A high-resolution power system multi-network cooperative operation optimization system

Abstract

The application relates to the field of power market analysis optimization, and discloses a multi-network coordinated operation optimization system of a high-resolution power system, wherein a model of the optimization system comprises a technical module, an energy module and a decision module; the decision module comprises a decision target module, the decision target module comprises total carbon emission budget and carbon emission reduction cost of the power system for realizing a temperature control target, the decision target module quantifies differences in low-carbon transformation of the power system according to low-carbon transformation paths of the power system under different scenarios under regulations, and determines a transformation strategy; a target function of the model is a total cost function, the target function comprises constraint conditions, the constraint conditions are obtained by simulating capacity planning and the transformation strategy, and a minimum-cost technical path of the power system in coordinated interaction between a large power grid and a microgrid is obtained. Compared with the prior art, the application constructs a real-time supply-demand balance simulation of an hourly power load, and the result is more detailed and accurate.

Description

Technical Field

[0001] This invention relates to the field of power market analysis and optimization, specifically to a high-resolution power system multi-grid collaborative operation optimization system. Background Technology

[0002] With the rapid development of society and technology, energy has become a core driving force for national development, its importance comparable to that of water for human survival. However, the over-exploitation and consumption of fossil fuels has not only exacerbated environmental problems but also highlighted the urgent need for a clean and reliable power supply. Improving and upgrading the global energy structure, especially promoting clean energy, has become an inevitable trend in social development. Against this backdrop, microgrids, as a form of distributed energy, have become crucial for integrating into the main grid and optimizing the energy structure due to their flexibility and reliability. Microgrids can utilize energy in a decentralized manner, improving power supply reliability, especially when large-scale distributed photovoltaic power is integrated, demonstrating their high efficiency and environmental friendliness. With the advancement of national new energy policies, the construction of smart grids, and power market reforms, the importance of microgrids is increasingly prominent. The synergistic relationship between microgrids and the main grid 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 and photovoltaics, as well as various energy storage devices. This complex structure gives microgrids flexible operating modes, ensuring power supply during severe weather or emergencies, whether operating in grid-connected or islanded mode, thus enhancing the overall stability of the grid.

[0003] Policy support and regulation are equally important for the development of microgrids. Reasonable policies can promote the application of microgrid technology, maximizing economic benefits while minimizing overall efficiency and pollution. Constructing models that conform to actual usage scenarios and providing low-cost, high-reliability dispatch schemes are of significant reference value for the actual operation of microgrids. However, currently, there is a lack of systems that can simultaneously consider the collaborative interaction and decision-making control of the large power grid and microgrids to minimize power system costs. Therefore, researching and designing a novel high-resolution multi-grid collaborative operation optimization system for power systems is of great significance for overcoming the problems in existing power market analysis and optimization, and achieving efficient, stable, and economical energy supply. Summary of the Invention

[0004] To address the lack of cost-minimizing power system technology paths in existing power system optimization systems that simultaneously consider the collaborative interaction and decision-making control of large power grids and microgrids, this invention provides a high-resolution power system multi-grid collaborative operation optimization system.

[0005] The technical solution adopted by this invention to achieve the above objectives is: a multi-grid collaborative operation optimization system for high-resolution power systems, wherein the model of the optimization system includes...

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

[0007] The energy module includes energy consumption parameters for different energy types, energy storage parameters for different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters, and energy supply parameters.

[0008] The decision-making module includes a decision-making objective module, which includes the total carbon emission budget and carbon reduction cost of the power system to achieve the temperature control target. The decision-making objective module quantifies the differences in the low-carbon transformation of the power system under different scenarios under the regulations and determines the transformation strategy.

[0009] The objective function is the overall cost function, and the objective function includes constraints. These constraints are obtained by simulating capacity planning and transition strategies to obtain a power system technology path that minimizes the cost of coordinated interaction between the large power grid and the microgrid.

[0010] This 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 that, when executed by the electronic device, cause the electronic device to perform the technical solutions of the second aspect of this application and any possible design of the second aspect.

[0011] This application also provides a computer-readable storage medium, which includes a computer program that, when run on an electronic device, causes the electronic device to execute the technical solutions of the second aspect of this application and any possible design of the second aspect.

[0012] This invention presents a high-resolution multi-grid collaborative operation optimization system for power systems. It introduces constraints to ensure real-time source-load balance and reserve constraints to address errors in load forecasting and renewable energy output forecasting, thereby enhancing the overall framework's flexibility. This method allows for a deeper analysis of the specific impact of decision constraints on market operation, providing a scientific basis for deploying flexible strategies for sustainable power systems. Compared to existing technologies, this method considers the real-time interaction between large power grids and microgrids, achieving coupling between long-term annual and short-term timescales. It constructs a high spatial resolution power system optimization model, considering both decision constraints and market operation, and combining meso-level power system and macro-level market regulation, thus providing clearer and more accurate guidance for the future development direction of power systems. Attached Figure Description

[0013] Figure 1This is a schematic diagram of the structure of the multi-grid collaborative operation optimization system for high-resolution power systems according to an embodiment of the present invention;

[0014] Figure 2 This is the emission reduction path for the power industry under the decision-making process in Embodiment 2 of the present invention;

[0015] Figure 3 This is a comparison of transformation costs under the regulations in Embodiment 2 of the present invention;

[0016] Figure 4 This is a comparison of cost differences under the regulations in Embodiment 2 of the present invention, wherein (a) is a comparison of cost differences under low electricity substitution rate; (b) is a comparison of cost differences under medium electricity substitution rate; and (c) is a comparison of cost differences under high electricity substitution rate.

[0017] Figure 5 The figures represent the annual costs of each power source under the regulations in Embodiment 2 of this invention, where (a) is the power source cost in 2025; (b) is the power source cost in 2030; (c) is the power source cost in 2035; and (d) is the power source cost in 2040.

[0018] Figure 6 The figures represent the annual costs of each power source under the regulations in Embodiment 2 of this invention, where (a) is the power source cost in 2045; (b) is the power source cost in 2050; (c) is the power source cost in 2055; and (d) is the power source cost in 2060.

[0019] Figure 7 This refers to the water consumption under different scenarios regulated in Embodiment 2 of the present invention;

[0020] Figure 8 This is a diagram of coal-fired power plant installed capacity under the regulations in Embodiment 2 of the present invention;

[0021] Figure 9 This is a diagram showing the total installed capacity of wind power and photovoltaic power under the regulations in Embodiment 2 of the present invention;

[0022] Figure 10 This refers to the installed capacity under the regulations in Embodiment 2 of the present invention, where (a) is the installed capacity in 2030; and (b) is the installed capacity in 2040.

[0023] Figure 11 This refers to the installed capacity under the regulations in Embodiment 2 of the present invention, where (a) represents the installed capacity in 2050; and (b) represents the installed capacity in 2060.

[0024] Figure 12 This refers to the power generation situation under the regulations in Embodiment 2 of the present invention, where (a) is the power generation situation in 2030; and (b) is the power generation situation in 2040.

[0025] Figure 13This refers to the power generation situation under the regulations in Embodiment 2 of the present invention, where (a) is the power generation situation in 2050; and (b) is the power generation situation in 2060.

