Power system evolution deduction method based on hierarchical collaborative simulation

Abstract

The invention provides an electric power system evolution deduction method based on hierarchical collaborative simulation, and belongs to the field of novel electric power system long-term planning. The method comprises the following steps: constructing an electric power system evolution boundary; simulating an electric power system distant view design; embedding electric power system source network load storage collaborative planning simulation of short-term operation simulation to form a current-stage power grid planning scheme; recording an evolution stage scheme; according to the method, the evolution process is summarized into simulation of three levels of long-term, middle-term and short-term according to the characteristics of different links of the power system, multi-factor and multi-time-scale coupling simulation is realized through modular modeling and solving of each layer of simulation, and the modeling complexity and accuracy are considered, so that the method can be used for large-scale system evolution deduction; and meanwhile, the modular modeling and solving support the self-defined assembly of a main link model and a solving method of the evolution system so as to keep better openness, and the expandability and adaptability of the evolution system are remarkably improved.

Description

Technical Field

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

[0002] A high proportion of new energy, a high proportion of power electronic equipment, and a high proportion of distributed resources are driving significant changes in the structure and operation of new power systems. The future power system will present a very different structure, operation characteristics, and control laws from the current power system. How to grasp the characteristics of the future power system in advance and then make good development plans and policy designs is a problem that needs to be solved in the long-term planning of the power system. Using numerical calculations as a tool to conduct sandbox simulations of the power system development process is an important way to understand the future power system. Specifically, the evolution of the power system refers to the comprehensive consideration of future power supply needs, changes in power production, transmission, storage, and consumption technologies, combined with power generation resource conditions and energy policy factors, to predict how the key links of the current power system, such as source-grid-load-storage, will change over a long period of time in the future, including power supply composition, scale and layout, grid interconnection and voltage level, energy storage form, capacity and layout, electricity consumption methods and composition scale, and other aspects. Therefore, the evolution of the power system is the result of the combined effect of multiple factors such as increased demand, technological progress and related policies. It involves multiple links such as policies, technologies, planning and operation and has the characteristics of multi-factor coupling. 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 types of power system evolution simulation methods at present: the first type regards the evolution of the power system as a multi-stage optimization decision-making problem, and realizes the development simulation by optimizing the power source, energy storage or grid configuration of each time section system. This type of method has two limitations: first, there are many decision variables and they may contain nonlinearity, which leads to the limitation of the scale of the solvable optimization problem due to computing power and needs to be simplified or only applicable to small-scale system evolution analysis; second, it is difficult to consider the increase of grid nodes and the fuzzy design (such as the planning of the prospective source and load center) and uncertainty in the process of system development. Another type of method is to analyze the evolution of the power grid by imitating the growth law of nature or the general network development law to avoid solving the optimization problem and use it for large-scale system analysis, but this type of method is only applicable to the evolution analysis of the power grid and cannot take into account the multi-factor coupling of source, network and storage, nor can it reflect the optimization planning and subjective prospect design in the evolution process. Therefore, the current evolution analysis method based on optimization theory and "imitation" theory does not consider the differences in characteristics of different links, nor can it consider source-storage synergy and subjective decision-making at the same time. In terms of evolution accuracy, precision and adaptability, it still does not meet the needs of large-scale new 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 method for power system evolution simulation based on hierarchical collaborative simulation includes the following steps:

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

[0008] Aggregate the starting system network nodes to form a power supply area that needs long-term planning; determine the net imbalance of regional power supply in combination with the regional economic development level and resource endowment, sort the regions according to the net imbalance of power supply, and select the regions with inflow and outflow power greater than the 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 transmission channels between regions;

[0009] 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 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 content 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 boundary of driving factors for the evolution of the power system, the boundary of wind and solar resources and construction land constraints, and the starting point variables describing the composition of the starting point system to form a time series of boundary variables and evolution starting point variables; aggregating the network nodes of the starting point system to form a power supply area that needs to be planned in the long term; 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 power channel schemes; construct a source-storage expansion planning model to form the total growth of power source and energy storage in the current stage, construct a power grid node growth model based on complex network theory to deduce future power grid growth, establish a source-grid-load-storage collaborative planning model based on a given total installed capacity target and power 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 in the boundary data of the next stage and continue to deduce the next stage. The present invention classifies 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 level 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 customized assembly of 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 A flow chart of a method for deducing power system evolution based on hierarchical collaborative simulation proposed in Embodiment 1 of the present invention;

