A method and system for power system multi-stage evolutionary path planning operation optimization

Abstract

The application belongs to the technical field of power system planning operation simulation, and discloses a power system multi-stage evolution path planning operation optimization method and system; including: obtaining starting horizontal year benchmark parameters, technical and economic parameters and boundary conditions of each planning year; updating investment cost parameters; bringing initialization data in the upper planning model into the lower simulation operation model, combining the technical and economic parameters and the boundary conditions to obtain the target solution result calculation upper grid evolution scheme total cost f t U , according to f t U updating the upper particle swarm, and recording the optimal value of each particle and the optimal value of the particle swarm; iteration is ended to obtain the optimal grid evolution scheme of the planning t year. The application is aimed at the future high proportion of new energy scale development demand, combined with the future grid pattern, the key factors considered are more comprehensive, and the power system multi-stage evolution path deduction simulation meeting the safety, economy, environmental protection, low carbon constraint can be realized.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and operation simulation technology, and specifically relates to a method and system for optimizing the planning and operation of multi-stage evolution paths of power systems. Background Technology

[0002] The large-scale grid connection of new energy sources and the integrated application of emerging technologies have a significant impact on the future form and structure of the power system, making the analysis of the power system evolution path a hot topic in the industry. On the one hand, high-quality economic and social development and the low-carbon transformation of energy and power pose new requirements for power system construction, with path dependence and technological progress jointly influencing the long-term evolution of the power system. On the other hand, while building a high-proportion new energy power system, it is still necessary to pay attention to the rational development of clean energy and system security. The grid connection and consumption of new energy sources are mutually restrictive with the power source and grid structure and the layout of flexible adjustment measures, requiring large-scale resource planning and time-series simulation verification.

[0003] Currently, relevant research has been carried out on the problem of power system transformation path planning. It mainly focuses on the construction of new power systems and the goal of clean and low-carbon energy transformation. Optimization algorithms are used to study the cost-optimal planning scheme under the constraints of energy demand and safe operation. The decision variables are usually installed capacity, transmission capacity and other technology choices.

[0004] Regarding the impact of key technological advancements and costs on the future transformation path of the power system, existing literature has proposed technology maturity and economic feasibility prediction methods. These methods set different levels of technological advancement based on the characteristics of different evolution scenarios, thereby predicting relevant techno-economic parameters and analyzing the changing trends of unit electricity costs in the power system. However, existing projection methods suffer from incomplete consideration of key factors, which will to some extent affect the later planning and layout of power sources / grids and the scale and trends of technology application. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for multi-stage evolution path planning and operation optimization of power systems. This method considers more comprehensive factors during the simulation process and can meet the constraints of safety, economy, environmental protection, and low carbon emissions in the simulation of multi-stage evolution paths of power systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for multi-stage evolution path planning and operation optimization of a power system, comprising:

[0008] Obtain the planning starting year T0 and the baseline parameters for the starting year; obtain the planning period T and the technical and economic parameters and boundary conditions for each planning year in the planning period T.

[0009] Update the investment cost parameters based on the cumulative installed capacity of each type of unit in year t-1 within the planning period;

[0010] By inputting the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and combining the aforementioned technical and economic parameters and boundary conditions, the objective solution result of minimizing the total system operating cost is obtained. And return to the upper-level planning model; using the sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, combined with the updated investment cost parameters, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains unchanged or the number of iterations reaches a set value, obtain the optimal power grid evolution scheme for the planning year t, as well as the minimum total construction cost and total operating cost;

[0011] Output the optimal power grid evolution scheme for year t of the plan.

[0012] Secondly, the present invention provides a multi-stage evolution path planning and operation optimization system for power systems, comprising:

[0013] The acquisition module is used to acquire the planning starting level year T0 and the baseline parameters of the starting level year; and to acquire the planning period T and the technical and economic parameters and boundary conditions of each planning year in the planning period.

[0014] The update module is used to update the investment cost parameters for year t based on the cumulative installed capacity and cumulative transmission line capacity of each type of unit in year t-1 within the planning period.

[0015] The optimization solution module is used to input the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and solve for the minimum total system operating cost by combining the aforementioned technical and economic parameters and boundary conditions. And return to the upper-level planning model; using the sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, combined with the updated investment cost parameters, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains unchanged or the number of iterations reaches a set value, obtain the optimal power grid evolution scheme for the planning year t, as well as the minimum total construction cost and total operating cost;

[0016] The output module is used to output the optimal power grid evolution scheme for year t of the plan.

[0017] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the aforementioned method for multi-stage evolution path planning and operation optimization of a power system.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the aforementioned method for multi-stage evolution path planning and operation optimization of a power system.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] This invention provides a method and system for multi-stage evolution path planning and operation optimization of power systems. In view of the future demand for high-proportion new energy development and combined with the future power grid structure, it considers more comprehensive key factors and realizes multi-stage evolution path simulation of power systems that meets the constraints of safety, economy, environmental protection and low carbon.

[0021] This invention, based on current energy strategic transformation requirements and reasonable predictions of future power system development scenarios, sets corresponding evolutionary boundary conditions within the planning period, and deduces power system transformation paths under different development scenarios. Under the constraints of economic and social development, safe and stable operation of the power system, and dual-carbon objectives, it optimizes the construction planning sequence and investment requirements of different power sources and inter-regional transmission lines, quantitatively presenting future power system transformation scenarios and development path schemes. It provides an operational optimization method for analyzing the impact of policy, technology, economic and other uncertainties on the evolution path, and provides a scientific basis for planning decisions on the timing, scale and spatial layout of related power source / grid construction.

[0022] This invention achieves the coordinated optimization of the multi-regional power supply structure, grid structure and new technologies of the future power system to meet the constraints of power system security, economy, environmental protection and low carbon, as well as the year-by-year prediction of the development pattern of the power system. It uses the optimization decision results of the previous planning year as the basis for the optimization scheme of the next planning year, and considers the path dependence characteristics of power system development from the perspective of temporal continuity, which has more significance for engineering practice guidance.

[0023] This invention fully considers the impact of the optimization results of decision variables in the previous stage within the planning cycle on the cost of key technologies such as power supply, power grid, and energy storage. In the planning of the next planning year, it combines the cumulative application planning of various equipment in the previous stage to dynamically correct the prediction results of the later technology costs, so as to realize the mutual iteration and synergistic optimization of power system planning development and technological progress.