[0026] Figure 14 This is a simulation of the baseline scenario under the regulations in Embodiment 2 of the present invention;

[0027] Figure 15 This is an operational simulation of the basic carbon market under regulation in Embodiment 2 of the present invention;

[0028] Figure 16 This is an operational simulation of the 90% renewable energy constraint under the regulations in Embodiment 2 of the present invention;

[0029] Figure 17 This is an operational simulation of a carbon budget with appropriate regulation under Embodiment 2 of the present invention;

[0030] Figure 18 This is an operational simulation with a smaller carbon budget under the regulations in Embodiment 2 of the present invention;

[0031] Figure 19 This is an operational simulation with a small carbon budget under the regulations in Embodiment 2 of the present invention. Detailed Implementation

[0032] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0033] Example 1: This example describes a high-resolution power system multi-grid collaborative operation optimization system, such as... Figure 1 As shown, the optimization system model includes a technology module, an energy module, and a decision module. The technology module includes a large power grid and microgrid interaction module and cost parameters. The energy module includes energy consumption parameters for different energy types, energy storage parameters for different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters, and energy supply parameters. The decision module includes a decision objective module, which includes the total carbon emission budget and carbon reduction cost of the power system to achieve temperature control targets. Based on different scenarios of low-carbon transition paths for the power system under regulation, the decision objective module quantifies the differences in low-carbon transition of the power system and determines the transition strategy. The objective function of the optimization system model is the overall cost function, which includes constraints. These constraints are obtained by simulating capacity planning and transition strategies to obtain the power system technology path that minimizes the cost of coordinated interaction between the large power grid and microgrids. Preferably, in this embodiment, the decision input in the decision module can be government policy.

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

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

[0036]

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

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

[0039]

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

[0041] Annual maintenance costs As shown in formula (3):

[0042]

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

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

[0045]

[0046] Where h represents the h-th hour, This represents the fuel cost per unit of electricity for the i-th type of generator set in year t. This represents the power generation of the i-th type of generator unit in the h-th hour of year t;

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

[0048]

[0049] in, This represents the cost of shutting down the power capacity of the i-th type of generator set in the h-th hour of year t. U represents the cost of activating the power capacity of the i-th type of generator set in the h-th hour of the t-th year. (i,t,h-1) U represents the on / off status of the i-th type of generator set in the (h-1)th hour of the t-th year. (i,t,h) U represents the on / off status of the i-th type of generator set in the h-th hour of the t-th year. (i,t,h-1) U (i,t,h) All are variables of 0 and 1, with 0 for closed and 1 for open.

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

[0051]

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

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

[0054]

[0055] Where, μ (i,t) This represents the percentage of CCS installations for the i-th type of generator set in year t. fm represents the unit capacity CCS investment cost of the i-th type of generator set in year t. (i,t) This represents the fixed operation and maintenance cost of the i-th type of generator set in year t. This represents the variable operation and maintenance cost of the i-th type of generator set in year t. This represents the power generation of the i-th type of generator unit with CCS installed capacity in the h-th hour of year t. Let represent the transportation cost per unit carbon emission of the i-th type of generator set in year t;

[0056] Residual value of equipment in year t As shown in formula (8):

[0057]

[0058] in, This represents the residual value factor of the unit power supply capacity of the i-th type of generator set in year t. This represents the power capacity of the i-th type of generator set to be decommissioned in year t. This represents the residual value coefficient per unit energy storage capacity for the j-th type of energy storage in year t. This represents the energy storage capacity of type j being decommissioned in year t.

[0059] It should be noted that, as a preferred embodiment, the constraints of the objective function include power balance constraints, energy balance constraints, power system expansion constraints, upper and lower limits of power output constraints, unit ramping constraints, maximum limit constraints of power output capacity, wind power generation constraints, photovoltaic power generation constraints, system adequacy constraints, operating reserve constraints, chemical energy storage constraints, physical energy storage constraints, installed capacity constraints, power balance expansion constraints, energy balance expansion constraints, decision constraints, power balance constraints of the large power grid, power balance constraints of the large power grid under different energy substitution rates, power balance constraints of the microgrid, power balance constraints of the large power grid under different energy substitution rates, energy balance constraints of the large power grid, energy balance constraints of the large power grid under different energy substitution rates, energy balance constraints of the microgrid, and energy balance constraints of the microgrid under different energy substitution rates.

[0060] Compared to other power system optimization systems, this embodiment constructs an hourly-level real-time power load supply and demand balance. 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.

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

[0062]

[0063] Among them, M ES M represents energy storage capacity. RE Indicates the capacity of new energy generating units. This represents the non-new energy power generation of the i-th type of generator unit in the h-th hour of year t. This represents the energy storage discharge of the i-th type of generator set in the h-th hour of year t. This represents the energy storage charging of the i-th type of generator set in the h-th hour of year t. This represents the renewable energy generation of the i-th type of generator unit in the h-th hour of year t. This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in the large power grid during the h-th hour of the t-th year. This represents the output load of the microgrid in the h-th hour of year t. This represents the output load of the large power grid in the h-th hour of year t. This represents the load of the large power grid in the h-th hour of year t. This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in a microgrid during the h-th hour of the t-th year. This represents the microgrid load in the h-th hour of year t. This represents the output coefficient of the i-th type of generator unit in the h-th hour of year t, which is a non-new energy unit. This represents the renewable energy output coefficient of the i-th type of generator unit in the h-th hour of year t. This represents the non-new energy power capacity of the i-th type of generator set in year t. This represents the renewable energy power capacity of the i-th type of generator set in year t.

[0064] The power balance of large power grids and microgrids involves the annual supply and demand balance of electricity, and the power balance constraints are shown in formulas (13) and (14):

[0065]

[0066]

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

[0068] Regarding the constraints on power system expansion, the existing installed capacity of power sources is determined based on the annual increase and decommissioning during the research period. The constraints on power system expansion are shown in formulas (15)-(20):

[0069]

[0070] Formulas (15)-(20) indicate that the existing capacity equals the capacity of the previous period plus the newly added capacity minus the decommissioned capacity. Wherein, This represents the existing generator capacity of type i, which is of capacity m, in year t. This represents the existing generator capacity of type i, which is of capacity m, in year t-1. This represents the newly added generator capacity of type m and type i in year t. This represents the capacity of the generator set of type m and type i that is decommissioned in year t. This represents the generator set of the m-th capacity type and the i-th type, with capacity tt. m,i Annual increase in generator unit capacity, t m,i This represents the operating life of the generator set of type i with capacity m. This represents the current energy storage capacity of the j-th type of energy storage in year t. This represents the current energy storage capacity of the j-th type of energy storage in year t-1. This represents the newly added energy storage capacity of type j in year t. This represents the energy storage capacity of type j to be decommissioned in year t. Represents the j-th type of energy storage tt j Annual new energy storage capacity, t j This represents the operational lifespan of the j-th type of energy storage. dinz represents the decommissioning capacity of the j-th type of energy storage in year t. (m,i,t) This represents the maximum increase in capacity for the m-th type and the i-th type of generator set in year t, cinz. (j,t) This represents the upper limit of the number of new energy storage units of type j in year t;

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

[0072]

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

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

[0075]

[0076] in, ru represents the actual output capacity of the i-th type of generator set in the h-1 hour of year t. (i,t) This represents the maximum uphill gradient rate of the i-th type of generator set in year t. This represents the capacity of the i-th type of generator set that is turned on in the h-th hour of the t-th year. This represents the capacity of the i-th type of generator unit that is shut down in the (h+1)-th hour of year t. Du represents the capacity of the i-th type of generator set shut down in the h-th hour of the t-th year. (i,t) This represents the maximum downhill gradient of the i-th type of generator set in year t. This represents the capacity of the i-th type of generator set that is turned on in the h-1 hour of year t;

