A source network planning method, system, medium and device

Abstract

The application discloses a kind of source network planning method, system, medium and equipment, establish the double-layer source network planning model including planning layer and optimization operation layer, construct multi-objective function, carry out network frame planning, power supply and energy storage planning respectively in two stages;In optimization operation layer, system power balance operation constraint is considered, and single-day operation cost is solved and returned to planning layer;Particle swarm optimization algorithm is used to solve double-layer source network planning model, and the position and capacity of network frame, power supply and energy storage are obtained based on the model of planning layer, and the model of optimization operation layer takes system economy as optimization target, through multi-scenario simulation calculation, the operation cost of receiving-end power grid is optimized, and the result is transmitted to planning layer, and the optimal scheme of source network collaborative planning is solved by the information iteration interaction of planning layer and optimization operation layer, to realize the system adequacy and transient stability level promotion under new energy access scene.The application can realize the system adequacy and transient stability level promotion under new energy access scene.

Description

Technical Field

[0001] This invention belongs to the field of energy planning technology, specifically relating to a source-grid planning method, system, medium, and equipment. Background Technology

[0002] Currently, the uncertainty and strong random fluctuations in the output of new energy sources create a sharp contradiction with the requirement to ensure a continuous and stable power supply. Following conventional development models inevitably leads to numerous problems such as the establishment of large-scale redundant backup power sources, inefficient power transmission, and disruptions to the safe and stable operation of the system, highlighting issues of power system stability and economics. Therefore, it is necessary to improve the utilization efficiency of new energy sources and the safety and stability of the power grid through a planning approach that coordinates sufficiency and transient voltage stability.

[0003] Most existing power system planning studies, both domestically and internationally, involve separate planning for power sources and power grids. However, new power systems possess flexible and varied operating strategies on both the power source and grid sides. Planning approaches that focus solely on one side are no longer adequate for the development of new power systems. Therefore, the consideration of coordinated planning between power sources and the grid has attracted widespread attention.

[0004] In the field of source-grid coordinated planning research, one approach is the source-grid coordinated extended planning model that considers reliability standards. Compared with individual power generation planning, coordinated planning offers better economic efficiency and can handle higher peak loads. However, current planning methods primarily aim to meet load demand and ensure the safe and stable operation of the power grid. They still lack theoretical research that comprehensively considers the adequacy of system regulation capacity and transient voltage stability, making it difficult to improve the power grid's objective capacity to support new energy sources. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a source-network planning method, system, medium and device to address the shortcomings of the prior art, and to solve the technical problem of source-network coordinated planning that balances sufficiency and transient voltage stability.

[0006] The present invention adopts the following technical solution:

[0007] A source-network planning method includes the following steps:

[0008] S1. Establish a two-layer source-grid planning model that includes a planning layer and an optimized operation layer. In the planning layer, consider the investment and construction costs, the expected cost of power shortage, and the transient voltage stability index. Construct a multi-objective function and carry out grid planning, power source and energy storage planning in two stages respectively. In the optimized operation layer, consider the system power balance operation constraints, solve the daily operating cost and return it to the planning layer.

[0009] S2. The particle swarm optimization algorithm is used to solve the two-layer source-grid planning model obtained in step S1. Based on the planning layer model, the location and capacity of the grid, power sources and energy storage are obtained. The optimization operation layer model takes system economy as the optimization objective. Through multi-scenario simulation calculation, the operating cost of the receiving-end grid is optimized and the results are passed to the planning layer. Through the information iterative interaction between the planning layer and the optimization operation layer, the optimal solution for source-grid collaborative planning is obtained, thereby improving the system adequacy and transient stability level under the scenario of new energy access.

[0010] Specifically, in step S1, the grid planning and power supply and energy storage planning are carried out in two phases:

[0011] The first phase aims to optimize the grid construction plan by balancing the construction cost and system reliability of the power lines. The second phase takes into account the construction costs of power sources and energy storage, as well as transient voltage stability indicators, to build a comprehensive optimization target and optimize the configuration capacity of generator sets, new energy sources, and energy storage.

[0012] Furthermore, the objective function min F1 for the first stage of the planning layer is as follows:

[0013] min F1=C linv +C r

[0014] The objective function min F2 for the second stage of the planning layer is as follows:

[0015] min F2=C dinv +w·ρ

[0016] Among them, C linv For the line investment cost, C r For the expected cost of insufficient power, C dinv ρ represents the investment and construction cost of conventional generator sets, new energy generator sets, and energy storage; w is the weighting coefficient of transient stability index; and ρ is the spectral radius of the small gain matrix.

