Multi-network cooperative operation optimization system of high-resolution power system

Abstract

The invention relates to the field of power market analysis and optimization, and discloses a multi-network cooperative operation optimization system of a high-resolution power system, and the model of the optimization system comprises a technical module, an energy module and a decision module. The decision-making module comprises a decision-making target module, the decision-making target module comprises an electric power system carbon emission budget total amount and carbon emission reduction cost for realizing a temperature control target, and the decision-making target module quantifies electric power system low-carbon transformation differences and determines a transformation strategy according to electric power system low-carbon transformation paths in different conditions under regulation; an objective function of the model is an overall cost function, the objective function comprises constraint conditions, and the constraint conditions are subjected to capacity planning and transformation strategy simulation to obtain a power system technical path of cost minimization of collaborative interaction of the large power grid and the micro power grid. Compared with the prior art, hour-level power load real-time supply and demand balance simulation is constructed, and the result is more detailed and accurate.

Description

Technical Field

[0001] The present invention relates to the field of power market analysis and optimization, and in particular to a multi-network coordinated operation optimization system for a high-resolution power system. Background Art

[0002] With the rapid development of society and technology, energy has become the core driving force of national development, and its importance is comparable to the necessity of water for human survival. However, the excessive exploitation and consumption of fossil energy has not only aggravated environmental problems, but also highlighted the urgent need for clean and reliable power supply. The improvement and upgrading of the global energy structure, especially the promotion of clean energy, has become an inevitable trend of social development. In this context, microgrids, as a form of distributed energy, have become the key to integrating into the main grid and optimizing the energy structure with their flexibility and reliability. Microgrids can locally disperse energy and improve power supply reliability, especially when large-scale access to distributed photovoltaic power is demonstrated to be efficient and environmentally friendly. With the advancement of national new energy decision-making, the construction of smart grids and the reform of the power market, the importance of microgrids has become increasingly prominent. The synergistic relationship between microgrids and large power grids is crucial. Microgrids include not only controllable power sources such as micro gas turbines and fuel cells, but also uncontrollable power sources such as wind power, photovoltaics, and a variety of energy storage devices. This complex structure gives microgrids a flexible operation mode. Whether it is grid-connected or islanded, it can ensure power supply in severe weather or emergencies and enhance the overall stability of the power grid.

[0003] Policy support and regulation are equally important for the development of microgrids. Reasonable policies can promote the popularization and application of microgrid technology, maximize economic benefits, and maximize comprehensive benefits and minimize pollution. Constructing a model that conforms to the actual usage scenario and providing a low-cost, high-reliability dispatching solution has important reference value for the actual operation of microgrids. However, there is currently a lack of systems that can simultaneously consider the coordinated interaction and decision-making and regulation of large power grids and microgrids to minimize the cost of the power system. Therefore, the research and design of a new type of high-resolution multi-grid collaborative operation optimization system for power systems is of great significance to overcome the problems in the existing power market analysis and optimization and achieve efficient, stable and economical energy supply. Summary of the invention

[0004] In order to solve the problem that the existing power system optimization system lacks a power system technical path that can minimize costs while considering the coordinated interaction and decision-making control of large power grids and microgrids, the present invention provides a multi-grid collaborative operation optimization system for a high-resolution power system.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a high-resolution power system multi-network coordinated operation optimization system, the optimization system model includes

[0006] Technical modules, including large grid and microgrid interaction modules and cost parameters;

[0007] Energy module, including energy consumption parameters of different energy types, energy storage parameters of different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters and energy supply parameters;

[0008] A decision module, including a decision target module, wherein the decision target module includes a total carbon emission budget of the power system and a carbon emission reduction cost for achieving a temperature control target, and the decision target module quantifies the difference in low-carbon transformation of the power system according to low-carbon transformation paths of the power system under different scenarios under regulation, and determines a transformation strategy;

[0009] The objective function is an overall cost function, and the objective function includes constraints. The constraints are obtained by simulating capacity planning and transformation strategies to obtain a technical path for the power system that minimizes the cost of coordinated interaction between large power grids and microgrids.

[0010] An embodiment of the present application also provides an electronic device, comprising: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device executes the second aspect of the embodiment of the present application and any possible design of the second aspect thereof.

[0011] An embodiment of the present application also provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the electronic device executes the second aspect of the embodiment of the present application and any possible design of the second aspect thereof.

[0012] A multi-grid collaborative operation optimization system for a high-resolution power system of the present invention introduces constraints to ensure real-time balance of system sources and loads, introduces standby constraints to cope with load forecasting and new energy output forecasting errors, and improves the flexibility of the overall framework. Through this method, the specific impact of decision-making constraints on market operation can be analyzed more deeply, providing a scientific basis for the deployment of flexibility strategies for sustainable power systems. Compared with existing technologies, this method takes into account the real-time interaction between large power grids and microgrids, realizes the coupling of long-term scales of years and short-term time, constructs a high-spatial resolution power system optimization model, takes into account decision-making constraints and market operation, combines meso-power systems with macro-market regulation, and can more clearly and accurately guide the future development direction of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1is a structural schematic diagram of a multi-network coordinated operation optimization system for a high-resolution power system according to an embodiment of the present invention;

[0014] Figure 2 It is the emission reduction path of the power industry under the decision in Example 2 of the present invention;

[0015] Figure 3 is the comparison of transformation costs under regulation in Example 2 of the present invention;

[0016] Figure 4 1 is a comparison of cost differences under the regulation in Example 2 of the present invention, wherein (a) is a comparison of cost differences under a low electricity substitution rate; (b) is a comparison of cost differences under a medium electricity substitution rate; (c) is a comparison of cost differences under a high electricity substitution rate;

[0017] Figure 5 is the annual cost of each power source under the regulation in Example 2 of the present invention, wherein (a) is the power source cost in 2025; (b) is the power source cost in 2030; (c) is the power source cost in 2035; (d) is the power source cost in 2040;

[0018] Figure 6 is the annual cost of each power source under the regulation in Example 2 of the present invention, wherein (a) is the power source cost in 2045; (b) is the power source cost in 2050; (c) is the power source cost in 2055; (d) is the power source cost in 2060;

[0019] Figure 7 is the water consumption in different scenarios under the regulation in Example 2 of the present invention;

[0020] Figure 8 is a coal-fired power generation capacity diagram under the regulation in Example 2 of the present invention;

[0021] Fig. 9 is a diagram of total installed capacity of wind power and photovoltaic power under the regulation in Embodiment 2 of the present invention;

[0022] Fig.10 is the installed capacity under the regulation in Example 2 of the present invention, wherein (a) is the installed capacity in 2030; (b) is the installed capacity in 2040;

[0023] Fig.11 is the installed capacity under the regulation in Example 2 of the present invention, wherein (a) is the installed capacity in 2050; (b) is the installed capacity in 2060;

[0024] Fig.12 It is the power generation situation under the regulation in Example 2 of the present invention, where (a) is the power generation situation in 2030; (b) is the power generation situation in 2040;

[0025] Fig.13It is the power generation under the regulation in Example 2 of the present invention, where (a) is the power generation in 2050; (b) is the power generation in 2060;

[0026] Fig.14 It is an operational simulation of the baseline scenario under regulation in Example 2 of the present invention;

[0027] Fig.15 It is a simulation of the operation of the basic carbon market under the regulation in Example 2 of the present invention;

[0028] Fig.16 It is an operation simulation of 90% new energy constraint under the regulation in Example 2 of the present invention;

[0029] Fig.17 It is an operation simulation with a moderate carbon budget under regulation in Example 2 of the present invention;

[0030] Fig.18 It is an operation simulation with a smaller carbon budget under regulation in Example 2 of the present invention;

[0031] Fig.19 This is an operation simulation with a small carbon budget under the regulation in Example 2 of the present invention. DETAILED DESCRIPTION

[0032] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0033] Embodiment 1: A high-resolution multi-grid coordinated operation optimization system for a power system according to this embodiment, such as Figure 1 As shown, the model of the optimization system includes a technical module, an energy module and a decision module. The technical module includes a large power grid and a microgrid interaction module and cost parameters; the energy module includes energy consumption parameters of different energy types, energy storage parameters of different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters and energy supply parameters; the decision module includes a decision target module, and the decision target module includes the total carbon emission budget and carbon emission reduction cost of the power system to achieve the temperature control target. The decision target module quantifies the difference in low-carbon transformation of the power system according to the low-carbon transformation path of the power system in different scenarios under regulation, and determines the transformation strategy; the objective function of the model of the optimization system is an overall cost function, and the objective function includes constraints. The constraints are obtained by simulating capacity planning and transformation strategies to obtain the technical path of the power system with the minimum cost of the coordinated interaction between the large power grid and the microgrid. As a preferred embodiment of this embodiment, the decision input into the decision module can be a government policy.

[0034] It should be noted that, as a preferred embodiment of this embodiment, cost parameters may include investment cost parameters, operation and maintenance cost parameters, fuel cost parameters, emission cost parameters, start and stop cost parameters, CCS cost parameters, and equipment residual value parameters; energy types may include non-new energy and new energy, non-new energy includes coal-fired power energy and biomass power energy; new energy may include wind power energy, photovoltaic energy, hydropower energy, gas power energy and nuclear power energy; energy storage capacity types may include physical energy storage and chemical energy storage.

[0035] The objective function of the optimization system model is shown in formula (1):

[0036]

[0037] Where t represents the tth year, T represents the planning period, and r represents the discount rate. represents the investment cost in year t, represents the operation and maintenance cost in the tth year, represents the fuel cost in year t, represents the start-up and shutdown cost in year t, represents the carbon emission cost in year t, represents the CCS cost in year t, Represents the residual value of equipment in year t.