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

[0078]

[0079] in, This represents the capacity of the i-th type of generator set that is turned on in the (h+1)-th hour of year t;

[0080] The constraints on wind power generation are shown in formula (25):

[0081]

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

[0083] The constraints on photovoltaic power generation are shown in formula (26):

[0084]

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

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

[0087]

[0088] in: This represents the confidence coefficient for the capacity of coal-fired power generation in year t. This represents the confidence coefficient for the capacity of wind power generation in year t. This represents the confidence coefficient for the capacity of the photovoltaic power generation in year t. This represents the confidence coefficient for the capacity of nuclear power sources in year t. This represents the confidence coefficient for the capacity of hydropower in year t. This represents the confidence coefficient for the capacity of the gas-fired power source in year t. This represents the confidence coefficient for the capacity of biomass power generation in year t. This represents the total installed capacity of coal-fired power plants of the i-th type of generator unit in year t. This represents the total installed nuclear power capacity of the i-th type of generator unit in year t. This represents the total installed hydropower capacity of the i-th type of generator unit in year t. This represents the total installed capacity of gas-fired power generation for the i-th type of generator set in year t. This represents the total installed capacity of biomass generator sets of type i in year t.

[0089] Due to forecasting errors and unforeseen circumstances, power systems have operational reserve requirements, and the operational reserve constraints are shown in formula (28):

[0090]

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

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

[0093] SC (j,t,h＝0) =SCO(29)

[0094]

[0095]

[0096] Among them: SC (j,t,h) SC represents the energy storage status of the energy storage device of type j in year t, hour h. (j,t,h＝0) This represents the energy storage status of the energy storage device of type j in year t at hour h=0, where SCO represents the initial energy storage status, and SC represents the initial energy storage status. (j,t,h+1) This represents the energy storage status of the energy storage device of type j in the (h+1)th hour of year t. This represents the charging efficiency of the j-th type of energy storage. This represents the discharge efficiency of the j-th type of energy storage. This represents the energy loss rate of the j-th type of energy storage. This represents the energy storage charging of the j-th type of energy storage device in the h-th hour of year t. This represents the energy storage discharge of the j-th type of energy storage device in the h-th hour of year t. This represents the lower limit coefficient for the j-th type of energy storage. This represents the upper limit coefficient for the j-th type of energy storage. Γ represents the energy storage capacity of the j-th type of energy storage in year t. (j,t) This represents the number of charge / discharge limits for the j-th type of energy storage in year t;

[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 equations (35) and (36):

[0099]

[0100] in, This represents the lower limit of the discharge power of a hydropower station for the j-th type of energy storage in the h-th hour of year t. CSX represents the upper limit of the discharge power of a hydropower station of type j energy storage in year t, hour h. (t,h) This represents the water storage capacity of the physical energy storage power station reservoir in the h-th hour of year t. XL represents the minimum energy storage capacity of the physical energy storage power station in year t. tThis represents the power generation efficiency of the physical energy storage turbine in year t.

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

[0102]

[0103] in, This represents the lower limit of the charging power of the physical energy storage power station in the h-th hour of year t. This represents the upper limit of the charging power of the physical energy storage power station in the h-th hour of year t. xlc represents the maximum energy storage value of the physical energy storage power station in year t. t This represents the charging efficiency of the physical energy storage turbine in year t.

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

[0105]

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

[0107] Minimum energy storage value of 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 power supply capacity restrictions involve five types of power sources: wind power, photovoltaic power, hydropower, gas power, and nuclear power. Wind power, photovoltaic power, hydropower, and gas power have limited capacity due to resource constraints, including wind intensity, solar intensity, and site constraints. For nuclear power, since controlled nuclear fusion technology has not yet been realized, and considering factors such as technological progress, uranium fuel supply, site resources, and power plant construction cycles, it is clearly stated that nuclear power will be strictly controlled in the future, and its installed capacity will also be limited. The installed capacity constraints are shown in formulas (41)-(45):

[0110]

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

[0112] In the models of optimization systems, nonlinear problems often exist, and these nonlinear problems are very likely to lead to local optima. In the power system optimization system model constructed in this embodiment, there are nonlinear problems in the objective function and constraints. In order to solve for the global optimum, this embodiment transforms some of the constraints in the model.

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

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

[0115]

[0116] This represents the actual output capacity of non-new energy power sources for the i-th type of generator unit in the h-th hour of year t.

[0117] Actual output capacity of non-new energy power source for generator set of type i in year t, hour h Appropriate constraints must also be added. The constraints are shown in formula (47):

[0118]

[0119] Let represent the actual online capacity of non-new energy power sources of the i-th type of generator set in the h-th hour of the t-th year. Thus, such an equation is transformed into an equation and an inequality constraint.

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

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

[0122]

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

[0124]

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

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

[0127]

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

[0129] (3) Linearize the multi-unit start-up and shutdown problem, and simplify the unit combination handling problem:

[0130] In the simulation, the start-up and shutdown of each coal-fired power unit corresponds to a specific state. The more units there are, the more 0-1 variables are needed to characterize them, which makes the solution of the model very complex. In this embodiment, a continuous variable modeling method is used to replace the original decision variables (0-1 variables). The specific changes are shown in formulas (55)-(57):

[0131]

[0132]

[0133] in, Indicates the maximum online capacity, x (i,t) Indicates installed capacity. Indicates that it is turned on. Indicates the enabled capacity. Indicates that it is turned on. Let represent the actual online capacity of the i-th type of generator set in the h-1 hour of year t. Indicates the shutdown capacity. This indicates that the service has been shut down.

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

[0135] It should be noted that, as a preferred embodiment, in the case of substitution of different energy types or energy storage capacity types, the power balance involves the annual power supply and demand balance, and the power balance extended constraints are shown in formulas (58)-(60):

[0136]

[0137] in, This represents the original load in the h-th hour of year t. ω represents the amount of wind and solar power curtailment of the i-th type of generator unit 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 substituted, the power balance extension constraint is shown in Equation (61):

[0139]

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

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

[0142]

[0143] in, TA represents the annual power generation of the i-th type of generator set in year t. co Indicates total carbon emissions. Indicates the proportion of electricity generated from non-fossil fuels. Indicates different power output states. This represents the existing generator capacity of type i in year t.

[0144] It should be noted that, as a preferred embodiment, carbon emission reduction cost can measure the economics of emission reduction, i.e., the additional cost required per unit of emission. Carbon emission reduction cost is also one of the standards 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 ΔTC represents the cost of carbon emission reduction, qh represents different scenario types, and ΔTC represents the cost of carbon emission reduction. qh ΔCCO represents the change in transformation costs compared to the baseline scenario. qh This indicates the change in carbon emissions compared to the baseline scenario.

[0147] It should be noted that, as a preferred embodiment, the interaction between the large power grid and the microgrid is crucial for the balance of the power system during operation. In order to study the planning and decision-making of the low-carbon transformation 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 load. The power balance constraints of the large power grid are shown in formulas (67)-(69):

[0148]

[0149] in, This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in the large power grid during the h-th hour of the t-th year. This represents the output load of the microgrid in the h-th hour of year t. This represents the output load of the large power grid in the h-th hour of year t. This represents the load of the large power grid in the h-th hour of year t.

