Novel power system time sequence production simulation method and system based on model decomposition

Abstract

The invention belongs to the technical field of electric power system operation, and provides a novel electric power system time sequence production simulation method and system based on model decomposition, which take system total operation cost optimization as an objective function, comprehensively consider system electric power balance constraint and line transmission out-of-limit constraint, introduce equipment-level constraint conditions, and improve system operation efficiency. Constructing a collaborative time sequence production simulation model; secondary compression is started and stopped; decomposing the collaborative timing sequence production simulation model into a source-load-storage collaborative timing sequence output simulation main problem model and a line transmission out-of-limit feasibility verification sub-problem model which do not contain line transmission constraints, decoupling unit start-stop state integer variables and output continuous variables, and constructing a unit start-stop-output inner-layer decomposition model; and carrying out optimization solution until each decomposition model converges to obtain an optimal solution. According to the method, various devices and coupling operation constraints thereof are comprehensively considered, and the operation process is described by adopting an optimality decomposition algorithm, so that the power balance risk is accurately evaluated.

Description

Technical Field

[0001] This invention belongs to the field of new power system operation, specifically relating to a new power system time-series production simulation method and system based on model decomposition. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, the grid-connected scale of renewable energy sources such as wind power and photovoltaics, as well as the installed capacity of energy storage, have both increased significantly. At the same time, the flexibility of demand side has been significantly improved, forming a new pattern of source-load interaction. Its operation characteristics are manifested in the interaction and coordination of large-scale, multi-type equipment at multiple time scales.

[0004] However, traditional production simulation methods typically employ low time resolution, performing weak time-series simplification on the operating characteristics of conventional units and loads (such as unit output and load demand), neglecting the temporal coupling relationships between equipment operating states, and making it difficult to accurately characterize the randomness and volatility of renewable energy output. Therefore, time-series production simulation that refines the power balance process has become an important development direction for new power system operation analysis.

[0005] However, existing main methods, such as using hourly high-time-resolution time-series production simulations, lead to a large-scale linear increase in model variables and constraint dimensions across spatiotemporal scales. The models contain numerous constraints describing the heterogeneous operating characteristics of various types of equipment, and specifically consider cross-time-period coupling constraints, resulting in extremely high model complexity. Furthermore, the numerous unit start-up and shutdown behaviors introduce integer variables, further exacerbating the difficulty of solving the model, ultimately forming a high-dimensional, strongly coupled mixed-integer linear programming problem.

[0006] To improve the efficiency of solving time-series production simulations, two main simplification methods are currently employed: dimensionality reduction methods and decomposition methods. Dimensionality reduction methods typically reduce the model's dimensionality based on clustering techniques, but may simplify or ignore some key physical operational constraints of the units (such as minimum output limits and ramp-up capabilities). Decomposition methods mainly include relaxation optimization decomposition, stochastic optimization decomposition, and optimality decomposition algorithms. Among these, relaxation optimization decomposition and stochastic optimization decomposition algorithms are highly adaptable to the problem structure, but they cannot strictly guarantee convergence to the theoretical optimal solution; while optimality decomposition algorithms can guarantee theoretical optimality, they usually have high requirements on the problem form, and their solution efficiency significantly depends on the decomposition strategy and cutting plane design method used. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a novel power system time-series production simulation method and system based on model decomposition. This invention comprehensively considers various types of equipment and their coupled operational constraints, and employs an optimality decomposition algorithm to describe their operation process as realistically as possible, thereby accurately assessing power balance risks.

[0008] According to some embodiments, the present invention adopts the following technical solution: A novel power system time-series production simulation method based on model decomposition includes the following steps: The operating boundary of the new power system is determined, with the optimal total operating cost of the system as the objective function. The system power balance constraint and line transmission limit exceedance constraint are comprehensively considered, and various equipment-level constraints of source, grid, load and storage are introduced to construct a collaborative time-series production simulation model. Based on engineering experience, a preliminary compression strategy for start-stop optimization space is constructed with economic optimization as the goal. The optimal system output solution under the all-time-space collaborative optimization framework is solved by relaxing start-stop, thereby achieving secondary compression of start-stop. The collaborative time-series production simulation model is decomposed into a main problem model for source-load-storage collaborative time-series output simulation without line transmission constraints and a sub-problem model for line transmission limit exceedance feasibility verification. Using these as the outer decomposition model, the integer variables of unit start-up and shutdown status and the continuous output variables are decoupled to construct the inner decomposition model of unit start-up and shutdown-output. The inner and outer decomposition models are optimized and solved. In the process of solving the outer decomposition model, the optimality decomposition algorithm is used to decouple the integer variables of the unit start-up and shutdown states from the continuous variables of output until each decomposition model converges and the optimal solution is obtained.

[0009] As an alternative implementation method, the process of determining the operating boundary of the new power system includes: determining the operating boundary of the new power system, calculating the maximum load of each node, and reconstructing it by combining the typical daily load curve and the annual daily maximum load change curve to form an input requirement that meets the time-series production simulation. Based on the average available density of wind and solar resources in each region, the power generation boundary of wind and solar resources in each region is calculated, and then a typical wind and solar power time series of each region is constructed. Determine the spatial distribution, capacity, and power generation technical parameters of thermal power units in various regions; Obtain the rated transmission capacity of the transmission line; Obtain the adjustable capacity ratio, adjustment duration, and response speed parameters of demand-side adjustable load resources at different time periods.

[0010] As an alternative implementation method, the process with the objective function of optimizing the total system operating cost includes: the total system operating cost includes the output cost of thermal power units, the start-up and shutdown cost of thermal power units, load shedding penalties and power curtailment penalties, and demand-side resource adjustment costs.

[0011] As an alternative implementation method, the process of comprehensively considering system power balance constraints and line transmission limit exceedance constraints, and introducing various equipment-level constraints of source, grid, load and storage includes: calculating the total power generation and total load power to form a source-load power balance constraint that the total power generation and total load are equal; Based on the power balance relationship of nodes, the active power expression of each node in the injected transmission network is constructed. Using the DC power flow model, the relationship expression between line power flow and node injected power is constructed. Based on the line power flow, the line transmission limit constraint expression is constructed. Using the load of each node, the output of wind and solar turbines, as well as the load shedding power and the power curtailed, as inputs, we construct the reserve demand constraint expression for thermal power units. Taking into account the active power output range constraints of thermal power units, the active power output range constraints including reserve output, the ramp rate constraints, and the minimum start / stop time constraints, the operating characteristic constraint expressions of thermal power units are constructed. After normalizing the wind and solar time-series power output curves, the active power output constraint expression of the wind and solar generator units is constructed using the wind and solar time-series power output curves as input. By considering the charging and discharging power constraints, state of charge range constraints, and energy balance constraints of the integrated energy storage device, an operational characteristic constraint expression for the energy storage device is constructed. By integrating the operating power range constraints, ramp rate constraints, dynamic recursive constraints of state of charge, and upper and lower limit boundary constraints of the adjustable load, an operating characteristic constraint expression for the adjustable load is constructed.