[0017] Specifically, in step S1, the objective function min F3 for optimizing the runtime layer is:

[0018] min F3=C gen +C ess

[0019] Among them, C gen For conventional unit operating costs, C ess This refers to the operating costs of energy storage.

[0020] Specifically, in step S2, each particle in the particle swarm optimization algorithm is encoded using a 2×N matrix. The first row of the matrix is ​​encoded with 0 / 1, representing wind turbines or photovoltaics, respectively, and the second row is encoded with integers, representing the capacity of power sources or energy storage equipment. The Monte Carlo method is used to sample wind speed and solar intensity for each time period. The K-means algorithm is used to cluster the sampled new energy output scenarios to obtain multiple typical scenarios. The planning results are obtained by combining the solution methods of grid planning, power source and energy storage capacity planning, and operation scheduling solution methods.

[0021] Furthermore, the solution process for the space frame planning problem is as follows:

[0022] S201. Initialize the population parameters and generate a particle population X of N network schemes;

[0023] S202. Based on the fitness function, select superior individuals from particle population X for crossover and mutation to generate N network planning schemes.

[0024] S203. Transfer the N grid schemes to the lower layer as conditions for solving power supply, energy storage planning and operation problems;

[0025] S204. Based on the power supply and energy storage planning scheme, calculate the fitness of particle population X. When the iteration termination condition is reached, output the solution corresponding to the optimal individual and end the search.

[0026] Furthermore, the solution process for the power supply and energy storage capacity planning problem is as follows:

[0027] S205. Monte Carlo and K-means algorithms were used to obtain several typical power output scenarios for new energy sources;

[0028] S206. Based on the particle population X of the grid scheme obtained from the upper-level grid planning, generate a particle population Y of power supply and energy storage access location codes and construction capacity.

[0029] S207. Based on the power and scenario probability of new energy generator units under typical power output scenarios, optimize the operation solution according to each power source and energy storage construction plan, and calculate the system operating cost based on the output of conventional units, wind power, and photovoltaic power.

[0030] S208. Based on the system operating cost and the investment and construction costs of power supply and energy storage, calculate the fitness of the particle population Y obtained in step S206. If the iteration termination condition is met, execute step S209; otherwise, update the population and return to step S206.

[0031] S209. Return the calculated overall objective function to the upper layer, update the optimal value of the objective function, and determine the optimal planning scheme.

[0032] In a second aspect, embodiments of the present invention provide a source-network planning system, comprising:

[0033] The module is constructed to establish a two-layer source-grid planning model that includes a planning layer and an optimized operation layer. In the planning layer, the investment and construction costs, the expected cost of power shortage, and the transient voltage stability index are considered. A multi-objective function is constructed, and grid planning, power source planning, and energy storage planning are carried out in two stages. In the optimized operation layer, the system power balance operation constraints are considered, the daily operating cost is solved, and the result is returned to the planning layer.

[0034] The planning module uses the particle swarm optimization algorithm to solve the two-layer source-grid planning model obtained from the construction module. Based on the planning layer model, the location and capacity of the grid structure, power sources and energy storage are obtained. The optimization operation layer model takes system economy as the optimization objective. Through multi-scenario simulation calculations, the operating cost of the receiving-end grid is optimized and the results are passed to the planning layer. Through the information iterative interaction between the planning layer and the optimization operation layer, the optimal solution for source-grid collaborative planning is obtained, thereby improving the system adequacy and transient stability level under the scenario of new energy access.

[0035] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described source-network planning method.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described source-network planning method.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects:

[0038] A source-grid planning method is proposed, which constructs a two-layer source-grid planning model. At the planning layer, investment and construction costs, expected costs due to power shortages, and transient voltage stability indicators are considered, and a multi-objective function is constructed. Network planning, power source planning, and energy storage planning are performed in two stages. At the optimization and operation layer, operational constraints such as system power balance are considered, and the daily operating cost is calculated and returned to the upper layer. A particle swarm optimization algorithm is used for the two-layer optimization solution, improving the system adequacy and transient stability in renewable energy integration scenarios.

[0039] Furthermore, the first phase aims to optimize the grid construction scheme with the goal of minimizing line construction costs and system reliability; the second phase takes into account the construction costs of power sources and energy storage, as well as transient voltage stability indicators, to construct a comprehensive optimization target, optimize the configuration capacity of generator sets, new energy sources, and energy storage, and solve optimization problems in stages to achieve coordinated planning of power sources and grids.