[0038] It should be noted that, as a preferred embodiment of this invention, the investment cost in the tth year is As shown in formula (2):

[0039]

[0040] Wherein, M represents the energy type, m represents non-new energy or new energy, i represents the i-th type of generator set, and the types of generator sets include coal-fired generator sets, photovoltaic generator sets, wind generator sets, hydropower generator sets, biomass generator sets, gas generator sets and nuclear generator sets. represents the newly added power source of the i-th type of generator set in year t, represents the newly added installed capacity of power generation units of the i-th type in the t-th year, K represents the energy storage capacity type, k represents physical energy storage or chemical energy storage, j represents the j-th type of energy storage, represents the unit capacity cost of the j-th type of energy storage added in year t, represents the newly added energy storage installed capacity of the jth type of energy storage in year t;

[0041] Operation and maintenance cost in the tth year As shown in formula (3):

[0042]

[0043] in, represents the fixed capacity unit cost of the i-th type of generator in the t-th year, represents the existing power capacity of the i-th type of generator set in year t, represents the power generation of the i-th type of generator in the t-th year, represents the current storage capacity of the jth type of energy storage in year t, represents the energy storage capacity of the jth type of energy storage in the tth year, represents the variable capacity unit cost of the i-th type of generator in the t-th year, represents the fixed energy storage unit cost of the j-th type of energy storage in year t, represents the unit cost of variable energy storage of the jth type of energy storage in year t;

[0044] Fuel cost in year t As shown in formula (4):

[0045]

[0046] Among them, h represents the hth hour, represents the fuel cost per unit of electricity of the ith type of generator in the tth year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year;

[0047] Start-up and shutdown costs in year t As shown in formula (5):

[0048]

[0049] in, represents the cost of shutting down the power capacity of the i-th type of generator at the h-th hour in the t-th year, It represents the cost of opening the power supply capacity of the i-th type of generator set at the h-th hour in the t-th year, U (i,t,h-1) represents the on / off status of the i-th type of generator set at the h-1 hour in the t-th year, U (i,t,h) represents the on / off status of the i-th type of generator set at the h-th hour in the t-th year, U (i,t,h-1) , U (i,t,h) Both are 0 and 1 variables, 0 is closed and 1 is opened.

[0050] Carbon emission cost in year t As shown in formula (6):

[0051]

[0052] in, represents the carbon price of the i-th type of generator in the t-th year, represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year, l represents different types of pollutants, including sulfur dioxide, nitrogen oxides and smoke, represents the unit pollutant tax of the i-th type of generator in the t-th year, It represents the pollutant emission coefficient of the i-th type of generator set in the t-th year;

[0053] CCS cost in year t As shown in formula (7):

[0054]

[0055] Among them, μ (i,t) represents the CCS installation ratio of the i-th type of generating unit in the t-th year, represents the CCS investment cost per unit capacity of the i-th type of generating unit in the t-th year, fm (i,t) represents the fixed operation and maintenance cost of the i-th type of generator set in the t-th year, represents the variable operation and maintenance cost of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set with CCS installed capacity at the h-th hour in the t-th year, represents the transportation cost of unit carbon emissions of the i-th type of generator in year t;

[0056] Equipment residual value in year t As shown in formula (8):

[0057]

[0058] in, It represents the residual value coefficient of unit power capacity of the i-th type of generator set in the t-th year, represents the power capacity of the i-th type of generator set retired in year t, It represents the residual value coefficient of unit energy storage capacity of the jth type of energy storage in the tth year, represents the retired energy storage capacity of the j-th type of energy storage in year t.

[0059] It should be noted that, as the preferred embodiment of the present invention, the constraints of the objective function include power balance constraints, power balance constraints, power system expansion constraints, upper and lower limit constraints on power output, unit climbing constraints, maximum limit constraints on power output capacity, wind power generation constraints, photovoltaic power generation constraints, system margin constraints, operating reserve constraints, chemical energy storage constraints, physical energy storage constraints, installed capacity constraints, power balance expansion constraints, power balance expansion constraints, decision constraints, power balance constraints of large power grids, power balance constraints of large power grids under different power substitution rates, power balance constraints of microgrids, power balance constraints of large power grids under different power substitution rates, power balance constraints of large power grids, power balance constraints of large power grids under different power substitution rates, power balance constraints of microgrids, and power balance constraints of microgrids under different power substitution rates.

[0060] Compared with other power system optimization systems, this embodiment builds a real-time supply and demand balance of hourly power load. In the large power grid and microgrid interaction module, the large power grid input includes wind power energy input, photovoltaic energy input, hydropower energy input, coal power energy input, nuclear power energy input, physical energy storage input, chemical energy storage input and microgrid input; microgrid input includes distributed photovoltaic energy input, physical energy storage input, chemical energy storage input and large power grid input;

[0061] The power balance constraints are shown in formulas (9)-(12):

[0062]

[0063] Among them, M ES Represents energy storage capacity, M RE Represents the capacity of new energy units, It represents the non-renewable energy generation of the i-th type of generator set in the h-th hour of the t-th year, It represents the energy storage discharge of the i-th type of generator set at the h-th hour in the t-th year, It represents the energy storage charging of the i-th type of generator set at the h-th hour in the t-th year, It represents the renewable energy generation of the i-th type of generator set at the h-th hour in the t-th year, It represents the amount of wind and solar power abandoned by the i-th type of generator set in the large power grid in the h-th hour of the t-th year, represents the output load of the microgrid at the hth hour in the tth year, represents the output load of the large power grid at the hth hour in the tth year, represents the large power grid load at the hth hour in the tth year, represents the amount of wind and solar power abandoned by the i-th type of generator set in the microgrid in the h-th hour of the t-th year, represents the microgrid load at hour h in year t, It represents the output coefficient of non-renewable energy unit of the i-th type of generator set at the h-th hour in the t-th year, It represents the new energy output coefficient of the i-th type of generator set in the h-th hour of the t-th year, represents the non-renewable energy capacity of the i-th type of generator set in the t-th year, It represents the new energy power capacity of the i-th type of generator set in the t-th year;

[0064] The power balance of the large power grid and microgrid involves the annual power supply and demand balance. The power balance constraints are shown in formulas (13) and (14):

[0065]

[0066]

[0067] Among them, E (i,t) represents the power generation of the i-th type of generator in the t-th year, represents the energy storage discharge of the i-th type of generator set in the t-th year, represents the energy storage charging amount of the i-th type of generator set in the t-th year; ESG represents energy storage discharge, ESS represents energy storage charging, Indicates the amount of power abandoned by the large power grid. Represents the output of the large power grid, represents the microgrid input, represents the power demand of the large power grid, Indicates the amount of power wasted in the microgrid. Represents the power demand of the microgrid.

[0068] In terms of power system expansion constraints, the existing installed capacity of power sources is determined based on the new additions and retirements in the horizontal year during the study period. The power system expansion constraints are shown in formulas (15)-(20):

[0069]

[0070] Formulas (15)-(20) indicate that the existing capacity is equal to the capacity of the previous period plus the newly added capacity minus the retired capacity. represents the existing capacity of the generator set of the i-th type of the m-th capacity type in the t-th year, represents the existing generating capacity of the i-th type of generating unit of the m-th capacity type in year t-1, It represents the newly added capacity of the generating unit of the i-th type of the m-th capacity type in the t-th year, represents the capacity of the generator set of the i-th type of the m-th capacity type retired in the t-th year, The ttth generator set of the mth capacity type and the ith type m,i Annual newly added generating unit capacity, t m,i It represents the operating life of the power supply of the ith type of generator set with the mth capacity type. represents the current storage capacity of the jth type of energy storage in year t, represents the current storage capacity of the jth type of energy storage in year t-1, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t, Indicates the jth type of energy storage tt j Annual new energy storage capacity, t j represents the operating life of the jth type of energy storage, represents the retired energy storage capacity of the jth type of energy storage in year t, dinz (m,i,t) represents the upper limit and cinz of the new generation units of the i-th type of capacity type in the t-th year (j,t) represents the upper limit of new additions of the j-th type of energy storage in year t;

[0071] The upper and lower limits of the power output are shown in formula (21):

[0072]

[0073] in, It represents the minimum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year;

[0074] The unit climbing constraints are shown in formulas (22) and (23):

[0075]

[0076] in, represents the actual output capacity of the i-th type of generator set at the h-1th hour in the tth year, ru (i,t) represents the maximum upward ramp rate of the i-th type of generator set in the t-th year, represents the capacity of the i-th type of generator set turned on at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at hour h+1 in year t, represents the capacity of the i-th type of generating unit shut down at the h-th hour in the t-th year, du (i,t) represents the maximum downward ramp rate of the i-th type of generator set in the t-th year, represents the capacity of the i-th type of generator set turned on at hour h-1 in year t;

[0077] The maximum limit constraint of the power supply output capacity is shown in formula (24):

[0078]

[0079] in, It represents the capacity of the i-th type of generator set turned on at hour h+1 in year t;

[0080] The wind power generation constraint is shown in formula (25):

[0081]

[0082] in, represents the wind power generation of the i-th type of generator set in the h-th hour of the t-th year, represents the wind power capacity factor of the i-th type of generator set at the h-th hour in the t-th year, represents the wind power installed capacity of the i-th type of generator in the t-th year, represents the newly added wind power installed capacity of the i-th type of generator in year t, represents the existing wind power installed capacity of the i-th type of generator in year t, represents the potential wind power installed capacity of the i-th type of generator in year t;

[0083] The photovoltaic power generation constraint is shown in formula (26):

[0084]

[0085] in, represents the photovoltaic power generation of the i-th type of generator set in the h-th hour of the t-th year, represents the photovoltaic capacity factor of the i-th type of generator set at the h-th hour in the t-th year, represents the photovoltaic installed capacity of the i-th type of generator in the t-th year, represents the newly added photovoltaic installed capacity of the i-th type of generator in year t, represents the existing photovoltaic installed capacity of the i-th type of generator in year t, represents the potential installed PV capacity of the i-th type of generator in year t;