[0012] As an alternative implementation method, the process of constructing a preliminary compression strategy for start-stop optimization space with the goal of economic optimization, based on engineering experience, includes: constructing an expression for the economic index of unit output, comprehensively considering the impact of reserve demand and the fluctuation of new energy output on thermal power output, estimating the upper and lower boundaries of the total thermal power output in each period, sorting all units in ascending order according to the economic index of thermal power unit output, and if the economic index is the same, then sorting them in ascending order again according to the minimum technical output, and prioritizing the selection of units with greater operational flexibility. Based on this priority, the unit output is set to its maximum technical output and accumulated. When the accumulated output first exceeds the upper limit of the total thermal power output, the process stops and the affected unit is determined to be a forced start-up optimized unit. For the remaining units, continue to sort them according to the above priority, set their output to the average of the maximum and minimum output and accumulate them until the lower boundary of the total thermal power output is reached. The units involved are then identified as units to be optimized and put into operation.

[0013] As an alternative implementation method, the process of achieving secondary compression through start-stop by solving the optimal system output scheme under the all-time-space cooperative optimization framework through relaxed start-stop includes: Taking into account the output cost of thermal power units, load shedding penalties, wind and solar power curtailment penalties, and regulation costs, and with the goal of minimizing the source-load-storage coordinated operation cost, an objective function expression for a linear relaxation output simulation model is constructed. Ignore all constraints that only include unit start-up and shutdown, and use constraints that only contain continuous variables and unit output constraints that relax start-up and shutdown state variables as model constraints; The start-stop state variables of the forced start and standby optimized units are fixed at 1, and the unit output constraint expression of the relaxed start-stop state variables is constructed. For units whose output exceeds the set value at the target time, their start-stop status at that time will be determined as the start-up status, and the corresponding start-stop status variable value will be fixed at 1 in subsequent optimizations and treated as a constant variable.

[0014] As an alternative implementation method, the process of decomposing the collaborative time-series production simulation model into a main problem model for source-load-storage collaborative time-series output simulation without line transmission constraints and a sub-problem model for line transmission limit exceedance feasibility verification includes: Taking into account the output cost of thermal power units, start-up and shutdown costs, load shedding penalties on the load side, wind and solar power curtailment penalties on the renewable energy side, and system regulation costs, the objective function expression of the source-load-storage coordinated time-series output simulation master problem model is constructed, which is the same as the objective function expression of the coordinated time-series production simulation model. The constraints related to line transmission limit exceedance in the constructed collaborative time-series production simulation model are decoupled, while the system power balance constraints and the operating characteristic constraints of various source, load, and storage devices are retained, which together constitute the constraint conditions of the main problem model. A DC power flow model is used to calculate the injected power value at each node in the transmission network and, combined with the line transmission capacity limit, calculate the transmission capacity over-limit value of each line in each time period. The feasibility of power flow for all transmission lines in the system is verified, and a transmission over-limit feasibility verification sub-problem is constructed.

[0015] As an alternative implementation method, the process of decoupling the integer variables of unit start-up and shutdown status from the continuous variables of output, and constructing the inner-layer decomposition model of unit start-up and shutdown-output includes: Construct the objective function expression for the main problem of unit start-up and shutdown optimization, whereby the objective function expression is the lower bound of the total start-up and shutdown cost of thermal power units and the objective function value of the sub-problem of output optimization under the objective start-up and shutdown scheme. The sum is minimized; Taking into account the minimum start / stop time constraints of the units, the hard constraints of the start / stop states of the units that must be turned on / off, and the objective function of the main problem of output optimization... Initial constraints and feasibility / optimality cutting plane constraints generated from subproblems constitute the constraints of the main problem of unit start-up and shutdown optimization. Given the unit start-up and shutdown schemes in the main problem, we construct the objective function expression for the output optimization sub-problem, which is to minimize the sum of the total output cost of thermal power units, the total load shedding penalty of the system, the total curtailment penalty of wind and solar power units, and the total adjustment cost of demand-side resources. Taking into account the system's source-load balance and equipment operation boundary constraints, the known start-up and shutdown state variable values ​​are substituted into the unit output constraints to form the constraint conditions of the output optimization sub-problem model. Solving the main unit start-up and shutdown optimization problem and the output optimization sub-problem will yield the source-load-storage output scheme for each time period.

[0016] As an alternative implementation method, the convergence determination process of the inner layer decomposition model includes: During the iteration process, the lower and upper bounds of the objective function of the inner decomposition model are constructed. In each round of decomposition iteration, the current lower and upper bounds of the objective function of the main problem are recorded. It is determined whether the absolute value of the difference between the lower and upper bounds is less than the preset convergence threshold. If it is satisfied, the inner decomposition model is considered to have converged to its optimal solution. Otherwise, based on the constraints of the output optimization sub-problem model, a feasibility / optimality cutting plane is generated and fed back to the main problem model. The unit start-up and shutdown scheme is iteratively optimized until the convergence condition is met.

[0017] As an alternative implementation method, the convergence determination process of the outer layer decomposition model includes: Substitute the transmission capacity of each line in all time periods into the following formula, and determine whether the absolute value of the ratio between the two is less than its over-limit threshold. If so, the current solution is determined to be the final solution; otherwise, generate a feasibility cutting plane constraint based on the constructed line transmission over-limit verification subproblem, and add it to the constructed source-load-storage coordinated time-series output simulation master problem. The solution of the master problem model is gradually corrected through the feedback of the cutting plane constraint until the convergence condition is met.

[0018] A novel power system time-series production simulation system based on model decomposition includes: The collaborative time-series production simulation model construction module is configured to determine the operating boundary of the new power system, with the optimal total operating cost of the system as the objective function. It comprehensively considers the system power balance constraints and line transmission limit exceedance constraints, and introduces various equipment-level constraints of sources, grids, loads and storage to construct a collaborative time-series production simulation model. The two-stage compression module is configured to combine engineering experience to construct an initial compression strategy for the start-stop optimization space with the goal of economic optimization, and to achieve a secondary compression of start-stop by solving the optimal system output scheme under the all-time-space collaborative optimization framework through relaxed start-stop. The inner and outer decomposition model construction module is configured to decompose the collaborative time-series production simulation model into a source-load-storage collaborative time-series output simulation main problem model without line transmission constraints and a line transmission limit exceedance feasibility verification sub-problem model. This is used as the outer decomposition model to decouple the unit start-stop state integer variables from the output continuous variables and construct the unit start-stop-output inner decomposition model. The solution module is configured to optimize the solution of the inner decomposition model and the outer decomposition model. In the process of solving the outer decomposition model, the optimality decomposition algorithm is used to decouple the integer variables of the unit start-up and shutdown states from the continuous output variables until each decomposition model converges and the optimal solution is obtained.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a collaborative time-series production simulation method adapted to the high proportion of renewable energy in new power systems. It meticulously considers the time-series operating characteristics of thermal power units, wind and solar power sources, energy storage devices, adjustable loads, and transmission lines to construct a time-series production simulation model. Furthermore, it incorporates practical engineering experience and, guided by the approximate optimal solution under the framework of objective optimality and collaborative optimization, constructs a unit start-up and shutdown optimization space compression strategy. On this basis, it constructs a decomposition model with a double-layer nested structure, and achieves reasonable decomposition and rapid iterative solution of the model through an efficient constraint feedback mechanism, thereby significantly improving the calculation speed of time-series production simulation.