[0040] Furthermore, by considering the reliability index C in the objective function rA comprehensive objective function is constructed using the system's construction cost, reliability, and transient stability index ρ, respectively, to achieve a holistic optimization of system construction cost, reliability, and transient stability.

[0041] Furthermore, by optimizing the objective function min F3 of the operation layer, considering the daily operating costs of the generator set and energy storage, the optimal operation scheme is obtained.

[0042] Furthermore, the Monte Carlo method is used to sample wind speed and light intensity at different times, and the K-means algorithm is used to cluster the sampled new energy power output scenarios to obtain multiple typical scenarios and their corresponding probabilities. Based on the power of new energy generator sets under typical power output scenarios, the optimization operation solution is performed, thereby realizing the modeling of the power output of new energy generator sets with uncertainty.

[0043] Furthermore, the particle swarm optimization algorithm is used to solve the source network planning problem, thereby achieving an effective solution to this complex nonlinear optimization model.

[0044] Furthermore, the coding of the upper-level grid planning problem is optimized, and the set of alternative grid planning schemes is used as input conditions for power supply, energy storage planning and optimized operation problems.

[0045] Furthermore, for the coding of the lower-level power supply and energy storage planning and optimization operation problems, the optimal grid structure and power supply and energy storage planning schemes are determined through iterative solutions based on the set of grid planning schemes.

[0046] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0047] In summary, this invention employs the particle swarm optimization algorithm to iteratively solve a two-level optimization problem, thereby obtaining a source-network planning scheme with optimal comprehensive objectives and improving the system adequacy and transient stability in new energy access scenarios.

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0049] Figure 1 This is a source network planning framework diagram of the present invention;

[0050] Figure 2 This is a flowchart of the two-layer particle swarm optimization solution of the present invention;

[0051] Figure 3 This is a schematic diagram of the optimal planning scheme A1 of the present invention;

[0052] Figure 4 This is a schematic diagram of the optimal planning scheme A2 of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0055] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" relationship.

[0057] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0058] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0059] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0060] Please see Figure 1 This invention provides a source-network planning method, based on the consideration of sufficiency and transient voltage stability, comprising the following steps:

[0061] S1. Establish a two-stage, two-layer source-grid planning model. In the planning layer, consider the investment and construction costs, the expected cost of insufficient power, and the transient voltage stability index. Construct a multi-objective function and carry out grid planning, power supply and energy storage planning in two stages respectively. In the optimization operation layer, consider the system power balance and other operational constraints, solve the daily operating cost and return it to the upper layer.

[0062] The two phases are as follows: The first phase aims to optimize the grid construction plan with the goal of minimizing line construction costs and system reliability; the second phase takes into account the construction costs of power sources and energy storage, as well as transient voltage stability indicators, to construct a comprehensive optimization target, optimize the configuration capacity of generator sets, new energy sources, and energy storage, and realize grid planning.

[0063] In the two-stage, two-layer source network planning model, the planning layer problem is optimized based on the decisions of the planning layer. At the same time, the planning results of the optimization layer are fed back to correct the planning parameters of the planning layer. The optimal solution is transmitted between the planning layer and the optimization layer to achieve iterative interaction of information between the two layers, thereby obtaining a solution that satisfies both the planning layer and the optimization layer.

[0064] In the planning decision-making model at the planning level, during the grid planning stage, considering both the investment and construction costs of transmission lines and the system reliability objective, the objective function min F1 of the grid planning model is constructed as follows:

[0065] min F1=C linv +C r

[0066] Among them, C linv For the line investment cost, C r Expected cost for insufficient power supply.

[0067]

[0068]

[0069] Among them, Clinv For the construction cost of transmission lines, κ t Here, x is the present value factor, r is the discount rate, and x is the present value factor. lt The variable is 0-1, representing whether line l has been constructed. l Indicates the length of line l. This indicates the construction cost per unit length of the line.

[0070] The reliability optimization objective is to reduce the expected EENS cost C due to insufficient power. r The details are as follows:

[0071]

[0072] Where VOLL is a constant, representing the cost of loss of load, and Γ jtk For the off-load power (MW), ρ k The probability of failure k occurring is defined based on the Bernoulli distribution:

[0073]

[0074] Where, x k U represents the 0-1 values ​​present in each line. k Let ρ0 represent the probability of failure for line k, and ρ0 represent the probability of no failure.

