A power system evolution simulation method based on hierarchical collaborative simulation

Abstract

The present invention proposes a method for power system evolution deduction based on hierarchical collaborative simulation, which belongs to the field of new power system long-term planning. The method includes the following steps: constructing the power system evolution boundary, simulating the power system vision design, and embedding the power system source-grid-load-storage collaborative planning simulation with short-term operation simulation to form the current stage power grid planning scheme and record the evolution stage scheme and make evolution termination judgment to connect each stage in series to form a long-term evolution result. The present invention summarizes the evolution process into three levels of simulation: long-term, medium-term and short-term according to the characteristics of different links of the power system. Through modular modeling and solution of each layer of simulation, multi-factor and multi-time scale coupling simulation is realized, and the modeling complexity and accuracy are taken into account, so it can be used for large-scale system evolution deduction; at the same time, modular modeling and solution support the customized assembly of the main link models and solution methods of the evolution system while maintaining good openness, significantly improving the scalability and adaptability of the evolution system.

Description

Technical Field

[0001] The present invention belongs to the technical field of long-term planning of power systems, and in particular relates to a power system evolution deduction method based on hierarchical collaborative simulation. Background Art

[0002] High proportions of renewable energy, power electronic equipment, and distributed resources are driving significant changes in the structure and operation of new power systems. Future power systems will exhibit distinctly different structures, operating characteristics, and control laws than the current ones. Understanding the characteristics of future power systems in advance and subsequently informing development planning and policy design is a crucial issue for long-term power system planning. Simulating power system evolution using numerical calculations is an important tool for understanding the future power system. Specifically, power system evolution refers to predicting how key components of the current power system, such as source-grid-load-storage, will evolve over a long period of time, taking into account future power demand, technological changes in power production, transmission, storage, and consumption, and factors such as power generation resource conditions and energy policy constraints. This includes factors such as power source composition, scale, and layout; grid interconnections and voltage levels; energy storage forms, capacity, and layout; and electricity consumption patterns and scale. Therefore, the evolution of the power system is the result of the combined effects of multiple factors such as increased demand, technological progress and relevant policies. It involves multiple links such as policies, technologies, planning and operations and has multi-factor coupling characteristics. It is necessary to take into account power supply security, economy and long-term design. It has multi-time scale coupling characteristics and also includes multiple development modes such as optimization decision-making and subjective design under resource and technology constraints. It is a high-dimensional nonlinear time-varying dynamic process under the influence of multiple influencing factors.

[0003] There are two main approaches to power system evolution simulation. The first approach treats power system evolution as a multi-stage optimization decision-making problem, achieving evolution simulation by optimizing the power source, energy storage, or grid configuration at each time interval. These approaches have two limitations: First, the large number of decision variables, which may include nonlinearities, limits the scale of the solvable optimization problem to computational capacity, necessitating simplification or applicability only to small-scale system evolution analysis. Second, they struggle to account for the increasing number of grid nodes and fuzzy design (such as long-term source-load center planning) and uncertainty associated with system evolution. Another approach analyzes power system evolution by mimicking natural growth patterns or general network development patterns, avoiding optimization problems and applying them to large-scale system analysis. However, these approaches are limited to power system evolution analysis and fail to account for the multi-factor coupling of power generation, grid, and storage, nor do they reflect the optimization planning and subjective long-term design involved in the evolutionary process. Therefore, current evolutionary analysis methods based on optimization theory and "imitation" theory fail to consider the differences in characteristics of different links, nor can they simultaneously account for source-storage synergy and subjective decision-making. Consequently, their accuracy, precision, and adaptability still fall short of the requirements for large-scale, novel power system evolution analysis. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a power system evolution deduction method based on hierarchical collaborative simulation, which improves the scalability and adaptability of power system deduction.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A power system evolution simulation method based on hierarchical collaborative simulation includes the following steps:

[0007] Construct the boundaries of driving factors of power system evolution, the boundaries of wind and solar resources and construction land constraints, and starting variables that describe the composition of the starting system, forming a time series of boundary variables and evolutionary starting variables;

[0008] Aggregate the starting system network nodes to form power supply areas that require long-term planning; determine the regional power supply net imbalance based on the regional economic development level and resource endowment, sort the regions according to the net power supply imbalance, and select regions with inflow and outflow power greater than a threshold as load centers and power supply centers; construct a regional interconnection channel optimization model, and use the regional interconnection channel optimization model to construct a set of candidate solutions for key inter-regional transmission channels;

[0009] A source-storage expansion planning model is constructed to form the total growth of power sources and energy storage in the current stage. A grid node growth model based on complex network theory is constructed to deduce future grid growth. A source-grid-load-storage collaborative planning model is established based on a given total installed capacity target and grid node growth. A source-grid-load-storage collaborative operation simulation model is constructed to respectively solve the current source-grid-storage site selection and sizing results and the operation evaluation indicators of each planning scheme. A heuristic algorithm is used to solve the source-grid-load-storage collaborative planning model to form a power system evolution scheme for the entire evolution range.

[0010] Record the evolution stage plan and make evolution termination judgment. When the current evolution stage is not equal to the maximum evolution stage, read the boundary data of the next stage and continue to deduce the next stage.

[0011] The effects provided in the summary of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0012] The present invention proposes a method for deducing the evolution of a power system based on hierarchical collaborative simulation, comprising the following steps: constructing the boundaries of driving factors for the evolution of the power system, the boundaries of constraints on wind and solar resources and construction land, and starting variables describing the composition of the starting system to form a time series of boundary variables and evolution starting variables; aggregating the network nodes of the starting system to form a power supply area that requires long-term planning; determining the net imbalance of regional power supply in combination with the regional economic development level and resource endowment, sorting the regions according to the net imbalance of power supply, and selecting the regions with inflow and outflow power greater than a threshold as load centers and power supply centers; constructing a regional interconnection channel optimization model, and using the regional interconnection channel optimization model to construct a key transmission network between regions. A set of candidate schemes for electric channels; construct a source-storage expansion planning model to form the total growth of power sources and energy storage in the current stage, construct a grid node growth model based on complex network theory to deduce future grid growth, establish a source-grid-load-storage collaborative planning model based on a given total installed capacity target and grid node growth, and construct a source-grid-load-storage collaborative operation simulation model to respectively solve the current stage source-grid-storage site selection and capacity determination results and the operation evaluation indicators of each planning scheme, and use a heuristic algorithm to solve the source-grid-load-storage collaborative planning model to form a power system evolution scheme for the entire evolution interval; record the evolution stage scheme and make evolution termination judgments. When the current evolution stage is not equal to the maximum evolution stage, read the boundary data of the next stage and continue to deduce the next stage. The present invention summarizes the evolution process into three levels of simulation: long-term, medium-term and short-term, based on the characteristics of different links in the power system. Through modular modeling and solution of each level of simulation, multi-factor and multi-time scale coupled simulation is realized, and the modeling complexity and accuracy are taken into account, so it can be used for large-scale system evolution deduction. At the same time, modular modeling and solution support the customized assembly of the models and solution methods of the main links of the evolutionary system while maintaining good openness, which significantly improves the scalability and adaptability of the evolutionary system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of a method for power system evolution deduction based on hierarchical collaborative simulation proposed in Example 1 of the present invention;

[0014] Figure 2 A flowchart for constructing the power system evolution boundary proposed in Example 1 of the present invention;