[0150] The power balance constraints of a large power grid under different energy substitution rates are shown in equation (70):

[0151]

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

[0153]

[0154] in, This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in a microgrid during the h-th hour of the t-th year. This represents the microgrid load at hour h in year t;

[0155] The power balance constraints of a large power grid under different energy substitution rates are shown in equation (74):

[0156]

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

[0158]

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

[0160] The power balance constraints of a large power grid under different power substitution rates are shown in formula (76):

[0161]

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

[0163]

[0164] in, This indicates the amount of electricity wasted by the microgrid. This indicates the electricity demand of the microgrid.

[0165] The energy balance constraints of microgrids under different energy substitution rates are shown in Equation (78):

[0166]

[0167] Example 2: This example focuses on the regulatory perspective. Based on a high-resolution power system multi-grid collaborative operation optimization system, it analyzes the multi-grid collaborative transformation path of the power system under different regulatory intensities, considering the constraints of new energy targets and carbon budgets under different levels of electricity substitution. It involves 27 scenarios, as shown in Table 1.

[0168] Table 1. Scenarios for Low-Carbon Transition in the Power Industry Considering Regulation

[0169]

[0170] The baseline scenario represents power system planning without considering decision-making constraints such as dual-carbon targets and clean energy requirements. Carbon budget constraints are referenced from Shu et al. and Zhuo et al. The main difference between the low carbon budget, moderate carbon budget, and moderate carbon budget scenarios lies in the allowable residual carbon emissions to achieve the carbon neutrality target. The low carbon budget, moderate carbon budget, and moderate carbon budget scenarios consider the carbon neutrality target (carbon neutrality scenario) and include all constraints listed in Table 1. Regarding clean energy constraints, this embodiment draws on the research of Chen et al., designing four different scenarios. Except for 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 the carbon neutrality target and clean energy proportion constraints, but only considers relaxed carbon market constraints, which include 90% free allowances and a pre-set carbon price. Free allowances and carbon prices are referenced from the research of Wu et al. and Zhang et al. In the basic carbon market scenario, the power industry can always purchase carbon allowances at a suitable price, regardless of total carbon emissions. Referring to Zhuo et al., the degree of electricity substitution considers low, medium, and high substitution rates, which are related to electricity demand. A high substitution rate results in high total electricity demand, and vice versa. Carbon neutrality is an important objective, and this embodiment sets it as a separate objective.

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

[0172] The emission reduction paths in the power industry differ significantly under different scenarios. For example... Figure 2As shown, under the baseline scenario, carbon emissions from the power sector will grow rapidly, reaching 1.9 to 2.3 times that of 2020 by 2060. Conversely, under scenarios with low, moderate, and moderate carbon budgets, the power sector will peak its carbon emissions around 2025. In the low-budget scenario, the power sector exhibits a pattern of rapid initial emission reductions followed by a gradual slowdown, reaching a plateau after 2040. In the low-budget scenario, this plateau occurs after 2050. To avoid the lock-in effect on coal-fired power and prevent the crowding out of renewable energy, all three scenarios show a "rapid initial, then slower" emission reduction path.

[0173] Under the basic carbon market constraint scenario, the power industry reaches its carbon emission peak in 2030, indicating that the carbon market design can effectively achieve this goal. However, after 2045, the power industry's carbon emissions rebound significantly. This rebound is mainly due to the 25-year lifespan of wind and solar power. The concentrated retirement of clean energy in 2045 will force the power industry to choose between coal-fired power investment and clean energy investment. The relaxed carbon market, high upfront costs of wind and solar power, and high systemic costs of new energy sources make coal-fired power capacity investment advantageous. This leads the power industry to choose coal-fired power investment, resulting in a coal-fired power lock-in effect and crowding out new energy investment. From 2030 to 2045, the power industry's temporary carbon emission reduction is due to investments in stable power sources such as gas-fired, nuclear, and hydropower, which replace the proportion of coal-fired power in the electricity mix. As investments in hydropower and nuclear power reach saturation, coal-fired power becomes the most economical choice.

[0174] Under a 75%-90% clean energy constraint, the power sector will reach peak carbon emissions in 2030 and 2035, respectively. Except for the 90% clean energy constraint scenario, all other scenarios show a brief increase in carbon emissions after 2050, but the increase will not exceed the peak. However, while the 90% clean energy constraint scenario can achieve peak carbon emissions for the power sector, it cannot achieve carbon neutrality, still maintaining 1.7 billion to 1.9 billion tons of carbon emissions in 2060. This will increase the pressure on other sectors to reduce emissions. Rapid emission reduction under strict regulations inevitably brings certain economic costs, mainly manifested in reduced carbon emission surplus leading to a tight transition time and high transition costs. On the other hand, slower emission reduction under relaxed regulations, while allowing for investment when costs are favorable and sufficient time to choose future low-cost investments, will also put pressure on other sectors to reduce emissions, potentially requiring them to be more proactive in achieving overall carbon neutrality. In some cases, carbon emissions from the power sector have rebounded. On the one hand, the increasing electricity demand without carbon neutrality targets would necessitate capacity investment, making the gradual expansion of coal-fired power a more economically viable option due to comparative advantages. On the other hand, achieving carbon neutrality incurs additional costs, including decommissioning coal-fired power plants, implementing CCS (Carbon Capacity Storage) technology, investing in biomass-based CCS systems, expanding clean energy capacity, and establishing stable energy storage systems to improve grid reliability. Relaxed decision-making constraints could lead to a rebound in carbon emissions during the power sector's transition; therefore, strengthening decision-making constraints and implementing appropriate decisions are crucial for the power sector's low-carbon transformation.

[0175] The carbon emission pathways in the power sector exhibit diversity under varying carbon budgets and electrification rates. Policymakers need to comprehensively consider multiple factors, including carbon market design and the proportion of clean energy, to develop more flexible and sustainable decarbonization paths for the power sector. Regarding carbon market design, emphasis should be placed on preventing a rebound in carbon emissions after 2030, while also stressing the role of regulation. Furthermore, the setting of the clean energy share must fully consider various factors to ensure sustainable carbon reduction targets can be achieved under different scenarios. Therefore, to achieve a low-carbon energy transition, supportive policies for new energy sources should be strengthened, encouraging more investment in clean energy, reducing the cost of new energy, and enhancing the competitiveness of new energy sources in the power sector.

[0176] Cost analysis of power systems involves multiple levels, including power system transition costs, decarbonization costs, and the specific costs of each power source. For example... Figure 2As shown, under the baseline scenario, investment costs, operation and maintenance costs, and fuel costs are the main cost components. These costs increase accordingly as the electricity substitution rate increases. Under the scenarios of low carbon budget, moderate carbon budget, and medium carbon budget, CCS costs increase significantly due to stricter carbon emission controls, especially under the low carbon budget scenario, where CCS costs are most significant. This reflects that under stricter carbon emission reduction targets, the power industry needs to adopt more expensive emission reduction technologies to achieve carbon neutrality. In terms of decarbonization costs, the costs are relatively high under the low carbon budget, moderate carbon budget, and medium carbon budget scenarios, ranging from -73.4 yuan / ton to -80.0 yuan / ton. This indicates that the power industry needs to pay a greater economic price to achieve stricter climate targets. These costs mainly involve investments required for using clean energy, adopting new technologies, and implementing carbon emission reduction measures.