[0075] The relevant variables in the construction plan must meet the following requirements:

[0076]

[0077] Where, x lt Let {0, 1} represent the construction status of line l, where 1 indicates construction has commenced and 0 indicates no construction has begun. The set Ω... L+ This is a collection of lines to be constructed.

[0078] The power grid must supply power to all load points and should avoid loop-shaped power supply structures, as shown in the following expression:

[0079]

[0080] Among them, Ψ LL Ψ is the set of branches contained in the ring structure. EL For the original line branch set, Ψ NL For the branch set of the line to be newly built, N LL For branch road collection Ψ LL The total number of branches contained in it, x e and x k These represent the states of branches e and k, respectively.

[0081] The construction capacity of transmission lines must meet the requirements.

[0082] Sl ≤S max

[0083] Among them, S l S represents the construction capacity corresponding to line l. max This represents the maximum allowable transmission capacity.

[0084] For the second-stage model of the planning layer, the optimization objective considers the investment and construction costs of conventional generator sets, new energy generator sets, and energy storage, as well as the transient voltage stability index, and establishes the comprehensive objective function as follows:

[0085] min F2=C dinv +w·ρ

[0086]

[0087]

[0088] Among them, C dinv The investment and construction costs for conventional generator sets, new energy generator sets, and energy storage. The investment costs per unit capacity for conventional generating units, wind turbines, photovoltaic units, and energy storage equipment are respectively, g i E w E p E S These represent the construction capacity of power supply and energy storage equipment, respectively. t ρ is the present value factor, and r is the discount rate. Based on the input-state stability theory, the spectral radius ρ of the small gain matrix represents the system's transient voltage stability index, and w is the weighting coefficient of the transient stability index.

[0089] The relevant variables of the construction plan must meet the following requirements.

[0090]

[0091]

[0092]

[0093] Where x gt ,x ret ,x st ∈{0,1} represents the construction status of generator g, new energy unit re, and energy storage device s, respectively, where 1 indicates construction and 0 indicates no construction. The set Ω G+ ,Ω RE+ ,Ω S+ This is a collection of components to be constructed.

[0094] The generator output constraint is expressed as:

[0095]

[0096] in, These represent the maximum and minimum capacities of generator i that are allowed to be constructed under the consideration of the grid planning scheme.

[0097] The capacity requirements for wind farms and photovoltaic power plants are as follows:

[0098] E w,min ≤E w ≤E w,max

[0099] E p,min ≤E p ≤E p,max

[0100] Among them, E w,max E w,min E represents the upper and lower limits of the planned capacity of the wind farm. p,max E p,min This refers to the upper and lower limits of the planned capacity of a photovoltaic power plant.

[0101] Energy storage configurations must meet the following constraints:

[0102] E s,min ≤E s ≤E s,max

[0103] Among them, E s,max E s,min Configure upper and lower limits for the capacity of energy storage device s.

[0104] The node power balance constraints are as follows:

[0105]

[0106] Where, p rt p represents the power generation of the new energy unit r during time period t. st f represents the power of the energy storage device s during time period t. lt d represents the active power flow of transmission line l connected to node n. jt Indicates load power. These correspond to the sets of generators, new energy units, energy storage and loads on node n, respectively, and r(l) and s(l) represent the first and last nodes of line l, respectively.

[0107] The existing power flow constraints are as follows:

[0108]

[0109] Extended line power flow constraint formula is as follows

[0110]

[0111] Among them, X l f represents the line reactance. l max Represents line capacity, θ s(l)t ,θ r(l)t These represent the voltage phase angles at the beginning and end nodes of line l, respectively. This indicates an existing set of routes.

[0112] The node voltage phase angle should be kept within the constraints, as expressed below:

[0113]

[0114] The proportion of renewable energy installed capacity to the total newly installed capacity of the system in that year should not be less than the prescribed installed capacity ratio requirement, as shown in the following expression:

[0115]

[0116] Where, ρ m This represents the monthly sequence of new energy installed capacity proportions, achieved by changing ρ. m The value P can be adjusted to control the proportion of new energy installed capacity in the total system installed capacity. i max Let i be the rated capacity of a power unit.

[0117] To ensure the stability of the system's transient voltage, the system's transient stability constraint is as follows:

[0118] ρ(G IOS )<1

[0119] G IOS =Γ IOS Z

[0120] Among them, G IOS Denotes the small gain matrix, ρ(G) IOS ) represents the small gain matrix G IOS spectral radius, Γ IOS Z is the input-output gain matrix, determined by the combined capacity of new energy and conventional power units at each node; Z is the input-output connection matrix, determined by the system network structure.