[0086] The system margin constraint ensures that there is sufficient installed capacity to meet the peak load requirements. The system margin constraint is shown in formula (27):

[0087]

[0088] in: represents the capacity confidence coefficient of coal-fired power supply in year t, represents the capacity confidence factor of wind power in year t, represents the capacity confidence coefficient of the photovoltaic power source in year t, represents the capacity confidence factor of nuclear power in year t, represents the capacity confidence factor of the hydropower source in year t, represents the capacity confidence factor of the gas power supply in year t, represents the capacity confidence factor of biomass power source in year t, represents the total installed capacity of coal-fired power generation units of type i in year t, represents the total installed nuclear power capacity of the i-th type of generating unit in year t, represents the total installed hydropower capacity of the i-th type of generating unit in year t, represents the total installed capacity of gas-fired power generation units of the i-th type in the t-th year, represents the total installed biomass capacity of the i-th type of generating unit in year t;

[0089] Due to forecast errors, unexpected situations, etc., the power system has operating reserve requirements. The operating reserve constraint is shown in formula (28):

[0090]

[0091] Among them, cg (i,t,h) The upper limit coefficient of the energy storage output of the i-th type of generator set in the h-th hour of the t-th year, represents the capacity of the i-th type of generating unit in year t, represents the capacity of energy storage occupied by the i-th type of generator set at the h-th hour in the t-th year, P represents the prediction error coefficient of the base load of the i-th type of generator set at the h-th hour in the t-th year, (t,h) represents the base load at the hth hour in the tth year, represents the forecast error coefficient of wind power generation at the hth hour in the tth year for the i-th type of generator set, It represents the prediction error coefficient of photovoltaic power generation at the hth hour in the tth year for the i-th type of generator set;

[0092] The participation of chemical energy storage technology in power system operation requires constraints on charging, discharging and energy storage levels based on the working principle of batteries. The chemical energy storage constraints are shown in formulas (29)-(34):

[0093] SC (j,t,h＝0) =SCO(29)

[0094]

[0095]

[0096] Of which: SC (j,t,h) represents the energy storage situation of the energy storage device of the jth type at the hth hour in the tth year, SC (j,t,h＝0) represents the energy storage situation of the energy storage device of the jth type in the tth year at hour h=0, SCO represents the initial energy storage situation, SC (j,t,h+1) It represents the energy storage situation of the energy storage device of the jth type at the h+1th hour in the tth year, represents the energy storage charging efficiency of the jth type, represents the energy storage discharge efficiency of the jth type, represents the energy loss rate of the jth type of energy storage, represents the energy storage charge of the energy storage device of the jth type at the hth hour in the tth year, It represents the energy storage discharge of the energy storage device of the jth type at the hth hour in the tth year, represents the lower limit coefficient of the jth type of energy storage, represents the upper limit coefficient of the jth type of energy storage, represents the energy storage capacity of the jth type of energy storage in the tth year, Γ (j,t) Indicates the number of charge and discharge restrictions for the j-th type of energy storage in the t-th year;

[0097] Physical energy storage constraints include physical energy storage discharge constraints and physical energy storage charging constraints;

[0098] The physical energy storage discharge constraints are shown in formulas (35) and (36):

[0099]

[0100] in, represents the lower limit of the discharge power of the hydropower station at the hth hour in the tth year with the jth type of energy storage, represents the upper limit of the discharge power of the hydropower station at the hth hour in the tth year with the jth type of energy storage, CSX (t,h) represents the water storage capacity of the physical energy storage power station reservoir at the hth hour in the tth year, represents the minimum energy storage value of the physical energy storage power station in year t, xl trepresents the power generation efficiency of the physical energy storage turbine in year t;

[0101] The physical energy storage charging constraints are shown in formulas (37) and (38):

[0102]

[0103] in, represents the lower limit of the charging power of the physical energy storage power station at the hth hour in the tth year, represents the upper limit of the charging power of the physical energy storage power station at the hth hour in the tth year, represents the maximum energy storage value of the physical energy storage power station in year t, xlc t represents the charging efficiency of the physical energy storage turbine in year t;

[0104] Water storage capacity of physical energy storage power station reservoir at hour h in year t CSX (t,h) As shown in formula (39):

[0105]

[0106] Among them, CSX (t+1,h) represents the water storage capacity of the physical energy storage power station reservoir at the time h in the year t+1,

[0107] Minimum energy storage value of the physical energy storage power station in year t and the maximum energy storage value of the physical energy storage power station in year t As shown in formula (40):

[0108]

[0109] The installed capacity restrictions on the power supply side involve five types of power sources: wind power, photovoltaic energy, hydropower, gas power, and nuclear power. The installed capacity of wind power, photovoltaic energy, hydropower, and gas power is limited due to resource constraints, namely wind intensity, light intensity, and site constraints. As for the constraints on nuclear power, controlled nuclear fusion technology has not been realized. Taking into account technological progress, uranium fuel supply, site resources, power station construction cycle and other conditions, it is clear that nuclear power will be strictly controlled in the future, and the installed capacity of nuclear power will also be limited. The installed capacity constraints are shown in formulas (41)-(45):

[0110]

[0111] Among them, M WRE Represents the capacity of wind turbine, M PRE Represents the capacity of the photovoltaic unit, M NRE Represents the capacity of nuclear power unit, M HRE Indicates the capacity of the hydropower unit, M GAS Indicates the capacity of the gas generator set. represents the upper limit of wind power installed capacity in year t, represents the upper limit of photovoltaic installed capacity in year t, represents the upper limit of nuclear power installed capacity in year t, represents the upper limit of hydropower installed capacity in year t, Represents the upper limit of gas-fired power generation capacity in year t.

[0112] In the optimization system model, there are often nonlinear problems, and nonlinear problems can easily lead to local optimal solutions. In the power system optimization system model constructed in this embodiment, there are nonlinear problems in the objective function and constraint conditions. In order to solve the global optimal solution, this embodiment transforms some constraint conditions in the model;

[0113] (1) Linearization of the problem of multiplying capacity and output coefficient:

[0114] In this embodiment, the product of power supply capacity and output coefficient is replaced by the change of capacity. The specific change is shown in formula (46):

[0115]

[0116] It represents the actual output capacity of non-renewable energy source of the i-th type of generator set at the h-th hour in the t-th year,

[0117] The actual output capacity of non-renewable energy sources of the i-th type of generator set at the h-th hour in the t-th year Corresponding constraints must also be added. The constraints are shown in formula (47):

[0118]

[0119] represents the actual online capacity of non-renewable energy power source of the i-th type of generator set at the h-th hour in the t-th year. Such an equation is transformed into an equality and an inequality constraint.

[0120] (2) Linearization of 0-1 variable multiplication problem

[0121] In this embodiment, the capacity change per unit time is used to replace the product of the 0-1 variable. The specific change is shown in formula (48):

[0122]

[0123] The start-stop capacity constraints are shown in formulas (49)-(51):

[0124]

[0125] in, represents the actual output capacity of the i-th type of generator set at the initial stage of year t, represents the unused unit capacity of the i-th type of generator set at the initial stage of year t, represents the capacity of the i-th type of generator set shut down at the h-κ hour in the t-th year, where κ∈(0,1,2…h), GT (i,t) represents the minimum shutdown time of the i-th type of generator in the t-th year;

[0126] The changes of relevant constraints are shown in formulas (52)-(54):

[0127]

[0128] in, KT represents the capacity of the i-th type of generator set at the initial stage of year t. (i,t) represents the minimum start-up time of the ith type of generator set in the tth year;

[0129] (3) Linearize the problem of starting and stopping multiple units and simplify the problem of unit combination processing:

[0130] In the operation simulation, the start and stop of each coal-fired power unit will correspond to a corresponding state. More units require more 0-1 variables to characterize, which makes the solution of the model very complicated. This embodiment uses a continuous variable modeling method to replace the original decision variables (0-1 variables). The specific changes are shown in formulas (55)-(57):

[0131]

[0132]

[0133] in, represents the maximum online capacity, x (i,t) Indicates installed capacity, Indicates opening, Indicates the opening capacity. Indicates opening, represents the actual online capacity of the i-th type of generator set at the h-1 hour in the t-th year, Indicates shutdown capacity, Indicates shutdown.

[0134] After processing, the traditional mixed integer programming model is transformed into a linear programming model. At the same time, the method of converting discrete variables into continuous processing also significantly reduces the number of decision variables. Especially in the hourly operation simulation in this embodiment, linear processing has obvious advantages in improving computational efficiency and handling nonlinear optimization.

[0135] It should be noted that, as a preferred embodiment of this invention, when different energy types or energy storage capacity types are replaced, the power balance involves the balance of annual power supply and demand, and the power balance expansion constraint is shown in formulas (58)-(60):

[0136]

[0137] in, represents the original load at the hth hour in the tth year, represents the amount of wind and solar power abandoned by the i-th type of generator in the h-th hour of the t-th year, and ω represents different degrees of electricity substitution;

[0138] When different energy types or energy storage capacity types are replaced, the power balance expansion constraint is shown in formula (61):

[0139]

[0140] in, Indicates the amount of power wasted. represents the original electricity in year t;

[0141] The decision is mainly based on the decision regulations, and the decision constraints are shown in formulas (62)-(65):

[0142]

[0143] in, represents the power generation of the i-th type of generator in the t-th year, TA co represents the total carbon emissions, represents the proportion of non-fossil energy electricity, Indicates different power output states. It represents the existing generating capacity of the i-th type of generating unit in year t.