[0177] like Figure 4 As shown, regarding the costs of power system transition, the transition costs caused by the carbon market are significantly higher than the baseline scenario, ranging from 6.34% to 6.69%. Relaxing carbon market constraints will allow the power industry to reach peak demand, but it will be difficult to achieve carbon neutrality, and it may also lead to a rebound in investment in coal-fired power capacity later on. Among the costs caused by renewable energy constraints, investment costs account for the largest proportion. Regulating renewable energy installations will increase costs by 3.26% to 4.31%. Costs caused by carbon neutrality account for a relatively high proportion (ranging from 5.95% to 6.59%), with investment costs being the main contributor to carbon neutrality costs. Fuel costs and emissions costs decrease, but CCS costs increase. As the carbon budget decreases, power industry transition will continue to increase, with CCS costs being the main contributor. Overall, emissions costs caused by the carbon market account for the highest proportion (6.34%), while the additional costs caused by carbon neutrality are relatively high (5.95%). Comparing different scenarios, investment costs, fuel costs, and operation and maintenance costs vary significantly; the higher the electricity substitution rate, the higher the additional costs caused by carbon market and carbon neutrality constraints.

[0178] Regarding the cost of various power sources, such as Figure 5As shown, costs vary significantly across different scenarios. In 2025, hydropower, nuclear power, and coal power will have higher costs due to their higher initial investment costs. In 2030, wind power will have the highest cost under scenarios with low, moderate, and moderate carbon budgets. This is because stricter carbon reduction targets will increase the demand for clean energy, leading to increased investment in new energy sources like wind power and consequently higher investment and operation and maintenance costs. By 2035, wind and solar power will account for a large proportion of costs, indicating that renewable energy is gradually becoming dominant. In some scenarios, coal power will still account for a significant proportion of costs due to insufficient constraints, resulting in a slower pace of low-carbon transition in the power sector. By 2040, onshore wind power investment costs will increase significantly, becoming the dominant factor; under various electricity substitution rate scenarios, developing onshore wind power will be more advantageous at this point.

[0179] like Figure 6 As shown, in 2045, under scenarios of low, moderate, and moderate carbon budgets, the cost of centralized photovoltaic (PV) power will increase, while wind power will still account for a large share. In 2050, under the same scenarios, distributed PV will have higher costs, and coal-fired power will also be more expensive due to increased investment in coal-fired power and the rising cost of CCS (Carbon Dioxide, Carbon Storage). During the low-carbon transition of the power system, coal-fired power plays a coordinating role in renewable energy output. The natural retirement of coal-fired power plants has led to insufficient installed capacity, affecting renewable energy absorption and load regulation. Furthermore, coal-fired power plants have a comparative advantage over other power sources such as energy storage, thus leading to the addition of some coal-fired power plants. In 2055, under the same scenarios, PV costs will be significantly higher than in other scenarios, with renewable energy and energy storage costs accounting for the majority. In 2060, gas-fired power and biomass power will be more expensive, with renewable energy and energy storage being the main cost components. Overall, the higher the electricity demand, the higher the total cost of the power system.

[0180] like Figure 7 As shown, in terms of water consumption, the baseline scenario predicts the highest water consumption in 2060, ranging from 5.17 million to 5.87 million tons. Under the base case carbon market scenario, water consumption in 2060 ranges from 3.51 million to 3.98 million tons. Under the scenario of 75%-90% clean energy constraints, water consumption in 2060 ranges from 2.14 million to 3.03 million tons. In contrast, the scenarios with low carbon budgets, moderate carbon budgets, and moderate carbon budgets show the lowest water consumption, at 1.44 million tons in 2060. This indicates that achieving carbon neutrality not only promotes cleaner electricity generation but also contributes to water conservation.

[0181] like Figure 8As shown, coal-fired power capacity plays a crucial and complex role in the future development of the power industry. Under various scenarios, a certain proportion of coal-fired power capacity is expected to be retained for future power supply, meaning 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 sources, the status and utilization of coal-fired power will undergo significant changes. In the baseline scenario, coal-fired power capacity 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 electricity demand, exhibiting a lock-in effect on traditional power energy. In contrast, under scenarios with low carbon budgets, moderate carbon budgets, and moderate carbon budgets, coal-fired power capacity peaks in 2025 and then decreases annually, with a capacity range of 404 million kilowatts to 537 million kilowatts in 2060. This reflects that, driven by low-carbon goals, coal-fired power will be gradually phased out to meet carbon emission reduction requirements. This shift signifies that, in the long-term development process, clean energy will gradually replace traditional coal-fired power to meet carbon neutrality requirements. Unlike past large-scale investments in coal-fired power, more resources are likely to flow into the development of new energy and low-carbon technologies to achieve a more sustainable and environmentally friendly power system. Furthermore, under a low carbon budget scenario, the proportion of coal-fired power plants equipped with CCS ranges from 56% to 65%, while under scenarios with a low carbon budget and a moderate carbon budget, the figures are 48% to 53% and 33% to 35%, respectively. This indicates that under more stringent carbon reduction targets, CCS technology will play a more important role in helping coal-fired power plants achieve more efficient carbon reduction. It is noteworthy that under all three scenarios, the deployment of CCS generally occurs after 2035, which is consistent with the results of other studies.

[0182] Under the basic carbon market scenario, coal-fired power capacity will peak between 2030 and 2035, followed by a U-shaped trend with an inflection point in 2045, and a range of 1.223 billion kilowatts to 1.46 billion kilowatts by 2060. This indicates that a relaxed carbon market design can effectively achieve carbon peaking for the power industry around 2030. However, with increasing electricity demand, coal-fired power capacity will rise again after 2045. This is because the concentrated retirement of clean energy in 2045 will force the power industry to choose between coal-fired power investment and clean energy investment. The combination of a relaxed carbon market, high upfront costs of wind and solar power, and high systemic costs of new energy sources makes coal-fired power capacity investment advantageous. Under the constraint of 75%-90% renewable energy, coal-fired power capacity will peak between 2030 and 2035. Under the scenarios of 75% and 80% clean energy, coal-fired power generation will increase again after 2050, ranging from 561 million kilowatts to 843 million kilowatts in 2060. This reflects the power industry's concern about power supply security and increased investment in stable power sources while maintaining the proportion of new energy capacity. This trend is consistent with the current situation in China's energy structure adjustment. To ensure a reliable power supply, local governments will continue to increase investment in coal-fired power, allowing it to remain dominant in the short term. Under the constraints of 85% and 90% new energy, coal-fired power generation will decline slowly after 2050, indicating that insufficient constraints have slowed the phasing out of coal-fired power, thus affecting the proportion of new energy.