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

[0015] Figure 3 A power system prospect design simulation flow chart proposed in Embodiment 1 of the present invention;

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

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

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

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

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

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

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

[0023] Fig.11 The technical progress coefficient curve proposed in Example 1 of the present invention;

[0024] Fig.12 The development difficulty coefficient curve proposed in Example 1 of the present invention;

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

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

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

[0028] Fig.16 The evolution results from 2020 to 2030;

[0029] Fig.17 The evolution results from 2030 to 2050;

[0030] Fig.18 The evolution results from 2050 to 2060;

[0031] Fig.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 the present 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 simplification 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 the description of known components and processing techniques 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 A flow chart of a method for deducing power system evolution based on hierarchical collaborative simulation proposed in Embodiment 1 of the present invention;

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

[0037] Construct the boundaries of driving factors of 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 to form a time series of boundary variables and evolutionary starting variables. Specifically, it includes three parts: the construction of the boundaries of driving factors of 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 flow chart for constructing the power system evolution boundary proposed in Embodiment 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, and the functional relationship between boundary variables and associated variables is fitted from the three aspects of power demand, technological progress and environmental policy. The associated variable data investigated and predicted by authoritative institutions 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 object system are obtained, and the correlation function parameters between the historical maximum power load and the population and economic output are obtained by fitting the historical maximum power load using formula (1) as the correlation function;

[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 loading and the associated variable;

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

[0044] In step 1.1.2, the annual daily load curve of the typical power load is obtained, and the load value of the daily load curve is normalized relative to its maximum value using formula (2) 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 load at time t and the power value of the original daily load curve of the r-th 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 r-th load in the s-th stage and the load proportion of the r-th load in the s-th 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 collection of typical load categories.

[0052] In step 1.1.3, the current costs of wind and solar power and energy storage are investigated, and the cost change law 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 sth stage, As the base 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 the time series of unit costs of wind, solar and energy storage.

[0056] In step 1.1.4, the carbon emission route 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 emission during the evolution period;

[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 of 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, and the specific process of using the wind and solar resource average density to calculate the boundary of wind and solar exploitable resources 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 affects the development cost based on the resource development ratio:

[0066]

[0067] in, is the incremental coefficient of development cost of region m in stage s; C s,m is the developed resources in area 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 form operating parameter tables such as the installed location and installed capacity, maximum and minimum output, ramp rate, minimum start and stop time, start and stop cost, coal consumption (water consumption, nuclear fuel consumption) characteristic curve parameters of typical thermal power, hydropower and nuclear power units;

[0070] Investigate the current wind and solar power installed capacity in the power system, and form a table of typical wind and solar power station grid-connected locations and installed capacity, annual curve of maximum power generation capacity, unit power abandonment penalty and other operating parameters;

[0071] Investigate the current installed capacity of energy storage in the power system, and form 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 life loss;

[0072] Investigate the current load situation of the power system and form operating parameter tables such as the daily load and annual load curves of each node, the maximum load that can be cut and the 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 in combination with the regional economic development level and resource endowment, and determine the load center and power center according to the positive and negative and amplitude of the net load; 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 A power system prospect design simulation flow chart proposed in Embodiment 1 of the present invention;

[0076] Step 2.1 Taking into account the node 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 relevance, x n is the nth sub-indicator, including the node's load level, resource development upper limit, geographical location, and administrative region; α n is the weight of sub-indicator 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 wind and solar power capacity development in each power supply area and the long-term power demand 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 first 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 comprehensive consideration of inter-regional geographical factors; 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] The linear programming solver is used to solve the regional interconnection channel optimization model to obtain the capacity of the interconnection transmission channels between each region, and the number of 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 the current power supply demand, 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. The above four steps are coordinated to complete the mid-term simulation to form the current grid construction plan, and embed the source-grid-load-storage multi-element collaborative operation daily model into the scheme evaluation to realize accurate evaluation of operation indicators based on power system operation status data at different time scales.

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

[0094] In step 3.1, ignoring the grid constraints, taking the total load balance of the whole system at all times and the upper limit of wind and solar resource development of the whole system as constraints, and taking the minimum comprehensive cost of annualized construction investment and operating expenses as the goal, 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 loads, outputs of conventional units, new energy and energy storage units in each region at each time in stage s; The type of machine to be optimized is assembled with capacity; F op,s For operating costs;

[0097] A linear programming solver is used to solve the source storage planning and 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 scheme 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, typical projects, national and industry substation design specifications (GB / T, IEEE, IEC standards) are investigated, and the power access or load power supply capabilities of 1000kV, 750kV and 500kV substations are summarized.