[0020] This invention is applicable to time-series production simulation of large-scale new power systems. It improves the solution efficiency of time-series production simulation by combining three parts: collaborative time-series production simulation model construction, start-stop optimization space compression, and reconstruction and solution of a double-layer nested decomposition model based on the optimality decomposition algorithm.

[0021] This invention constructs an optimization objective function that covers thermal power output costs, start-up and shutdown costs, load shedding penalties, curtailment penalties, and demand-side adjustment costs. Then, it constructs system-level constraints based on system power balance and transmission line capacity limitations, and constructs equipment-level operational constraints based on the fluctuating output boundaries of new energy units and the cross-period energy balance characteristics of energy storage devices. This results in a collaborative time-series production simulation model that adapts to the operating characteristics of new power systems.

[0022] This invention addresses the large number of 0-1 integer variables introduced by unit start-up and shutdown behavior and the resulting mixed integer programming problem by proposing a two-stage compression strategy: combining engineering experience, a preliminary compression strategy for the start-up and shutdown optimization space is constructed based on the economic optimality objective; by relaxing start-up and shutdown to solve the near-optimal system output solution under the all-time-space collaborative optimization framework, a secondary compression strategy for start-up and shutdown is constructed, thereby achieving model dimensionality reduction.

[0023] Based on the principle of avoiding line transmission limit violations, this invention employs a logical optimality decomposition algorithm to decompose the outer layer of the model: the collaborative time-series production simulation model is decomposed into a main problem model for source-load-storage collaborative time-series output simulation without line transmission constraints and a sub-problem model for verifying the feasibility of line transmission limit violations. In the outer main problem, the optimality decomposition algorithm is used to decouple the integer variables of unit start-up and shutdown states from the continuous output variables, thereby significantly reducing the difficulty of solving the collaborative time-series production simulation.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings, which form part of this invention, 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 improper limitation of the invention.

[0026] Figure 1 This is a flowchart of a novel power system time-series production simulation based on model decomposition in one embodiment; Figure 2 A flowchart for constructing a collaborative time-series production simulation model in one embodiment; Figure 3 This is a flowchart illustrating the implementation of a start / stop compression strategy in one embodiment. Figure 4 This is a flowchart illustrating the decomposition, reconstruction, and solution process of a collaborative time-series production simulation model in one embodiment. Figure 5 This is a schematic diagram of a double-layer nested decomposition model structure in one embodiment; Figure 6(a) is a diagram of the starting system configuration of an application example in one embodiment; Figure 6(b) is a typical load curve diagram of an application example in one embodiment; Figure 6(c) is a typical wind power timing output curve of an application example in one embodiment; Figure 6(d) is a typical photoelectric timing output curve of an application example in one embodiment; Figure 6(e) is a comparison of the effects of starting and stopping compression strategies in one embodiment; Figure 6(f) is a diagram of the convergence process of an outer layer transmission over-limit line in one embodiment. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0030] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0031] Example 1 As described in the background section, traditional production simulation is limited by low time resolution and often uses weak time-series approximations to treat the operating characteristics of conventional units and loads. It is usually difficult to explicitly characterize the time-series coupling effect of equipment status, and thus cannot accurately depict the stochastic characteristics of renewable energy output and the time-varying response mechanism of energy storage equipment and adjustable loads.

[0032] In contrast, time-series production simulation of new power systems needs to comprehensively consider the heterogeneous operating characteristics constraints of various types of equipment across power sources, grids, loads, and storage, as well as their complex coupling relationships. In particular, cross-time-period coupling constraints cause the model to exhibit large-scale, strongly coupled mixed-integer programming characteristics, resulting in excessively long solution times or even failure to obtain results. Therefore, how to design an efficient optimality decomposition framework to improve the solution efficiency of the model has become a key problem that urgently needs to be solved in time-series production simulation of new power systems.

[0033] To address the aforementioned issues, this embodiment provides a novel time-series power system production simulation method based on model decomposition. First, a collaborative time-series production simulation model with hourly time resolution is constructed to characterize the stochastic fluctuations in renewable energy output and the dynamic response capabilities of storage-load equipment. Based on this, guided by the objective optimality under a single time period and the approximate optimal solution over the entire time period, a two-stage compression strategy for the unit start-up and shutdown optimization space is designed: initial compression of units that must be shut down and secondary compression of units that must be started. In the compressed start-up and shutdown optimization space, this method constructs outer and inner decomposition models based on the highly coupled characteristics of source-grid-load-storage operation constraints and their mixed-integer programming structure. This significantly improves the solution speed while ensuring controllable accuracy sacrifice, thereby supporting the time-series production simulation of large-scale new power systems.

[0034] The following is a detailed introduction.

[0035] like Figure 1 As shown, this method includes the following steps: Step 1: Construction of Collaborative Time-Sequence Production Simulation Model The new power system time-series production simulation uses the source-grid-load-storage planning scheme as the boundary to simulate the system's power balance process time-by-time. The model is further constructed in four parts: first, determining the operating boundary of the new power system; second, constructing the objective function of the collaborative time-series production simulation model; third, constructing system-level constraints; and finally, forming various equipment-level constraints for sources, grids, loads, and storage. See the flowchart for the collaborative time-series production simulation model construction. Figure 2 The specific steps are as follows: Step 1.1 Determine the operating boundary parameters of the new power system based on surveys or data extrapolation, specifically including: (1) Investigate reports published by social and economic research institutions, collect economic and population forecast data for various regions published in the target year, and substitute them into formula (1) to form the maximum load of each node: (1) In the formula: The target year's maximum electricity load forecast results, For the corresponding economic output and population projections, The economic and population correlation coefficients of the load are obtained by fitting historical data on the maximum power load, economy, and population in previous years. After obtaining the annual maximum load of each node, the typical daily load curve and the annual daily maximum load change curve released by the power sector are further combined, and the hourly load time series of the target year is reconstructed using normalization and interpolation methods to meet the input requirements in time-series production simulation.

[0036] (2) Investigate the distribution map of wind and solar energy resources released by the land and resources department, calculate the average available density of wind and solar resources in each region, and substitute it into the following formula to calculate the power generation boundary of wind and solar resources in the region.

[0037] (2) In the formula: , , For nodes i The density of wind and solar resources in the region, the usable area of ​​the region, and the upper limit of wind and solar power generation. The theoretical maximum conversion efficiency of wind / photovoltaic power sources is related to the current level of power generation technology. Its value is based on the upper limit of the technical specifications published by the equipment manufacturer.