[0024] This invention addresses the prominent issue of frequency stability in future power systems caused by the large-scale development of new energy sources. Using the system inertial time constant as an indicator, it proposes a minimum system inertial time constant constraint at the planning level, enabling the optimization results to provide reasonable decision-making guidance for the planning and regional layout of new energy and synchronous power generation capacity. Attached Figure Description

[0025] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0026] Figure 1 This is a flowchart illustrating a multi-stage evolution path planning and operation optimization method for power systems according to the present invention.

[0027] Figure 2 This is a structural block diagram of another power system multi-stage evolution path planning and operation optimization method according to the present invention;

[0028] Figure 3 This is a structural block diagram of a multi-stage evolution path planning and operation optimization system for power systems according to the present invention.

[0029] Figure 4 This is a structural block diagram of an electronic device according to the present invention. Detailed Implementation

[0030] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0031] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0032] Current research on long-term optimization planning and economic operation of power systems still needs to consider the following key factors:

[0033] First, in existing technologies, the cost prediction of key technologies is often independent of the power system planning and operation optimization process. The prediction results are used as input parameters in the optimization in a one-way direction, without considering the optimization results of the scale of technology application as a decision variable within the planning period. This will have a certain impact on the trend of technology cost changes in the later period.

[0034] Secondly, in terms of planning and optimizing the operation of large power grid systems for medium and long term, existing research mainly takes power balance as the starting point and selects system reserve capacity indicators to set security constraints. However, with the large-scale development of new energy sources becoming an inevitable trend in the future, the reduction of system equivalent inertia caused by their low inertia / zero inertia characteristics will lead to more prominent frequency stability issues. System inertia level should also be included as an important indicator in medium and long term planning.

[0035] Example 1

[0036] Please see Figure 1 As shown, this invention provides a method for optimizing the operation of multi-stage evolution path planning in a power system, comprising the following steps:

[0037] S101: Obtain the planning starting year T0, planning period T, and starting year benchmark parameters, and determine the equivalent topology of the power grid based on the prospective power grid morphology analysis; the starting year benchmark parameters include the cumulative installed capacity and historical installed capacity sequence of power sources in each region, the capacity of inter-regional transmission channels, the load curves of each region, and the characteristic curves of wind and photovoltaic power generation.

[0038] S102: Obtain the technical and economic parameters and boundary conditions for each planning year in the planning period T; the technical and economic parameters and boundary conditions include: carbon emissions, pollutant emissions constraints, regional load forecast results, operation constraints, regional reserve capacity constraints, expansion rate constraints, minimum inertia time constant constraints, operation parameters, and cost parameters.

[0039] S103: Update the investment cost parameters based on the cumulative installed capacity and transmission line construction capacity in year t-1 of the planning period;

[0040] S104: Obtain the number of particles, initial velocity, number of iterations and convergence conditions, and randomly generate a planning scheme that satisfies the upper-level constraints;

[0041] S105: Using the various types of generating units and line expansion capacity from the upper-level planning model as initial data, input them into the lower-level simulation operation model to obtain the objective solution result that minimizes the total system operating cost. And return to the upper-level planning model;

[0042] S106: The sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm.

[0043] S107: If the optimal solution remains unchanged or the number of iterations reaches the set value, the solution for year t is completed, and the optimal power grid evolution scheme for year t, as well as the minimum total construction cost and total operating cost, are output; otherwise, the particle velocity and position are updated to form a new planning scheme, and the process proceeds to step S105.

[0044] S108: Based on the optimization results of year t, carry out the optimization process of year t+1. Repeat steps S103-107. The model evolves and simulates in stages until the set time limit is reached, and the solution ends.

[0045] In one specific embodiment, the upper-level planning model of the present invention is as follows:

[0046] The upper-level planning model aims to solve power system power source (including energy storage) and grid planning problems, and is described as follows:

[0047]

[0048]

[0049] In the formula, f t U Let f be the objective function of the higher-level planning model, representing the sum of the annual investment costs of all newly built power sources and transmission lines in the first t years of the planning period and the system operating costs in year t; t,inv f represents the annual value of the investment cost for equipment construction in year t of the plan. t,inv1 To plan the annual value of unit expansion costs in year t, without considering the cost of unit decommissioning and recovery, the types of new generating units (TG) include six types of power generation units (TN): coal-fired, gas-fired, hydropower, nuclear power, wind power, and photovoltaic power, as well as two types of energy storage (TS): pumped storage and electrochemical energy storage. t,inv2 This represents the annual value of the inter-regional transmission line expansion cost in year t. Let be the objective function of the lower-level simulation model, representing the planned system operating cost in year t. The overall planning period is T years.

[0050] 1) Objective function

[0051] The objective function of the upper-level planning model is shown in equation (1).

[0052] Unit expansion cost f t,inv1 The calculation formula is:

[0053]

[0054]

[0055] In the formula, Let be the discount factor for the annual value of the p-th type of equipment, and i be the benchmark discount rate. Let p be the lifespan (in years) of the p-th type of equipment; To plan the unit capacity investment cost of type p equipment in year i, the construction costs of all newly built units from year 1 to year t during the planning period are allocated to the annual value of that year. The plan specifies the new construction capacity of type p equipment in year i; NP represents the number of unit types, including 8 types of units: coal-fired, gas-fired, hydropower, nuclear power, wind power, photovoltaic, pumped storage, and electrochemical energy storage.

[0056] Cost of expanding inter-regional power transmission lines f t,inv2 The calculation formula is:

[0057]

[0058]

[0059] In the formula, I L T is the annual value conversion factor for transmission line investment costs. L The lifespan of the transmission line (in years); To plan the unit capacity line expansion cost in year i, To plan the additional capacity of the line in year i.

[0060] The investment and construction costs of various energy technologies have a significant impact on the model optimization results, and the scale of technology development is an important and inevitable factor affecting these costs. Therefore, the optimization results of the unit construction scale in the early planning stage will influence the later optimization. To address this issue, a dynamic adjustment mechanism for cost parameters based on unit scale (or transmission line construction capacity) is set up. Specifically, a function c is constructed using mathematical statistics methods to describe the relationship between unit investment cost (or unit transmission line construction cost) and cumulative application scale. t =f(X) t-1 Based on the cumulative installed capacity planned for year t-1, the cost parameters of various equipment in year t are dynamically adjusted according to the following method:

[0061]

[0062]

[0063]

[0064]

[0065] In equation (7), This represents the cumulative installed capacity of the p-th type of generating unit in the system after the (t-1)th year of the planning period. This represents the cumulative installed capacity of Class p units in the planning baseline year. This represents the planned new installed capacity of type p units in year i. This represents the planned retirement capacity of the p-th type of generating unit in the i-th year; in equation (8), c t,p The unit investment cost of the p-th type of generating unit in year t after dynamic adjustment; in equation (9), This represents the cumulative transmission line capacity in the system after the (t-1)th year of the planning period. This indicates the cumulative transmission line capacity in the planning baseline year. This represents the planned capacity of new transmission lines to be built in year i; in equation (10), This represents the unit investment cost of the transmission line in year t after dynamic adjustment.