[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 it 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 whole system and compare it with the total capacity of new power sources added in this stage. If the remaining grid-connected capacity does not meet the demand for new power sources, new power supply nodes are added in order of the resource richness of each region until the remaining total grid-connected capacity meets the needs of 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 by 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 node set within the power supply range; is the load value of the existing node j within the power supply range of the candidate growth point; ξ d,i For power supply shortage;

[0106] According to the power supply capacity shortage sorting, the maximum value point is selected 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 supply within the power supply range of node i;v,i To develop potential characteristic parameters;

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

[0111] In step 3.2.3, formula (16) is used to calculate the node preference factor of all candidate connection nodes within the growth node connection range:

[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 the newly added power node being connected to node i; α, β, γ are non-negative weight coefficients that characterize the influence of degree, distance and injected 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 into 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 with economic optimization as the goal is established to determine the installation locations of each power source and energy storage and the supporting grid upgrade plan. It is further divided into four steps: constructing a planning objective function with economic optimization, constructing planning constraints that integrate the long-term source-storage planning capacity, short-term process rolling simulation and operation indicator evaluation based on the source-grid-load-storage collaborative operation model, and solving the model based on 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 is to minimize the comprehensive cost of operating expenses and annualized investment, and form the objective function of the source-grid-storage collaborative planning model:

[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] The annualized investment of the sum of the annualized investment of the units and lines is modeled using formula (18):

[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 the 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 development capacity upper limit of each node of each type of power source, the total installed capacity and power balance, and the upper limit of line installation.

[0124]

[0125]

[0126]

[0127] in, The installed capacity of new energy and the upper limit of developable capacity to be optimized for node i; The total installed capacity target 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 operating economy, a source-grid-load-storage collaborative operation model is constructed by comprehensively considering the power system and equipment operation constraints. The source-grid-load-storage collaborative operation model is called on a daily basis to perform short-term process simulation to obtain the operating status data of the planning scheme, and the annual operating cost is calculated based on the operating status data.

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

[0130] f 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; r is the standby cost; f uc is the unit start-up and shutdown cost; f p Penalties for load shedding and power abandonment;

[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, k is the carbon dioxide emission cost per unit of coal consumption; 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; is the unit cost of upper and lower standby of the unit; Up and down standby power provided for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the start-up and shutdown cost of the unit; c drop 、c shed is the power curtailment and load shedding penalty factor, Q drop , Q shed is the amount of power abandonment and load shedding, Allowed values ​​for the corresponding variable.

[0138] Taking into account the resource endowment of each node and the overall source and storage increase target of the power system, 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, the kth type The output of a type 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 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 reserve 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 the output of wind and solar power units; W - % is the second reserve requirement coefficient reserved for the output of wind and solar power units; g + It is the positive and standby sign of thermal power unit; g － It is the negative standby sign of thermal power unit; k VRE is the VRE unit number; is the VRE unit number; For unit k VRE The amount of power abandoned 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 constraints are formed:

[0146]

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

[0148] Considering the upper and lower limits of the output when the thermal power unit provides backup demand, the output constraint of the thermal power unit is 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; They are the maximum output and minimum output of thermal power units respectively; are positive and negative reserve coefficients; For the kth G The output of a thermal power unit at time t.

[0151] Taking into account the restrictions on the power rise and fall of thermal power units per unit time, a ramp constraint is constructed 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 running 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-stop process of the thermal power unit, a start-stop constraint is constructed in which the start-up and shutdown time of the thermal power unit 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 quantity of the thermal power unit at time t-1;

[0158] Construct upper and lower limits of new energy unit output:

[0159]

[0160] In the formula, They are the maximum output and minimum output of the new energy unit at time t respectively; For the kth VRE The output of a 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 state of charge of the energy storage unit are constructed:

[0165]

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

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

[0168]