[0038] To meet the accuracy requirements of time series simulation, typical wind and solar power time series for each region were constructed by surveying historical measured meteorological and power data provided by institutions such as NREL.

[0039] (3) Investigate the installed capacity data and product catalogs released by local energy management departments and mainstream thermal power equipment manufacturers to clarify the spatial distribution, capacity scale and power generation technical parameters of thermal power units in various regions.

[0040] (4) Investigate power transmission technology standards and engineering cases, compile statistics on commonly used conductor types and cross-sectional areas at voltage levels such as 500kV and 1000kV, and obtain the rated power transmission capacity of the transmission line accordingly.

[0041] (5) Investigate the demand-side adjustable load resources, and focus on statistics of parameters such as the adjustable capacity ratio, adjustment duration and response speed of typical loads such as air conditioning and hydrogen production at different time periods.

[0042] Step 1.2 With the goal of optimizing the total system operating cost, construct the objective function for the collaborative time-series production simulation model. Specifically, this includes: (1) Taking into account the fuel cost, carbon emission cost, and standby cost of thermal power units, the following formula is used to construct the expression for the output cost of thermal power units: (3) In the formula: The total output cost of thermal power units; This represents the number of system cycles. This is the set of sites for thermal power plants; , The unit cost of coal consumption and the cost of carbon emissions; This is the coal consumption coefficient for thermal power units; , , They are nodes Thermal power unit time The amount of active power output and the positive and negative reserves provided; , These represent the unit costs for positive and negative standby power units, respectively.

[0043] (2) Substitute into the following formula to construct the expression for the start-up and shutdown costs of thermal power units: (4) In the formula: Total start-up and shutdown costs for thermal power units; , These are the costs of a single start-up and shutdown of a thermal power unit; , For nodes Thermal power unit time Start-up and shutdown indicator variables.

[0044] (3) Substitute into the following formula to construct the expressions for load shedding penalty and power curtailment penalty: (5) In the formula: Total load shedding penalty for the system; For the location set of the load; Penalty per unit power for system load shedding; For nodes At the moment The load shedding power; Total penalties for curtailment of wind and solar power units; For the site selection of wind and solar power units; Fines per unit power for abandoned electricity from wind and solar power units; node At the moment The amount of wind and solar power curtailed. (4) Substitute into the following formula to construct the expression for demand-side resource adjustment costs: (6) In the formula: The total adjustment cost of demand-side resources; For the location set of adjustable loads; The unit adjustment cost for adjustable load; For nodes At the moment Adjustable load regulation power.

[0045] (5) Total output cost of thermal power units Total start-up and shutdown costs of thermal power units Total system load shedding penalty Total curtailment penalties for wind and solar power units and the total adjustment cost of demand-side resources Add them together to construct the expression for the total system operating cost: (7) In the formula: This represents the total operating cost of the system.

[0046] Step 1.3 Taking into account both system power balance constraints and line transmission limit exceedance constraints, construct the system-level constraints for the collaborative time-series production simulation model. The specific steps are as follows: (1) Substitute into the following formula to calculate the total power generation and total load power, forming a source-load power balance constraint expression where the total power generation equals the total load: (8) In the formula: This refers to the set of locations for energy storage devices. , They are time points At the node Active power of wind and solar turbines and energy storage equipment; The corresponding power load is determined by the external input power load sequence; For a moment At the node Adjustable load power baseline.

[0047] (2) Based on the power balance relationship of integrated nodes, the DC power flow model, and the transmission capacity limit of the transmission line, construct the transmission limit constraint expression for the transmission line. The specific steps are as follows: First, based on the node power balance relationship, substitute the following formula to construct the expression for the active power injected into each node in the transmission network: (9) In the formula: For a moment Power flows into the node from other nodes via the transmission network. The active power; It is the set of bus nodes.

[0048] Furthermore, using a DC power flow model, the following expression is used to construct the relationship between line power flow and node injected power: (10) In the formula: For each line t The active power vector is always present. The system power transfer distribution factor matrix; For each node t Inject active power vectors at all times.

[0049] Finally, substituting the line power flow into the following formula, we construct the line transmission limit constraint expression: (11) In the formula: For the line At any moment The active power transmitted; , These are the corresponding line transmission capacity limits; This is a collection of power transmission lines.

[0050] Step 1.4 Taking into account the operational characteristics of various equipment including power sources, grids, loads, and storage, construct the equipment-level constraints for the collaborative time-series production simulation model, specifically including: (1) Using the load of each node and the output of wind and solar turbines, as well as the load shedding power and the power curtailed, as inputs, the reserve demand constraint expression of thermal power units is constructed by substituting them into the following formula: (12) (13) In the formula: , , , These are the reserve requirement coefficients reserved for load and wind and solar turbine output, respectively.

[0051] (2) Taking into account the active power output range constraints of thermal power units, the active power output range constraints including reserve output, the ramp rate constraints, and the minimum start / stop time constraints, the operating characteristic constraint expressions of thermal power units are constructed as follows: First, using the start-up and shutdown status and active power output of each node unit as input, the active power output constraint expression of the thermal power unit is constructed by substituting it into the following formula: (14) In the formula: For nodes thermal power units It is always in a running state and is a 0-1 variable; , These are the lower and upper limits of the output of thermal power units, respectively.

[0052] Then, taking the active power output and its limitations of the unit as input, we substitute them into the following formula to construct the active power output constraint expression of the thermal power unit considering its reserve output: (15) Furthermore, substituting the active power output of the unit into the following formula, we construct the ramp-up constraint for the thermal power unit: (16) In the formula: , These represent the maximum ascent and descent ramp rates of the thermal power unit, respectively.

[0053] Finally, substituting the start-up and shutdown indicator variables of the unit into the following formula, the minimum start-up and shutdown time constraints of the thermal power unit are constructed: (17) In the formula: , This refers to the minimum start-up and shutdown time for thermal power units.

[0054] (3) After normalizing the wind and solar time-series power output curves, use the wind and solar time-series power output curves as input and substitute them into the following formula to construct the active power output constraint expression of the wind and solar generator sets: (18) In the formula: For the first Typhoon solar generator time The normalized maximum output is determined by the externally input wind and solar maximum output sequence.

[0055] (4) Based on the charging and discharging power constraints, state of charge range constraints, and energy balance constraints of the integrated energy storage equipment, construct the operating characteristic constraint expression of the energy storage equipment.

[0056] First, taking the charging and discharging power of the energy storage device as input, we substitute it into the following formula to construct the constraint expression for the charging and discharging power of the energy storage device: (19) In the formula: These represent the maximum charging and discharging power of the energy storage.

[0057] Then, substituting into the following equation, we construct the state-of-charge constraint expression for the energy storage device: (20) In the formula: For at any time node The state of charge of the energy storage device.

[0058] Finally, substituting the state of charge and charging / discharging power of the energy storage device into the following equation, we can construct the energy balance constraint expression for the energy storage device: (twenty one) In the formula: , For nodes The maximum and minimum energy storage ratios of the corresponding energy storage devices; The rated capacity of the energy storage; This is the time step for production simulation.