[0066] Due to differences in policy environment, resource conditions, and economic foundation across regions, the unit investment cost varies. A cost adjustment coefficient can be set to further refine the cost parameters for each region.

[0067]

[0068]

[0069] in, These represent the unit investment cost and cost adjustment coefficient for the p-th type of unit m in the region during the planned year t. τ t,i,j These represent the unit investment cost and cost adjustment coefficient for the inter-regional transmission line between regions i and j in year t of the plan.

[0070] The third term in the objective function is the comprehensive operating cost of each type of unit. The details will be elaborated in the lower-level simulation model.

[0071] 2) Constraints

[0072] ① Reserve capacity constraints

[0073] Power system reserve capacity includes maintenance reserve capacity, contingency reserve capacity, and load reserve capacity. Maintenance reserve capacity refers to the additional capacity added to account for anticipated major and minor repairs of power equipment; contingency reserve capacity refers to the additional capacity added to ensure power supply according to prescribed reliability standards even during power system accidents; and load reserve capacity refers to the additional capacity added to ensure power system frequency complies with standards. Total reserve capacity can be determined based on system reliability analysis, subject to the following constraints:

[0074]

[0075] In the formula, To plan the effective capacity of the units in year t, To plan for the maximum load demand in year t, The system capacity reserve factor is used to plan for year t.

[0076] ② Constraints on the rate of power / line expansion

[0077] During the development of the power grid, in each planning period, the construction of various types of power sources and inter-regional transmission channels within the region should adapt to constraints such as load demand, regional resource conditions, construction capacity, and environmental carrying capacity. Upper limit constraints should be set on the expansion rate of various types of power sources and lines.

[0078]

[0079] In the formula, These are the planned expansion capacity of various power sources in region m in year t and the corresponding expansion rate limits; These are the planned expansion capacity and expansion rate limits for inter-regional transmission lines between regions i and j in year t.

[0080] ③ System minimum inertia time constant constraint

[0081] New energy power generation has low / zero inertia characteristics. As its installed capacity increases, the power system inertia gradually decreases, leading to a significant increase in the initial frequency change rate and maximum frequency deviation during faults, posing a greater risk to the safe and stable operation of the power system. In the absence of disruptive technologies, maintaining a certain proportion of synchronous power sources is fundamental to ensuring the safe and stable operation of the power system. The inertial time constant is an important indicator describing the dynamic process of power system frequency, and its value is directly related to the proportion of conventional (synchronous) units in the system. Therefore, the inertial time constant is selected as the characterization indicator, and the following safety constraints are set:

[0082]

[0083]

[0084] Equation (15) represents the minimum inertia time constant constraint of region m in year t of the planning, H t,m , χ represents the inertial time constant of region m in year t of the plan and the lower limit of the inertial time constant set to ensure the stability of the system frequency, respectively. The lower limit can be obtained based on scheduling operation experience or simulation calculation results; Equation (16) represents the simplified calculation method of the inertial time constant of the power grid in each region, χ t,m,p To plan the proportion of synchronous generators of type p in region m to the total installed capacity in region t in year t, H p SYN represents the inertial time constant of type p unit, SYN represents the set of synchronous units, including four types of units: coal-fired, gas-fired, hydropower, and nuclear power, and EQS represents some wind turbine units that have the ability to provide rotational inertia.

[0085] In one specific embodiment, the lower-level simulation operation model in this invention is as follows:

[0086] The lower-level simulation operation model is based on the grid structure and installed capacity distribution determined by the upper-level planning model. It uses the output value of each unit as the decision variable and aims to minimize the overall operating cost, which is the sum of the total operating costs of all units in the system, while maximizing renewable energy generation. The lower-level simulation operation model is described as follows:

[0087]

[0088]

[0089] In the formula, f t,ope1 This represents the total operating cost of the system, including fuel costs for various types of power sources, operating costs, peak-shaving costs, and environmental costs. t,ope2 To improve the efficiency of new energy power generation, the lower-level simulation operation model selects the load, wind power, and photovoltaic output curves of four typical days in the planning year based on seasonal characteristics to optimize the economic operation of the system. Each typical day can be divided into 24 time periods according to hours.

[0090] 1) Objective function

[0091] The optimization objective function of the lower-level simulation model is expressed as:

[0092]

[0093] In the formula f t,ope1 This represents the total operating cost of all types of power sources for the entire year of year t, including fuel cost c. t,fuel Operating costs c t,ope Peak shaving cost c t,peak and environmental costs c t,envir ;f t,ope2 For the benefit of new energy power generation, E t,NE M represents the amount of new energy power generation, and M is the benefit coefficient.

[0094] Annual fuel cost c t.fuel Operating costs c t.ope Peak shaving cost c t.peak and environmental costs c t.envir Calculated using equation (20):

[0095]

[0096] In the formula, These represent the fuel cost, variable operating cost, equivalent peak-shaving cost, and environmental cost for unit p of type m in region m during a typical day in year t of the planned year. The annual fixed operating cost of the p-th type of generating unit in region m in year t of the plan is related to the unit's investment cost. Carbon emissions and pollutant emission levels of various power generation methods and their corresponding monetary treatment costs need to be considered; NM represents the number of planned areas; NP represents the total number of unit types; fuel costs involve thermal power units and nuclear power units; operating costs involve all units; peak shaving costs involve thermal power, hydropower, nuclear power, pumped storage units and energy storage; and environmental costs involve thermal power units.

[0097] The future development of the power grid aims to rationally absorb the power generation of new energy sources, and optimizes the power generation of new energy sources by incorporating the benefit coefficient M into the objective function.

[0098] 2) Constraints

[0099] ① Power balance constraints

[0100] The sum of power generation and power received in each region should be balanced in real time with the sum of load and power transmitted in that region.

[0101]

[0102] In the formula, These represent the power generation, input power, load, and output power of the region in the h-hour period of a typical day in year t of the planned region.

[0103] ②Power constraints of transmission lines

[0104]

[0105] In the formula, P t,i,j,day,h To plan the real-time transmission power of the transmission channel capacity in the h-th hour of a typical day between regions i and j in year t, To plan the upper limit of the power transmission capacity of the transmission channel between regions i and j in year t, P t,i,j,day,h A positive value indicates that power is being supplied from region i to region j, while a negative value indicates the opposite; a scheduling curve can also be set according to scheduling needs.