[0121] Based on the location and capacity of the grid structure, power source, and energy storage obtained from the two-stage planning model at the planning layer, an optimized operation model at the operation layer is established.

[0122] The objective function, which minimizes the overall system operating cost, is expressed as follows:

[0123] min F3=C gen +C ess

[0124]

[0125]

[0126] Among them, C gen For conventional unit operating costs, G i Du is the unit operating cost coefficient ($ / MWh). t Let t be the duration of the time interval, and g be the value of the interval. it Let C be the power generation of unit i during time period t. ess For energy storage operating costs, p st Let γ be the power exchanged between the energy storage device s and the grid during time period t, and let γ be the energy storage operating cost coefficient.

[0127] To ensure time-series power balance, a variable for wind and solar power curtailment is introduced, allowing load and generation balance to be met through wind curtailment measures. In addition, since the network modeling adopts a DC power flow model, losses during power transmission are not considered.

[0128] The constraints are as follows:

[0129]

[0130] Among them, P rt E represents the curtailment power of renewable energy unit r at node n during time period t. r p represents the installed capacity of new energy sources on node n. rkt This refers to the per-unit value of new energy power generation.

[0131] Generator output constraints are expressed as

[0132]

[0133] Among them, g it This represents the output power of generator i during time period t. These are the maximum and minimum power outputs of generator i during time period t, respectively, and are decision variables related to the unit's operating status and ramp-up capability.

[0134] Given the high proportion of renewable energy integration, the equivalent net load of the system changes more dramatically, and conventional units should be able to provide sufficient ramp / down ramp capacity to compensate for the power imbalance in the system.

[0135] The generator set's ramping constraints are as follows:

[0136]

[0137] in, These represent the generator set's climbing and descending capabilities per unit time, respectively.

[0138] The charging and discharging power of energy storage should not exceed the rated power, as constrained by the following formula:

[0139] -p smax ≤p st ≤p smax

[0140] Where, p smax This indicates the maximum charging and discharging power of the energy storage.

[0141] Meanwhile, the energy of the energy storage system should not exceed the upper and lower limits during operation:

[0142] e st =e st-1 +α st p st du t-1

[0143] e s,min ≤e st ≤e s,max

[0144]

[0145] Among them, e st e represents the remaining electricity of energy storage device s during time period t. s,max ,e s,min α represents the upper and lower limits of energy storage capacity. st This represents the charge / discharge efficiency coefficient.

[0146] To ensure the cyclic regulation capability of the energy storage system during optimized operation, the final energy level of the energy storage system should be restored to the same level as the initial energy level at the end of the operating cycle, satisfying the requirement that the initial and final energy states should be equal.

[0147] e s0 =e sN

[0148] Among them, e s0 e represents the initial amount of energy stored. sN It is used to store the remaining electricity at the end of the operating cycle.

[0149] The output of wind farms and photovoltaic power plants must meet upper and lower limits:

[0150]

[0151] Where, p w,max ,p w,min This represents the upper and lower limits of the active power output of the wind farm, p. p,max ,p p,min This indicates the upper and lower limits of the active power output of a photovoltaic power station.

[0152] To ensure the safe, reliable, and continuous generation and transmission of power, the system must have a certain reserve capacity of generating units to cope with uncertainties such as failures of new energy generating units and deviations in predicted output.

[0153] The constraints are as follows:

[0154]

[0155] in, This is to determine the system's required reserve capacity in both the positive and negative directions during time period t.

[0156] S2. The particle swarm optimization algorithm is used to solve the two-layer source-network planning model obtained in step S1. Based on the location and capacity of the grid, power sources and energy storage obtained from the two-stage planning model of the planning layer, the model of the operation layer is optimized with the system economy as the optimization objective. Through multi-scenario simulation calculations, the operating cost of the receiving-end grid is optimized and the results are passed to the planning layer. Through the information iterative interaction between the planning layer and the optimized operation layer, the optimal solution for source-network collaborative planning is obtained, thereby improving the system adequacy and transient stability level under the scenario of new energy access.

[0157] Heuristic algorithms are used to solve the source-network planning model.

[0158] The construction and deployment of conventional generator sets, new energy generator sets, and energy storage are affected by factors such as the uncertainty of new energy output, and belong to mixed integer nonlinear problems. Therefore, the particle swarm optimization algorithm is used to solve them. Each particle is encoded using a 2×N matrix. The first row of the matrix is ​​encoded with 0 / 1, representing wind turbines or photovoltaics, respectively. The second row is encoded with integers, representing the capacity of the power source or energy storage equipment.

