more reasonable the site selection, the smaller the need for transmission line expansion and the lower the investment cost of the system.

[0095] ③ If a large-capacity energy storage system is configured in certain areas, power fluctuations can be absorbed locally, which may reduce the pressure of long-distance power transmission and reduce the need for transmission line expansion. However, excessive energy storage configuration will increase investment costs. If the transmission line itself already has sufficient transmission capacity, it may be more economical to expand the line. Therefore, a trade-off is needed between capacity and line expansion to seek the optimal solution for the overall system.

[0096] The relationship between the decision variables of the upper-level planning model and the lower-level planning model:

[0097] ① Upper-level decisions affect lower-level decisions: Upper-level planning schemes directly affect lower-level operational decisions. The location and capacity of energy storage determine the flexibility resources available during system operation, while the expansion of transmission lines affects the capacity and stability of the entire power transmission system. Therefore, upper-level planning determines the physical configuration and operational constraints of the lower-level model;

[0098] ② Feedback from the lower layer affects the decision-making of the upper layer: The operating costs (including penalty costs, flexibility load rate, etc.) calculated by the lower layer in a specific operating scenario will be fed back to the upper layer as an important basis for the upper layer to conduct the next round of optimization. If the operating costs of the lower layer are too high, it indicates that the planning scheme of the upper layer may be insufficient (for example, unreasonable energy storage site selection or insufficient expansion of transmission lines), and the upper layer needs to adjust the planning scheme to reduce the overall cost;

[0099] ③In this two-level programming model, optimization is accomplished through an iterative process of the upper and lower levels. The specific process is as follows:

[0100] 1. Upper-layer optimization: The upper layer first provides an initial planning scheme (e.g., energy storage site selection, energy storage capacity configuration, and transmission line expansion);

[0101] 2. Lower-layer optimization: The lower layer performs operation optimization based on the upper-layer solution and calculates the actual operation cost and risk;

[0102] 3. Feedback iteration: The lower layer feeds back the operating results, and the upper layer adjusts its planning scheme based on these feedbacks (such as operating costs, penalty costs) until the optimal solution is found.

[0103] The specific steps are as follows: construct a two-layer multi-stage planning model for the coordination of energy storage and transmission lines, including an upper-layer planning model and a lower-layer planning model; the upper-layer planning model uses the investment location, capacity and power of energy storage and the investment location and number of new circuits of transmission lines as decision variables, with the goal of minimizing the sum of investment and construction costs and operating costs; the lower-layer planning model uses the unit output as a decision variable, with the goal of minimizing operating costs. The calculation formula of the planning layer objective function F is:

[0104] minF=F inv +F opr (5)

[0105] In formula (5), F inv is the investment and construction cost; F opr It is the running cost.

[0106] The calculation formula for investment and construction cost is:

[0107]

[0108] In formula (6), k and K represent the kth stage and the number of planning stages respectively; y and Y k denote the yth year of the kth stage and the number of years of the kth stage respectively; r is the discount rate; They represent the investment and construction costs of the k-th stage transmission line and energy storage that have not yet been converted to present value; R L , R E Respectively represent the depreciation coefficients of transmission lines and energy storage; z k,l , L l are the number of expansions of the transmission line l in the kth stage, the expansion cost per unit length of the transmission line in the kth stage, and the length of the transmission line l; x k,i is a 01 variable, indicating whether energy storage is built at node i in stage k; E k,i and P k,iThey represent the capacity and power of the energy storage configuration of node i in the kth stage respectively; and They represent the construction cost of energy storage per unit capacity and power at the kth stage respectively; T A Indicates the age of the equipment;

[0109] The formula for calculating the operating cost is:

[0110]

[0111] In formula (7), T is the total operation period; σ is the operation cost conversion coefficient, which is 365; P k,s,g,t is the output of thermal power unit g at time t under scenario s in stage k; N g is the number of thermal power units; a g 、b g 、c g is the fuel coefficient cost of thermal power unit g; wt and χ load They are the penalty costs for wind curtailment and load shedding per unit power; and are respectively the wind abandonment and load shedding of node i at time t under scenario s in the kth stage.