[0144] It should be noted that, as a preferred embodiment of this invention, the carbon emission reduction cost can measure the economic feasibility of emission reduction, that is, the additional cost required for unit emission. The carbon emission reduction cost is also one of the criteria for comparing the transformation paths of the power industry. The carbon emission reduction cost is shown in formula (66):

[0145]

[0146] Among them, CCOST qh represents the carbon reduction cost, qh represents different scenario types, ΔTC qh represents the change in transition cost compared to the baseline scenario, ΔCCO qh Represents the change in carbon emissions compared to the baseline scenario.

[0147] It should be noted that, as a preferred embodiment of this embodiment, during the operation of the power system, the interaction between the large power grid and the microgrid is the key to the balance of the power system. In order to study the low-carbon transformation planning decision of the power industry, this embodiment models the interaction between the large power grid and the microgrid. Compared with other power systems, this embodiment constructs a real-time supply and demand balance of hourly power loads. The power balance constraints of the large power grid are shown in formulas (67)-(69):

[0148]

[0149] in, It represents the amount of wind and solar power abandoned by the i-th type of generator set in the large power grid in the h-th hour of the t-th year, represents the output load of the microgrid at the hth hour in the tth year, represents the output load of the large power grid at the hth hour in the tth year, represents the large power grid load at the hth hour in the tth year,

[0150] The power balance constraint of the large power grid under different electricity substitution rates is shown in formula (70):

[0151]

[0152] The power balance constraints of the microgrid are shown in formulas (71)-(73):

[0153]

[0154] in, represents the amount of wind and solar power abandoned by the i-th type of generator set in the microgrid in the h-th hour of the t-th year, represents the microgrid load at hour h in year t;

[0155] The power balance constraint of the large power grid under different electricity substitution rates is shown in formula (74):

[0156]

[0157] The power balance constraint of the large power grid is shown in formula (75):

[0158]

[0159] Among them, E (i,t) represents the power generation of the i-th type of generator in the t-th year, represents the energy storage discharge of the i-th type of generator set in the t-th year, represents the energy storage charging amount of the i-th type of generator set in the t-th year, ESG represents energy storage discharge, ESS represents energy storage charging, Indicates the amount of power abandoned by the large power grid. Represents the output of the large power grid, represents the microgrid input, represents the power demand of the large power grid, Indicates the amount of power wasted in the microgrid. Represents the power demand of the microgrid.

[0160] The power balance constraint of the large power grid under different power substitution rates is shown in formula (76):

[0161]

[0162] The power balance constraint of the microgrid is shown in formula (77):

[0163]

[0164] in, Indicates the amount of power wasted in the microgrid. Represents the power demand of the microgrid.

[0165] The microgrid power balance constraint under different power substitution rates is shown in formula (78):

[0166]

[0167] Example 2: This example focuses on the regulatory perspective. Based on a high-resolution power system multi-grid coordinated operation optimization system, the new energy target constraints and carbon budget constraints under different degrees of electricity substitution are analyzed to analyze the multi-grid coordinated transformation paths of power systems with different regulatory intensities, involving 27 scenarios, as shown in Table 1:

[0168] Table 1 Low-carbon transformation scenarios of the power industry considering regulation

[0169]

[0170] Among them, the baseline scenario represents the power system planning without considering decision constraints such as dual carbon goals and clean energy requirements. The carbon budget constraint refers to Shu Yinbiao et al. and Zhuo et al. The difference between the scenarios of small carbon budget, small carbon budget and moderate carbon budget lies mainly in the remaining carbon emissions allowed to achieve the carbon neutrality target. The scenarios of small carbon budget, small carbon budget and moderate carbon budget consider the carbon neutrality target (carbon neutrality scenario) and include all the constraints listed in Table 1. Regarding clean energy constraints, this embodiment draws on the research of Chen et al. and designs four different scenarios. In addition to not considering the carbon neutrality target, the 75%, 80%, 85% and 90% scenarios represent clean energy proportions similar to the carbon neutrality scenario. The basic carbon market constraint scenario does not consider command-and-control constraints such as carbon neutrality targets and clean energy proportion constraints, but only considers loose carbon market constraints, which include 90% free quotas and pre-set carbon prices. The free quotas and carbon prices refer to the research of Wu et al. and Zhang Xiliang et al. In the basic carbon market scenario, the power industry can always buy carbon quotas at a suitable price without considering the total carbon volume constraint. Referring to Zhuo et al., the degree of electricity substitution considers low electricity substitution rate, medium electricity substitution rate and high electricity substitution rate, which involves electricity demand. The higher the electricity substitution rate, the higher the total electricity demand, and vice versa. Carbon neutrality is an important goal, and this embodiment sets it separately.

[0171] According to the China Nuclear Power Development Center and Global Energy Monitor, other constraints include restrictions on renewable energy abandonment rate, clean energy ratio in 2030, and new energy installed capacity in 2030. In the context of energy transformation, a series of decisions have been implemented to guide and regulate the development of the power industry. The renewable energy abandonment rate refers to the ratio of renewable energy that has not been fully utilized due to various reasons. By setting an upper limit on the abandonment rate, it aims to encourage power companies to improve the utilization efficiency of renewable energy, reduce energy waste, and promote the sustainable use of clean energy. In addition, a target for the clean energy ratio in 2030 has been set. The clean energy ratio refers to the proportion of clean energy in total energy consumption in 2030. By setting a clean energy ratio target, power companies are encouraged to increase investment and development in clean energy to reduce dependence on traditional high-carbon energy and promote the development of the power structure in a cleaner and more sustainable direction. In addition, restrictions have been imposed on the scale of new energy installed capacity.

[0172] The emission reduction paths of the power industry show obvious differences under different scenarios. Figure 2As shown in the figure, in the baseline scenario, carbon emissions in the power industry grow rapidly, and by 2060 they will be 1.9 to 2.3 times that of 2020. On the contrary, in the scenarios of low carbon budget, small carbon budget and moderate carbon budget, the power industry reaches its carbon peak around 2025. In the scenario of low carbon budget, the power industry shows a high intensity of emission reduction in the early stage, a gradual slowdown in emission reduction in the later stage, and enters a flat period after 2040. In the scenario of small carbon budget, the flat period appears after 2050. In order to avoid the lock-in effect on coal-fired power and prevent the run on the share of new energy power, these three scenarios all show a "fast first, slow later" emission reduction path.

[0173] In the scenario of basic carbon market constraints, the power industry reaches its carbon emission peak in 2030, which shows that the carbon market design can effectively achieve the carbon peak of the power industry in 2030. However, after 2045, the carbon emissions of the power industry rebounded significantly. The rebound was mainly due to the service life of wind power and photovoltaic power is 25 years. The centralized retirement of clean energy in 2045 will lead to the power industry facing the choice of coal power investment and clean energy investment. Due to the loose carbon market, the high cost of wind and photovoltaic power in the early stage, and the high systemic cost brought by new energy, coal power installed capacity investment has advantages, which leads to the power industry choosing coal power investment, resulting in coal power lock-in effect and the emergence of new energy investment. From 2030 to 2045, the short-term carbon emission reduction in the power industry is due to the investment in stable power sources such as gas power, nuclear power, and hydropower, which replaces the proportion of coal power in electricity. With the saturation of investment in hydropower, nuclear power, etc., coal power has become the most economical choice.

[0174] Under the 75%-90% clean energy constraint, the power industry reaches its carbon emission peak in 2030 and 2035 respectively. Except for the 90% clean energy constraint, the other scenarios all see a short-term increase in carbon emissions after 2050, but the growth rate does not exceed the peak. However, the 90% clean energy constraint scenario can achieve the carbon emission peak of the power industry, but cannot achieve carbon neutrality. In 2060, there will still be 1.7 billion tons to 1.9 billion tons of carbon emissions, which will increase the pressure on other industries to reduce emissions. Too fast emission reduction under high-intensity regulation is bound to bring certain economic costs, mainly manifested in the reduction of carbon emission surplus, resulting in tight transition time and high transformation costs. Under loose regulations, the emission reduction speed is slow. Although investment can be made when the cost adapts, and there is enough time to choose low-cost investment in the future, this development model will also bring emission reduction pressure to other industries, which may cause other industries to need to reduce emissions more actively to achieve the overall carbon neutrality goal. In some cases, carbon emissions in the power industry have rebounded. On the one hand, the growing demand for electricity without the restriction of carbon neutrality targets will increase capacity investment, and the gradual expansion of coal power is a more economically feasible option due to comparative advantages. On the other hand, achieving carbon neutrality brings additional costs, including retiring coal-fired power plants, implementing CCS technology, investing in biomass portfolio CCS, expanding clean energy installed capacity, and establishing stable energy storage systems to improve grid reliability. Loose decision constraints may lead to a rebound in carbon emissions during the transformation of the power industry. Therefore, strengthening decision constraints and implementing appropriate decisions are crucial to the low-carbon transformation of the power sector.

[0175] Under different carbon budgets and electrification rates, the carbon emission paths of the power industry are diverse. For decision makers, it is necessary to comprehensively consider multiple factors such as carbon market design and the proportion of clean energy to develop a more flexible and sustainable decarbonization path for the power industry. In terms of carbon market design, attention should be paid to avoiding a rebound in carbon emissions after 2030, while paying attention to the role of regulation. In addition, the setting of the proportion of clean energy also needs to fully consider various factors to ensure that sustainable carbon emission reduction targets can be achieved under different scenarios. Therefore, in order to achieve low-carbon energy transformation, support decisions for new energy can be strengthened, more clean energy investment can be encouraged, the cost of new energy can be reduced, and the competitiveness of new energy in the power industry can be improved.