[0106] ③ Unit ramp-up rate constraint

[0107] To ensure the safe and stable operation of all types of generating units, the ramp-up rate of various peak-shaving generating units is constrained:

[0108] |P t,m,p,h -P t,m,p,h-1 |≤ΔP t,m,p,h (twenty three)

[0109] In the formula, P t,m,p,h-1 and P t,m,p,h ΔP represents the power output of unit p of type m in region m in year t at time h-1 and time h, respectively. Unit types include thermal power, hydropower, nuclear power, and pumped storage. t,m,p,hTo plan the ramping capacity limit of unit p of type m in region m at time h in year t, for the sake of simplifying the calculation, it is assumed that the same parameters are used for both upward and downward ramping of the unit.

[0110] ④ Upper and lower limits of unit output constraints

[0111] During the optimized operation phase, the output of each unit should meet its upper and lower output limits.

[0112]

[0113] In the formula, P t,m,p,h , P t,m,p,h , These represent the real-time output, lower limit, and upper limit of the p-th type of unit in region m in year t of the plan. The output of new energy units should not exceed their maximum output, and nuclear power units are set to operate with base load.

[0114] The operational constraints of energy storage units mainly include charging and discharging power constraints and capacity constraints.

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] In the formula: Equations (25) and (26) represent the constraints on the energy storage charging power and discharging power in the h-th hour of a typical day m in the region during the t-th year of the planning year, respectively; Equation (27) indicates that the energy storage charging and discharging states cannot occur simultaneously. These represent the energy storage charging and discharging power, respectively. These are the upper and lower limits of energy storage charging power, respectively. These represent the upper and lower limits of energy storage discharge power. These are the 0-1 variables representing the charging and discharging states of the energy storage, respectively; Equation (28) is the formula for calculating the energy stored at time h, which is determined by the energy stored at time h-1 and the charging and discharging power and efficiency at time h; Equation (29) is the constraint on the charging and discharging depth of the energy storage considering operational safety and economy. The energy storage charge states at times h and h-1 are respectively. To define the upper and lower limits of energy storage, These are the energy storage charging efficiency and the discharge efficiency, respectively; Equation (30) indicates that the energy stored by the energy storage device remains unchanged at the beginning and end of each typical day.

[0122] ⑤ Total carbon emissions and pollutant emissions constraints

[0123] In the process of power grid evolution, in order to respond to the requirements of clean and low-carbon development, and considering the limitations of regional environmental capacity, international organization regulations and emission limits set by the government, the carbon emissions and pollutant emissions of all types of generating units should not exceed the limits.

[0124]

[0125] In the formula, To plan the typical daily emissions of pollutants EM in region m in year t, pollutants EM include CO2, SO2, and NO. x , This represents the upper limit for pollutant EM emissions.

[0126] In one specific embodiment, the solution algorithm in this invention is as follows:

[0127] In the standard particle swarm optimization algorithm, each particle updates its search speed based on its individual extreme value, population extreme value, and inertia weight, as shown in the following formula.

[0128]

[0129] In the formula, w represents the particle's search velocity in the t-th and t+1-th searches, respectively. t The weights are inertia weights, c1 and c2 are learning factors, usually taken as c1 = c2 = 2, and r1 and r2 are random numbers between [0, 1]. Let t be the position of the particle during its t-th search. Let $t$ be the global extreme value and $t$ be the individual extreme value for the $t$-th search.

[0130] In the later stages of iteration, the particle velocity gradually decreases as the inertia weight w decreases, initiating a refined local search. If the global optimum is only a local optimum at this point, the entire population may focus on searching within the neighborhood of that point, ignoring better solutions that other particles might find. To minimize the risk of particles getting trapped in local optima, the velocity update formula of the standard particle swarm optimization algorithm is adjusted.

[0131] First, unlike the standard particle swarm optimization algorithm where the inertia weight decreases linearly with the number of iterations, a nonlinear dynamic inertia weight method is adopted to avoid the problem of the linearly decreasing inertia weight strategy getting trapped in local extrema. Its mathematical expression is:

[0132]

[0133] In the formula, w start w end These represent the maximum and minimum values ​​of the inertial weight variation range, respectively. k is a control factor used to adjust the smoothness of the curves showing the change in w and t. Multiple tests were conducted using a multi-peak function. The experimental data were obtained and the performance was stable when the k value was in the range of (3.0, 4.0).

[0134] Second, the historical population fitness values ​​are sorted from largest to smallest, and the average of the positions of the top g historical extreme values ​​is selected as the global extreme value. When adjusting their search behavior, particles not only consider their own and their companions' experiences but also refer to the average information of historical extreme values, effectively improving the algorithm's tendency for particle search to stagnate in the later stages. The improved particle velocity update formula is:

[0135]

[0136] In the formula, g0 is the number of particles, t max p is the maximum number of iterations. gd,j The position of the individual particle with fitness value j.

[0137] Third, to address the premature convergence problem of the particle swarm optimization (PSO) algorithm, an adaptive mutation operation is introduced. A mutation threshold TH is set during the iteration process, allowing certain particle variables to change with a certain probability. The mutation operation expands the diversity of the particle population, increasing the likelihood of the algorithm finding the optimal value.

[0138]

[0139] In the formula: η is a random variable that follows a Gaussian distribution in [0,1], and μ is a random variable in [0,1].

[0140] The improved particle swarm optimization algorithm process is as follows:

[0141] 1) Set the number of particles, initial position, number of iterations, and convergence criteria;

[0142] 2) Calculate the fitness values ​​of all particles in the population and the extreme fitness values ​​of individual particles;

[0143] 3) Sort all particles by fitness values ​​from largest to smallest;

[0144] 4) The average value of the historical extreme values ​​of the first g terms is used as the global extreme value. The particle velocity is adjusted, and the new particle population begins the next iteration search.

[0145] 5) If the number of iterations reaches the maximum value or the number of consecutive unchanged optimal solutions reaches the set value, terminate the iteration; otherwise, repeat steps 2)-5).

[0146] Example 2

[0147] Please see Figure 2 As shown, this invention provides a method for optimizing the operation of multi-stage evolution path planning in a power system, comprising:

[0148] S1. Obtain the planning starting year T0 and the baseline parameters for the starting year; obtain the planning period T and the technical and economic parameters and boundary conditions for each planning year in the planning period.