[0112] The constraints of the upper-level planning model include the number of transmission lines and the existence of energy storage site constraints.

[0113] 1) The number of transmission lines is constrained as follows:

[0114]

[0115] In formula (8), is the maximum number of expansions of transmission line l.

[0116] 2) The constraints on energy storage site selection are:

[0117]

[0118] In formula (9), N E The number of nodes that are allowed to configure energy storage.

[0119] The constraints of the lower-level planning model include power balance constraints, thermal power unit operation constraints, energy storage operation constraints, node voltage phase angle constraints, line flow constraints, node wind curtailment and load shedding constraints.

[0120] 1) The power balance constraint is:

[0121]

[0122] In formula (10), and They are respectively the wind power output, energy storage charging and discharging power and load power at node i at time t in the kth stage scenario s.

[0123] 2) The operating constraints of thermal power units are:

[0124]

[0125] Where P g,min and P g,max are the upper and lower limits of the output of thermal power unit g; r g,down and r g,up are the downward and upward climbing rates of the thermal power unit g respectively; Δt is the time scale of operation.

[0126] 3) Energy storage operation constraints are:

[0127]

[0128] In formula (12), E k,s,i,t is the energy storage capacity of node i at time t in the kth stage scenario s; μ min and μ max are the minimum and maximum states of charge of energy storage, respectively.

[0129] 4) The node voltage phase angle constraint is:

[0130] θ i,min ≤θ k,s,i,t ≤θ i,max (13)

[0131] In formula (13), θ k,s,i,t is the voltage phase angle of node i at time t in the kth stage scenario s; θ i,min ,θ i,max are the upper and lower limits of the voltage phase angle of node i respectively.

[0132] 5) Line flow constraints are:

[0133] -P l max ≤P k,s,l,t ≤P l max (14) In formula (14), P k,s,l,t is the power passing through the transmission line l at time t in the kth stage scenario s; P l max is the maximum power allowed to pass through the transmission line l.

[0134] 6) The node wind abandonment and load shedding constraints are:

[0135]

[0136] In formula (15), r wt and r load are the maximum allowable proportions of wind curtailment and load shedding, respectively.

[0137] Step S4: Solve the energy storage and transmission line coordinated planning model to obtain the optimal operation scheduling plan for the coordination of energy storage and transmission lines.

[0138] The specific steps are as follows: using the particle swarm algorithm to solve the upper-level planning model to determine the energy storage configuration and transmission network expansion plan; using the GUROBI solver to solve the lower-level planning model to obtain the optimal operation scheduling plan.

[0139] As attached Figure 3 As shown, it includes the following steps: Step 1: Input the parameters in the energy storage and transmission line collaborative planning model and the particle swarm algorithm parameters; Step 2: Generate the initial population of the particle swarm of the upper-level planning model, and the initial population of the particle swarm includes the energy storage site selection and capacity determination and the new transmission line; Step 3: Input the upper-level planning scheme obtained by the upper-level planning model into the lower-level planning model, and input the typical wind power and load scenarios of each stage; Step 4: Call the GUROBI solver to solve the optimal operation of the lower-level planning model, and return the result to the upper-level planning model; Step 5: Calculate the comprehensive cost, that is, calculate the investment and construction cost and the operating cost; Step 6, iterative optimization, compare and update the optimal individual and the global optimum until the optimal solution is found.

[0140] In step 3, the upper-level planning solution specifically refers to the solution obtained in the upper-level planning model. The upper-level planning solution obtained by the upper-level planning model is input into the lower-level planning model, that is, the optimization results of the upper-level planning model (i.e., energy storage site selection, capacity configuration, and transmission line expansion plan) are used as input and passed to the lower-level planning model. Specifically, the upper-level planning solution includes:

[0141] ① Site selection and capacity of energy storage configuration: The upper-level planning model determines which nodes in the system to install energy storage devices, as well as the capacity and power of each energy storage device through optimization calculations.