[0176] The cost analysis of the power system involves multiple levels, including the cost of power system transformation, the cost of decarbonization, and the specific cost of each power source. Figure 2As shown in the figure, in the baseline scenario, investment costs, operation and maintenance costs, and fuel costs are the main cost components. As the electricity substitution rate increases from low to high, these costs also increase accordingly. In the scenarios of low carbon budget, small carbon budget, and moderate carbon budget, due to the strict control of carbon emissions, the cost of CCS increases significantly, especially in the scenario of low carbon budget, where the cost of CCS is the most significant. This reflects that under more stringent carbon reduction targets, the power industry needs to adopt more expensive emission reduction technologies to achieve carbon neutrality. From the perspective of decarbonization costs, the costs under the scenarios of low carbon budget, small carbon budget, and moderate carbon budget are relatively high, ranging from -73.4 yuan / ton to -80.0 yuan / ton. This shows that the power industry needs to pay a greater economic price when achieving stricter climate targets. These costs mainly involve the investment required to use clean energy, adopt new technologies, and take carbon reduction measures.

[0177] like Figure 4 As shown in the figure, in terms of the cost of power system transformation. Compared with the baseline scenario, the transformation cost caused by the carbon market is relatively large, ranging from 6.34% to 6.69%. Loosening carbon market constraints will enable the power industry to reach its peak, but it will be difficult to achieve carbon neutrality, and it may also lead to a rebound in coal-fired power capacity investment in the later stage. Among the costs caused by new energy constraints, investment costs account for the largest proportion. Regulating new energy installations will increase costs by 3.26% to 4.31%. The cost caused by carbon neutrality accounts for a high proportion (ranging from 5.95% to 6.59%), and investment costs are the main contributor to carbon neutrality costs. Fuel costs and emission costs decrease, but CCS costs increase. As the carbon budget decreases, the transformation of the power industry will continue to increase, among which CCS costs are the main contributor. Overall, the proportion of emission costs caused by the carbon market is the highest (accounting for 6.34%), and the additional costs caused by carbon neutrality are relatively high (accounting for 5.95%); compared with different scenarios, investment costs, fuel costs and operation and maintenance costs vary greatly; the higher the electricity substitution rate, the higher the additional costs caused by the carbon market and carbon neutrality constraints.

[0178] The cost of each power supply is as follows: Figure 5As shown in the figure, the costs of various scenarios show obvious changes. In 2025, the costs of hydropower, nuclear power and coal power are relatively high, which is due to the high costs of these power sources in the initial investment stage. In 2030, in the scenarios of small carbon budget, low carbon budget and moderate carbon budget, the cost of wind power is the largest. This is because under more stringent carbon reduction targets, the demand for clean energy increases, which increases investment in new energy such as wind power, which in turn causes an increase in investment costs and operation and maintenance costs. In 2035, the costs of wind power and photovoltaic power account for a large proportion, indicating that renewable energy gradually dominates. In some scenarios, the cost of coal power still accounts for a large proportion. This is because the constraints of these scenarios are not strong enough, resulting in a slow low-carbon transformation of electricity. In 2040, the investment cost of onshore wind power increases significantly and occupies a major position; under various electricity substitution rate scenarios, it is more advantageous to develop onshore wind power at this time.

[0179] like Figure 6 As shown in the figure, in 2045, under the scenarios of small carbon budget, low carbon budget and moderate carbon budget, the cost of centralized photovoltaic power increases, and wind power still accounts for a large share. In 2050, under the scenarios of small carbon budget, low carbon budget and moderate carbon budget, the cost of distributed photovoltaic power is high, and the cost of coal-fired power is also high, because of the new investment in coal-fired power and the increase in CCS costs. In the process of low-carbon transformation of the power system, coal-fired power plays a role in coordinating the output of new energy. Due to the natural retirement of coal-fired power, the installed capacity of coal-fired power is insufficient, which affects the consumption of new energy and load regulation. At the same time, it has a comparative advantage over other power sources such as energy storage, so some coal-fired power is added. In 2055, under the scenarios of small carbon budget, low carbon budget and moderate carbon budget, the cost of photovoltaic power is much higher than other scenarios, and the cost of new energy and energy storage accounts for the main part. In 2060, the cost of gas-fired power and biomass power is high, and new energy and energy storage are the main cost components. In general, the higher the demand for electricity, the higher the total cost of the power system.

[0180] like Figure 7 As shown in the figure, in terms of water consumption, under the baseline scenario, water consumption is the largest in 2060, ranging from 5.17 million tons to 5.87 million tons. Under the basic carbon market scenario, water consumption in 2060 ranges from 3.51 million tons to 3.98 million tons. Under the 75%-90% clean energy constraint scenario, water consumption in 2060 ranges from 2.14 million tons to 3.03 million tons. In contrast, water consumption is lowest in the scenarios of small carbon budget, low carbon budget and moderate carbon budget, reaching 1.44 million tons in 2060. This shows that achieving the carbon neutrality goal is not only conducive to the clean use of electricity, but also helps save water.

[0181] like Figure 8As shown in the above figure, coal-fired power generation plays an important and complex role in the future development of the power industry. For future power supply, a certain proportion of coal-fired power generation capacity is expected to be retained in various scenarios, which means that coal-fired power plays an indispensable role in the stability of the future power system. However, at the same time, with the promotion of low-carbon goals and the rise of new energy, the status and utilization of coal-fired power will also undergo significant changes. In the baseline scenario, the installed capacity of coal-fired power shows an upward trend, reaching a range of 2.107 billion kilowatts to 2.457 billion kilowatts in 2060, becoming the main power source. In this scenario, the power industry relies on coal-fired power to meet the growing demand for electricity, showing a lock-in effect on traditional power energy. In contrast, in the scenarios of low carbon budget, low carbon budget and moderate carbon budget, the installed capacity of coal-fired power will peak in 2025, and then decrease year by year, with the capacity range of 404 million kilowatts to 537 million kilowatts in 2060. This reflects that under the promotion of low-carbon goals, coal-fired power will be gradually phased out to meet the requirements of carbon emission reduction. This change indicates that in the long-term development process, clean energy will gradually replace traditional coal-fired power to meet the requirements of carbon neutrality. Unlike the large-scale investment in coal-fired power in the past, more resources may flow to the development of new energy and low-carbon technologies in the future to achieve a more sustainable and environmentally friendly power system. In addition, in the scenario of low carbon budget, the proportion of coal-fired power equipped with CCS ranges from 56% to 65%, while in the scenarios of low carbon budget and moderate carbon budget, it is 48% to 53% and 33% to 35% respectively. This shows that under more stringent carbon reduction targets, CCS technology will play a more important role and help coal-fired power achieve more efficient carbon reduction. It is worth noting that in these three scenarios, the deployment time of CCS is generally after 2035, which is consistent with the results of other studies.

[0182] Under the basic carbon market scenario, coal-fired power generation capacity will peak in 2030-2035, and then show a U-shaped change, with an inflection point in 2045, and the range in 2060 will be between 1.223 billion kilowatts and 1.460 billion kilowatts. This shows that the design of a loose carbon market can effectively achieve the carbon peak in the power industry around 2030, but with the increase in electricity demand, coal-fired power in the power industry will rise again after 2045. This is because the concentrated retirement of clean energy in 2045 will lead to the power industry facing the choice of coal-fired power investment and clean energy investment. The comprehensive loose carbon market, the high cost of wind and solar power in the early stage, and the high systemic cost brought by new energy make coal-fired power generation investment have advantages. Under the constraints of 75%-90% of new energy, coal-fired power generation capacity will peak in 2030-2035. Under the 75% and 80% clean energy scenarios, coal-fired power generation will increase again after 2050, ranging from 561 million kilowatts to 843 million kilowatts in 2060. This reflects that the power industry is concerned about the security of power supply and has increased investment in stable power sources while maintaining the proportion of new energy installed capacity. This trend is consistent with the current situation in China's energy structure adjustment. In order to ensure a reliable supply of electricity, local governments will continue to increase investment in coal-fired power, making it still dominant in the short term. Under the constraints of 85% and 90% new energy, coal-fired power generation will show a slow decline after 2050, indicating that the lack of constraint strength has slowed down the elimination of coal-fired power, which in turn affects the proportion of new energy.

[0183] As a representative of clean energy, wind power will play an increasingly important role in the future power system. In different scenarios, wind power installed capacity shows obvious differences. Fig. 9As shown in the figure, wind power installed capacity will remain relatively stable before 2035 under the baseline scenario, indicating that other power sources can meet electricity demand at this stage and there is no need to increase wind power installed capacity on a large scale. However, as the development of other power sources such as hydropower reaches its limit after 2035, the growth in electricity demand will need to be met by increasing wind power, so the capacity peak will be reached in 2040. Thereafter, as part of the wind power installed capacity is naturally retired, the wind power capacity will gradually decrease and eventually stabilize at 720 million kilowatts to 760 million kilowatts in 2060. Compared with the baseline scenario, wind power capacity shows a significant growth trend under the scenarios of low carbon budget, low carbon budget and moderate carbon budget. Under these scenarios, wind power installed capacity increases rapidly before 2040. After 2040, the growth rate of wind power installed capacity slows down. By 2060, the wind power capacity ranges from 3.2 billion kilowatts to 3.8 billion kilowatts, including 400 million kilowatts of offshore wind power installed capacity. This also means that wind power will become one of the main pillars of the power system in the future, making great contributions to carbon neutrality and clean energy transformation. Under the basic carbon market scenario, wind power capacity shows a trend of first growth and then decline. After reaching its peak in 2050, its capacity will gradually decrease, mainly due to the retirement caused by the limitation of wind power operation life. In 2060, its capacity ranges from 1.9 billion kilowatts to 2.13 billion kilowatts. Under the 75%-90% new energy constraint scenario, wind power capacity shows obvious differences in different years. Under the 75% and 80% scenarios, the capacity reaches its peak in 2055, ranging from 2.97 billion kilowatts to 3.37 billion kilowatts and 3.0 billion kilowatts to 3.37 billion kilowatts, respectively. Under the 85% and 90% scenarios, its capacity tends to stabilize after 2055. In 2060, its capacity ranges from 3 billion kilowatts to 3.4 billion kilowatts and 3.05 billion kilowatts to 3.74 billion kilowatts, respectively. This shows that under the dual constraints of a higher proportion of clean energy and the carbon market, wind power capacity reaches its peak at an earlier time and then stabilizes. At the same time, it also means that decision-making regulations and market mechanism guidance are crucial to the sustainable development of wind power.