[0183] As a representative of clean energy, wind power will play an increasingly important role in future power systems. The installed capacity of wind power varies significantly depending on the specific circumstances. For example... Figure 9As shown, under the baseline scenario, wind power capacity will remain relatively stable before 2035, indicating that other power sources can meet electricity demand during this period, and a large-scale increase in wind power capacity is not necessary. 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, thus reaching a peak capacity in 2040. Thereafter, with the natural retirement of some wind power capacity, wind power capacity will gradually decrease, eventually stabilizing at 720-760 million kilowatts by 2060. Compared to the baseline scenario, wind power capacity shows a significant growth trend under scenarios with low carbon budgets, relatively low carbon budgets, and moderate carbon budgets. Under these scenarios, wind power capacity increases rapidly before 2040. After 2040, the growth rate of wind power capacity slows down. By 2060, wind power capacity will range between 3.2 billion and 3.8 billion kilowatts, including 400 million kilowatts of offshore wind power capacity. This also means that wind power will become one of the main pillars of the power system in the future, making a significant contribution to carbon neutrality and the clean energy transition. Under a basic carbon market scenario, wind power capacity shows a trend of first increasing and then decreasing. Its capacity will peak in 2050 and then gradually decrease, mainly due to the limited lifespan of wind turbines leading to their retirement. In 2060, its capacity will range from 1.9 billion kilowatts to 2.13 billion kilowatts. Under a renewable energy constraint scenario of 75%-90%, wind power capacity shows significant differences in different years. Under the 75% and 80% scenarios, capacity will peak in 2055, ranging from 2.97 billion kilowatts to 3.37 billion kilowatts and from 3 billion kilowatts to 3.37 billion kilowatts, respectively. Under the 85% and 90% scenarios, its capacity will stabilize after 2055. In 2060, its capacity will range from 3 billion kilowatts to 3.4 billion kilowatts and from 3.05 billion kilowatts to 3.74 billion kilowatts, respectively. This indicates that under the dual constraints of a higher proportion of clean energy and a carbon market, wind power capacity peaked earlier and then stabilized. At the same time, this also means that policy regulation and market mechanisms are crucial for the sustainable development of wind power.

[0184] As a clean energy source, photovoltaic (PV) power generation will play a crucial role in future power systems. However, due to the intermittent nature of PV power generation, its capacity growth is influenced by energy storage technology; therefore, investment in energy storage must be considered during the investment process. Under the baseline scenario, PV capacity remains relatively stable due to the relatively stable demand for energy storage at current investment levels and the comparative advantage of coal-fired power. Figure 9As shown in the diagram. In this scenario, since energy storage cannot be significantly increased and photovoltaic power generation cannot achieve stable output, maintaining the existing photovoltaic capacity is considered the best economic option. Photovoltaic capacity tends to increase under scenarios with low, moderate, and moderate carbon budgets. Under these three scenarios, the power industry's demand for clean energy is higher, prompting the expansion of photovoltaic power generation facilities. By 2060, photovoltaic capacity has not yet reached a plateau, indicating that photovoltaics will continue to play a high role in the future power system with its high penetration rate and huge power generation potential to meet the growing electricity demand, such as... Figure 9 As shown, the photovoltaic (PV) capacity will range from 2.6 billion kilowatts to 3.25 billion kilowatts by 2060. Under the basic carbon market scenario, PV capacity will grow rapidly before 2050, then stabilize. This indicates that the introduction of the carbon market has a positive impact on PV investment, thereby increasing the proportion of clean energy in the power system. By 2060, PV capacity will range from 740 million kilowatts to 780 million kilowatts, stabilizing at a relatively low level. Under the 75%-90% renewable energy constraint scenario, PV capacity shows a clear increasing trend. Under the 75%-85% scenario, capacity will stabilize in 2050 and 2055. Under the 90% scenario, capacity growth will be relatively slow before 2045, and then rapid after 2050.

[0185] like Figure 10 and Figure 11 As shown, from the perspective of installed capacity, the installed capacity under scenarios with low carbon budgets, moderate carbon budgets, and moderate carbon budgets is higher than that under other scenarios. By 2030, coal-fired power, nuclear power, hydropower, and wind power will become the main power sources among various power generation capacity, such as... Figure 10 As shown, in scenarios with low, moderate, and moderate carbon budgets, wind power capacity significantly exceeds that of other power sources, reflecting the crucial role of clean energy in these scenarios. In these scenarios, the power industry reduces its reliance on high-carbon energy sources and achieves carbon reduction targets by increasing the proportion of renewable energy. By 2040, wind power capacity will become the primary power source in most scenarios, reflecting advancements in wind power technology and cost reductions, making it more competitive. Meanwhile, due to limited hydropower resources and planning constraints on nuclear power, hydropower and nuclear power have reached saturation, while wind power is gradually emerging as a rapidly deployable form of clean energy. Figure 11As shown, by 2050, wind and solar power will become the main power sources, except for the baseline scenario. In scenarios with low, moderate, and moderate carbon budgets, centralized and distributed solar power will become the second largest power source. Simultaneously, for power systems under these low-carbon scenarios, coal-fired power will gradually fade from the main stage, while stable power sources such as gas-fired power, biomass, and hydropower will play a regulating and auxiliary role. By 2060, the installed capacity under the low, moderate, and moderate carbon budget scenarios will be significantly higher than in other scenarios. In these three scenarios, energy storage capacity will range from 1.07 billion kilowatts to 1.64 billion kilowatts, reflecting the rapid development of new energy sources accompanied by a huge demand for energy storage. Its role in the power system is becoming increasingly prominent, becoming a key component in balancing power supply and demand and ensuring system operation. As a carbon negative element, biomass + CCS will play a significant role in the later stages, serving as one of the important means to achieve carbon neutrality. Compared to the baseline scenario, biomass power generation accounts for a larger proportion in other scenarios.

[0186] like Figure 12 and Figure 13 As shown, clean electricity is gradually taking a dominant position in the future development of power systems. Figure 12 As shown, in 2030, under scenarios with low, moderate, and moderate carbon budgets, the main components of electricity will include coal-fired power, wind power, nuclear power, and hydropower. In contrast, in other scenarios, coal-fired power will remain the primary source of electricity, indicating that under relaxed regulations, the development of clean energy will be slower, and traditional thermal power will still dominate. By 2040, wind power will gradually replace coal-fired power as the main power source, and photovoltaic power generation will also gradually increase. This is a significant shift, demonstrating the increasing importance of renewable energy in the electricity structure. Especially in scenarios with a high proportion of clean electricity, wind and photovoltaic power generation will become the main sources of electricity and make positive contributions to the decarbonization and cleanliness of the power system. Figure 13As shown, by 2050, wind power will gradually replace coal power, and photovoltaic power generation will gradually increase, with clean energy becoming 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 scenarios, wind power and coal power will become the main sources of electricity, while clean power such as hydropower and nuclear power will reach saturation ahead of schedule due to restrictions. This embodiment is still relatively optimistic about coal power, believing that a certain amount of coal power 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 some regions have increased the installed capacity of coal power to improve the stability of the power system in order to avoid insufficient power supply. In terms of energy storage, the charging and discharging of energy storage is more obvious in the scenarios of low carbon budget, low carbon budget, and moderate carbon budget. 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 stored is not particularly large, which is similar to the study of Chen et al.

[37] . This also illustrates that during the construction of new power systems, energy storage will be used more extensively to smooth out fluctuations in renewable energy and ensure grid stability. Overall, in all scenarios, although coal-fired power is gradually being replaced by wind and solar power, it still plays a role in the power system to ensure its stability.

[0187] This embodiment also presents an operational optimization analysis of large power grids and microgrids under command-and-control environmental regulations: During the low-carbon transformation of the power system, clean energy is gradually replacing traditional energy sources and becoming the dominant force in the future. Among these, microgrids, as a flexible and sustainable power system configuration scheme, are of great significance for achieving efficient utilization of clean energy and stable operation of the power system. For example... Figure 14 As shown, this embodiment will explore the load interaction of the power system under different scenarios. In the baseline scenario, the power system still mainly relies on coal-fired power, nuclear power, and hydropower. The interaction between the large power grid and microgrids is infrequent; microgrids mainly rely on the input power from the large power grid, while the amount of power input from microgrids to the large power grid is relatively small.