[0142] ② Investment planning of transmission lines: The upper-level planning also involves the planning of new or expanded transmission lines, including the location of the lines that need to be expanded and the number of loops of the new lines to meet the future needs of the system.

[0143] A specific example is as follows:

[0144] The present invention adopts an actual system in a province in northwest China. The system topology is as follows: Figure 4As shown. The system has a total of 37 nodes. The planning period is set to 9 years, divided into 3 stages, each stage is 3 years, and it is assumed that equipment investment and construction will be carried out at the beginning of the first year of each stage. Taking into account the increase in load and the increase in the proportion of wind power installed capacity, the load growth rate of each stage is set to 15%, and the wind turbine installed capacity increase rate is 25%. Considering 4 typical wind power output scenarios and 4 typical load scenarios, 16 typical scenarios are arranged and combined, and each scenario considers 24 operating time points. Assuming that the output of each wind power plant in the same scenario is only related to the installed capacity, the candidate node for energy storage configuration is set on the wind power side, and the maximum number of channel lines is set to 4. The initial unit power cost and capacity cost of energy storage are set to 821,100 yuan / MW and 1,563,800 yuan / (MWh) respectively, with a service life of 10 years; the line cost is 1 million yuan / km, and the economic service life is 25 years. For other parameter designs, refer to Table 1.

[0145] Table 1 Planning model parameters

[0146]

[0147] The present invention provides three solutions. Solution 1: Only energy storage configuration; Solution 2: Only transmission network expansion planning; Solution 3: The energy storage and transmission line coordinated planning proposed by the present invention. The cost comparison of each solution is shown in Table 2.

[0148] Table 2 Cost comparison of various solutions

[0149]

[0150] As can be seen from Table 2, the investment and construction cost of Option 1 is the highest. Although energy storage configuration can improve the flexibility and reliability of the power grid to a certain extent, its improvement effect on the overall power system is limited due to the lack of expansion of the transmission network. Option 2 has the highest total cost. This option improves the transmission capacity of the power grid by expanding the transmission network lines. Although its construction investment cost is the lowest among the three options, its operating cost is the highest. Option 3 combines the advantages of energy storage configuration and transmission network expansion, showing the highest cost-effectiveness. Its total cost is the lowest among the three options. It is not only the most advantageous in total cost, but also shows higher economy and benefits in cost allocation at each stage. Option 3 achieves the optimal allocation of resources and the comprehensive improvement of the power system through the coordinated planning of energy storage and transmission network.

[0151] In order to further evaluate the operating cost-effectiveness of each scheme, a comparative analysis of the penalty costs under different schemes was conducted. Figure 5 is the penalty cost of each plan at different planning stages. Figure 5It can be seen that the penalty costs of the three schemes gradually increase in each stage. The total penalty cost of Scheme 3 is the lowest, and the penalty cost between stages increases slowly. In contrast, the penalty costs of Schemes 1 and 2 are higher, and the penalty costs in the third stage are much higher than those of Scheme 3. Scheme 3 effectively reduces the cost of wind curtailment, load shedding, and the risk cost of insufficient line flexibility through the coordinated planning of energy storage configuration and transmission network expansion.

[0152] In order to further analyze the impact of line flexibility load rate on penalty cost and investment and construction cost, the present invention sets different maximum line flexibility load rates LFLR allowed regardless of operation risk between [0.74, 0.86] m . Figure 6 The penalty cost and investment cost of the planning results under different values ​​are shown. m As the penalty cost increases, the penalty cost also increases. m When it is greater than 0.82, the increase becomes very small. m This indicates that setting the LFLR too small m Although it will reduce penalty costs and increase flexibility resources, it will also lead to equipment redundancy, thereby increasing investment costs. m This will increase penalty costs, leading to more wind curtailment, load shedding, and line overload in the system, which does not meet the system's operational flexibility and reliability requirements. m The value of is an effective way to reduce system risk and improve system economy in the coordinated planning of energy storage and transmission lines.