[0184] As a clean energy source, photovoltaic power generation will also play an important role in the future power system. However, due to the intermittent nature of photovoltaic power generation, its capacity growth is affected by energy storage technology, so the supporting investment in energy storage needs to be considered when investing. In the baseline scenario, photovoltaic capacity remains relatively stable. This is because the demand for energy storage is relatively stable under the current investment level and coal-fired power has more comparative advantages, such as Fig. 9As shown. In this case, since photovoltaic power generation cannot achieve stable output without a significant increase in energy storage, maintaining the existing photovoltaic capacity is considered to be the best economic option. In the scenarios of small carbon budget, small carbon budget and moderate carbon budget, photovoltaic capacity shows an upward trend. In these three scenarios, the power industry has a higher demand for clean energy, which prompts the expansion of photovoltaic power generation facilities. By 2060, photovoltaic capacity has not reached a plateau, which indicates that photovoltaics will continue to play its high penetration rate and huge power generation potential in future power systems to meet the growing electricity demand, such as Fig. 9 As shown. In 2060, its capacity ranges from 2.60 billion kilowatts to 3.25 billion kilowatts. Under the basic carbon market scenario, photovoltaic capacity grows rapidly before 2050 and then stabilizes. This shows that the introduction of the carbon market has a positive impact on photovoltaic investment, thereby increasing the proportion of clean energy in the power system. By 2060, the photovoltaic capacity range is 740 million kilowatts to 780 million kilowatts, stabilizing at a relatively low level. Under the 75%-90% new energy constraint scenario, photovoltaic capacity shows a clear increasing trend. Under the 75%-85% scenario, the capacity stabilizes in 2050 and 2055. Under the 90% scenario, before 2045, the capacity grows slowly, and after 2050, it grows rapidly.

[0185] like Fig.10 and Fig.11 As shown in Figure 2, from the perspective of installed capacity, the installed capacity in the scenarios with small carbon budget, small carbon budget and moderate carbon budget is higher than that in other scenarios. In 2030, coal power, nuclear power, hydropower and wind power will become the main power sources among various power generation sources, such as Fig.10 As shown. In the scenarios of small carbon budget, low carbon budget and moderate carbon budget, wind power installed capacity is significantly higher than other power sources, which reflects the important position of clean energy in these scenarios. In these scenarios, the power industry reduces its dependence on high-carbon energy and achieves carbon reduction targets by increasing the proportion of renewable energy. In 2040, wind power installed capacity becomes the main power source in most scenarios, reflecting the advancement of wind power technology and the reduction of costs, making it more competitive. At the same time, due to limited hydropower resources and planning constraints of nuclear power, hydropower and nuclear power have reached a saturation stage, while wind power is gradually emerging as a rapidly deployable form of clean energy. As Fig.11As shown in the figure, in 2050, except for the baseline scenario, wind power and photovoltaic power will become the main power sources. In the scenarios of low carbon budget, small carbon budget and moderate carbon budget, centralized photovoltaic power and distributed photovoltaic power will become the second largest power source. At the same time, for the power systems in these low-carbon scenarios, coal power will gradually withdraw from the main stage, and stable power sources such as gas power, biomass and hydropower will play a regulatory and auxiliary role. In 2060, the installed capacity in the scenarios of low carbon budget, small carbon budget and moderate carbon budget will be much higher than that in other scenarios. In these three scenarios, the energy storage capacity ranges from 1.07 billion kilowatts to 1.64 billion kilowatts, which reflects that the rapid development of new energy is accompanied by huge demand for energy storage. Its role in the power system is becoming more and more prominent, becoming a key component to balance power supply and demand and ensure system operation. As a negative carbon role, biomass + CCS plays a huge role in the later stage, and it is one of the important means to achieve carbon neutrality. Compared with the baseline scenario, biomass power generation accounts for a large proportion in other scenarios.

[0186] like Fig.12 and Fig.13 As shown in Figure 2, clean electricity will gradually take a leading role in the development of future power systems. Fig.12 As shown, in 2030, under the scenarios of small carbon budget, low carbon budget and moderate carbon budget, the main components of electricity include coal power, wind power, nuclear power and hydropower. In contrast, in other scenarios, coal power is still the main source of electricity, which shows that under loose regulations, the development of clean energy is slow and traditional thermal power still occupies a dominant position. In 2040, wind power gradually replaces coal power and becomes the main power source, and photovoltaic power generation also gradually increases. This is an obvious transformation, showing the increasing status of renewable energy in the power structure. Especially when the proportion of clean electricity is high, wind power and photovoltaic power generation have become the main sources of electricity and have made positive contributions to the low carbonization and cleanness of the power system. As Fig.13As shown in the figure, by 2050, wind power will gradually replace coal power, photovoltaic power generation will also gradually increase, and clean energy will become the main power source. In the scenarios of low carbon budget, low carbon budget and moderate carbon budget, wind power and solar power will become the main power sources in 2060. In other cases, wind power and coal power will become the main sources of electricity, and clean power such as hydropower and nuclear power will have reached saturation in advance due to restrictions. This embodiment is still optimistic about coal power and believes that a certain amount of coal power installed capacity will still need to be retained in the future. Although coal power will be gradually replaced by wind power after 2040, it will still have room to play a regulatory and stabilizing role in 2060. This is consistent with the current status of my country's power system, because in order to avoid insufficient power supply, some regions have increased coal power installed capacity and improved the stability of the power system. In terms of energy storage, in the scenarios of low carbon budget, low carbon budget and moderate carbon budget, the charging and discharging of energy storage is more obvious. In these scenarios, energy storage mainly plays the role of regulating the balance of the power grid rather than playing the role of power supply. Therefore, the amount of energy storage is not particularly large, and this result is similar to the study of Chen et al.

[37] . This also shows that in the process of building a new power system, energy storage will be used more to smooth the fluctuations of renewable energy and ensure the stability of the power grid. Overall, in all scenarios, although coal power is gradually replaced by wind power and solar power, it still plays a role in the power system to ensure the stability of the power system.

[0187] This embodiment also provides an optimization analysis of the operation of large power grids and microgrids under command-control environment regulation: During the low-carbon transformation of the power system, clean energy gradually replaces traditional energy and becomes the dominant energy in the future. Among them, microgrids, as a flexible and sustainable power system configuration solution, are of great significance for achieving efficient use of clean energy and stable operation of the power system. Fig.14 As shown, this embodiment will further explore the load interaction of the power system under different scenarios. In the baseline scenario, the power system still mainly relies on coal power, nuclear power and hydropower. The interaction between the large power grid and the microgrid is not frequent. The microgrid mainly relies on the input power of the large power grid, while the power input of the microgrid to the large power grid is relatively small.

[0188] like Fig.15 As shown in the figure, under the basic carbon market scenario, coal-fired power and gas-fired power still dominate, but wind power and photovoltaic power need to take a series of measures to adapt to load demand, including adjusting photovoltaic output and wind turbine speed. This adjustment shows that the power system makes flexible adjustments to adapt to load balance. Under the basic carbon market scenario, distributed photovoltaics play an important role in microgrids, while the power input from the large power grid still accounts for a large proportion of microgrids.

[0189] like Fig.16As shown in the figure, under the 90% clean energy constraint scenario, the volatility of wind power and photovoltaic power requires traditional coal-fired power to adjust its output frequently. In this case, the interaction between distributed photovoltaic and energy storage in the microgrid becomes more frequent, achieving more efficient power supply. At the same time, the energy storage of the microgrid is charged and discharged more frequently. The interaction between the large power grid and the microgrid is also relatively frequent, and the power input from the large power grid still accounts for a large proportion. In this scenario, the microgrid cannot independently achieve a balance between power supply and demand.

[0190] like Fig.17 As shown in the figure, in the moderate carbon budget scenario, the supply structure of the power system has changed significantly. Clean power sources such as wind power and centralized photovoltaic power have become the main power supply, followed by hydropower and nuclear power. However, in the peak load regulation process, gas power and coal power still play a key role. In the process of load interaction, the intermittent nature of photovoltaic power generation has a great impact on the power grid, and it is easy to show the typical "duck curve" characteristics, which requires other power units to adjust frequently and quickly. Therefore, solving this challenge requires a large investment in energy storage and more flexible thermal power generating units. At this time, the interaction between the large power grid and the microgrid is more frequent and important. The amount of electricity input from the microgrid to the large power grid has increased and become more frequent, and the proportion of the large power grid input to the microgrid has been greatly reduced. Distributed photovoltaics support most of the electricity of the microgrid.

[0191] like Fig.18 As shown in the figure, in the scenario with a smaller carbon budget, the supply structure of the power system is similar to that in the scenario with a moderate carbon budget. Clean power sources such as wind power and centralized photovoltaic power generation become the main power supply. However, during the peak load regulation process, gas power and coal power have changed from a dominant position to a supporting position. The interaction between microgrids and large power grids is closer. Microgrids can adjust the load balance of large power grids, while large power grids can also provide power for microgrids. In this scenario, the charging and discharging of energy storage is more frequent.

[0192] like Fig.19 As shown in the figure, in the low carbon budget scenario, the supply structure of the large power grid is similar to that in the moderate carbon budget and low carbon budget scenarios. However, gas-fired power and coal-fired power still play a key role in the peak load regulation process. In this scenario, thermal power has more peak load regulation and frequency regulation, and the power system is more volatile. The interactive mode between microgrids and large power grids enables microgrids to better cope with power demand in different scenarios and achieve more flexible and sustainable power supply.