[0149] S2. Update the investment cost parameters based on the cumulative installed capacity of each type of unit and the cumulative transmission line capacity in year t-1 of the planning period;

[0150] S3. Input the various types of generating units and line expansion capacity in the upper-level planning model as initial data into the lower-level simulation operation model, and solve for the minimum total system operating cost by combining the aforementioned technical and economic parameters and boundary conditions. And return to the upper-level planning model; using the sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, combined with the updated investment cost parameters, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains unchanged or the number of iterations reaches a set value, obtain the optimal power grid evolution scheme for the planning year t, as well as the minimum total construction cost and total operating cost;

[0151] S4. Output the optimal power grid evolution scheme for year t of the plan.

[0152] In one specific implementation, the baseline parameters for the initial horizontal year include: the cumulative installed capacity and historical installation time series of power sources in each region, the capacity of inter-regional transmission channels, the load curves of each region, and the characteristic curves of wind and solar power generation.

[0153] The technical and economic parameters and boundary conditions include: carbon emissions, pollutant emissions constraints, regional load forecast results, operational constraints, regional reserve capacity constraints, expansion rate constraints, minimum inertia time constant constraints, unit operating parameters, and cost parameters.

[0154] In one specific implementation, the step of updating the investment cost parameters based on the planning year-t-1 baseline parameters in the initial level year baseline parameters includes:

[0155] Based on the cumulative installed capacity planned for year t-1, dynamically adjust the cost parameters of various equipment for year t in the plan:

[0156]

[0157]

[0158]

[0159]

[0160] In equation (7), This represents the cumulative installed capacity of the p-th type of generating unit in the system after the (t-1)-th year of the planning period. This represents the cumulative installed capacity of Class p units in the planning baseline year. This represents the planned new installed capacity of type p units in year i. This represents the planned retirement capacity of the p-th type of generating unit in the i-th year; in equation (8), c t,p The unit investment cost of the p-th type of generating unit in year t after dynamic adjustment; in equation (9), This represents the cumulative transmission line capacity in the system after the (t-1)th year of the planning period. This indicates the cumulative transmission line capacity in the planning baseline year. This represents the planned capacity of new transmission lines to be built in year i; in equation (10), This represents the unit investment cost of the transmission line in year t after dynamic adjustment.

[0161] Due to differences in policy environment, resource conditions, and economic foundation across regions, the unit investment cost varies. A cost adjustment coefficient can be set to further refine the cost parameters for each region.

[0162]

[0163]

[0164] in, τ t,m,p These represent the unit investment cost and cost adjustment coefficient for the p-th type of unit m in the region during the planned year t. τ t,i,j These represent the unit investment cost and cost adjustment coefficient for the inter-regional transmission line between regions i and j in year t of the plan.

[0165] In one specific implementation, the various types of generating units and line expansion capacities in the upper-level planning model are used as initialization data and input into the lower-level simulation operation model. Combined with the technical and economic parameters and boundary conditions, the solution is obtained to achieve the objective of minimizing the total system operating cost. Then, in the step of returning to the upper-level planning model, the upper-level planning model is:

[0166]

[0167]

[0168] In the formula, f t U The objective function of the upper-level planning model is the sum of the annual investment costs of all newly built power sources and transmission lines in the first t years of the planning period and the system operating costs in year t. t,inv f represents the annual value of the investment cost for equipment construction in year t of the plan. t,inv1 To plan the annual value of the unit expansion cost in year t, f t,inv2 This represents the annual value of the inter-regional transmission line expansion cost in year t. The objective function of the lower-level simulation model;

[0169] The lower-level simulation operation model is as follows:

[0170]

[0171]

[0172] In the formula, f t,ope1 f represents the total operating cost of all types of power sources for the entire year of year t in the planning process. t,ope2 For the benefit of new energy power generation;

[0173] The objective function of the lower-level simulation model is:

[0174]

[0175] In the formula, f t,ope1 Including fuel costs c t,fuel Operating costs c t,ope Peak shaving cost c t,peak and environmental costs c t,envir M is the benefit coefficient, E t,NE This is for new energy power generation.

[0176] In one specific implementation, in the upper-level planning model:

[0177] Unit expansion cost f t,inv1 The calculation formula is:

[0178]

[0179]

[0180] In the formula, Let be the discount factor for the annual value of the p-th type of equipment, and i be the benchmark discount rate. Let p be the lifespan (in years) of the p-th type of equipment; To plan the unit capacity investment cost of type p equipment in year i; This refers to the planned new construction capacity of type p equipment in year i; NG represents the number of unit types.

[0181] Cost of expanding inter-regional power transmission lines f t,inv2 The calculation formula is:

[0182]

[0183]

[0184] In the formula, I L T is the annual value conversion factor for transmission line investment costs. L The lifespan of the transmission line (in years); To plan the unit capacity line expansion cost in year i, To plan for the increase in line capacity in year i;

[0185] The reserve capacity constraint of the upper-level planning model is:

[0186]

[0187] In the formula, To plan the effective capacity of the units in year t, To plan for the maximum load demand in year t, To plan the system capacity reserve factor for year t;

[0188] The power / line expansion rate constraint of the upper-level planning model is:

[0189]

[0190] In the formula, These are the planned expansion capacity of various power sources in region m in year t and the corresponding expansion rate limits; These are the planned expansion capacity and expansion rate limits for inter-regional transmission lines between regions i and j in year t.

[0191] The system inertia time constant constraint of the upper-level planning model is as follows:

[0192]

[0193]

[0194] Equation (15) represents the minimum inertia time constant constraint of region m in year t of the planning, H t,m , χ represents the regional inertial time constant for year t and the lower limit of the inertial time constant set to ensure system frequency stability, respectively; Equation (16) represents the simplified calculation method of the regional power grid inertial time constant, χt,m,p To plan the proportion of synchronous generators of type p in region m to the total installed capacity in region t in year t, H p Let be the inertial time constant of type p unit, SYN represent the set of synchronous units, and EQS represent the partial wind turbine units with the ability to provide rotational inertia.

[0195] In one specific implementation, in the lower-level simulation operation model:

[0196] Annual fuel cost c t.fuel Operating costs c t.ope Peak shaving cost c t.peak and environmental costs c t.envir Calculated using equation (20):

[0197]

[0198] In the formula, These represent the fuel cost, variable operating cost, equivalent peak-shaving cost, and environmental cost for unit p of type m in region m during a typical day in year t of the planned year. The annual fixed operating cost of the p-th type unit in region m in the planning year t, To account for the carbon and pollutant emission levels of various power generation methods and convert them into monetary terms for treatment costs; NM represents the number of planned areas; NP represents the total number of unit types;

[0199] The power balance constraints of the lower-level simulation operation model are:

[0200]

[0201] In the formula, These represent the power generation, power received, load, and power transmitted in the h-hour period of a typical day (day) in region m during year t of the planning year.