[0169] in, For the kth ess The charge state 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 on a daily basis to perform short-term process simulation to obtain the operation status data of the planning scheme and solve the system operation 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, and 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) and mutated (randomly changed scheme parameters) with the original schemes to form a new generation of scheme sets, and the investment and operation costs of each new generation of schemes 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: Conduct operation simulation for the source and storage site selection and capacity determination and power grid upgrade plan. Calculate key indicators based on the operation status data and verify whether they meet the set targets. If the key indicators do not meet the requirements, revise the total installed capacity target and restart the mid-term simulation of this stage. It is further divided into four steps: short-term operation simulation of the planning plan, calculation of key evaluation indicators, indicator satisfaction verification and installation plan revision, including:

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

[0177] Step 3.4.2 Use 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 of 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 , They 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 go to the next step;

[0184] In step 3.4.4, if the carbon emission or load shedding rate exceeds the set value, the installed capacity of new energy generators will be increased by considering the replacement of excess carbon emission electricity and the corresponding electricity and peak capacity shortage of load shedding:

[0185]

[0186] Among them, r Adj To adjust the margin system; EMI over , They are carbon emission, 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 of new energy units and the peak capacity coefficient; 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 added value of energy storage installed capacity and the average ratio of new energy storage; Usually around 0.2.

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

[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 of evolutionary plans and determination of evolution termination

[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: evolution result data saving and stage number verification.

[0195] Step 4.1 After the evolution simulation is completed, save the evolution result data at the current moment. 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 status of the nodes; the operation results include the load size at each moment of the optimal plan, 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 in 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 provincial power grid simplified system is taken 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, and includes two voltage levels of 500kV and 100kV, with an initial maximum load of 100GW.

[0199] First, the evolution boundary model of the system is constructed according to step 1. Based on the population and economic total forecast data of authoritative institutions, the power demand of the province at each stage can be obtained by substituting it 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. Fig. 9 is a typical load curve proposed in Example 1 of the present invention, and the combination Figure 8 and Fig. 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. Fig.10 This is the carbon emission limit for each stage proposed in Example 1 of the present invention.

[0202] By investigating the potential for future unit technology development in the region, a model for the technological advancement of wind, photovoltaic and energy storage can be formed. In the technological advancement, the costs of wind power, photovoltaic and energy storage will drop to 53.4%, 34.5% and 34.2% of the current costs respectively. The technological advancement curve of the region changes over time as shown in the figure below: Fig.11 As shown; Fig.11 This is the technical 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 can be formed for the region. 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 upper limit of development as follows: Fig.12 As shown, Fig.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 of the system, we can form a table of operating parameters of each device of this coefficient. The parameters of some thermal power units, wind and solar units, energy storage units and lines of this system are shown in Table 2-Table 5. Fig.13 This is a typical wind power output curve proposed in Example 1 of the present invention; Fig.14 This is a typical photovoltaic output curve proposed in Example 1 of the present invention.

[0207] Table 2: Equipment status and parameters of some node thermal power plants

[0208]

[0209]

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

[0215]

[0216] Secondly, according to step 2, the long-term regional planning and regional interconnection line planning of the province are constructed based on the existing boundary conditions. It is assumed that the northern and eastern parts of the province have rich offshore wind power resources and solar energy resources, respectively, the western and eastern parts are political and economic centers, forming two major load centers, the southern hilly areas have certain wind power resources, and other areas have certain photovoltaic resources.

[0217] Based on this, the northern and eastern parts of the city can be designed as the future new energy power supply center, 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. Fig.15 This is the system partitioning and key power transmission channels proposed in Example 1 of the present invention.

[0218] Subsequently, according to step 3, the future development of the system is deduced stage by stage based on the long-term planning 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 regional transmission routes are installed in the system. The main output of the system in this stage is still dominated by thermal power units. Fig.16 For the evolution results from 2020 to 2030; Fig.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] Phase 5 and 6 are based on Phase 4. The system strengthens several regional transmission routes. The main energy supply task of the system in this phase is mainly undertaken by wind power and photovoltaic units, and thermal power units undertake certain system regulation work. Fig.17 This is the evolution result from 2030 to 2050.

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

[0222] Phase 8 is based on Phase 7. The system load grows 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. Fig.18 This is the evolution result from 2050 to 2060.

[0223] Finally, in the last stage of the evolution simulation, the program determines that the evolution stage has reached the maximum number of stages, the system stops evolving, and outputs the evolution results of each stage generated in the above processes. Table 6 gives the short-term simulation operation result indicators of the unit in each stage.