[0159] Considering the uncertainty of the output of new energy units, the Monte Carlo method is used to sample the wind speed and light intensity at different times. The K-means algorithm is then used to cluster the sampled new energy output scenarios to obtain several typical scenarios.

[0160] Please see Figure 2 Combining the solution methods for grid planning, power supply and energy storage capacity planning, and operation scheduling, the overall solution process for the model is as follows:

[0161] Planning layer coding:

[0162] S201. Using Monte Carlo and K-means algorithms, several typical power output scenarios for new energy sources were obtained.

[0163] S202. Initialize the population parameters and generate population X for N network structure schemes;

[0164] S203. With the comprehensive goal of optimizing the construction cost and reliability of the transmission lines, determine the receiving-end power grid planning scheme;

[0165] S204. Transfer N space frame schemes to the optimized operation layer;

[0166] S205. Calculate the fitness of the particle population X, determine whether the iteration termination condition has been met, if so, output the solution corresponding to the best individual and end the search, otherwise update the population and return to step S203.

[0167] Optimize runtime layer coding:

[0168] S206. Based on the grid scheme X obtained from the planning layer, generate a population Y of power supply and energy storage access location codes and construction capacity;

[0169] S207. Optimize the operation solution based on each power source and energy storage construction plan, and calculate the system operating cost based on the output of conventional units, wind power, and photovoltaic power.

[0170] S208. Calculate the fitness of the particle population Y (if the power flow balance constraint is not met, return to step S207 to determine whether the iteration termination condition has been met. If yes, proceed to step S209; otherwise, update the population and return to step S206).

[0171] S209. Return the calculated overall objective function to the planning layer.

[0172] In another embodiment of the present invention, a source-network planning system is provided, which can be used to implement the above-mentioned source-network planning method. Specifically, the source-network planning system includes a construction module and a planning module.

[0173] The construction module establishes a two-layer source-grid planning model that includes a planning layer and an optimized operation layer. In the planning layer, the investment and construction costs, the expected cost of power shortage, and the transient voltage stability index are considered. A multi-objective function is constructed, and grid planning, power source planning, and energy storage planning are carried out in two stages. In the optimized operation layer, the system power balance operation constraints are considered, the daily operating cost is solved, and the result is returned to the planning layer.

[0174] The planning module uses the particle swarm optimization algorithm to solve the two-layer source-grid planning model obtained from the construction module. Based on the planning layer model, the location and capacity of the grid structure, power sources and energy storage are obtained. The optimization operation layer model takes system economy as the optimization objective. Through multi-scenario simulation calculations, the operating cost of the receiving-end grid is optimized and the results are passed to the planning layer. Through the information iterative interaction between the planning layer and the optimization operation layer, the optimal solution for source-grid collaborative planning is obtained, thereby improving the system adequacy and transient stability level under the scenario of new energy access.

[0175] The planning module includes a grid planning submodule and a power supply and energy storage capacity planning submodule, as detailed below:

[0176] The grid planning submodule initializes the population parameters and generates a particle population X of N grid schemes. Based on the fitness function, it selects superior individuals from particle population X for crossover and mutation to generate N grid planning schemes. The N grid schemes are then transferred to the lower layer as conditions for solving the power supply and energy storage planning and operation problems. Based on the power supply and energy storage planning schemes, the fitness of particle population X is calculated. When the iteration termination condition is met, the solution corresponding to the optimal individual is output and the search ends.

[0177] The power supply and energy storage capacity planning submodule uses Monte Carlo and K-means algorithms to obtain multiple typical power output scenarios for new energy sources. Based on the particle population X of the grid scheme obtained from the upper-level grid planning, a particle population Y is generated for the access location codes and construction capacity of power supply and energy storage. Based on the power of new energy generator units and scenario probabilities under typical power output scenarios, optimization operation is performed according to each power supply and energy storage construction scheme. The system operating cost is calculated based on the output of conventional units and wind and photovoltaic power. Based on the system operating cost and the construction cost of power supply and energy storage, the fitness of particle population Y is calculated. When the iteration termination condition is reached, the calculated total objective function is returned to the upper layer to update the optimal value of the objective function and determine the optimal planning scheme. Otherwise, the population is updated and the calculation is repeated.