[0202] The power constraints of the transmission lines in the lower-level simulation operation model are:

[0203]

[0204] In the formula, P t,i,j,day,h To plan the real-time transmission power of the transmission channel capacity in the h-th hour of a typical day between regions i and j in year t, To plan the upper limit of the power transmission capacity of the transmission channel between regions i and j in year t, P t,i,j,day,h A positive value indicates that power is supplied from region i to region j, and a negative value indicates the opposite.

[0205] The unit ramp-up rate constraint for the lower-level simulated operation model is:

[0206] |P t,m,p,h -P t,m,p,h-1|≤ΔP t,m,p,h (twenty three)

[0207] In the formula, P t,m,p,h-1 and P t,m,p,h Let ΔP be the output of the p-th type m unit in the region during the planned year t at time h-1 and time h, respectively; t,m,p,h To limit the ramp-up capability of Class p units in region m in year t;

[0208] The upper and lower limits of unit output in the lower-level simulation operation model are constrained as follows:

[0209]

[0210] In the formula, P t,m,p,h , P t,m,p,h , These are the real-time output, lower limit, and upper limit of the p-type unit in region m in year t of the plan;

[0211] The operational constraints of energy storage units include charging and discharging power constraints and capacity constraints.

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] In the formula: These represent the energy storage charging and discharging power at hour h on a typical day in region m during year t of the planning year. These are the upper and lower limits of energy storage charging power, respectively. These represent the upper and lower limits of energy storage discharge power. These are 0-1 variables representing the charging and discharging states of energy storage, respectively. The energy storage charge states at times h and h-1 are respectively. To define the upper and lower limits of energy storage, These are energy storage charging efficiency and discharging efficiency, respectively.

[0219] The total carbon emissions and pollutant emissions constraints for the lower-level simulation model are as follows:

[0220]

[0221] In the formula, The typical daily emissions of pollutant EM in the planning area are given. This corresponds to the upper limit value.

[0222] Example 3

[0223] Please see Figure 3 As shown, the present invention provides a power system multi-stage evolution path planning and operation optimization system, comprising:

[0224] The acquisition module is used to acquire the planning starting level year T0 and the baseline parameters of the starting level year; and to acquire the planning period T and the technical and economic parameters and boundary conditions of each planning year in the planning period.

[0225] The update module is used to update the investment cost parameters for year t based on the cumulative installed capacity and cumulative transmission line capacity of each type of unit in year t-1 within the planning period.

[0226] The optimization solution module is used to input the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and solve for the minimum total system operating cost by combining the aforementioned technical and economic parameters and boundary conditions. And return to the upper-level planning model; using the sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, combined with the updated investment cost parameters, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains unchanged or the number of iterations reaches a set value, obtain the optimal power grid evolution scheme for the planning year t, as well as the minimum total construction cost and total operating cost;

[0227] The output module is used to output the optimal power grid evolution scheme for year t of the plan.

[0228] In one specific implementation, the upper-level planning model is:

[0229]

[0230]

[0231] In the formula, f t U The objective function of the upper-level planning model is the sum of the annual investment costs of all newly built power sources and transmission lines in the first t years of the planning period and the system operating costs in year t. t,inv f represents the annual value of the investment cost for equipment construction in year t of the plan. t,inv1To plan the annual value of the unit expansion cost in year t, f t,inv2 This represents the annual value of the inter-regional transmission line expansion cost in year t. The objective function of the lower-level simulation model;

[0232] The lower-level simulation operation model is as follows:

[0233]

[0234]

[0235] In the formula, f t,ope1 f represents the total operating cost of all types of power sources for the entire year of year t in the planning process. t,ope2 For the benefit of new energy power generation;

[0236] The objective function of the lower-level simulation model is:

[0237]

[0238] In the formula, f t,ope1 Including fuel costs c t,fuel Operating costs c t,ope Peak shaving cost c t,peak and environmental costs c t,envir E t,NE M represents the amount of new energy power generation, and M is the benefit coefficient.

[0239] Example 4

[0240] Please see Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a multi-stage evolution path planning operation optimization method for a power system; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0241] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the power system multi-stage evolution path planning operation optimization method described in Embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0242] The at least one processor 102 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. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0243] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-stage evolution path planning and operation optimization for a power system, and the processor 102 can execute the multiple instructions to achieve the following:

[0244] Obtain the planning starting year T0 and the baseline parameters for the starting year; obtain the planning period T and the technical and economic parameters and boundary conditions for each planning year in the planning period T.

[0245] The investment cost parameters are updated based on the cumulative installed capacity of each type of generating unit and the cumulative transmission line capacity in year t-1 of the planning cycle.

[0246] By inputting the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and combining the aforementioned technical and economic parameters and boundary conditions, the objective solution result of minimizing the total system operating cost is obtained. And return to the upper-level planning model; using the sum of total construction cost and total operating cost f t U As the fitness function of the upper-level planning particle swarm optimization algorithm, combined with the updated investment cost parameters, the total cost f of the upper-level power grid evolution scheme is calculated. t U According to f t U Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains unchanged or the number of iterations reaches a set value, obtain the optimal power grid evolution scheme for the planning year t, as well as the minimum total construction cost and total operating cost;

[0247] Output the optimal power grid evolution scheme for year t of the plan.

[0248] Example 5

[0249] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they 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, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0250] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0251] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0252] 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.

[0253] 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.