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

[0225]

[0226]

[0227] Embodiment 1 of the present invention proposes a method for the evolution and deduction of an electric power system based on hierarchical collaborative simulation. According to the characteristics of different links of the electric power system, the evolution process is summarized into three levels of simulation: long-term, medium-term and short-term. 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 customized assembly of 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.

[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 that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment that includes a series of elements are inherent to the elements. In the absence of more restrictions, the elements limited by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or equipment that includes the elements. In addition, the above-mentioned technical solution provided in the embodiment of the present application is consistent with the corresponding technical solution in the prior art in principle, and the part is not described in detail, so as not to repeat too much.

[0229] Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A method for power system evolution deduction based on hierarchical collaborative simulation, characterized in that: The following steps are involved: Construct the boundary of driving factors of power system evolution, the boundary of wind and solar resources and construction land constraints, and the starting point variables describing the composition of the starting point system, forming a time series of boundary variables and evolutionary starting point variables; 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 in combination with the regional economic development level and resource endowment, sort the regions according to the net imbalance of power supply, and select the regions with inflow and outflow power greater than the 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 transmission channels between regions; 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 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 is characterized in that: The specific process of constructing the boundary of driving factors of power system evolution, the boundary of wind and solar resources and construction land constraints, and the starting point variables describing the composition of the starting point system, and forming the time series of boundary variables and evolution starting point variables includes: Taking the maximum load, typical load curve, equipment unit investment and carbon emissions as boundary variables, fitting the correlation function relationship between the boundary variables and the associated variables from the three aspects of power demand, technological progress and environmental policy, and inputting the predicted associated variables into the correlation function relationship to calculate the boundary variable time series; Using the wind and solar resource distribution map to find the average density of wind and solar resources in each region, and using the wind and solar resource average density 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 associated variable; a 2,L is the third correlation function between the maximum loading and the associated variable; Obtain the annual daily load curve of 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; in, are the power value of the normalized daily load curve of the r-th load at time t and the power value of the original daily load curve of the r-th 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 r-th load in the s-th stage and the load proportion of the r-th load in the s-th 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 sth stage, As the base unit investment, is the coefficient of technological progress that decreases as the evolutionary stage increases; Normalize the carbon emission route 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 emission 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 of 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 boundary of wind and solar exploitable resources 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 sources 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 affects the development cost based on the resource development ratio: in, is the incremental coefficient of development cost of region m in stage s; C s,m is the developed resources in area 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 1 is characterized in that: The specific process of aggregating the starting system network nodes to form a power supply area that needs 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 relevance, x n is the nth sub-indicator, including the node's load level, resource development upper limit, geographical location, and administrative region; α n is the weight of sub-indicator n; Assign each node to the nearest center point to form a preliminary regional 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 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, 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 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 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: 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 first 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 comprehensive consideration of inter-regional geographical factors; 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 each region, 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 forming the total growth amount of power supply and energy storage in the current stage through source and storage planning to construct a source and storage expansion planning model includes: taking the total load balance of the whole system at all times in this stage and the upper limit of the development of wind and solar resources in the whole system as constraints, and taking the minimum comprehensive cost of annualized construction investment and operating expenses as the goal, constructing a 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 loads, outputs of conventional units, new energy and energy storage units in each region at each time in stage s; Assemble the capacity of the type machine to be optimized; F op,s For operating costs; A linear programming solver is used to solve the source storage planning and 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 simulating the growth scheme of power grid nodes based on complex network theory 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 it 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 difference between the total load within the power supply radius of each candidate point and the existing power supply capacity is calculated using formula (14) to form the power supply capacity shortfall: 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 node set within the power supply range; is the load value of the existing node j within the power supply range of the candidate growth point; ξ d,i For power supply shortage; According to the power supply capacity shortage sorting, the maximum value point is selected 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 supply within the power supply range of node i; v,i To develop potential characteristic parameters; According to the development potential and ranking, select the maximum value point as the new power node; Formula (16) is used to calculate the node optimization factor of all candidate connection nodes within the growth node connection range: Ω c A set of connectable nodes that belong