[0178] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a source-network planning method, including:

[0179] A two-layer source-grid planning model, comprising a planning layer and an optimization operation layer, is established. The planning layer considers investment and construction costs, expected costs due to power shortages, and transient voltage stability indicators, constructing a multi-objective function and performing grid planning, power source planning, and energy storage planning in two phases. The optimization operation layer considers system power balance operation constraints, solves for daily operating costs, and returns the results to the planning layer. A particle swarm optimization algorithm is used to solve the two-layer source-grid planning model obtained from the construction module. Based on the planning layer model, the location and capacity of the grid, power sources, and energy storage are obtained. The optimization operation layer model uses system economy as the optimization objective, optimizes the receiving-end grid operating costs through multi-scenario simulation calculations, and transmits the results to the planning layer. Through iterative information interaction between the planning and optimization operation layers, the optimal solution for source-grid collaborative planning is obtained, improving system sufficiency and transient stability under new energy access scenarios.

[0180] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0181] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the source-network planning method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0182] A two-layer source-grid planning model, comprising a planning layer and an optimization operation layer, is established. The planning layer considers investment and construction costs, expected costs due to power shortages, and transient voltage stability indicators, constructing a multi-objective function and performing grid planning, power source planning, and energy storage planning in two phases. The optimization operation layer considers system power balance operation constraints, solves for daily operating costs, and returns the results to the planning layer. A particle swarm optimization algorithm is used to solve the two-layer source-grid planning model obtained from the construction module. Based on the planning layer model, the location and capacity of the grid, power sources, and energy storage are obtained. The optimization operation layer model uses system economy as the optimization objective, optimizes the receiving-end grid operating costs through multi-scenario simulation calculations, and transmits the results to the planning layer. Through iterative information interaction between the planning and optimization operation layers, the optimal solution for source-grid collaborative planning is obtained, improving system sufficiency and transient stability under new energy access scenarios.

[0183] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0184] Taking the improved Garver-6 node system as an example, a source-network planning method that integrates system adequacy and transient voltage stability is simulated and solved.

[0185] Assume the total load in the system is 1GW, with the load ratio of nodes 1 to 6 being 0.10:0.30:0.05:0.20:0.30:0.05, and the characteristics of each load node being consistent.

[0186] The cost of the line is 1 million yuan per km;

[0187] The unit capacity construction cost of the wind turbine is 2.3 million yuan / MW;

[0188] The unit capacity construction cost of photovoltaic power is 3.2 million yuan / MW;

[0189] The loss-of-load cost (VOLL) is set at 10,000 yuan / MWh.

[0190] The power cost and capacity cost of the energy storage device are RMB 1.1 million / MW and RMB 2.2 million / MWh, respectively.

[0191] The discount rate r is 5%;

[0192] The population size and number of generations for the particle swarm optimization algorithm are both set to 100.

[0193] The crossover rate and mutation rate were 0.9 and 0.1, respectively.

[0194] Table 1 compares the economics and expected load-bearing capacity of the two planning and construction schemes, as detailed below:

[0195] Table 1 Comparison and Analysis of Planning Schemes

[0196]

[0197] Based on the proposed model, the resulting planning and construction scheme is as follows: Figure 3 As shown in Figure A1, the dashed line represents the newly constructed line under the planning scheme, with conventional, photovoltaic, and wind turbine generators installed at nodes 1, 3, and 6, respectively.

[0198] Without considering system adequacy and transient voltage stability optimization in the objective function, the obtained planning scheme is as follows: Figure 4 As shown in Table 1, denoted by A2, the dashed lines represent newly constructed lines. The planning cost, reliability, and transient stability indicators are shown in Table 1.

[0199] In summary, the present invention provides a source-network planning method, system, medium, and device that uses the particle swarm optimization algorithm to iteratively solve a two-level optimization problem, thereby obtaining a source-network planning scheme with optimal comprehensive objectives. This can improve the system adequacy and transient stability in new energy access scenarios.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0201] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0202] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0203] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0204] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0205] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0206] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0207] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0208] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0209] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0210] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A source network planning method, characterized by, Includes the following steps: S1, a double-layer source-grid planning model including a planning layer and an optimal operation layer is established, investment construction cost, expected cost of power shortage and transient voltage stability index are considered in the planning layer, a multi-objective function is constructed, and grid planning, power supply and energy storage planning are respectively performed in two stages; in the optimal operation layer, system power balance operation constraints are considered, single-day operation cost is solved and returned to the planning layer, and the target function of the first stage of the planning layer is as follows: Objective function for second stage of planning layer As follows: in, For line investment costs, For the expected cost of insufficient power, The investment and construction costs for conventional generator sets, new energy generator sets, and energy storage. w The weighting coefficients for the transient stability index are... The spectral radius of the small gain matrix; S2. The particle swarm optimization algorithm is used to solve the two-layer source-network planning model obtained in step S1. Based on the planning layer model, the location and capacity of the network structure, power sources and energy storage are obtained. The optimization operation layer model takes the system economy as the optimization objective and passes the results to the planning layer. Through the information iterative interaction between the planning layer and the optimization operation layer, the optimal solution for source-network collaborative planning is obtained.