[0015] Figure 3 This is a flow chart of the power system prospective design simulation proposed in Example 1 of the present invention;

[0016] Figure 4 The power system single-stage optimization planning simulation process embedded with short-term operation simulation proposed in Example 1 of the present invention;

[0017] Figure 5 This is the power network node growth simulation process proposed in Example 1 of the present invention;

[0018] Figure 6 This is the source-network-storage collaborative planning process embedded with short-term operation simulation proposed in Example 1 of the present invention;

[0019] Figure 7 This is a diagram of the starting system structure proposed in Example 1 of the present invention;

[0020] Figure 8 This is the load growth curve proposed in Example 1 of the present invention;

[0021] Figure 9 This is a typical load curve proposed in Example 1 of the present invention;

[0022] Figure 10 The carbon emission limits for each stage proposed in Example 1 of the present invention;

[0023] Figure 11 This is the technology progress coefficient curve proposed in Example 1 of the present invention;

[0024] Figure 12 This is the development difficulty coefficient curve proposed in Example 1 of the present invention;

[0025] Figure 13 Typical wind power output curve proposed in Example 1 of the present invention

[0026] Figure 14 Typical photovoltaic output curve proposed in Example 1 of the present invention

[0027] Figure 15 The system partitions and key transmission channels proposed in Example 1 of the present invention;

[0028] Figure 16 This is the evolution result from 2020 to 2030;

[0029] Figure 17 This is the evolution result from 2030 to 2050;

[0030] Figure 18 This is the evolution result from 2050 to 2060;

[0031] Figure 19 It is the system power balance curve at each stage of evolution. DETAILED DESCRIPTION

[0032] In order to clearly illustrate the technical features of this solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplicity and clarity and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits descriptions of well-known components and processing technologies and processes to avoid unnecessary limitations on the present invention.

[0033] Example 1

[0034] Embodiment 1 of the present invention proposes a flow chart of a method for power system evolution deduction based on hierarchical collaborative simulation, which is used to solve the technical problems existing in the power system evolution deduction in the prior art.

[0035] Figure 1 This is a flow chart of a method for power system evolution deduction based on hierarchical collaborative simulation proposed in Example 1 of the present invention;

[0036] In step 1, the power system evolution boundary is constructed.

[0037] Construct the boundaries of the driving factors of the power system evolution, the constraints of wind and solar resources and construction land, and the starting variables that describe the composition of the starting system, and form a time series of boundary variables and evolutionary starting variables. Specifically, it includes three parts: the construction of the boundaries of the driving factors of the power system evolution, the construction of the constraints of wind and solar resources and construction land, and the construction of starting variables that describe the composition of the starting system.

[0038] Figure 2 A flowchart for constructing the power system evolution boundary proposed in Example 1 of the present invention;

[0039] In step 1.1, the maximum load, typical load curve, equipment unit investment, and carbon emissions are used as boundary variables. The functional relationship between the boundary variables and the associated variables is fitted from the three aspects of power demand, technological progress, and environmental policy. The associated variable data from authoritative research and forecasts are further substituted into the associated function relationship to calculate the boundary variable time series. Specifically, it includes:

[0040] In step 1.1.1, the historical maximum power load, economic output, and population of the evolving system are obtained. Formula (1) is used as the correlation function to fit the historical maximum power load to obtain the correlation function parameters between the maximum power load and the population and economic output.

[0041]

[0042] Among them, Ps L is the maximum power load; E s 、N s is the economic output and population data of the corresponding year; a 0,L is the first correlation function between the maximum load and the associated variable; a 1,L is the second correlation function between the maximum load and the correlation variable; a 2,L is the third correlation function between the maximum load and the correlation variable;

[0043] Investigate reports published by social and economic research institutions, collect the predicted values ​​of economic aggregate and population time series data of each system and each region in each stage of the evolution cycle, and substitute them into formula (1) to form the maximum load time series of each system and each region in each stage of the evolution cycle.

[0044] In step 1.1.2, obtain the annual daily load curve of the typical power load, and use formula (2) to normalize the load value of the daily load curve relative to its maximum value to form the standardized daily load curve of each type of load;

[0045]

[0046] in, are the power value of the normalized daily load curve of the r-th type of load at time t and the power value of the original daily load curve of the r-th type of load at time t respectively; is the annual maximum value of the daily load curve of the rth type of load;

[0047] Predict the proportion of each type of typical load in each stage of the evolution cycle, and use formula (3) to calculate the scale of each type of load based on the total load in each stage:

[0048] in, β s,r are the load scale of the rth type of load in the sth stage and the load proportion of the rth type of load in the sth stage respectively;

[0049] Multiply and superimpose the maximum values ​​of various loads with the annual daily load curve, and use formula (4) to synthesize the annual daily load curve of the entire system in each stage;

[0050]

[0051] in, is the system annual daily load curve; Ω L A typical load category set.

[0052] In step 1.1.3, the current costs of wind and solar power and energy storage are investigated, and their cost changes as the evolution stage increases, forming the equipment unit investment change function shown in formula (5):

[0053]

[0054] in, Investment in various equipment units for the s stage, As the benchmark unit investment, is the coefficient of technological progress that decreases as the evolutionary stage increases;

[0055] Substituting the survey data into the above formula forms a time series of unit costs of wind, solar, and energy storage.

[0056] In step 1.1.4, the carbon emission path curve is normalized to form the future long-term carbon emission curve;

[0057]

[0058] in, are the values ​​of the normalized carbon emission curve at the sth stage of evolution and the values ​​of the original carbon emission curve at the sth stage of evolution respectively; is the initial value of carbon emissions during the evolution cycle;

[0059] Multiply the carbon emissions of the power industry in the initial evolution stage by the normalized carbon emission curve to calculate the time series of carbon emission limits for the power industry during the evolution cycle:

[0060]

[0061] in, is the value of the carbon emission limit curve of the power system in the evolution cycle at the sth stage, is the initial value of carbon emissions from the power system.

[0062] In step 1.2, the wind and solar resource distribution map is used to find the average density of wind and solar resources in each region. The specific process of using the wind and solar resource average density to calculate the exploitable wind and solar resource boundaries in each region includes:

[0063]

[0064] in, is the average density of wind and solar resources in region m; S m is the area of ​​region m; is the upper limit of resource development in region m; η VRE For the highest conversion efficiency of wind and solar power related to the level of power generation technology, the maximum value can be selected from the data published by the wind and solar generator set manufacturer.

[0065] Investigate the difficulty of wind and solar power construction in each region and construct a function that influences the resource development ratio on development costs:

[0066]

[0067] in, C is the incremental cost coefficient for development of region m in stage s; s,m is the developed resources in region m at stage s; η s,m is the development level of region m in stage s; F re (·) is a non-decreasing function that is positively correlated with the degree of development.