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

[0189] like Figure 16As shown, under a 90% clean energy constraint scenario, the volatility of wind and solar power necessitates frequent adjustments to the output of traditional coal-fired power plants. In this situation, the interaction between distributed solar power and energy storage in microgrids becomes more frequent, achieving more efficient power supply. Simultaneously, the charging and discharging of energy storage in microgrids is more frequent. The interaction between the main grid and microgrids is also relatively frequent, with the main grid still contributing a large proportion of the electricity. Under this scenario, microgrids cannot independently achieve power supply and demand balance.

[0190] like Figure 17 As shown, in a moderate carbon budget scenario, the power system supply structure changes significantly. Clean energy sources such as wind power and centralized photovoltaic (PV) become the main sources of electricity supply, followed by hydropower and nuclear power. However, gas-fired and coal-fired power still play a crucial role in peak shaving. During load interaction, the intermittent nature of PV power generation has a significant impact on the grid, easily exhibiting a typical "duck curve" characteristic, requiring frequent and rapid adjustments from other power sources. Therefore, addressing this challenge requires substantial investment in energy storage and more flexible thermal power generating units. In this scenario, the interaction between the main grid and microgrids becomes more frequent and important. The amount of electricity input from microgrids to the main grid increases and becomes more frequent, while the proportion of electricity input from the main grid to microgrids decreases significantly, with distributed PV supporting the majority of the electricity generated by microgrids.

[0191] like Figure 18 As shown, in the scenario with a low carbon budget, the power system supply structure is similar to that in the scenario with a moderate carbon budget. Clean energy sources such as wind power and centralized photovoltaic power become the main sources of electricity supply. However, during peak shaving, gas-fired power and coal-fired power shift from a dominant to a secondary role. The interaction between microgrids and the main grid becomes closer. Microgrids can regulate the load balance of the main grid, while the main grid can also provide power to microgrids. In this scenario, the charging and discharging of energy storage becomes more frequent.

[0192] like Figure 19 As shown, in the low carbon budget scenario, the supply structure of the large power grid is similar to that in the moderate and low carbon budget scenarios. However, gas-fired and coal-fired power still play a crucial role in peak shaving. In this scenario, coal-fired power plants perform peak shaving and frequency regulation more frequently, resulting in greater power system volatility. The interaction between microgrids and the large power grid enables microgrids to better meet electricity demand under different scenarios, achieving a more flexible and sustainable power supply.

[0193] In summary, microgrids play a crucial role in the sustainable evolution of regulated power systems. First, by supporting the overall development of the power system with distributed energy output, microgrids improve the utilization rate of clean energy. Second, the flexibility of microgrids allows them to better adapt to the needs of the main grid under different scenarios, especially in the face of the volatility and intermittency of clean energy, where microgrids can achieve a more stable power supply through energy storage and smart dispatch. In the future, with continuous innovation in clean energy technologies and supportive decision-making, the power system will undergo further transformation. 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, this application also provides an electronic device, the electronic device including: 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, which, when executed by the electronic device, cause the electronic device to perform the method provided in the above embodiments.

[0195] Based on the above embodiments, this application also provides a computer storage medium storing a computer program, which, when executed by a computer, causes the computer to perform the method provided in the above embodiments.

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

[0197] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0198] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0201] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A multi-grid collaborative operation optimization system for a high-resolution power system, characterized in that, The model for optimizing the system includes Technical modules, including large power grid and microgrid interaction modules, and cost parameters; The energy module includes energy consumption parameters for different energy types, energy storage parameters for different energy storage capacity types, pollutant emission parameters, carbon emission parameters, energy demand parameters, and energy supply parameters. The decision-making module includes a decision-making objective module, which includes the total carbon emission budget and carbon reduction cost of the power system to achieve the temperature control target. The decision-making objective module quantifies the differences in the low-carbon transformation of the power system under different scenarios under the regulations and determines the transformation strategy. The objective function is a total cost function, and the objective function includes constraints. These constraints are obtained by simulating capacity planning and transition strategies to find a power system technology path that minimizes the cost of coordinated interaction between large power grids and microgrids. The cost parameters include investment cost parameters, operation and maintenance cost parameters, fuel cost parameters, emission cost parameters, start-up and shutdown 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-fired power and biomass power; new energy includes wind power, photovoltaic power, hydropower, gas-fired power and nuclear power. The energy storage capacity types include physical energy storage and chemical energy storage; The objective function of the optimization system model is shown in Equation (1): （1） Where t represents year t, and T represents the planning period. Indicates the discount rate. This represents the investment cost in year t. This represents the maintenance cost in year t. This represents the fuel cost in year t. This represents the start-up and shutdown cost in year t. This represents the carbon emission cost in year t. This represents the CCS cost in year t. This represents the residual value of the equipment in year t. The constraints of the objective function include power balance constraints, energy balance constraints, power system expansion constraints, upper and lower limits of power output constraints, unit ramping constraints, maximum limit constraints of power output capacity, wind power generation constraints, photovoltaic power generation constraints, system adequacy constraints, operating reserve constraints, chemical energy storage constraints, physical energy storage constraints, installed capacity constraints, power balance expansion constraints, energy balance expansion constraints, decision constraints, power balance constraints of large power grids, power balance constraints of microgrids under different energy substitution rates, power balance constraints of microgrids, power balance constraints of large power grids under different energy substitution rates, energy balance constraints of large power grids, energy balance constraints of large power grids under different energy substitution rates, energy balance constraints of microgrids, and energy balance constraints of microgrids under different energy substitution rates. The decision constraints are shown in formulas (50)-(53): （50） （51） （52） （53） in, This represents the power generation of the i-th type of generator set in year t. Indicates total carbon emissions. Indicates the proportion of electricity generated from non-fossil fuels. Indicates different power output states. This represents the existing generator capacity of type i in year t. M represents the energy type, m represents non-new energy or new energy, and i represents the i-th type of generator set; This represents the carbon absorption rate of the i-th type of generator set in year t. This represents the carbon emission coefficient of the i-th type of generator set in year t; This represents the amount of wind and solar power curtailment for the i-th type of generator unit in the h-th hour of the t-th year; The carbon emission reduction cost is shown in formula (54): （54） in, Indicates the cost of carbon emission reduction. Indicates different scenario types, This indicates the change in transformation costs compared to the baseline scenario. This indicates the change in carbon emissions compared to the baseline scenario.