[0059] (5) By combining the operating power range constraints, ramp rate constraints, dynamic recursive constraints of charge state and upper and lower limit constraints of the adjustable load, the operating characteristic constraint expression of the adjustable load is constructed.

[0060] First, taking the adjustable load's regulating power and power baseline as inputs, substitute them into the following formula to construct the operating power range constraint expression for the adjustable load: (twenty two) In the formula: These are the operating power limits for adjustable loads.

[0061] Then, substituting into the following equation, we construct the ramp rate constraint expression for the adjustable load: (twenty three) In the formula: , This represents the maximum ramp rate for adjustable loads.

[0062] Furthermore, substituting into the following equation, we construct a dynamic recursive constraint expression for the charge state of an adjustable load: (twenty four) In the formula: For at any time node State of charge of adjustable load; For nodes State transition coefficient for adjustable load.

[0063] Finally, using the state of charge of the adjustable load as input, its upper and lower boundary constraint expressions are constructed: (25) Step 2: Start and stop optimization space compression This section addresses the high-dimensional discrete non-convex optimization problem caused by unit start-up and shutdown behavior in a collaborative time-series production simulation model. It employs initial and secondary compression strategies for the start-up and shutdown optimization space to achieve model dimensionality reduction. The implementation process for the start-up and shutdown compression strategy is detailed in [link to implementation details]. Figure 3 The specific steps are as follows: Step 2.1 To narrow the search space for unit start-up and shutdown optimization, drawing on the experience principle of prioritizing the use of units with better economic performance in engineering practice, a preliminary strategy for compressing the start-up and shutdown optimization space based on the unit output economic indicators is proposed. The specific steps are as follows: First, substitute the following formula to construct the expression for the unit's output economic index.

[0064] (26) In the formula: For at any time node The unit's output economic indicators.

[0065] Then, taking into account the impact of reserve demand and the fluctuation of new energy output on thermal power output, the upper and lower boundaries of total thermal power output in each period are estimated by substituting into the following formula.

[0066] (27) (28) In the formula: For time period Estimated upper and lower limits of total thermal power output; The system's reserve and spinning reserve factors are adjusted downwards to address the risks of power surplus and shortage. For time period Maximum wind and solar power curtailment; For time period Maximum load shedding power; For time period Maximum charging and discharging power of energy storage devices.

[0067] Furthermore, based on the output economic indicators of thermal power units All units are sorted in ascending order. If economic indicators are the same, a second ascending order is made based on minimum technical output, prioritizing units with greater operational flexibility. Following this priority, the unit output is sequentially set to its maximum technical output and accumulated. When the accumulated output first exceeds... If the operation stops immediately, the affected units are identified as forced-start optimization units. For the remaining units, continue to prioritize them according to the above priority, and set their output to the average of the maximum and minimum outputs, accumulating the values ​​until a certain threshold is reached. The units involved were identified as units awaiting startup and optimization.

[0068] The start-up and standby start-up optimization units retain their start-up and standby start-up status as 0-1 integer decision variables in subsequent optimizations. The remaining unselected units are determined to be mandatory shutdown units, and their start-up and standby status variables are fixed at 0 in the model and treated as known constants.

[0069] Step 2.2 Within the framework of all-time and space-time collaborative optimization, an approximate optimal solution for power output is obtained by solving the linear relaxation power output simulation model to identify thermal power units with obvious power output tendencies. Based on this, a start-stop secondary compression strategy is constructed. The specific steps are as follows: First, taking into account the output cost of thermal power units, load shedding penalties, wind and solar power curtailment penalties, and regulation costs, we substitute these into the following formula to construct the objective function expression for the linear relaxation output simulation model, with the goal of minimizing the source-load-storage coordinated operation cost: (29) In the formula: This represents the objective function value of the linear relaxation output simulation model.

[0070] Then, ignoring all constraints that only involve unit start-up and shutdown, the model constraints are constructed in two parts: constraints containing only continuous variables and unit output constraints with relaxed start-up and shutdown state variables.

[0071] Taking into account system load balance, equipment operation boundary and other constraints, the constraints are composed of only continuous variables. The specific constraint expressions are as follows: Equation (8) in step 1.3 above, and Equations (12)-(13) and (18)-(25) in step 1.4.

[0072] Furthermore, by fixing the start-up and standby start-up state variables of the optimized units to 1, and substituting them into the following formula, we construct the unit output constraint expression for relaxing the start-up and stop-up state variables: (30) (31) (32) In the formula: This refers to the set of all units except those that must be shut down as specified in step 2.1. Finally, regarding the time... t The output is greater than its minimum technical output. For units with a power density of 1 times that of the specified power generation unit, the start / stop status at that moment will be determined as the start state, and the corresponding start / stop status variable value will be fixed at 1 in subsequent optimizations and treated as a constant variable.

[0073] Step 3: Reconstruction and solution of the double-nested decomposition model based on the optimality decomposition algorithm This section primarily employs the Benders optimality decomposition algorithm to reconstruct the collaborative time-series production simulation model based on a two-level nested decomposition structure. See the flowchart for the model reconstruction and solution process. Figure 4 See the schematic diagram of the two-level nested decomposition model. Figure 5 The specific steps are as follows: Step 3.1 Based on the feasibility logic of avoiding line transmission limit violations, the constraints related to line transmission in the original model are decoupled, and the main problem of source-load-storage coordinated time-series output simulation and the sub-problem of line transmission limit violation verification are constructed. The specific steps are as follows: (1) Constructing a master problem model for simulating the time-series output of source-load-storage coordinated power generation First, taking into account the output cost of thermal power units, start-up and shutdown costs, load shedding penalties on the load side, wind and solar power curtailment penalties on the renewable energy side, and system regulation costs, we construct the objective function expression of the main problem model for source-load-storage coordinated time-series output simulation, which is consistent with the form of formula (7) in step1.2.

[0074] Then, the constraints related to line transmission over-limit in the collaborative time-series production simulation model constructed in step 1 (i.e., equations (9) to (11) constructed in step 1.3) are decoupled, and the system power balance constraints and the operating characteristic constraints of various equipment such as source, load and storage are retained, which together constitute the constraint conditions of the main problem model.

[0075] (2) Construct a sub-problem model for verifying the feasibility of line transmission exceeding limits. Based on the power output schemes provided by the main problem, a sub-problem for verifying the feasibility of transmission capacity exceeding limits is constructed. Using a DC power flow model, the injected power values ​​at each node in the transmission network are calculated. Combined with the limitations of line transmission capacity, these values ​​are substituted into the following formula to calculate the transmission capacity exceeding limits for each line in each time period, thus verifying the feasibility of power flow for all transmission lines in the system.

[0076] (33) In the formula: , These represent the over-limit power for forward and reverse transmission of the line, respectively.

[0077] when or When this occurs, it indicates that the current power output scheme has caused the line to exceed the transmission limit, and this exceedance phenomenon needs to be suppressed in the next iteration.