[0068] In step 1.3, investigate the current power system grid topology and line and transformer electrical parameters to form a starting system network parameter table;

[0069] Investigate the current installed capacity of thermal power, hydropower, and nuclear power in the power system, and develop operating parameter tables including the installed location and capacity, maximum and minimum output, ramp rate, minimum on / off time, start / stop costs, and coal (water and nuclear fuel) consumption characteristic curve parameters for typical thermal, hydro, and nuclear power units;

[0070] Investigate the current installed capacity of wind and solar power in the power system, and develop a table of typical wind and solar power station grid-connected locations and installed capacities, annual curves of maximum power generation capacity, unit power curtailment penalties, and other operating parameters;

[0071] Investigate the current status of energy storage installed in the power system and develop a table of typical energy storage station grid-connected locations and operating parameters such as maximum storage capacity, maximum power, charge and discharge efficiency, and lifespan reduction;

[0072] Investigate the current load situation of the power system and form operating parameter tables such as daily load and annual load curves of each node, maximum load that can be cut, and unit load cutting penalty.

[0073] Step 2: Power system vision design simulation.

[0074] Aggregate the starting system network nodes to form a power supply area suitable for long-term planning; calculate the net load level based on the regional economic development level and resource endowment, and determine the load center and power center based on the positive and negative net load and amplitude; construct a regional interconnection channel optimization model, and use the constructed regional interconnection channel optimization model to select key transmission channels between regions.

[0075] Figure 3 This is a flow chart of the power system prospective design simulation proposed in Example 1 of the present invention;

[0076] Step 2.1 Taking into account the node's load level, resource development limit, geographical location, and administrative region, the starting system network nodes are aggregated to form a power supply area suitable for long-term planning, including:

[0077] Randomly select several nodes as the initial center points representing different areas;

[0078] Taking into account the multi-dimensional characteristics such as geographical location, load level, resource development capacity and administrative division, the comprehensive evaluation index of the correlation between the remaining nodes and the center point is calculated using formula (10):

[0079]

[0080] Among them, F long,area is the comprehensive evaluation index of regional division correlation, x n is the nth sub-index, including the node's load level, resource development upper limit, geographical location, and administrative region; α n is the weight of sub-index n;

[0081] Assign each node to the nearest center point to form a preliminary area division;

[0082] Recalculate the evaluation index of the nodes in each area and update the center point position of each area until the area allocation of each node no longer changes.

[0083] In step 2.2, based on the maximum load forecast sequence of each node and the wind and solar resource endowment, formula (11) is used to calculate the difference between the upper limit of the future wind and solar power capacity development and the future power demand of each power supply area to form the regional power supply net imbalance:

[0084]

[0085] in, is the net imbalance of region m; is the maximum development capacity of renewable resources at node i in region m; P i L is the future load level of node i in region m.

[0086] Sort the regions based on the net imbalance of power supply and select the top M with the largest net outflow power. B areas as power centers; select the top M with the largest net inflow power B area as the load center; among them, M B (Usually 2 to 5).

[0087] Taking the power exchange requirements of each power supply area as the constraint and the channel construction cost as the target, a regional interconnection channel optimization model is constructed:

[0088]

[0089] Where M is the total number of regions; p∈(1,...M), q∈(1,...M), The capacity of the transmission channel between regions p and q reflects the capacity transmitted from region p to region q; It is the transmission channel cost per unit capacity after comprehensively considering the geographical factors between regions; is the upper limit of the transmission channel that can be constructed between regions p and q; is the net imbalance of region p;

[0090] A linear programming solver is used to solve the regional interconnection channel optimization model to obtain the capacity of the interconnection transmission channels between regions, and the number of inter-regional interconnection lines is determined based on the typical transmission capacity of 500kV and 1000kV high-voltage transmission lines.

[0091] Step 3: Single-stage optimization planning simulation of the power system embedded in the short-term operation simulation.

[0092] This section integrates long-term goals with current power supply needs, and forms the total growth of power sources and energy storage in the current stage through source and storage planning, realizes grid node growth based on complex network theory, completes source and storage site selection and capacity determination and grid upgrade based on source-grid-storage collaborative planning, and implements scheme verification, correction and update based on key parameter verification. Through the above four steps, a mid-term simulation is completed to form the current stage of grid construction plan, and a multi-dimensional collaborative operation daily model of source, grid, load and storage is embedded in the scheme evaluation to realize accurate evaluation of operating indicators based on power system operating status data at different time scales.

[0093] Figure 4 The power system single-stage optimization planning simulation process embedded with short-term operation simulation proposed in Example 1 of the present invention;

[0094] In step 3.1, ignoring grid constraints, the total load balance of the entire system at all times in this stage and the upper limit of wind and solar resource development in the entire system are used as constraints, and the goal is to minimize the combined cost of annual construction investment and operating expenses. A source and storage expansion planning model is constructed:

[0095]

[0096] in, The unit investment of each equipment in stage s is to comprehensively consider the influence of technology progress coefficient and development difficulty; is the wind, solar and storage installed capacity to be optimized in the sth stage; r dis 、m type They are the wind, solar and storage installation discount rate and the wind, solar and storage installation equipment life; They are the load, output of conventional units, new energy and energy storage units in each region at each time in stage s; The installed capacity of the type machine to be optimized; F op,s For operating costs;

[0097] A linear programming solver is used to solve the source and storage planning expansion planning model to obtain the various types of power capacity that need to be added in the current stage.

[0098] In step 3.2, the grid node growth plan is implemented based on complex network theory, which is further divided into three steps: whether to grow, growth point site selection, and growth point access. Figure 5 This is the power network node growth simulation process proposed in Example 1 of the present invention.

[0099] In step 3.2.1, research typical projects, national, and industry substation design specifications (GB / T, IEEE, and IEC standards), and summarize the power access or load power supply capabilities of 1000kV, 750kV, and 500kV substations.

[0100] Check each load node one by one to see if the off-grid load exceeds the power supply capacity of the substation, and if so, split the load to the surrounding area to generate additional load nodes;

[0101] Calculate the sum of the remaining grid-connected capacity of all power supply nodes in the entire system and compare it with the total capacity of new power sources in this stage. If the remaining grid-connected capacity does not meet the demand for new power sources, new power supply nodes are added according to the resource richness of each region until the remaining total grid-connected capacity meets the demand for new power sources.

[0102] In step 3.2.2, if the node to be split is a load node, the transferable range of the power supply load is determined with the node as the center, and the candidate points are further determined through grid division.

[0103] Formula (14) is used to calculate the difference between the total load within the power supply radius of each candidate point and the existing power supply capacity to form the power supply capacity shortfall:

[0104]

[0105] Where, i∈(1,...I), j∈(1,...I); I is the total number of nodes; is the total load value within the power supply range of the selected growth point i; Ω sup It is the set of nodes within the power supply range; is the load value of the existing node j within the power supply range of the selected growth point; d,i For power supply shortage;

[0106] Sort by power supply capacity shortfall and select the maximum value point as the new load node;

[0107] If the node to be split is a power node, the sum of the exploitable potential in the area of ​​the point is calculated using formula (15) to form the characteristic parameter;

[0108]

[0109] Among them, P i po Develop the potential of power within the power supply range of node i; ξv,i To develop potential characteristic parameters;

[0110] Based on the development potential and ranking, the maximum value point is selected as the new power node.

[0111] In step 3.2.3, the node preference factors of all candidate connection nodes within the growth node connection range are calculated using formula (16):

[0112]

[0113] Ω c A set of connectable nodes that belong to the same local world as the newly added power node; is the probability of a new power node being connected to node i; α, β, γ are non-negative weight coefficients representing the influence of degree, distance, and injection power; k i is the degree of node i; k j is the degree of node j; l i is the Euclidean distance between node i and the newly added power node; l j is the Euclidean distance between node j and the newly added power node; P net,i Inject power to node i; P net,j Inject power into node j;

[0114] A random simulation is performed based on the probability of adding new power nodes to the local world point set, and the connection method between the new power nodes and the original network is formed according to the random simulation results.