[0153] The beneficial effects of the present invention are:

[0154] 1. This invention proposes and defines "line flexibility load rate" to measure and evaluate the flexibility of line transmission. This indicator can effectively reflect the ability of the line to meet the node flexibility requirements and more comprehensively and accurately evaluate the transmission capacity and operation risk of the line;

[0155] 2. The present invention adopts a two-layer multi-stage planning method to organically combine energy storage with transmission network expansion. This method not only takes into account the long-term factors in power grid planning, but also effectively balances the investment and construction costs and operating costs through iterative optimization of upper-level planning and lower-level operation, avoiding the limitations of single planning. This method not only improves system flexibility, but also minimizes costs.

[0156] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for coordinated planning of energy storage and transmission lines considering transmission flexibility, characterized in that: The method for coordinated planning of energy storage and transmission lines considering transmission flexibility comprises the following steps: Step S1: defining and calculating a line flexibility load rate, where the line flexibility load rate is used to characterize the ability of a transmission line to flexibly transmit loads; Step S2: Based on the conditional risk value theory, quantify the operation risk of the line flexibility load rate exceeding the limit, and obtain the line risk cost of insufficient line transmission flexibility; Step S3: construct a two-layer multi-stage planning model for coordinated energy storage and transmission lines with constraints, the two-layer multi-stage planning model for coordinated energy storage and transmission lines includes an upper-layer planning model and a lower-layer planning model; the upper-layer planning model takes the investment location, capacity and power of energy storage and the investment location and number of new loops of transmission lines as decision variables, with the goal of minimizing the sum of investment and construction costs and operating costs; the lower-layer planning model takes the unit output as a decision variable, with the goal of minimizing operating costs; the operating costs are obtained based on the line risk cost of insufficient line transmission flexibility; Step S4: Solve the energy storage and transmission line coordinated planning model to obtain the optimal operation scheduling plan for the coordination of energy storage and transmission lines.

2. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 1, characterized in that: In the step S1, the line transmission flexibility demand is defined and calculated, and the line flexibility load rate is calculated based on the line transmission flexibility demand.

3. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 2, characterized in that: The line transmission flexibility requirement is: In the formula, F l,t is the line transmission flexibility requirement of line l at time t; N is the number of nodes; PTDF l,i is the power transfer distribution factor of line l at time t; is the flexibility requirement of node i at time t; The line flexibility load rate is: where, LFLR l,t is the line flexibility load rate of line l at time t; P l max is the maximum permissible transmission capacity of line l; P l,t-1 is the transmission power of line l at time t-1.

4. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 3 is characterized in that: Flexibility requirement of node i at time t for: In the formula, is the net load power of node i at time t, is the net load power of node i at time t+1.

5. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 1, characterized in that: In step S2, the line risk cost of insufficient line transmission flexibility is: In the formula, is the line risk cost of insufficient transmission flexibility of the line at time t; δ is the auxiliary decision variable for calculating conditional risk value; α is the confidence level; s and S are the dimension and scenario set of the scenario respectively; ρ s is the probability of the sth scenario; line is the penalty cost for exceeding the line flexibility load rate limit by 1%; Ω l For transmission line collection; LFLR s,l,t is the line flexibility load rate of line l at time t in the sth scenario; LFLR m The maximum line flexibility load rate allowed regardless of operational risk (LFLR s,l,t -LFLR m ) + represents max{LFLR s,l,t -LFLR m ,0}, express 6. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 1, characterized in that: In step S3, the calculation formula of the investment and construction cost is: Where k and K represent the kth stage and the number of planning stages respectively; y and Y k denote the yth year of the kth stage and the number of years of the kth stage respectively; r is the discount rate; They represent the investment and construction costs of the k-th stage transmission line and energy storage that have not yet been converted to present value; R L , R E Respectively represent the depreciation coefficients of transmission lines and energy storage; z k,l , L l are the number of expansions of the transmission line l in the kth stage, the expansion cost per unit length of the transmission line in the kth stage, and the length of the transmission line l; x k,i is a 01 variable, indicating whether energy storage is built at node i in stage k; E k,i and P k,i They represent the capacity and power of the energy storage configuration of node i in the kth stage respectively; and They represent the construction cost of energy storage per unit capacity and power at the kth stage respectively; T A Indicates the age of the equipment; The calculation formula of the operating cost is: Where, T is the total operation period; σ is the operation cost conversion coefficient; P k,s,g,t is the output of thermal power unit g at time t under scenario s in stage k; N g is the number of thermal power units; a g 、b g 、c g is the fuel coefficient cost of thermal power unit g; wt and χ load They are the penalty costs for wind curtailment and load shedding per unit power; and are respectively the wind abandonment and load shedding of node i at time t under scenario s in the kth stage.