[0078] Step 3.2 Given the strong coupling between unit start-up / shutdown states and output decisions in the main problem of source-load-storage coordinated time-series output simulation, and the significant mixed-integer characteristics of the model due to a large number of 0-1 start-up / shutdown decision variables, the integer variables of unit start-up / shutdown states are decoupled from the continuous output variables to construct an inner-layer decomposition model of unit start-up / shutdown and output. The specific steps are as follows: (1) Constructing the main problem model for unit start-up and shutdown optimization First, substituting into the following formula, we construct the objective function expression for the main problem of unit start-up and shutdown optimization: (34) In the formula: The objective function value of the main problem for optimizing unit start-up and shutdown; for The lower bound of the objective function value of the output optimization subproblem under the alternative start-stop scheme.

[0079] Then, taking into account the minimum start / stop time constraints of the units, the hard constraints of the start / stop states of the units that must be turned on / off, and the objective function of the main problem of output optimization... Initial constraints and feasibility / optimality cutting plane constraints generated from subproblems, substituted into the following equation, constitute the constraints of the main unit start-up and shutdown optimization problem: (35) In the formula: for The dual variable value vector obtained by dualizing the subproblem; This is the vector of constant terms in the constraints of the subproblem; The coefficient matrix of the coupling variables between the master and subproblems in the constraints of the subproblems; for The coupling variable vector between the principal and subproblems; , These are the fixed sets of units that must be shut down in step 2.1 and the fixed sets of units that must be turned on in step 2.2, respectively.

[0080] (2) Construct a model for the output optimization sub-problem First, given the unit start-up and shutdown schemes in the main problem, substitute the following equation to construct the objective function expression for the output optimization subproblem: (36) In the formula: To optimize the objective function value of the subproblem.

[0081] Then, taking into account constraints such as system source-load balance and equipment operating boundaries, the specific constraint expressions are as follows: Equation (8) in step 1.3 above, and Equations (12)-(13) and (18)-(25) in step 1.4. At the same time, the known start-up and shutdown state variable values ​​are substituted into the unit output constraints (i.e., Equations (14)-(16)) to form the output optimization sub-problem constraints. Further, the following equation is substituted to construct the feasibility / optimality cutting plane constraint expression. In summary, the constraint conditions of the output optimization sub-problem model are formed.

[0082] (37) Finally, by solving the main problem of unit start-up and shutdown optimization and the sub-problem of output optimization, the source-load-storage output scheme for each time period will be obtained.

[0083] Step 3.3 Construct the convergence conditions of the inner decomposition model and determine whether the inner decomposition model has reached convergence.

[0084] First, substituting into the following formula, we construct the expressions for the lower and upper bounds of the objective function in the inner decomposition model during the iteration process: (38) In the formula: , These are the lower and upper bounds of the overall objective function of the inner decomposition model, respectively.

[0085] In each iteration of the decomposition, the lower and upper bounds of the objective function of the main problem are recorded. Then, the values ​​are substituted into the following formula to determine whether the absolute value of the difference between the lower and upper bounds is less than a preset convergence threshold, which serves as the convergence condition for the inner decomposition model.

[0086] (39) In the formula: This is the convergence threshold between the upper and lower bounds of the objective function.

[0087] Finally, if the above convergence conditions are met, the inner decomposition model is considered to have converged to its optimal solution. If the above convergence conditions are not met, a feasibility / optimality cutting plane will be generated in the subproblem of step 3.2 (2) according to the form of equation (37) and fed back to the main problem model of step 3.2 (1). The unit start-up and shutdown scheme will be iteratively optimized until the convergence conditions are met.

[0088] Step 3.4 Construct the convergence conditions of the outer decomposition model and determine whether the outer decomposition model has reached convergence.

[0089] First, substitute the transmission capacity of each line in all time periods with its transmission capacity limit into the following formula, and determine whether the absolute value of the ratio between the two is less than its over-limit threshold, which serves as the convergence condition of the outer decomposition model.

[0090] (40) In the formula: This is the threshold for exceeding the transmission limit on the line.

[0091] Then, determine whether the convergence condition is met. If the convergence condition is met, the current solution is determined to be the final solution. Otherwise, substitute into the following formula to generate the feasibility cutting plane constraint from the line transmission limit check subproblem constructed in step (2) of step 3.1, and add it to the source-load-storage coordinated time-series output simulation master problem constructed in step 3.1: (41) In the formula: These are the positive and negative over-limit power vectors of the over-limit line at time t, respectively; The active power vector injected into each node of the previous generation during the iteration process; This is the set of times when the line exceeds the limit.

[0092] Finally, the solution to the main problem in step 3.1 is gradually corrected through feedback from the cutting plane constraint until the convergence condition is met.

[0093] Application Examples The effectiveness of the method of the present invention is verified by taking a simplified provincial power grid system as an example. The starting system topology is shown in Figure 6(a), with a total of 90 nodes and 200 lines. In terms of power supply structure, it is equipped with 77.98GW of thermal power plants, 30.22GW of wind power plants, 65.34GW of photovoltaic power plants, and 2GW of energy storage stations, and includes two voltage levels: 500kV and 100kV. The load side includes 84.80GW of base load, 9.58GW of air conditioning load, and 15.57GW of hydrogen production load.

[0094] First, construct a collaborative time-series production simulation model based on step 1.

[0095] The population and economic data of the region were investigated, and a typical load curve was constructed as shown in Figure 6(b). Combined with the wind and solar resource distribution map of the province, the upper limit of wind power and photovoltaic power generation capacity of each node was calculated as shown in Table 1. The typical wind power and photovoltaic time-series output curves are shown in Figure 6(c) and Figure 6(d). Taking into account the current technical level of mainstream equipment manufacturers, the upper limit of power generation, regulation power and transmission capacity of thermal power, energy storage equipment, adjustable load and transmission line in each region was investigated and is shown in Table 1 and Table 2, respectively. Substitute the above data into the aforementioned equations (3)-(25) to complete the construction of the collaborative time-series production simulation model.

[0096] Table 1. Maximum Power Generation Capacity of Thermal, Wind, Solar, Load and Storage Resources at Each Node

[0097] Table 2 Transmission Line Capacity Parameters

[0098] Time-series production simulations were conducted using timescales of 4 days, 7 days, 30 days, 90 days, and 180 days, covering short-term and medium-to-long-term demand. Then, in step 2, a start-stop optimization space compression strategy was adopted: in the initial compression stage, upper and lower limit estimates were set based on the actual total output of the thermal power units, with an upper and lower margin range of [missing information]. Based on this, units that are not generating power within the optimization range are identified, designated as units that must be shut down, and their start / stop status value is fixed at 0. In the secondary compression phase, units whose approximate output solution is greater than their minimum technical output are designated as units that must be started, and their start / stop status value is fixed at 1. Taking a 7-day simulation scenario as an example, the effect comparison diagram of this start / stop compression strategy is shown in Figure 6(e). Each cell in the figure represents a unit, with dark blue squares representing units that must be started and light blue squares representing units that must be shut down.