[0115] In step 3.3, based on the given total installed capacity target and grid nodes, a source-grid-storage collaborative planning model is established with the goal of optimal economic efficiency. The installation locations of each power source and energy storage, as well as the supporting grid upgrade plan, are determined. This is further divided into four steps: constructing a planning objective function with optimal economic efficiency, constructing planning constraints that integrate the long-term source-storage planning capacity, conducting short-term rolling simulation and operating indicator evaluation based on the source-grid-load-storage collaborative operation model, and solving the model using a genetic algorithm. Figure 6 This is the source-network-storage collaborative planning process embedded with short-term operation simulation proposed in Example 1 of the present invention.

[0116] In step 3.3.1, the objective function of the source-grid-storage collaborative planning model is formed with the goal of minimizing the combined cost of operating expenses and annualized investment:

[0117] minF=F op +F in ; (17)

[0118] Among them, F op 、F in , F are operating cost, annualized investment and comprehensive cost respectively;

[0119] Formula (18) is used to model the annualized investment of the sum of the annualized investment of units and lines:

[0120]

[0121] type∈{VRE,ess,line};(18)

[0122] in, is the unit installation cost of type type unit at node i; is the installed capacity of type type unit at node i; r dis 、m type are the discount rate and equipment life respectively.

[0123] In step 3.3.2, the planning constraints of the source-grid-storage collaborative planning model are constructed by considering the upper limit of development capacity of each node of each type of power source, total installed capacity and power balance, and upper limit of line installation.

[0124]

[0125]

[0126]

[0127] in, The installed capacity of new energy to be optimized and the upper limit of developable capacity for node i; The total installed capacity targets for various power sources and energy storage in this phase; is the upgrade capacity and upgrade capacity upper limit of the lth line to be optimized.

[0128] In step 3.3.3, with the goal of optimizing operational economics, a source-grid-load-storage collaborative operation model is constructed by comprehensively considering the power system and equipment operating constraints. The source-grid-load-storage collaborative operation model is called on a daily basis to perform short-term process simulations to obtain the operating status data of the planning scheme. The annual operating cost is calculated based on this operating status data.

[0129] Comprehensively consider the coal consumption and carbon emission costs of coal-fired units c , standby cost f r , unit start-up and shutdown costs f uc , load shedding and power abandonment penalty f p etc., and construct the objective function of the source-grid-load-storage collaborative planning model:

[0130] minF op =f c +f r +f uc +f p ;(twenty two)

[0131] Among them, f c is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalties for load shedding and power curtailment;

[0132] The cost calculation method for each sub-item is as follows:

[0133]

[0134]

[0135]

[0136]

[0137] Among them, c coal is the unit coal consumption cost, is the carbon dioxide emission cost per unit coal consumption; k G Number the thermal power units; kth G Coal consumption characteristic function of thermal power units; For the kth G The output of the thermal power unit at time t; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the cost of starting and stopping the unit; c drop 、c shed is the penalty factor for power curtailment and load shedding, Q drop , Q shed is the amount of power abandonment and load shedding, Allowed values ​​for the corresponding variables.

[0138] Taking into account the resource endowment of each node and the overall power system source and storage increase target, the constraints of the source-grid-load-storage coordinated operation model are constructed:

[0139] Considering that the total node power injection is equal to the load, the power balance constraint is constructed:

[0140]

[0141] in, For node i k type The output of each type of unit at time t; represents the active power transmitted in line l at time t; is the active power of the load at node i at time t; is the load shedding amount of node i at time t; Ω from ,Ω to They represent the set of lines starting with node i and ending with node i; k type The type unit number.

[0142] Taking into account the load reserve demand and the new energy reserve demand, a reserve constraint is established in which the reserve supply is greater than the system reserve demand:

[0143]

[0144] in, is the positive and negative standby coefficient; Ω d is the set of nodes connected to the load; L + % is the first reserve requirement coefficient reserved for load output; L - % is the second reserve requirement coefficient reserved for load output; W + % is the first reserve requirement coefficient reserved for wind and solar power unit output; W - % is the second reserve requirement coefficient reserved for wind and solar power unit output; g + It is the positive and standby sign of thermal power unit; g － It is the negative standby flag of thermal power unit; k VRE Number the VRE unit; Number the VRE unit; k for unit VRE The amount of power wasted at time t; here, the backup needs come from load fluctuations and wind and solar power output forecast errors, and the backup supply is only provided by thermal power units.

[0145] Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraint is formed:

[0146]

[0147] in, is the maximum transmission capacity of line l;

[0148] Considering the upper and lower limits of thermal power generation units' output when providing backup demand, the output constraints of thermal power generation units are constructed:

[0149]

[0150] in, is the operating state variable of the kth thermal power unit at time t, the operating state is 1 and the shutdown state is 0; are the maximum output and minimum output of thermal power units respectively; are the positive and negative spare coefficients; For the kth G The output of the thermal power unit at time t.

[0151] Taking into account the limitations on the power rise and fall of thermal power units per unit time, a ramp constraint is constructed to ensure that the power change of thermal power units per unit time is less than the ramp upper limit:

[0152]

[0153] in, is the upward power change of the thermal power unit under the operating time; is the downward power change of the thermal power unit under the running time; For the kth G The output of the thermal power unit at time t-1.

[0154] Considering the start-stop state changes and start-stop time restrictions during the start-up and shutdown process of thermal power units, a start-stop constraint is constructed to ensure that the start-up and shutdown time of the thermal power units is greater than the minimum start-up and shutdown time:

[0155]

[0156]

[0157] in, is the minimum startup and shutdown duration of coal-fired unit k; For the kth G The start-up mark quantity of the thermal power unit at time t-1; For the kth G The shutdown mark value of the thermal power unit at time t-1;

[0158] Establish upper and lower output constraints for new energy units:

[0159]

[0160] Where, are the maximum output and minimum output of the new energy unit at time t respectively; For the kth VRE The output of the thermal power unit at time t.

[0161] Construct upper and lower power constraints for energy storage units:

[0162]

[0163] in, is the maximum discharge power of the energy storage unit; For the kth ess The output of the energy storage unit at time t.

[0164] Considering the energy storage capacity limitation during normal operation of the energy storage unit, the upper and lower limit constraints of the energy storage unit's state of charge are constructed:

[0165]

[0166] in, Indicates the kth ess The state of charge of the energy storage unit at time t, kth ess The maximum and minimum nuclear power states of the energy storage units.

[0167] The state of charge of the energy storage unit is:

[0168]

[0169] in, For the kth ess The state of charge of the energy storage unit at time t-1; For the kth ess The rated energy storage capacity of each energy storage unit; Δt is the time step; For the kth ess The output of the energy storage unit at time t-1.

[0170] The source-grid-load-storage collaborative operation model is called daily to perform short-term process simulations to obtain the operating status data of the planned scheme and solve the system operating status at each moment of the day;

[0171] The system operation status data such as unit start and stop, energy storage charge status, etc. at the last moment of the previous day is used as the initial moment status of the next day. The daily operation results are connected in series to form a complete 8760 time series operation results.