2. The source-network planning method according to claim 1, characterized in that, In step S1, the grid planning and power supply and energy storage planning are carried out in two phases, as follows: The first phase aims to optimize the grid construction plan by balancing the construction cost and system reliability of the power lines. The second phase takes into account the construction costs of power sources and energy storage, as well as transient voltage stability indicators, to build a comprehensive optimization target and optimize the configuration capacity of generator sets, new energy sources, and energy storage.

3. The source-network planning method according to claim 1, characterized in that, In step S1, the objective function of the runtime layer is optimized. for: in, For the operating costs of conventional generating units, This refers to the operating costs of energy storage.

4. The source-network planning method according to claim 1, characterized in that, In step S2, each particle in the particle swarm optimization algorithm is used with 2× N The matrix encoding uses 0 / 1 codes in the first row, representing wind turbines or photovoltaic power plants, respectively, and the second row uses integer codes, representing the capacity of power sources or energy storage equipment. The Monte Carlo method was used to sample wind speed and light intensity at different times, combined with... K The mean algorithm clusters the sampled new energy output scenarios to obtain multiple typical scenarios. The planning results are obtained by combining the solution methods of grid planning, power supply and energy storage capacity planning, and operation scheduling solution methods.

5. The source-network planning method according to claim 4, characterized in that, The solution process for the space frame planning problem is as follows: S201. Initialize population parameters and generate... N Particle population of a network scheme X ; S202. Select the particle population based on the fitness function. X Superior individuals are crossbred and mutated to generate N A space frame planning scheme; S203, will N The individual grid structure scheme is transferred to the lower layer as a condition for solving power supply, energy storage planning, and operation problems; S204. Calculate the particle population based on the power supply and energy storage planning scheme. X The fitness of the individual is determined, and when the iteration termination condition is met, the solution corresponding to the optimal individual is output and the search ends.

6. The source-network planning method according to claim 4, characterized in that, The solution process for the power supply and energy storage capacity planning problem is as follows: S205, adopting Monte Carlo and K The -means algorithm yields multiple typical power output scenarios for new energy sources; S206. Particle population based on the grid structure scheme obtained from the upper-level grid structure planning. X A particle swarm is generated to encode the location of power sources and energy storage access and the capacity of the deployed power. Y ; S207. Based on the power and scenario probability of new energy generator units under typical power output scenarios, optimize the operation solution according to each power source and energy storage construction plan, and calculate the system operating cost based on the output of conventional units, wind power, and photovoltaic power. S208. Based on the system operating costs and the investment costs of power supply and energy storage, calculate the particle population obtained in step S206. Y The fitness of the population is determined. If the iteration termination condition is met, step S209 is executed; otherwise, the population is updated and the process returns to step S206. S209. Return the calculated overall objective function to the upper layer, update the optimal value of the objective function, and determine the optimal planning scheme.

7. A source-network planning system, characterized in that, include: The module constructs a two-layer power grid planning model, comprising a planning layer and an optimization operation layer. The planning layer considers investment and construction costs, expected costs due to power shortages, and transient voltage stability indicators, constructing a multi-objective function and performing grid planning, power source planning, and energy storage planning in two phases. The optimization operation layer considers system power balance constraints, solves for daily operating costs, and returns the results to the planning layer. The objective function of the first phase of the planning layer is... as follows: Objective function of the second stage of the planning layer as follows: in, For line investment costs, For the expected cost of insufficient power, The investment and construction costs for conventional generator sets, new energy generator sets, and energy storage. w The weighting coefficients for the transient stability index are... The spectral radius of the small gain matrix; The planning module uses the particle swarm optimization algorithm to solve the two-layer source-network planning model obtained from the construction module. Based on the planning layer model, the location and capacity of the grid, power sources and energy storage are obtained. The optimization operation layer model takes system economy as the optimization objective and passes the results to the planning layer. Through the information iterative interaction between the planning layer and the optimization operation layer, the optimal solution for source-network collaborative planning is obtained.

8. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 6.

9. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the method of any one of claims 1 to 6.

Images