[0099] Furthermore, based on the compressed start-stop optimization space, the double-layer nested structure decomposition model is reconstructed and solved in step 3. First, in the outer master problem of source-load-storage coordinated time-series output simulation, an inner decomposition model of unit start-stop-output optimization is constructed, and its convergence threshold is set to 0.01% of the initial lower bound of the objective function of the inner decomposition model, thus ensuring that the output scheme passed to the outer model is highly close to the optimal solution in the compressed space. After the inner decomposition model converges iteratively, the obtained source-load-storage output scheme is passed to the outer sub-problem of line transmission over-limit feasibility verification, and the convergence threshold of the outer decomposition model is set to the transmission capacity of each line being less than its transmission capacity limit in all time periods, thus ensuring the hard constraint requirements for the safe operation of the power grid. Finally, after the outer decomposition model converges iteratively, the final source-load-storage coordinated time-series output simulation scheme that satisfies all constraints can be obtained. Taking the transmission over-limit convergence process of line number 46 in the 7-day simulation scenario as an example, Figure 6(f) shows the convergence curve of the outer decomposition model.

[0100] The original method (hereinafter referred to as "the original method") was compared with the method of the present invention, and the results are shown in Tables 3 and 4. The results show that in simulation scenarios of 4 days, 7 days, 30 days, and 90 days, the computation time of the method of the present invention is 15.7%, 7.5%, 5.9%, and 5.3% of that of the original method, respectively, with the optimal overall cost difference being only 0.1%, 1.4%, 2.8%, and 3.7%. In a long-term simulation scenario of 180 days, the original method was interrupted due to memory overflow, while the method of the present invention only requires 10.2% of the execution time of the original method to successfully obtain a feasible solution. This indicates that the method of the present invention achieves a significant speedup compared to the original method, with a computation speed improvement of 5-20 times. While ensuring an accuracy loss of less than 5%, this method effectively reduces computational complexity. Furthermore, this method can support time-series simulations of up to 180 days, overcoming the memory limitation bottleneck faced by the original method. In summary, the method of this invention significantly improves computational efficiency, obtaining a time-series production simulation scheme similar to the original method while greatly shortening the solution time and expanding the scale of solvable problems.

[0101] Table 3 Comparison of solution time

[0102] Table 4 Comparison of Optimal Objective Values ​​Solved

[0103] Example 2 A novel power system time-series production simulation system based on model decomposition includes: The collaborative time-series production simulation model construction module is configured to determine the operating boundary of the new power system, with the optimal total operating cost of the system as the objective function. It comprehensively considers the system power balance constraints and line transmission limit exceedance constraints, and introduces various equipment-level constraints of sources, grids, loads and storage to construct a collaborative time-series production simulation model. The two-stage compression module is configured to combine engineering experience to construct an initial compression strategy for the start-stop optimization space with the goal of economic optimization, and to achieve a secondary compression of start-stop by solving the optimal system output scheme under the all-time-space collaborative optimization framework through relaxed start-stop. The inner and outer decomposition model construction module is configured to decompose the collaborative time-series production simulation model into a source-load-storage collaborative time-series output simulation main problem model without line transmission constraints and a line transmission limit exceedance feasibility verification sub-problem model. This is used as the outer decomposition model to decouple the unit start-stop state integer variables from the output continuous variables and construct the unit start-stop-output inner decomposition model. The solution module is configured to optimize the solution of the inner decomposition model and the outer decomposition model. In the process of solving the outer decomposition model, the optimality decomposition algorithm is used to decouple the integer variables of the unit start-up and shutdown states from the continuous output variables until each decomposition model converges and the optimal solution is obtained.

[0104] The processing flow for each model is as described in Example 1.

[0105] 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 one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A novel time-series power system production simulation method based on model decomposition, characterized in that, Includes the following steps: The operating boundary of the new power system is determined, with the optimal total operating cost of the system as the objective function. The system power balance constraint and line transmission limit exceedance constraint are comprehensively considered, and various equipment-level constraints of source, grid, load and storage are introduced to construct a collaborative time-series production simulation model. Based on engineering experience, a preliminary compression strategy for start-stop optimization space is constructed with economic optimization as the goal. The optimal system output solution under the all-time-space collaborative optimization framework is solved by relaxing start-stop, thereby achieving secondary compression of start-stop. The collaborative time-series production simulation model is decomposed into a main problem model for source-load-storage collaborative time-series output simulation without line transmission constraints and a sub-problem model for line transmission limit exceedance feasibility verification. Using these as the outer decomposition model, the integer variables of unit start-up and shutdown status and the continuous output variables are decoupled to construct the inner decomposition model of unit start-up and shutdown-output. The inner and outer decomposition models are optimized and solved. In the process of solving the outer decomposition model, the optimality decomposition algorithm is used to decouple the integer variables of the unit start-up and shutdown states from the continuous variables of output until each decomposition model converges and the optimal solution is obtained.

2. The novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process of determining the operating boundary of the new power system includes: determining the operating boundary of the new power system, calculating the maximum load of each node, and reconstructing it by combining the typical daily load curve and the annual daily maximum load change curve to form the input requirements that meet the time-series production simulation. Based on the average available density of wind and solar resources in each region, the power generation boundary of wind and solar resources in each region is calculated, and then a typical wind and solar power time series of each region is constructed. Determine the spatial distribution, capacity, and power generation technical parameters of thermal power units in various regions; Obtain the rated transmission capacity of the transmission line; Obtain the adjustable capacity ratio, adjustment duration, and response speed parameters of demand-side adjustable load resources at different time periods.

3. The novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process with the objective function of optimizing the total system operating cost includes: the total system operating cost includes the output cost of thermal power units, the start-up and shutdown cost of thermal power units, load shedding penalties and power curtailment penalties, and demand-side resource adjustment costs.

4. The novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process of comprehensively considering system power balance constraints and line transmission limit exceedance constraints, and introducing various equipment-level constraints of source, grid, load and storage includes: calculating the total power generation and total load power to form a source-load power balance constraint that the total power generation and total load are equal; Based on the power balance relationship of nodes, the active power expression of each node in the injected transmission network is constructed. Using the DC power flow model, the relationship expression between line power flow and node injected power is constructed. Based on the line power flow, the line transmission limit constraint expression is constructed. Using the load of each node, the output of wind and solar turbines, as well as the load shedding power and the power curtailed, as inputs, we construct the reserve demand constraint expression for thermal power units. Taking into account the active power output range constraints of thermal power units, the active power output range constraints including reserve output, the ramp rate constraints, and the minimum start / stop time constraints, the operating characteristic constraint expressions of thermal power units are constructed. After normalizing the wind and solar time-series power output curves, the active power output constraint expression of the wind and solar generator units is constructed using the wind and solar time-series power output curves as input. By considering the charging and discharging power constraints, state of charge range constraints, and energy balance constraints of the integrated energy storage device, an operational characteristic constraint expression for the energy storage device is constructed. By integrating the operating power range constraints, ramp rate constraints, dynamic recursive constraints of state of charge, and upper and lower limit boundary constraints of the adjustable load, an operating characteristic constraint expression for the adjustable load is constructed.