[0254] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for multi-stage evolution path planning and operation optimization of a power system, characterized in that, include: Obtain the planning starting level year T 0 and initial horizontal year baseline parameters; obtain planning period T and the technical and economic parameters and boundary conditions for each planning year in the planning cycle; Determine the equivalent topology of the power grid based on the prospective power grid morphology analysis; According to the planning cycle, the first t- Cumulative installed capacity of various types of generating units and cumulative transmission line capacity over one year, updated [number]. t Annual investment cost parameters; By inputting the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and combining the aforementioned technical and economic parameters and boundary conditions, the objective solution result of minimizing the total system operating cost is obtained. And return to the upper-level planning model; the sum of total construction cost and total operating cost. As the fitness function of the upper-level planning particle swarm optimization algorithm, it is used in conjunction with the updated investment cost parameters to calculate the total cost of the upper-level power grid evolution scheme. ,in accordance with Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains continuous or the number of iterations reaches a set value, obtain the planning... t The optimal power grid evolution scheme for the year and the minimum total construction cost and total operating cost; Output the planned number t Optimal power grid evolution scheme for the year; According to the planning period, the first t- Cumulative installed capacity of various types of generating units and cumulative transmission line capacity over one year, updated [number]. t The steps involved in determining annual investment cost parameters include: A function describing the relationship between unit investment cost or unit transmission line construction cost and cumulative application scale is constructed using mathematical statistics methods. According to the plan t- The cumulative installed capacity over one year will be dynamically adjusted according to the plan. t Annual cost parameters for various equipment: （7） （8） （9） （10） in, Indicates the planning of the first t- After one year, the first in the system p Cumulative installed capacity of similar units Indicates the planning base year. p Cumulative installed capacity of similar units Indicates the planning of the first i Year p New installed capacity of similar units Indicates the planning of the first i Year p Decommissioning capacity of similar generating units; For the dynamically adjusted planning t Year p The unit investment cost of this type of unit; Indicates the planning of the first t- After one year, the cumulative transmission line capacity in the system This indicates the cumulative transmission line capacity in the planning baseline year. Indicates the planning of the first i Annual capacity of newly built transmission lines; For the dynamically adjusted planning t Annual unit investment cost of transmission lines; Set cost adjustment factors to correct cost parameters for each region: （11） （12） in, , The respective planning number t Year Region m No. p Unit investment cost and cost adjustment coefficient for this type of generating unit; , The respective planning number t Year Region i and j Unit investment cost and cost adjustment coefficient for inter-regional power transmission lines; Obtain the number of particles, initial velocity, number of iterations, and convergence conditions, and randomly generate a planning scheme that satisfies the upper-level constraints; The process involves inputting the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and then solving the problem using the aforementioned technical and economic parameters and boundary conditions to obtain the objective solution that minimizes the total system operating cost. The step then returns to the upper-level planning model, which is: （1） （2） In the formula, Let be the objective function of the higher-level planning model, representing the objective function of the previous period within the planning cycle. t The annual value of investment costs for all newly constructed power sources and transmission lines in the year and the [year] [number]th [year]. t The total annual system operating costs; Indicates the planning of the first t Annual investment cost for equipment construction For planning the first t Annual value of unit expansion costs Indicates the first t Annual cost of expanding inter-regional power transmission lines; The objective function of the lower-level simulation model; The lower-level simulation operation model is as follows: （17） （18） In the formula, Indicates the planning of the first t Annual comprehensive operating cost of all types of power sources For the benefit of new energy power generation; The objective function of the lower-level simulation model is: （19） In the formula, Including fuel costs Operating costs Peak shaving costs and environmental costs ; M For the benefit coefficient, The target function is to optimize the power generation from new energy sources by incorporating the power generation from new energy sources into the objective function through the benefit coefficient M. In the upper-level planning model: Unit expansion cost The calculation formula is: （3） （4） In the formula, For the first p Annual value conversion factor for different types of equipment i Using the benchmark discount rate, For the first p Life cycle of various equipment types, in years; For planning the first i Year p Investment cost per unit capacity for each type of equipment; For planning the first i Year p New construction capacity for various types of equipment; NP Number of unit types; Cost of expanding inter-regional power transmission lines The calculation formula is: （5） （6） In the formula, This is the annual value conversion factor for transmission line investment costs. The lifespan of a transmission line is expressed in years. For planning the first i Annual unit capacity line expansion cost For planning the first i Annual increase in line capacity; The reserve capacity constraint of the upper-level planning model is: （13） In the formula, For planning the first t Annual effective capacity of generating units For planning the first t Annual maximum load demand For planning the first t Annual system capacity reserve factor; The power / line expansion rate constraint of the upper-level planning model is: （14） In the formula , The respective planning number t Year Region m Expansion capacity of various power sources and corresponding expansion rate limits; , The respective planning number t Year Region i , j Limits on the capacity and expansion rate of inter-regional power transmission lines; The system inertia time constant constraint of the upper-level planning model is as follows: （15） （16） Equation (15) represents the first planning step. t Year Region m The system's minimum inertia time constant constraint, , The respective planning number t Year Region m The inertial time constant and the lower limit of the inertial time constant set to ensure system frequency stability; Equation (16) represents the simplified calculation method of the inertial time constant of the power grid in each region. For planning the first t Year Region m type p The proportion of synchronous machine assemblies in the total installed capacity of this region is as follows: For type p The inertial time constant of the unit, SYN Represents a set of synchronous generator units. EQS This refers to a portion of the wind turbine that has the capability to provide rotational inertia.

2. The method for multi-stage evolution path planning and operation optimization of a power system according to claim 1, characterized in that, The baseline parameters for the initial horizontal year include: the cumulative installed capacity and historical installation time series of power sources in each region, the capacity of inter-regional transmission channels, the load curves of each region, and the characteristic curves of wind and solar power generation. The technical and economic parameters and boundary conditions include: carbon emissions, pollutant emissions constraints, regional load forecast results, operational constraints, regional reserve capacity constraints, expansion rate constraints, minimum inertia time constant constraints, operational parameters, and cost parameters.

3. The method for multi-stage evolution path planning and operation optimization of a power system according to claim 1, characterized in that, In the lower-level simulation model: Annual fuel cost Operating costs Peak shaving costs and environmental costs Calculated using equation (20): （20） In the formula, , , , The respective planning number t Year Region m No. p Type of unit in typical days day The Middle h Hourly fuel costs, variable operating costs, equivalent peak-shaving costs, and environmental costs. For the region m No. p Type 1 generator unit in the planning stage t Annual fixed operating costs Specifically, this involves considering the environmental costs of converting the carbon emissions and pollutant emission levels of various power generation methods into monetary terms; NM Number of planned areas; NP This represents the total number of unit types. The power balance constraints of the lower-level simulation operation model are: （21） In the formula, , , , The respective planning number t Year Region m typical day day The Middle h Hourly power generation, input power, load, and output power; The power constraints of the transmission lines in the lower-level simulation operation model are: （22） In the formula, For planning the first t Year Region i , j Typical day between day The Middle h Real-time power transmission capacity of hourly transmission channels For planning the first t Year Region i , j The upper limit of transmission capacity of the power transmission channel between them Positive values ​​indicate that the value comes from the region. i To the region j A positive value indicates power transmission, while a negative value indicates the opposite. The unit ramp-up rate constraint for the lower-level simulation operation model is: （23） In the formula, and The respective planning number t Year Region m No. p Type of unit in h -1 time and h Efforts made at all times; For planning the first t Year Region m No. p Limited ramp-up capability of similar units; The upper and lower limits of unit output in the lower-level simulation operation model are constrained as follows: （24） In the formula, The respective planning number t Year Region m No. p Real-time output, lower limit, and upper limit of the generator unit; The operational constraints of energy storage units include charging and discharging power constraints and capacity constraints. （25） （26） （27） （28） （29） （30） In the formula: , They are respectively the planning year number t Year Region m typical day day The Middle h Hourly energy storage charging and discharging power, , These are the upper and lower limits of energy storage charging power, respectively. , These represent the upper and lower limits of energy storage discharge power. These are 0-1 variables representing the charging and discharging states of energy storage, respectively. , They are respectively h , h- At time 1, the energy storage state of charge, , To define the upper and lower limits of energy storage, , These are energy storage charging efficiency and discharging efficiency, respectively. The total carbon emissions and pollutant emissions constraints for the lower-level simulation model are as follows: （31） In the formula, For planning the first t Year Region m typical day day pollutants EM Emissions pollutants EM Maximum emission limits.