[0172] In step 3.3.4, the source-grid-load-storage collaborative planning model is solved based on the genetic algorithm to initially randomly generate the source-storage installation location capacity and line upgrade plan parameters, and calculate the investment and operating costs of each plan.

[0173] The former NS schemes with the lower sum of costs are crossed (exchanged scheme parameters), mutated (randomly changed scheme parameters) and combined with the original schemes to form a new generation of schemes. The investment and operating costs of each scheme in the new generation are calculated.

[0174] The above process is repeated until the cost difference of the optimal solution between the two generations is less than the iterative convergence criterion ε.

[0175] Step 3.4 conducts operational simulations for the source and storage site selection and capacity determination, as well as the grid upgrade plan. Key indicators are calculated based on operational status data and verified to ensure they meet the set targets. If key indicators do not meet the requirements, the total installed capacity target is revised and the mid-term simulation of this phase is restarted. This is further divided into four steps: short-term operational simulation of the planning plan, calculation of key evaluation indicators, indicator satisfaction verification, and installation plan revision. These steps include:

[0176] Step 3.4.1 The source-grid-load-storage collaborative operation model is called on a daily basis to conduct short-term operation simulations to obtain the annual operation status data of the selected scheme.

[0177] Step 3.4.2 uses formula (37) to calculate the key evaluation indicators of the planning scheme:

[0178]

[0179]

[0180]

[0181] Among them, EMI is the total annual carbon emissions of the planning scheme; Carbon emissions per unit coal consumption of thermal power units; T total is the total simulation time; Coal consumption characteristic function of thermal power units; For the kth G The output of the thermal power unit at time t;

[0182] are the system load shedding rate and power abandonment rate respectively; Q Lshed 、 are the total load shedding power, the abandoned power of new energy and the total power generation; Q total is the total power generation.

[0183] In step 3.4.3, compare whether all the above key indicators are less than the target value. If so, jump to step 4, otherwise proceed to the next step;

[0184] In step 3.4.4, if carbon emissions or load shedding rates exceed the set values, the installed capacity of new energy generators will be increased, taking into account the replacement of excess carbon emissions with electricity, the electricity corresponding to load shedding, and the peak capacity shortfall:

[0185]

[0186] Among them, r Adj To adjust the margin system; EMI over 、 They are carbon emissions, average load shedding rate and maximum load shedding rate excess; They are the CO2 emission intensity per kilowatt-hour of fossil energy units, the annual utilization hours and peak capacity coefficient of new energy units; are the maximum load and the added value of new energy installed capacity respectively; r Adj The adjustment margin system is 1 to 1.05.

[0187] In order to ensure the new energy consumption capacity, the energy storage installed capacity is increased according to formula (41):

[0188]

[0189] in, They are the newly added value of energy storage installed capacity and the average ratio of new energy storage; Usually around 0.2.

[0190] If the curtailment rate exceeds the set value, the energy storage capacity is increased according to formula (42) considering the system flexibility adjustment requirements:

[0191]

[0192] in, The power abandonment rate exceeds the limit; Q VRE is the total power generation of the VRE units; It is the annual utilization hours of new energy units.

[0193] Step 4: Recording the evolutionary plan and determining when to terminate the evolution

[0194] This part is responsible for saving the evolution results of the current stage and judging whether the current evolution simulation is terminated based on the current stage number. It mainly includes two steps: saving the evolution result data and checking the stage number.

[0195] Step 4.1 After the evolution simulation is completed, save the current evolution result data. The data to be saved includes optimization results, planning results, and operation results;

[0196] The optimization results include all the plans scanned by the genetic algorithm and the investment and operating costs of the plan; the planning results include the location and capacity of various source and storage installed capacity before and after correction, the line reinforcement status and the growth of nodes; the operation results include the load size of the optimal plan at each moment, the unit output data, the charging and discharging data of the energy storage and the line transmission energy data, as well as the power balance of each node.

[0197] Step 4.2 determines whether the current evolution stage is equal to the maximum evolution stage. If they are equal, terminate the current simulation and input the evolution result data of each stage; otherwise, increase the stage number by one, read the boundary data of the next stage and return to step 4 to continue executing the next stage of evolution.

[0198] In order to illustrate the power system evolution deduction method based on hierarchical collaborative simulation proposed in Example 1 of the present invention, a simplified system of a provincial power grid is used as an example to verify the effect of the patent method. The starting system topology diagram is shown in FIG. Figure 7The system has a total of 90 nodes, 200 lines, 83.2GW thermal power plants, 13GW wind power plants, 18GW photovoltaic power plants, and a total of 2GW energy storage stations. It includes two voltage levels of 500kV and 100kV, and the maximum initial load is 100GW.

[0199] First, the evolution boundary model of the system is constructed according to step 1. Based on the population and economic forecast data of authoritative institutions, the power demand of the province at each stage can be obtained by substituting them into formula (1). It can be predicted that the maximum load in the planned year will increase to 229GW. Figure 8 This is the load growth curve proposed in Example 1 of the present invention.

[0200] Investigate the typical load change curve in the area, Figure 9 This is the typical load curve proposed in Example 1 of the present invention, with a combination of Figure 8 and Figure 9 , the load change curve of each stage can be formed.

[0201] Taking into account the progress of carbon sequestration technologies such as carbon capture and carbon sinks, the carbon emission limit within the evolution cycle can be obtained. Figure 10 This is the carbon emission limit for each stage proposed in Example 1 of the present invention.

[0202] By investigating the future technological development potential of the region's turbines, a model for the advancement of wind, solar, and energy storage technologies can be formed. With technological advancement, the costs of wind power, photovoltaics, and energy storage will drop to 53.4%, 34.5%, and 34.2% of their current costs, respectively. The technological advancement curve of the region changes over time as shown below. Figure 11 As shown; Figure 11 This is the technology progress coefficient curve proposed in Example 1 of the present invention.

[0203] By investigating the national unit installation cost data, a development difficulty coefficient model for the region can be formed. The upper limit of wind and solar resource development at each node is shown in Table 1. The unit development difficulty coefficient of the region changes with the development upper limit as follows: Figure 12 As shown, Figure 12 This is the development difficulty coefficient curve proposed in Example 1 of the present invention.

[0204] Table 1: Upper limit of wind and solar resource development at each node

[0205]

[0206] By investigating the installation data of various power sources, energy storage, and lines in this system, we can generate a table of operating parameters for each device within this coefficient. The parameters for some of the system's thermal power units, wind and solar units, energy storage units, and lines are shown in Tables 2-5. Figure 13 This is a typical wind power output curve proposed in Example 1 of the present invention; Figure 14 This is a typical photovoltaic output curve proposed in Example 1 of the present invention.

[0207] Table 2: Equipment and parameters of some thermal power plants at certain nodes

[0208]

[0209]

[0210] Table 3: Installation locations and parameters of some wind and solar turbines in the system

[0211]

[0212] Table 4: System energy storage unit configuration and parameters

[0213] ESS Number Node number Pcmax(MW) Pdmax(MW) Emax(MWh) SOCmax SOCmin SOCInit SOCEnd 1 41 1000 1000 6000 0.9 0.1 0.5 0.5 2 36 1000 1000 6000 0.9 0.1 0.5 0.5

[0214] Table 5: Installation locations and parameters of some system lines

[0215]

[0216] Next, follow Step 2 to construct the province's long-term regional plan and regional interconnection plan based on the existing boundary conditions. Assume that the province has abundant offshore wind and solar resources in the north and east, respectively. The west and east are the political and economic centers, forming two major load centers. The southern hilly region has some wind power resources, and other regions have some photovoltaic resources.