5. A novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, Based on engineering experience, the process of constructing a preliminary compression strategy for start-stop optimization space with the goal of economic optimization includes: constructing an expression for the economic index of unit output, comprehensively considering the impact of reserve demand and the fluctuation of new energy output on thermal power output, estimating the upper and lower boundaries of the total thermal power output in each period, sorting all units in ascending order according to the economic index of thermal power unit output, and if the economic index is the same, sorting them in ascending order again according to the minimum technical output, and giving priority to units with greater operational flexibility. Based on this priority, the unit output is set to its maximum technical output and accumulated. When the accumulated output first exceeds the upper limit of the total thermal power output, the process stops and the affected unit is determined to be a forced start-up optimized unit. For the remaining units, continue to sort them according to the above priority, set their output to the average of the maximum and minimum output and accumulate them until the lower boundary of the total thermal power output is reached. The units involved are then identified as units to be optimized and put into operation.

6. The novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process of achieving secondary compression through start-stop optimization, using a relaxed start-stop approach to find the optimal system output within a spatiotemporal collaborative optimization framework, includes: Taking into account the output cost of thermal power units, load shedding penalties, wind and solar power curtailment penalties, and regulation costs, and with the goal of minimizing the source-load-storage coordinated operation cost, an objective function expression for a linear relaxation output simulation model is constructed. Ignore all constraints that only include unit start-up and shutdown, and use constraints that only contain continuous variables and unit output constraints that relax start-up and shutdown state variables as model constraints; The start-stop state variables of the forced start and standby optimized units are fixed at 1, and the unit output constraint expression of the relaxed start-stop state variables is constructed. For units whose output exceeds the set value at the target time, their start-stop status at that time will be determined as the start-up status, and the corresponding start-stop status variable value will be fixed at 1 in subsequent optimizations and treated as a constant variable.

7. A novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process of decomposing the collaborative time-series production simulation model into a main problem model of source-load-storage collaborative time-series output simulation without line transmission constraints and a sub-problem model of line transmission limit exceedance feasibility verification includes: Taking into account the output cost of thermal power units, start-up and shutdown costs, load shedding penalties on the load side, wind and solar power curtailment penalties on the renewable energy side, and system regulation costs, the objective function expression of the source-load-storage coordinated time-series output simulation master problem model is constructed, which is the same as the objective function expression of the coordinated time-series production simulation model. The constraints related to line transmission limit exceedance in the constructed collaborative time-series production simulation model are decoupled, while the system power balance constraints and the operating characteristic constraints of various source, load, and storage devices are retained, which together constitute the constraint conditions of the main problem model. A DC power flow model is used to calculate the injected power value at each node in the transmission network and, combined with the line transmission capacity limit, calculate the transmission capacity over-limit value of each line in each time period. The feasibility of power flow for all transmission lines in the system is verified, and a transmission over-limit feasibility verification sub-problem is constructed.

8. A novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process of decoupling the integer variables of unit start-up and shutdown status from the continuous variables of output, and constructing the inner decomposition model of unit start-up and shutdown-output includes: Construct the objective function expression for the main problem of unit start-up and shutdown optimization, whereby the objective function expression is the lower bound of the total start-up and shutdown cost of thermal power units and the objective function value of the sub-problem of output optimization under the objective start-up and shutdown scheme. The sum is minimized; Taking into account the minimum start / stop time constraints of the units, the hard constraints of the start / stop states of the units that must be turned on / off, and the objective function of the main problem of output optimization... Initial constraints and feasibility / optimality cutting plane constraints generated from subproblems constitute the constraints of the main problem of unit start-up and shutdown optimization. Given the unit start-up and shutdown schemes in the main problem, we construct the objective function expression for the output optimization sub-problem, which is to minimize the sum of the total output cost of thermal power units, the total load shedding penalty of the system, the total curtailment penalty of wind and solar power units, and the total adjustment cost of demand-side resources. Taking into account the system's source-load balance and equipment operation boundary constraints, the known start-up and shutdown state variable values ​​are substituted into the unit output constraints to form the constraint conditions of the output optimization sub-problem model. Solving the main unit start-up and shutdown optimization problem and the output optimization sub-problem will yield the source-load-storage output scheme for each time period.

9. A novel power system time-series production simulation method based on model decomposition as described in claim 1, characterized in that, The process for determining the convergence of the inner-layer decomposition model includes: During the iterative process, the lower and upper bounds of the objective function in the inner decomposition model are constructed. In each round of decomposition iteration, the current lower and upper bounds of the main problem objective function are recorded. It is determined whether the absolute value of the difference between the lower and upper bounds is less than the preset convergence threshold. If it is satisfied, the inner decomposition model is considered to have converged to its optimal solution; otherwise, based on the constraints of the output optimization sub-problem model, a feasibility / optimality cutting plane is generated and fed back to the main problem model. The unit start-up and shutdown scheme is iteratively optimized until the convergence condition is met. Alternatively, the process for determining the convergence of the outer-layer decomposition model includes: Substitute the transmission capacity of each line in all time periods into the following formula, and determine whether the absolute value of the ratio between the two is less than its over-limit threshold. If so, the current solution is determined to be the final solution; otherwise, generate a feasibility cutting plane constraint based on the constructed line transmission over-limit verification subproblem, and add it to the constructed source-load-storage coordinated time-series output simulation master problem. The solution of the master problem model is gradually corrected through the feedback of the cutting plane constraint until the convergence condition is met.

10. A novel power system time-series production simulation system based on model decomposition, characterized in that, include: The collaborative time-series production simulation model construction module is configured to determine the operating boundary of the new power system, with the optimal total operating cost of the system as the objective function. It comprehensively considers the system power balance constraints and line transmission limit exceedance constraints, and introduces various equipment-level constraints of sources, grids, loads and storage to construct a collaborative time-series production simulation model. The two-stage compression module is configured to combine engineering experience to construct an initial compression strategy for the start-stop optimization space with the goal of economic optimization, and to achieve a secondary compression of start-stop by solving the optimal system output scheme under the all-time-space collaborative optimization framework through relaxed start-stop. The inner and outer decomposition model construction module is configured to decompose the collaborative time-series production simulation model into a source-load-storage collaborative time-series output simulation main problem model without line transmission constraints and a line transmission limit exceedance feasibility verification sub-problem model. This is used as the outer decomposition model to decouple the unit start-stop state integer variables from the output continuous variables and construct the unit start-stop-output inner decomposition model. The solution module is configured to optimize the solution of the inner decomposition model and the outer decomposition model. In the process of solving the outer decomposition model, the optimality decomposition algorithm is used to decouple the integer variables of the unit start-up and shutdown states from the continuous output variables until each decomposition model converges and the optimal solution is obtained.

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