to the same local world as the newly added power node; is the probability of the newly added power node being connected to node i; α, β, γ are non-negative weight coefficients that characterize the influence of degree, distance and injected 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 into 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 solve the current source-grid-storage site selection and capacity determination 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 interval includes: With the goal of minimizing the comprehensive 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; The annualized investment of the sum of the annualized investment of the units and lines is modeled using formula (18): in, is the unit installation cost of type type unit at node i; is the installed capacity of the type type unit at node i; r dis 、m type are the discount rate and equipment life respectively; Construct the planning constraints of the source-network-storage collaborative planning model: in, The installed capacity of new energy and the upper limit of developable capacity to be optimized for node i; The total installed capacity target for various power sources and energy storage in this phase; is the upgrade capacity and the upper limit of the upgrade capacity of the lth line to be optimized; With the goal of optimizing operation economy, a source-grid-load-storage collaborative operation model is constructed by comprehensively considering the power system and equipment operation constraints. The source-grid-load-storage collaborative operation model is rolled out on a daily basis to perform short-term process simulation to obtain the operation status data of the planning scheme, and the annual operation cost is calculated based on the operation 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; r is the standby cost; f uc is the start-up and shutdown cost of the unit; p Penalties for load shedding and power abandonment; The cost calculation method for each sub-item is: Among them, c coal is the unit coal consumption cost, k is the carbon dioxide emission cost per unit of coal consumption; 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; is the unit cost of upper and lower standby of the unit; Up and down standby power provided for thermal power units; It is the startup and shutdown state variable of the thermal power unit; is the start-up and shutdown cost of the unit; c drop 、c shed is the power curtailment and load shedding penalty factor, 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, the kth type The output of a type 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; Taking into account the load reserve demand and 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 reserve 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 the output of wind and solar power units; W - % is the second reserve requirement coefficient reserved for the output of wind and solar power units; g + It is the positive and standby sign of thermal power unit; g － It is the negative standby sign of thermal power unit; k VRE is the VRE unit number; is the VRE unit number; For unit k VRE The amount of power abandoned at time t; Considering that the line transmission power is less than the maximum transmission capacity of the line, the line operation constraints are formed: in, is the maximum transmission capacity of line l; Considering the upper and lower limits of the output when the thermal power unit provides backup demand, the output constraint of the thermal power unit is 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; They are the maximum output and minimum output of thermal power units respectively; are positive and negative reserve coefficients; For the kth G The output of the thermal power unit at time t; Taking into account the power increase and decrease limits of thermal power units per unit time, a ramp constraint is constructed 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 running 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-stop process of the thermal power unit, a start-stop constraint is constructed in which the start-up and shutdown time of the thermal power unit 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 quantity of the thermal power unit at time t-1; Construct upper and lower limits on the output of new energy units: In the formula, They 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 state of charge of the energy storage unit are constructed: in, Indicates the kth ess The state of charge of the energy storage unit at time t, k ess The maximum and minimum nuclear power status of each energy storage unit; The charge state of the energy storage unit is: in, For the kth ess The charge state 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 of the optimal solution between 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 operation simulation on the current planning scheme, calculating key indicators based on the operation status data and verifying whether the set target is met, and if the key indicators do not meet the requirements, revising the total installed capacity target and restarting the planning of this stage. The specific process includes: The source-grid-load-storage collaborative operation model is called on a daily basis to conduct short-term operation simulation and obtain the annual operation status data of the selected scheme: Formula (37-39) is used to calculate the key evaluation indicators of the planning scheme: Among them, EMI is the total annual carbon emissions of the planning scheme; Carbon emissions per unit of 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; are the system load shedding rate and power abandonment rate respectively; Q Lshed , They 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 value, the installed capacity of new energy generators will be increased: Among them, r Adj To adjust the margin system; EMI over , They are carbon emission, 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 of new energy units and the peak capacity coefficient; They are the maximum load and the added value of installed capacity of new energy sources; In order to ensure the new energy consumption capacity, the energy storage installed capacity is increased according to formula (41): in, They are the added value of energy storage installed capacity and the average ratio of new energy storage; If the power abandonment rate exceeds the set value, the energy storage installed capacity is increased according to formula (42) considering the system flexibility adjustment demand: 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: The specific process of recording the evolution stage plan and making evolution termination judgment, reading the boundary data of the next stage when the current evolution stage is not equal to the maximum evolution stage, and continuing to deduce the next stage until all evolution intervals are solved to form a complete evolution plan includes: Save the evolution result data at the current moment, 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 cost of the scheme. The planning results include the location and capacity of various source and storage installations before and after the correction, the line reinforcement and the growth of the nodes. The operation results include the load size of the optimal scheme 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. 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 in the boundary data of the next stage and continue to execute the next stage of evolution.

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