7. The method for coordinated planning of energy storage and transmission lines considering transmission flexibility according to claim 6, characterized in that: The value of σ is 365.

8. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 6, characterized in that: The constraints of the upper-level planning model include the number of transmission lines and the existence of energy storage site selection constraints; The line number constraint of the transmission channel is: In the formula, is the maximum number of expansions of the transmission line l; The energy storage site selection has the following constraints: Where N E The number of nodes that allow energy storage to be configured; The constraints of the lower-level planning model include power balance constraints, thermal power unit operation constraints, energy storage operation constraints, node voltage phase angle constraints, line flow constraints, node wind abandonment and load shedding constraints; The power balance constraint is: In the formula, and They are respectively the wind power output, energy storage charging and discharging power and load power at time t on node i in the kth stage scenario s; The operating constraints of the thermal power unit are: Where P g,min and P g,max are the upper and lower limits of the output of thermal power unit g; r g,down and r g,up are the downward and upward climbing rates of thermal power unit g respectively; Δt is the time scale of operation; The energy storage operation constraints are: In the formula, E k,s,i,t is the energy storage capacity of node i at time t in the kth stage scenario s; μ min and μ max are the minimum and maximum states of charge of energy storage, respectively; The node voltage phase angle constraint is: i i,min ≤θ k,s,i,t ≤θ i,max , In the formula, θ k,s,i,t is the voltage phase angle of node i at time t in the kth stage scenario s; θ i,min ,θ i,max are the upper and lower limits of the voltage phase angle of node i respectively; The line power flow constraint is: -P l max ≤P k,s,l,t ≤P l max , Where P k,s,l,t is the power passing through the transmission line l at time t in the kth stage scenario s; P l max is the maximum power allowed to pass through the transmission line l; The node wind abandonment and load shedding constraints are: In the formula, r wt and r load are the maximum allowable proportions of wind curtailment and load shedding, respectively.

9. The method for collaborative planning of energy storage and transmission lines considering transmission flexibility according to claim 1, characterized in that: In step S4, a particle swarm algorithm is used to solve the upper-level planning model to determine the energy storage configuration and the transmission network expansion plan; and a GUROBI solver is used to solve the lower-level planning model to obtain the optimal operation scheduling plan.

10. The method for coordinated planning of energy storage and transmission lines considering transmission flexibility according to claim 9, characterized in that: The step S4 comprises the following steps: Step 1: Input the parameters of the energy storage and transmission line coordinated planning model and the particle swarm algorithm parameters; Step 2: Generate an initial particle swarm population of the upper-level planning model, wherein the initial particle swarm population includes energy storage site selection and capacity determination and new transmission lines; Step 3: inputting the upper-level planning scheme obtained by using the upper-level planning model into the lower-level planning model, and inputting typical wind power and load scenarios of each stage; Step 4: Call the GUROBI solver to solve the optimal operation of the lower-level planning model, and return the result to the upper-level planning model; Step 5: Calculate the investment and construction cost and the operating cost; Step 6: Iterate and optimize, compare and update the optimal individual and global optimal until the optimal solution is found.

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