[0193] In general, microgrids play a key role in the sustainable evolution of the regulated power system. First, microgrids improve the utilization rate of clean energy by supporting the overall development of the power system with distributed energy output. Second, the flexibility of microgrids enables them to better adapt to the needs of large power grids in different scenarios. In particular, when faced with the volatility and intermittency of clean energy, microgrids can achieve a more stable power supply through energy storage and intelligent scheduling. In the future, with the continuous innovation of clean energy technology and the support of decision-making, the power system will usher in more changes. As a flexible and efficient power system configuration solution, microgrids will play an increasingly important role in the future.

[0194] Based on the above embodiments, an embodiment of the present application also provides an electronic device, which includes: one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device executes the method provided in the above embodiments.

[0195] Based on the above embodiments, the embodiments of the present application further provide a computer storage medium, in which a computer program is stored. When the computer program is executed by a computer, the computer executes the method provided in the above embodiments.

[0196] The storage medium may be any available medium that can be accessed by a computer. For example, but not limited to, a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0197] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0198] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0201] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A high-resolution multi-grid coordinated operation optimization system for power systems, characterized in that: The optimization system model includes Technical modules, including large grid and microgrid interaction modules and cost parameters; Energy module, including energy consumption parameters of different energy types, energy storage parameters of different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters and energy supply parameters; A decision module, including a decision target module, wherein the decision target module includes a total carbon emission budget of the power system and a carbon emission reduction cost for achieving a temperature control target, and the decision target module quantifies the difference in low-carbon transformation of the power system according to low-carbon transformation paths of the power system under different scenarios under regulation, and determines a transformation strategy; The objective function is an overall cost function, and the objective function includes constraints. The constraints are obtained by simulating capacity planning and transformation strategies to obtain a technical path for the power system that minimizes the cost of coordinated interaction between large power grids and microgrids.

2. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 1, characterized in that: The cost parameters include investment cost parameters, operation and maintenance cost parameters, fuel cost parameters, emission cost parameters, start and stop cost parameters, CCS cost parameters, and equipment residual value parameters; The energy types include non-new energy and new energy. Non-new energy includes coal power energy and biomass power energy. New energy includes wind power energy, photovoltaic energy, hydropower energy, gas power energy and nuclear power energy. The energy storage capacity types include physical energy storage and chemical energy storage.

3. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 2, characterized in that: The objective function of the model of the optimization system is shown in formula (1): Where t represents the tth year, T represents the planning period, and r represents the discount rate. represents the investment cost in year t, represents the operation and maintenance cost in the tth year, represents the fuel cost in year t, represents the start-up and shutdown cost in year t, represents the carbon emission cost in year t, represents the CCS cost in year t, Represents the residual value of equipment in year t.

4. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 3, characterized in that: The investment cost in year t As shown in formula (2): Wherein, M represents the energy type, m represents non-new energy or new energy, i represents the i-th type of generator set, and the types of generator sets include coal-fired generator sets, photovoltaic generator sets, wind generator sets, hydropower generator sets, biomass generator sets, gas generator sets and nuclear generator sets. represents the newly added power source of the i-th type of generator set in year t, represents the newly added installed capacity of power generation units of the i-th type in the t-th year, K represents the energy storage capacity type, k represents physical energy storage or chemical energy storage, j represents the j-th type of energy storage, represents the unit capacity cost of the j-th type of energy storage added in year t, represents the newly added energy storage installed capacity of the jth type of energy storage in year t; The operation and maintenance cost in the tth year As shown in formula (3): in, represents the fixed capacity unit cost of the i-th type of generator in the t-th year, represents the existing power capacity of the i-th type of generator set in year t, represents the power generation of the i-th type of generator in the t-th year, represents the current storage capacity of the jth type of energy storage in year t, represents the energy storage capacity of the jth type of energy storage in the tth year, represents the variable capacity unit cost of the i-th type of generator in the t-th year, represents the fixed energy storage unit cost of the j-th type of energy storage in year t, represents the unit cost of variable energy storage of the jth type of energy storage in year t; The fuel cost in the tth year As shown in formula (4): Among them, h represents the hth hour, represents the fuel cost per unit of electricity of the ith type of generator in the tth year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year; The start-up and shutdown costs in the tth year As shown in formula (5): in, represents the cost of shutting down the power capacity of the i-th type of generator at the h-th hour in the t-th year, It represents the cost of opening the power supply capacity of the i-th type of generator set at the h-th hour in the t-th year, U (i,t,h-1) represents the on / off status of the i-th type of generator set at the h-1 hour in the t-th year, U (i,t,h) represents the on / off status of the i-th type of generator set at the h-th hour in the t-th year, U (i,t,h-1) , U (i,t,h) Both are 0 and 1 variables, 0 is closed and 1 is opened. The carbon emission cost in year t As shown in formula (6): in, represents the carbon price of the i-th type of generator in the t-th year, represents the carbon absorption rate of the i-th type of generator in the t-th year, represents the carbon emission coefficient of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set in the h-th hour of the t-th year, l represents different types of pollutants, including sulfur dioxide, nitrogen oxides and smoke, represents the unit pollutant tax of the i-th type of generator in the t-th year, It represents the pollutant emission coefficient of the i-th type of generator set in the t-th year; The CCS cost in year t As shown in formula (7): Among them, μ (i,t) represents the CCS installation ratio of the i-th type of generating unit in the t-th year, represents the CCS investment cost per unit capacity of the i-th type of generating unit in the t-th year, fm (i,t) represents the fixed operation and maintenance cost of the i-th type of generator set in the t-th year, represents the variable operation and maintenance cost of the i-th type of generator set in the t-th year, represents the power generation of the i-th type of generator set with CCS installed capacity at the h-th hour in the t-th year, represents the transportation cost of unit carbon emissions of the i-th type of generator in year t; The residual value of the equipment in year t As shown in formula (8): in, It represents the residual value coefficient of unit power capacity of the i-th type of generator set in the t-th year, represents the power capacity of the i-th type of generator set retired in year t, It represents the residual value coefficient of unit energy storage capacity of the jth type of energy storage in the tth year, represents the retired energy storage capacity of the j-th type of energy storage in year t.

5. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 4, characterized in that: The constraints of the objective function include power balance constraints, power balance constraints, power system expansion constraints, upper and lower limit constraints on power output, unit climbing constraints, maximum limit constraints on power output capacity, wind power generation constraints, photovoltaic power generation constraints, system margin constraints, operating reserve constraints, chemical energy storage constraints, physical energy storage constraints, installed capacity constraints, power balance expansion constraints, power balance expansion constraints, decision constraints, power balance constraints of large power grids, power balance constraints of large power grids under different power substitution rates, power balance constraints of microgrids, power balance constraints of large power grids under different power substitution rates, power balance constraints of large power grids, power balance constraints of large power grids under different power substitution rates, power balance constraints of microgrids, and power balance constraints of microgrids under different power substitution rates.

6. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 5, characterized in that: The power balance constraints are shown in formulas (9)-(12): Among them, M ES Represents energy storage capacity, M RE Represents the capacity of new energy units, It represents the non-renewable energy generation of the i-th type of generator set in the h-th hour of the t-th year, It represents the energy storage discharge of the i-th type of generator set at the h-th hour in the t-th year, It represents the energy storage charging of the i-th type of generator set at the h-th hour in the t-th year, It represents the renewable energy generation of the i-th type of generator set at the h-th hour in the t-th year, It represents the amount of wind and solar power abandoned by the i-th type of generator set in the large power grid in the h-th hour of the t-th year, represents the output load of the microgrid at the hth hour in the tth year, represents the output load of the large power grid at the hth hour in the tth year, represents the large power grid load at the hth hour in the tth year, represents the amount of wind and solar power abandoned by the i-th type of generator set in the microgrid in the h-th hour of the t-th year, represents the microgrid load at hour h in year t, It represents the output coefficient of non-renewable energy unit of the i-th type of generator set at the h-th hour in the t-th year, It represents the new energy output coefficient of the i-th type of generator set in the h-th hour of the t-th year, represents the non-renewable energy capacity of the i-th type of generator set in the t-th year, It represents the new energy power capacity of the i-th type of generator set in the t-th year; The power balance constraint is shown in formulas (13) and (14): Among them, E (i,t) represents the power generation of the i-th type of generator in the t-th year, represents the energy storage discharge of the i-th type of generator set in the t-th year, represents the energy storage charging amount of the i-th type of generator set in the t-th year, ESG represents energy storage discharge, ESS represents energy storage charging, Indicates the amount of power abandoned by the large power grid. Represents the output of the large power grid, represents the microgrid input, represents the power demand of the large power grid, Indicates the amount of power wasted in the microgrid. represents the power demand of the microgrid; The power system expansion constraints are shown in formulas (15)-(20): in, represents the existing capacity of the generator set of the i-th type of the m-th capacity type in the t-th year, represents the existing generating capacity of the i-th type of generating unit of the m-th capacity type in year t-1, It represents the newly added capacity of the generating unit of the i-th type of the m-th capacity type in the t-th year, represents the capacity of the generator set of the i-th type of the m-th capacity type retired in the t-th year, The ttth generator set of the mth capacity type and the ith type m,i Annual newly added generating unit capacity, t m,i It represents the operating life of the power supply of the ith type of generator set with the mth capacity type. represents the current storage capacity of the jth type of energy storage in year t, represents the current storage capacity of the jth type of energy storage in year t-1, represents the newly added energy storage capacity of the jth type of energy storage in year t, represents the retired energy storage capacity of the jth type of energy storage in year t, Indicates the jth type of energy storage tt j Annual new energy storage capacity, t j represents the operating life of the jth type of energy storage, represents the retired energy storage capacity of the jth type of energy storage in year t, dinz (m,i,t) represents the upper limit and cinz of the new generation units of the i-th type of capacity type in the t-th year (j,t) represents the upper limit of new additions of the j-th type of energy storage in year t; The upper and lower limits of the power output are shown in formula (21): in, It represents the minimum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, It represents the maximum output coefficient of the power supply of the i-th type of generator set at the h-th hour in the t-th year, represents the actual output capacity of the i-th type of generator set at the h-th hour in the t-th year, represents the actual online capacity of the i-th type of generator set at the h-th hour in the t-th year; The unit climbing constraint is shown in formulas (22) and (23): in, represents the actual output capacity of the i-th type of generator set at the h-1th hour in the tth year, ru (i,t) represents the maximum upward ramp rate of the i-th type of generator set in the t-th year, represents the capacity of the i-th type of generator set turned on at the h-th hour in the t-th year, represents the capacity of the i-th type of generating unit shut down at hour h+1 in year t, represents the capacity of the i-th type of generating unit shut down at the h-th hour in the t-th year, du (i,t) represents the maximum downward ramp rate of the i-th type of generator set in the t-th year, represents the capacity of the i-th type of generator set turned on at hour h-1 in year t; The maximum limit constraint of the power supply output capacity is shown in formula (24): in, It represents the capacity of the i-th type of generator set turned on at hour h+1 in year t; The wind power generation constraint is shown in formula (25): in, represents the wind power generation of the i-th type of generator set in the h-th hour of the t-th year, represents the wind power capacity factor of the i-th type of generator set at the h-th hour in the t-th year, represents the wind power installed capacity of the i-th type of generator in the t-th year, represents the newly added wind power installed capacity of the i-th type of generator in year t, represents the existing wind power installed capacity of the i-th type of generator in year t, represents the potential wind power installed capacity of the i-th type of generator in year t; The photovoltaic power generation constraint is shown in formula (26): in, represents the photovoltaic power generation of the i-th type of generator set in the h-th hour of the t-th year, represents the photovoltaic capacity factor of the i-th type of generator set at the h-th hour in the t-th year, represents the photovoltaic installed capacity of the i-th type of generator in the t-th year, represents the newly added photovoltaic installed capacity of the i-th type of generator in year t, represents the existing photovoltaic installed capacity of the i-th type of generator in year t, represents the potential installed PV capacity of the i-th type of generator in year t; The system margin constraint is shown in formula (27): in: represents the capacity confidence coefficient of coal-fired power supply in year t, represents the capacity confidence factor of wind power in year t, represents the capacity confidence coefficient of the photovoltaic power source in year t, represents the capacity confidence factor of nuclear power in year t, represents the capacity confidence factor of the hydropower source in year t, represents the capacity confidence factor of the gas power supply in year t, represents the capacity confidence factor of biomass power source in year t, represents the total installed capacity of coal-fired power generation units of type i in year t, represents the total installed nuclear power capacity of the i-th type of generating unit in year t, represents the total installed hydropower capacity of the i-th type of generating unit in year t, represents the total installed capacity of gas-fired power generation units of the i-th type in the t-th year, represents the total installed biomass capacity of the i-th type of generating unit in year t; The running reserve constraint is shown in formula (28): Among them, cg (i,t,h) The upper limit coefficient of the energy storage output of the i-th type of generator set in the h-th hour of the t-th year, represents the capacity of the i-th type of generating unit in year t, represents the capacity of energy storage occupied by the i-th type of generator set at the h-th hour in the t-th year, P represents the prediction error coefficient of the base load of the i-th type of generator set at the h-th hour in the t-th year, (t,h) represents the base load at the hth hour in the tth year, represents the forecast error coefficient of wind power generation at the hth hour in the tth year for the i-th type of generator set, It represents the prediction error coefficient of photovoltaic power generation at the hth hour in the tth year for the i-th type of generator set; The chemical energy storage constraints are shown in formulas (29)-(34): SC (j,t,h＝0) =SCO(29) Of which: SC (j,t,h) represents the energy storage situation of the energy storage device of the jth type at the hth hour in the tth year, SC (j,t,h＝0) represents the energy storage situation of the energy storage device of the jth type in the tth year at hour h=0, SCO represents the initial energy storage situation, SC (j,t,h+1) It represents the energy storage situation of the energy storage device of the jth type at the h+1th hour in the tth year, represents the energy storage charging efficiency of the jth type, represents the energy storage discharge efficiency of the jth type, represents the energy loss rate of the jth type of energy storage, represents the energy storage charge of the energy storage device of the jth type at the hth hour in the tth year, It represents the energy storage discharge of the energy storage device of the jth type at the hth hour in the tth year, represents the lower limit coefficient of the jth type of energy storage, represents the upper limit coefficient of the jth type of energy storage, represents the energy storage capacity of the jth type of energy storage in the tth year, Γ (j,t) Indicates the number of charge and discharge restrictions for the j-th type of energy storage in the t-th year; The physical energy storage constraints include physical energy storage discharge constraints and physical energy storage charging constraints; The physical energy storage discharge constraints are shown in formulas (35) and (36): in, represents the lower limit of the discharge power of the hydropower station at the hth hour in the tth year with the jth type of energy storage, represents the upper limit of the discharge power of the hydropower station at the hth hour in the tth year with the jth type of energy storage, CSX (t,h) represents the water storage capacity of the physical energy storage power station reservoir at the hth hour in the tth year, represents the minimum energy storage value of the physical energy storage power station in year t, xl t represents the power generation efficiency of the physical energy storage turbine in year t; The physical energy storage charging constraints are shown in formulas (37) and (38): in, represents the lower limit of the charging power of the physical energy storage power station at the hth hour in the tth year, represents the upper limit of the charging power of the physical energy storage power station at the hth hour in the tth year, represents the maximum energy storage value of the physical energy storage power station in year t, xlc t represents the charging efficiency of the physical energy storage turbine in year t; The water storage capacity of the physical energy storage power station reservoir at the hth hour in the tth year CSX (t,h) As shown in formula (39): Among them, CSX (t+1,h) represents the water storage capacity of the physical energy storage power station reservoir at the time h in the year t+1, The minimum energy storage value of the physical energy storage power station in the tth year and the maximum energy storage value of the physical energy storage power station in year t As shown in formula (40): The installed capacity constraints are shown in formulas (41)-(45): Among them, M WRE Represents the capacity of wind turbine, M PRE Represents the capacity of the photovoltaic unit, M NRE Represents the capacity of nuclear power unit, M HRE Indicates the capacity of the hydropower unit, M GAS Indicates the capacity of the gas generator set. represents the upper limit of wind power installed capacity in year t, represents the upper limit of photovoltaic installed capacity in year t, represents the upper limit of nuclear power installed capacity in year t, represents the upper limit of hydropower installed capacity in year t, Represents the upper limit of gas-fired power generation capacity in year t.

7. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 6, characterized in that: The power balance expansion constraint is shown in formulas (46)-(48): in, represents the original load at the hth hour in the tth year, represents the amount of wind and solar power abandoned by the i-th type of generator in the h-th hour of the t-th year, and ω represents different degrees of electricity substitution; The power balance expansion constraint is shown in formula (49): in, Indicates the amount of power wasted. represents the original electricity in year t; The decision constraints are shown in formulas (50)-(53): in, represents the power generation of the i-th type of generator in the t-th year, TA co represents the total carbon emissions, represents the proportion of non-fossil energy electricity, Indicates different power output states. It represents the existing generating capacity of the i-th type of generating unit in year t.

8. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 7, characterized in that: The carbon emission reduction cost is shown in formula (54): Among them, CCOST qh represents the carbon reduction cost, qh represents different scenario types, ΔTC qh represents the change in transition cost compared to the baseline scenario, ΔCCO qh Represents the change in carbon emissions compared to the baseline scenario.

9. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 8, characterized in that: In the large power grid and microgrid interaction module, the large power grid input includes wind power energy input, photovoltaic energy input, hydropower energy input, coal power energy input, nuclear power energy input, physical energy storage input, chemical energy storage input and microgrid input; the microgrid input includes distributed photovoltaic energy input, physical energy storage input, chemical energy storage input and large power grid input.

10. A high-resolution multi-network coordinated operation optimization system for power systems according to claim 9, characterized in that: The power balance constraint of the large power grid is shown in formula (55)-(55): in, It represents the amount of wind and solar power abandoned by the i-th type of generator set in the large power grid in the h-th hour of the t-th year, represents the output load of the microgrid at the hth hour in the tth year, represents the output load of the large power grid at the hth hour in the tth year, represents the large power grid load at the hth hour in the tth year, The power balance constraint of the large power grid under different electricity substitution rates is shown in formula (58): The power balance constraints of the microgrid are shown in formulas (59)-(61): in, represents the amount of wind and solar power abandoned by the i-th type of generator set in the microgrid in the h-th hour of the t-th year, represents the microgrid load at hour h in year t; The power balance constraint of the large power grid under different electricity substitution rates is shown in formula (62): The power balance constraint of the large power grid is shown in formula (63): Among them, E (i,t) represents the power generation of the i-th type of generator in the t-th year, represents the energy storage discharge of the i-th type of generator set in the t-th year, represents the energy storage charging amount of the i-th type of generator set in the t-th year, ESG represents energy storage discharge, ESS represents energy storage charging, Indicates the amount of power abandoned by the large power grid. Represents the output of the large power grid, represents the microgrid input, represents the power demand of the large power grid, Indicates the amount of power wasted in the microgrid. represents the power demand of the microgrid; The power balance constraint of the large power grid under different power substitution rates is shown in formula (64): The power balance constraint of the microgrid is shown in formula (65): in, Indicates the amount of power wasted in the microgrid. represents the power demand of the microgrid; The microgrid power balance constraint under different power substitution rates is shown in formula (66):

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