[0217] Based on this, the northern and eastern parts of the city can be designed as future new energy power supply centers, while the western and eastern parts can be designed as dual load centers, and they will be interconnected through 17 500kV and 10 1000kV lines. Figure 15 This is the system partitioning and key power transmission channels proposed in Example 1 of the present invention.

[0218] Then, according to step 3, the future development of the system is deduced stage by stage based on the long-term plan and boundary conditions. In stage 1, the load increases rapidly based on the starting system. At this time, thermal power units are added in the western region and the existing transmission lines are upgraded and strengthened, while a small number of wind power and photovoltaic units are deployed in the eastern region. In stage 2, based on stage 1, a small number of east-west transmission routes are installed in the system. The main output of the system in this stage is still mainly thermal power units. Figure 16 For the evolution results from 2020 to 2030; Figure 19 The system power balance curves at each stage of evolution are given.

[0219] Phases 3 and 4 are based on Phase 2. The system installs a large number of wind, solar and storage units in the northern and eastern power centers, and builds more west-northwest and east-west transmission routes to connect the load centers and power centers.

[0220] Phases 5 and 6 build on Phase 4 by strengthening several regional transmission routes. Wind power and photovoltaic units will be responsible for the system's main energy supply, while thermal power units will be responsible for certain system regulation tasks. Figure 17 This is the evolution result from 2030 to 2050.

[0221] In Phase 7, based on Phase 6, the development of the eastern and northern regions of the system has reached saturation, and wind and solar turbines have begun to be installed in the central and western regions. During this phase, the system has installed a large number of energy storage units, and the location of energy storage units tends to be at nodes with larger wind and solar turbine capacities.

[0222] Phase 8 is based on Phase 7. The system load increases slowly, the system development slows down, the high renewable ratio system has been fully formed, the main regulation task of the system is undertaken by the energy storage unit, and the thermal power unit has almost no output. Figure 18 This is the evolution result from 2050 to 2060.

[0223] Finally, in the final stage of the evolutionary simulation, the program determines that the maximum number of evolutionary stages has been reached, the system stops evolving, and outputs the evolutionary results of each stage generated in the above process. Table 6 shows the indicators of the short-term simulation operation results of the unit at each stage.

[0224] Table 6: Short-term simulation operation results of units at each stage

[0225]

[0226]

[0227] Example 1 of the present invention proposes a method for power system evolution deduction based on hierarchical collaborative simulation, which summarizes the evolution process into three levels of simulation: long-term, medium-term and short-term according to the characteristics of different links of the power system. Through modular modeling and solution of each layer of simulation, multi-factor and multi-time scale coupling simulation is realized, and the modeling complexity and accuracy are taken into account, so it can be used for large-scale system evolution deduction; at the same time, modular modeling and solution support the customized assembly of the models and solution methods of the main links of the evolution system while maintaining good openness, significantly improving the scalability and adaptability of the evolution system.

[0228] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device comprising a series of elements are inherent to the elements. In the absence of further restrictions, the elements limited by the statement "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device comprising the elements. In addition, the above-mentioned technical solutions provided in the embodiments of the present application are not described in detail in accordance with the corresponding technical solutions in the prior art to achieve the same principle, so as to avoid excessive elaboration.

[0229] Although the above description is of specific embodiments of the present invention in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or variations can be made based on the above description. It is not necessary and impossible to list all embodiments here. Based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without expending creative effort are still within the scope of protection of the present invention.

Claims

1. A power system evolution deduction method based on hierarchical collaborative simulation, characterized in that: The following steps are involved: Construct the boundaries of driving factors of power system evolution, wind and solar resources and construction land constraint boundaries, and starting variables describing the composition of the starting system, forming a time series of boundary variables and a table of network parameters of the starting system; Aggregate the starting system network nodes to form a power supply area that requires long-term planning; Determine the net imbalance of regional power supply based on the regional economic development level and resource endowment, sort the regions according to the net imbalance, and select regions with inflow and outflow power greater than a threshold as load centers and power supply centers; construct a regional interconnection channel optimization model, and use the regional interconnection channel optimization model to construct a set of candidate solutions for key inter-regional transmission channels; A source-storage expansion planning model is constructed to form the total growth of power sources and energy storage in the current stage. A grid node growth model based on complex network theory is constructed to deduce future grid growth. A source-grid-load-storage collaborative planning model is established based on a given total installed capacity target and grid node growth. A source-grid-load-storage collaborative operation simulation model is constructed to respectively solve the current source-grid-storage site selection and sizing results and the operation evaluation indicators of each planning scheme. A heuristic algorithm is used to solve the source-grid-load-storage collaborative planning model to form a power system evolution scheme for the entire evolution range. Record the evolution stage plan and make evolution termination judgment. When the current evolution stage is not equal to the maximum evolution stage, read the boundary data of the next stage and continue to deduce the next stage.

2. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 1, characterized in that: The specific process of constructing the boundary of the driving factors of power system evolution, the boundary of wind and solar resources and construction land constraints, and the starting variables describing the composition of the starting system, and forming the boundary variable time series and the starting system network parameter table includes: Taking the maximum load, typical load curve, equipment unit investment and carbon emissions as boundary variables, the correlation function relationship between the boundary variables and the associated variables is fitted from the three aspects of power demand, technological progress and environmental policy. The predicted associated variables are input into the correlation function relationship to calculate the boundary variable time series sequence; Using the wind and solar resource distribution map to find the average density of wind and solar resources in each region, and using the average density of wind and solar resources to calculate the boundaries of wind and solar exploitable resources in each region; Based on the obtained current power system grid topology and line and transformer electrical parameters, a starting system network parameter table is formed.

3. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 2 is characterized in that: The specific process of fitting the correlation function relationship between the boundary variables and the correlation variables from the three aspects of power demand, technological progress and environmental policy by taking the maximum load, typical load curve, equipment unit investment and carbon emissions as boundary variables includes: Obtain the historical maximum power load, economic output and population of the evolving object system, use formula (1) as the correlation function, fit the historical maximum power load to obtain the correlation function parameters between the maximum power load and the population and economic output; in, is the maximum power load; E s 、N s is the economic output and population data of the corresponding year; a 0,L is the first correlation function between the maximum load and the associated variable; a 1,L is the second correlation function between the maximum load and the correlation variable; a 2,L is the third correlation function between the maximum load and the correlation variable; Obtain the annual daily load curve of typical power loads, and use formula (2) to normalize the load value of the daily load curve relative to its maximum value to form the standardized daily load curve of each type of load; in, are the power value of the normalized daily load curve of the r-th type of load at time t and the power value of the original daily load curve of the r-th type of load at time t respectively; is the annual maximum value of the daily load curve of the rth type of load; Calculate the size of various loads: in, β s,r are the load scale of the rth type of load in the sth stage and the load proportion of the rth type of load in the sth stage respectively; Multiply and superimpose the maximum values ​​of various loads with the annual daily load curve, and use formula (4) to synthesize the annual daily load curve of the entire system in each stage; in, is the system annual daily load curve; Ω L is a set of typical load categories; Get the equipment unit investment change function: in, Investment in various equipment units for the s stage, As the benchmark unit investment, is the coefficient of technological progress that decreases as the evolutionary stage increases; Normalize the carbon emission path curve to form a long-term carbon emission curve in the future; in, are the values ​​of the normalized carbon emission curve at the sth stage of evolution and the values ​​of the original carbon emission curve at the sth stage of evolution respectively; is the initial value of carbon emissions during the evolution period; Multiply the carbon emissions of the power industry in the initial evolution stage by the normalized carbon emission curve to calculate the time series of carbon emission limits for the power industry during the evolution cycle: in, is the value of the carbon emission limit curve of the power system in the evolution cycle at the sth stage, is the initial value of carbon emissions from the power system; The specific process of finding the average density of wind and solar resources in each region using the wind and solar resource distribution map and calculating the exploitable wind and solar resource boundaries in each region using the wind and solar resource average density includes: in, is the average density of wind and solar resources in region m; S m is the area of ​​region m; is the upper limit of resource development in region m; η VRE The highest conversion efficiency of wind and solar power related to the level of power generation technology; Investigate the difficulty of wind and solar power construction in each region and construct a function that influences the resource development ratio on development costs: in, C is the incremental cost coefficient for development of region m in stage s; s,m is the developed resources in region m at stage s; η s,m is the development level of region m in stage s; F re (·) is a non-decreasing function that is positively correlated with the degree of development.

4. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 3 is characterized in that: The specific process of aggregating the starting system network nodes to form a power supply area requiring long-term planning includes: Randomly select several nodes as the initial center points representing different areas; Considering multiple sub-indicators, the comprehensive evaluation index of the correlation between the remaining nodes and the center point is calculated using formula (10); Among them, F long,area is the comprehensive evaluation index of regional division correlation, x n is the nth sub-index, including the node's load level, resource development upper limit, geographical location, and administrative region; α n is the weight of sub-index n; Assign each node to the nearest center point to form a preliminary area division; Recalculate the evaluation index of the nodes in each area and update the center point position of each area until the area allocation of each node no longer changes.

5. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 4 is characterized in that: The process of determining a regional power supply net imbalance based on the regional economic development level and resource endowment, sorting regions according to the power supply net imbalance, selecting regions with inflow and outflow power greater than a threshold as load centers and power supply centers, constructing a regional interconnection channel optimization model, and selecting key transmission channels between regions using the regional interconnection channel optimization model specifically includes: Based on the maximum load forecast sequence of each node and the wind and solar resource endowment, formula (11) is used to calculate the difference between the upper limit of wind and solar power capacity development and the long-term power demand of each power supply area to form the regional power supply net imbalance: in, is the net imbalance of region m; is the maximum development capacity of renewable resources at node i in region m; P i L is the prospective load level of node i in region m; Sort the regions based on the net imbalance of power supply and select the top M with the largest net outflow power. B areas as power centers; select the top M with the largest net inflow power B Areas are used as load centers; Taking the power exchange requirements of each power supply area as the constraint and the channel construction cost as the target, a regional interconnection channel optimization model is constructed: Where M is the total number of regions; p∈(1,...M), q∈(1,...M), The capacity of the transmission channel between regions p and q reflects the capacity transmitted from region p to region q; It is the transmission channel cost per unit capacity after comprehensively considering the geographical factors between regions; is the upper limit of the transmission channel that can be constructed between regions p and q; is the net imbalance of region p; The regional interconnection channel optimization model is solved by a linear programming solver to obtain the capacity of the interconnection transmission channels between regions, and the number of regional interconnection lines is determined in combination with the typical transmission capacity of high-voltage transmission lines.

6. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 5 is characterized in that: The process of constructing the source and storage expansion planning model to determine the total growth amount of power supply and energy storage in the current stage includes: using the total load balance of the entire system at all times in this stage and the upper limit of wind and solar resource development in the entire system as constraints, and aiming to minimize the combined cost of annualized construction investment and operating expenses, to construct the source and storage expansion planning model: in, The unit investment of each equipment in stage s is to comprehensively consider the influence of technology progress coefficient and development difficulty; is the wind, solar and storage installed capacity to be optimized in the sth stage; r dis 、m type They are the wind, solar and storage installation discount rate and the wind, solar and storage installation equipment life; They are the load, output of conventional units, new energy and energy storage units in each region at each time in stage s; The installed capacity of the type machine to be optimized; F op,s For operating costs; A linear programming solver is used to solve the source and storage expansion planning model to obtain the various types of power capacity that need to be added in the current stage.

7. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 6 is characterized in that: The process of constructing a grid node growth model based on complex network theory to deduce future grid growth includes: Check each load node one by one to see if the off-grid load exceeds the power supply capacity of the substation, and if so, split the load to the surrounding area to generate additional load nodes; If the node to be split is a load node, the transferable range of the power supply load is determined with the node as the center, and the candidate points are further determined by grid division. The power supply capacity shortage is calculated by using formula (14) to calculate the difference between the total load within the power supply radius of each candidate point and the existing power supply capacity: Where, i∈(1,...I), j∈(1,...I); I is the total number of nodes; is the total load value within the power supply range of the selected growth point i; Ω sup A collection of nodes within the power supply range; is the load value of the existing node j within the power supply range of the selected growth point; d,i For power supply shortage; Sort by power supply capacity shortfall and select the maximum value point as the new load node; If the node to be split is a power node, the sum of the exploitable potential in the area of ​​the point is calculated using formula (15) to form the characteristic parameter; Among them, P i po Develop the potential of power within the power supply range of node i; v,i To develop potential characteristic parameters; Based on the development potential and ranking, select the maximum point as the new power node; Formula (16) is used to calculate the node preference factors of all candidate connection nodes within the connection range of the growing node: Ω c A set of connectable nodes that belong to the same local world as the newly added power node; is the probability of a new power node being connected to node i; α, β, γ are non-negative weight coefficients representing the influence of degree, distance, and injection power; k i is the degree of node i; k j is the degree of node j; l i is the Euclidean distance between node i and the newly added power node; l j is the Euclidean distance between node j and the newly added power node; P net,i Inject power to node i; P net,j Inject power into node j; A random simulation is performed based on the probability of adding new power nodes to the local world point set, and the connection method between the new power nodes and the original network is formed according to the random simulation results.

8. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 7 is characterized in that: Based on the given total installed capacity target and grid node growth, a source-grid-load-storage collaborative planning model is established, and a source-grid-load-storage collaborative operation simulation model is constructed to respectively solve the current source-grid-load-storage site selection and sizing results and the operation evaluation indicators of each planning scheme. The specific process of using a heuristic algorithm to solve the source-grid-load-storage collaborative planning model to form a power system evolution scheme for the entire evolution range includes: With the goal of minimizing the combined cost of operating expenses and annualized investment, the objective function of the source-grid-storage collaborative planning model is formed: minF=F op +F in (17) Among them, F op 、F in , F are operating cost, annualized investment and comprehensive cost respectively; Formula (18) is used to model the annualized investment of the sum of the annualized investment of units and lines: type∈{VRE,ess,line};(18) in, is the unit installation cost of type type unit at node i; is the installed capacity of type type unit at node i; r dis 、m type They are the wind, solar and storage installation discount rate and the wind, solar and storage installation equipment life; Constructing the planning constraints of the source-network-storage collaborative planning model: in, The installed capacity of new energy to be optimized and the upper limit of developable capacity for node i; The total installed capacity targets for various power sources and energy storage in this phase; is the upgrade capacity and upgrade capacity upper limit of the lth line to be optimized; With the goal of optimizing operational economics, a source-grid-load-storage collaborative operation model is constructed by comprehensively considering the power system and equipment operating constraints. This model is then used daily to perform short-term process simulations to obtain operational status data for the planned scheme. Annual operating costs are then calculated based on this operational status data. minF op =f c +f r +f uc +f p (22) Among them, f c is the coal consumption and carbon emission cost of coal-fired units; f r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalties for load shedding and power curtailment; The cost calculation method for each sub-item is: Among them, c coal is the unit coal consumption cost, is the carbon dioxide emission cost per unit coal consumption; k G Number the thermal power units; kth G Coal consumption characteristic function of thermal power units; For the kth G The output of the thermal power unit at time t; The unit cost of upper and lower standby of the unit; Provide upper and lower standby power for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the cost of starting and stopping the unit; c drop 、c shed is the penalty factor for power curtailment and load shedding, Q drop , Q shed is the amount of power abandonment and load shedding, Allowed values ​​for the corresponding variables; Considering that the total node power injection is equal to the load, the power balance constraint is constructed: in, For node i k type The output of each type of unit at time t; represents the active power transmitted in line l at time t; is the active power of the load at node i at time t; is the load shedding amount of node i at time t; Ω from、 Ω to They represent the set of lines starting with node i and ending with node i; k type The unit number is type; Taking into account the load reserve demand and the new energy reserve demand, a reserve constraint is established in which the reserve supply is greater than the system reserve demand: in, is the positive and negative standby coefficient; Ω d is the set of nodes connected to the load; L + % is the first reserve requirement coefficient reserved for load output; L - % is the second reserve requirement coefficient reserved for load output; W + % is the first reserve requirement coefficient reserved for wind and solar power unit output; W - % is the second reserve requirement coefficient reserved for wind and solar power unit output; g + It is the positive and standby sign of thermal power unit; g － It is the negative standby flag of thermal power unit; k VRE Number the VRE unit; Number the VRE unit; k for unit VRE The amount of power wasted at time t; Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraint is formed: in, is the maximum transmission capacity of line l; Considering the upper and lower limits of thermal power generation units' output when providing backup demand, the output constraints of thermal power generation units are constructed: in, is the operating state variable of the kth thermal power unit at time t, the operating state is 1 and the shutdown state is 0; are the maximum output and minimum output of thermal power units respectively; are the positive and negative spare coefficients; For the kth G The output of the thermal power unit at time t; Taking into account the limitations on the power rise and fall of thermal power units per unit time, a ramp constraint is constructed to ensure that the power change of thermal power units per unit time is less than the ramp upper limit: in, is the upward power change of the thermal power unit under the operating time; is the downward power change of the thermal power unit under the running time; For the kth G The output of the thermal power unit at time t-1; Considering the start-stop state changes and start-stop time restrictions during the start-up and shutdown process of thermal power units, a start-stop constraint is constructed to ensure that the start-up and shutdown time of the thermal power units is greater than the minimum start-up and shutdown time: in, is the minimum startup and shutdown duration of coal-fired unit k; For the kth G The start-up mark quantity of the thermal power unit at time t-1; For the kth G The shutdown mark value of the thermal power unit at time t-1; Establish upper and lower output constraints for new energy units: Where, are the maximum output and minimum output of the new energy unit at time t respectively; For the kth VRE The output of the thermal power unit at time t; Construct upper and lower power constraints for energy storage units: in, is the maximum discharge power of the energy storage unit; For the kth ess The output of the energy storage unit at time t; Considering the energy storage capacity limitation during normal operation of the energy storage unit, the upper and lower limit constraints of the energy storage unit's state of charge are constructed: in, Indicates the kth ess The state of charge of the energy storage unit at time t, kth ess The maximum and minimum nuclear power states of each energy storage unit; The state of charge of the energy storage unit is: in, For the kth ess The state of charge of the energy storage unit at time t-1; For the kth ess The rated energy storage capacity of each energy storage unit; Δt is the time step; For the kth ess The output of the energy storage unit at time t-1; Based on the genetic algorithm, the source-grid-load-storage collaborative planning model is solved to initially randomly generate the source-storage installation location capacity and line upgrade plan parameters, and calculate the investment and operating costs of each plan. The process is repeated until the cost difference between the best solutions of two generations is less than the iterative convergence criterion.

9. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 8, characterized in that: The method further includes: performing an operation simulation on the current planning scheme, calculating key indicators based on the operation status data and verifying whether the set targets are met; if the key indicators do not meet the requirements, revising the total installed capacity target and restarting the planning of the current stage. The specific process includes: The source-grid-load-storage collaborative operation model is called on a daily basis to conduct short-term operation simulations and obtain the annual operation status data of the selected scheme: Use formula (37-39) to calculate the key evaluation indicators of the planning scheme: Among them, EMI is the total annual carbon emissions of the planning scheme; For the kth G The carbon emissions per unit coal consumption of thermal power units; T is the total simulation time; For the kth G Coal consumption characteristic function of thermal power units; For the kth G The output of the thermal power unit at time t; are the system load shedding rate and power abandonment rate respectively; Q Lshed 、 are the total load shedding power, the abandoned power of new energy and the total power generation; Q total is the total power generation; If all key evaluation indicators are not less than the target values, the installed capacity of new energy generators will be increased: Among them, r Adj To adjust the margin system; EMI over 、 They are carbon emissions, average load shedding rate and maximum load shedding rate excess; They are the CO2 emission intensity per kilowatt-hour of fossil energy units, the annual utilization hours and peak capacity coefficient of new energy units; are the maximum load and the added value of new energy installed capacity respectively; In order to ensure the new energy consumption capacity, the energy storage installed capacity is increased according to formula (41): in, They are the newly added value of energy storage installed capacity and the average ratio of new energy storage; If the curtailment rate exceeds the set value, the energy storage capacity is increased according to formula (42) considering the system flexibility adjustment requirements: in, The power abandonment rate exceeds the limit; Q VRE is the total power generation of the VRE units; It is the annual utilization hours of new energy units.

10. The method for power system evolution deduction based on hierarchical collaborative simulation according to claim 9, characterized in that: Record the evolution stage plan and make evolution termination judgment. When the current evolution stage is not equal to the maximum evolution stage, read the boundary data of the next stage and continue to deduce the specific process of the next stage, including: Save the evolution result data at the current moment, specifically including: optimization results, planning results, and operation results; among them, the optimization results include all the schemes scanned by the genetic algorithm and the investment and operation costs of the scheme; the planning results include the location and capacity of various source and storage installed capacity before and after correction, the line reinforcement status, and the growth of nodes; the operation results include the load size of the optimal scheme at each moment, unit output data, energy storage charging and discharging data, line transmission energy data, and the power balance of each node; Determine whether the current evolution stage is equal to the maximum evolution stage. If they are equal, terminate the current simulation and input the evolution result data of each stage; otherwise, increase the stage number by one, read the boundary data of the next stage and continue to execute the next stage of evolution.

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