4. A multi-stage evolution path planning and operation optimization system for power systems, characterized in that, include: The acquisition module is used to obtain the planning starting horizontal year. T 0 and initial horizontal year baseline parameters; obtain planning period T and the technical and economic parameters and boundary conditions for each planning year in the planning cycle; Determine the equivalent topology of the power grid based on the prospective power grid morphology analysis; The update module is used to update the plan based on the number of iterations within the planning period. t- Cumulative installed capacity of various types of generating units and cumulative transmission line capacity over one year, updated [number]. t Annual investment cost parameters; Optimize the solver module. By inputting the various types of generating units and line expansion capacity from the upper-level planning model as initial data into the lower-level simulation operation model, and combining the aforementioned technical and economic parameters and boundary conditions, the objective solution result of minimizing the total system operating cost is obtained. And return to the upper-level planning model; the sum of total construction cost and total operating cost. As the fitness function of the upper-level planning particle swarm optimization algorithm, it is used in conjunction with the updated investment cost parameters to calculate the total cost of the upper-level power grid evolution scheme. ,in accordance with Update the upper-level particle swarm and record the optimal value of each particle and the optimal value of the particle swarm; if the optimal solution remains continuous or the number of iterations reaches a set value, obtain the planning... t The optimal power grid evolution scheme for the year and the minimum total construction cost and total operating cost; The output module is used to output the plan number. t Optimal power grid evolution scheme for the year; According to the planning period, the first t- Cumulative installed capacity of various types of generating units and cumulative transmission line capacity over one year, updated [number]. t The steps involved in determining annual investment cost parameters include: A function describing the relationship between unit investment cost or unit transmission line construction cost and cumulative application scale is constructed using mathematical statistics methods. According to the plan t- The cumulative installed capacity over one year will be dynamically adjusted according to the plan. t Annual cost parameters for various equipment: （7） （8） （9） （10） in, Indicates the planning of the first t- After one year, the first in the system p Cumulative installed capacity of similar units Indicates the planning base year. p Cumulative installed capacity of similar units Indicates the planning of the first i Year p New installed capacity of similar units Indicates the planning of the first i Year p Decommissioning capacity of similar generating units; For the dynamically adjusted planning t Year p The unit investment cost of this type of unit; Indicates the planning of the first t- After one year, the cumulative transmission line capacity in the system This indicates the cumulative transmission line capacity in the planning baseline year. Indicates the planning of the first i Annual capacity of newly built transmission lines; For the dynamically adjusted planning t Annual unit investment cost of transmission lines; Set cost adjustment factors to correct cost parameters for each region: （11） （12） in, , The respective planning number t Year Region m No. p Unit investment cost and cost adjustment coefficient for this type of generating unit; , The respective planning number t Year Region i and j Unit investment cost and cost adjustment coefficient for inter-regional power transmission lines; Obtain the number of particles, initial velocity, number of iterations, and convergence conditions, and randomly generate a planning scheme that satisfies the upper-level constraints; The upper-level planning model is as follows: （1） （2） In the formula, The objective function of the higher-level planning model is the planning period within the previous planning cycle. t The annual value of investment costs for all newly constructed power sources and transmission lines in the year and the [year] [number]th [year]. t The total annual system operating costs; Indicates the planning of the first t Annual investment cost for equipment construction For planning the first t Annual value of unit expansion costs Indicates the first t Annual cost of expanding inter-regional power transmission lines; The objective function of the lower-level simulation model; The lower-level simulation operation model is as follows: （17） （18） In the formula, Indicates the planning of the first t Annual comprehensive operating cost of all types of power sources For the benefit of new energy power generation; The objective function of the lower-level simulation model is: （19） In the formula, Including fuel costs Operating costs Peak shaving costs and environmental costs ; M For the benefit coefficient, The target function is to optimize the power generation from new energy sources by incorporating the power generation from new energy sources into the objective function through the benefit coefficient M. In the upper-level planning model: Unit expansion cost The calculation formula is: （3） （4） In the formula, For the first p Annual value conversion factor for different types of equipment i Using the benchmark discount rate, For the first p Life cycle of various equipment types, in years; For planning the first i Year p Investment cost per unit capacity for each type of equipment; For planning the first i Year p New construction capacity for various types of equipment; NP Number of unit types; Cost of expanding inter-regional power transmission lines The calculation formula is: （5） （6） In the formula, This is the annual value conversion factor for transmission line investment costs. The lifespan of a transmission line is expressed in years. For planning the first i Annual unit capacity line expansion cost For planning the first i Annual increase in line capacity; The reserve capacity constraint of the upper-level planning model is: （13） In the formula, For planning the first t Annual effective capacity of generating units For planning the first t Annual maximum load demand For planning the first t Annual system capacity reserve factor; The power / line expansion rate constraint of the upper-level planning model is: （14） In the formula , The respective planning number t Year Region m Expansion capacity of various power sources and corresponding expansion rate limits; , The respective planning number t Year Region i , j Limits on the capacity and expansion rate of inter-regional power transmission lines; The system inertia time constant constraint of the upper-level planning model is as follows: （15） （16） Equation (15) represents the first planning step. t Year Region m The system's minimum inertia time constant constraint, , The respective planning number t Year Region m The inertial time constant and the lower limit of the inertial time constant set to ensure system frequency stability; Equation (16) represents the simplified calculation method of the inertial time constant of the power grid in each region. For planning the first t Year Region m type p The proportion of synchronous machine assemblies in the total installed capacity of this region is as follows: For type p The inertial time constant of the unit, SYN Represents a set of synchronous generator units. EQS This refers to a portion of the wind turbine that has the capability to provide rotational inertia.

5. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement a power system multi-stage evolution path planning operation optimization method as described in any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements a power system multi-stage evolution path planning and operation optimization method as described in any one of claims 1 to 2.

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