2. The multi-grid collaborative operation optimization system for a high-resolution power system according to claim 1, characterized in that, The investment cost in year t As shown in formula (2): （2） Where M represents the energy type, m represents non-new energy or new energy, and i represents the i-th type of generator set. Generator set types include coal-fired power units, photovoltaic power units, wind power units, hydropower units, biomass power units, gas power units, and nuclear power units. This represents the newly added power supply of the i-th type of generator set in year t. Let K represent the newly installed power generation capacity of the i-th type of generator set in year t, K represent the energy storage capacity type, k represent physical energy storage or chemical energy storage, and j represent the j-th type of energy storage. This represents the unit capacity cost of newly added energy storage of type j in year t. This represents the newly installed energy storage capacity of type j in year t; The maintenance cost in year t As shown in formula (3): （3） in, This represents the unit cost of fixed capacity for the i-th type of generator set in year t. This represents the existing power supply capacity of the i-th type of generator set in year t. This represents the power generation of the i-th type of generator set in year t. This represents the current energy storage capacity of the j-th type of energy storage in year t. This represents the energy storage capacity of the j-th type of energy storage in year t. This represents the variable capacity unit cost of the i-th type of generator set in year t. This represents the unit cost of fixed energy storage for the j-th type in year t. This represents the variable unit cost of energy storage of type j in year t; The fuel cost in year t As shown in formula (4): （4） Where h represents the h-th hour, This represents the fuel cost per unit of electricity for the i-th type of generator set in year t. This represents the power generation of the i-th type of generator unit in the h-th hour of year t; The start-up and shutdown costs in year t As shown in formula (5): （5） in, This represents the cost of shutting down the power capacity of the i-th type of generator set in the h-th hour of year t. This represents the cost of activating the power capacity of the i-th type of generator set in the h-th hour of year t. This indicates the on / off status of the i-th type of generator set in the (h-1)th hour of the t-th year. This indicates the on / off status of the i-th type of generator set in the h-th hour of year t. , All are variables of 0 and 1, 0 is for closed and 1 is for open; The carbon emission cost in year t As shown in formula (6): （6） in, This represents the carbon price of the i-th type of generator set in year t. This represents the carbon absorption rate of the i-th type of generator set in year t. This represents the carbon emission coefficient of the i-th type of generator set in year t. This represents the power generation of the i-th type of generator unit in the h-th hour of year t. This indicates different types of pollutants, including sulfur dioxide, nitrogen oxides, and particulate matter. This represents the unit pollutant tax revenue of the i-th type of generator set in year t. This represents the pollutant emission coefficient of the i-th type of generator set in year t; The CCS cost in year t As shown in formula (7): （7） in, This represents the percentage of CCS installations for the i-th type of generator set in year t. This represents the unit capacity CCS investment cost of the i-th type of generator set in year t. This represents the fixed operation and maintenance cost of the i-th type of generator set in year t. This represents the variable operation and maintenance cost of the i-th type of generator set in year t. This represents the power generation of the i-th type of generator unit with CCS installed capacity in the h-th hour of year t. Let represent the transportation cost per unit carbon emission of the i-th type of generator set in year t; The equipment residual value in year t As shown in formula (8): （8） in, This represents the residual value factor of the unit power supply capacity of the i-th type of generator set in year t. This represents the power capacity of the i-th type of generator set to be decommissioned in year t. This represents the residual value coefficient per unit energy storage capacity for the j-th type of energy storage in year t. This represents the energy storage capacity of type j being decommissioned in year t.

3. The multi-grid collaborative operation optimization system for a high-resolution power system according to claim 2, characterized in that, The power balance constraints are shown in formulas (9)-(12): （9） （10） （11） （12） in, Indicates energy storage capacity. Indicates the capacity of new energy generating units. This represents the non-new energy power generation of the i-th type of generator unit in the h-th hour of year t. This represents the energy storage discharge of the i-th type of generator set in the h-th hour of year t. This represents the energy storage charging of the i-th type of generator set in the h-th hour of year t. This represents the renewable energy generation of the i-th type of generator unit in the h-th hour of year t. This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in the large power grid during the h-th hour of the t-th year. This represents the output load of the microgrid in the h-th hour of year t. This represents the output load of the large power grid in the h-th hour of year t. This represents the load of the large power grid in the h-th hour of year t. This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in a microgrid during the h-th hour of the t-th year. This represents the microgrid load in the h-th hour of year t. This represents the output coefficient of the i-th type of generator unit in the h-th hour of year t, which is a non-new energy unit. This represents the renewable energy output coefficient of the i-th type of generator unit in the h-th hour of year t. This represents the non-new energy power capacity of the i-th type of generator set in year t. This represents the renewable energy power capacity of the i-th type of generator set in year t. The power balance constraints are shown in formulas (13) and (14): （13） （14） in, This represents the power generation of the i-th type of generator set in year t. This represents the energy storage discharge of the i-th type of generator set in year t. This represents the energy storage charging amount of the i-th type of generator set in year t, where ESG represents energy storage discharging and ESS represents energy storage charging. This indicates the amount of electricity abandoned by the large power grid. Indicates the output of the large power grid. Indicates the input of the microgrid. This indicates the electricity demand of the large power grid. This indicates the amount of electricity wasted by the microgrid. This indicates the electricity demand of the microgrid; The power system expansion constraints are shown in equations (15)-(20): （15） （16） （17） （18） （19） （20） in, This represents the existing generator capacity of type i, which is of capacity m, in year t. This represents the existing generator capacity of type i, which is of capacity m, in year t-1. This represents the newly added generator capacity of type m and type i in year t. This represents the capacity of the generator set of type m and type i that is decommissioned in year t. This represents the generator set of the m-th capacity type and the i-th type. Annual increase in generator unit capacity, This represents the operating life of the generator set of type i with capacity m. This represents the current energy storage capacity of the j-th type of energy storage in year t. This represents the current energy storage capacity of the j-th type of energy storage in year t-1. This represents the newly added energy storage capacity of type j in year t. This represents the energy storage capacity of type j to be decommissioned in year t. Represents the j-th type of energy storage Annual increase in energy storage capacity This represents the operational lifespan of the j-th type of energy storage. This represents the energy storage capacity of type j to be decommissioned in year t. This represents the maximum increase in capacity for the m-th type and the i-th type of generator set in year t. This represents the upper limit of the number of new energy storage units of type j in year t; The upper and lower limits of the power output are constrained as shown in formula (21): （21） in, This represents the minimum power output coefficient of the i-th type of generator set in the h-th hour of year t. This represents the maximum power output coefficient of the i-th type of generator set in the h-th hour of year t. This represents the actual output capacity of the i-th type of generator set in the h-th hour of year t. This represents the actual online capacity of the i-th type of generator set in the h-th hour of year t; The unit ramping constraints are shown in formulas (22) and (23): （22） （23） in, This represents the actual output capacity of the i-th type of generator set in the h-1 hour of year t. This represents the maximum uphill gradient rate of the i-th type of generator set in year t. This represents the capacity of the i-th type of generator set that is turned on in the h-th hour of the t-th year. This represents the capacity of the i-th type of generator unit that is shut down in the (h+1)-th hour of year t. This represents the capacity of the i-th type of generator set shut down in the h-th hour of the t-th year. This represents the maximum downhill gradient of the i-th type of generator set in year t. This represents the capacity of the i-th type of generator set that is turned on in the h-1 hour of year t; The maximum limit constraint of the power supply output capacity is shown in formula (24): （24） in, This represents the capacity of the i-th type of generator set that is turned on in the (h+1)-th hour of year t; The constraints on wind power generation are shown in formula (25): （25） in, This represents the wind power generation of the i-th type of generator unit in the h-th hour of year t. This represents the wind power capacity factor for the i-th type of generator unit in the h-th hour of year t. This represents the installed wind power capacity of the i-th type of generator set in year t. This represents the newly added wind power installed capacity of the i-th type of generator set in year t. This represents the existing wind power installed capacity of the i-th type of generator set in year t. This represents the potential wind power installed capacity of the i-th type of generator set in year t; The photovoltaic power generation constraints are shown in formula (26): （26） in, This represents the photovoltaic power generation of the i-th type of generator set in the h-th hour of the t-th year. This represents the photovoltaic capacity factor of the i-th type of generator set in the h-th hour of year t. This represents the photovoltaic installed capacity of the i-th type of generator set in year t. This represents the newly added photovoltaic installed capacity of the i-th type of generator set in year t. This represents the existing photovoltaic installed capacity of the i-th type of generator set in year t. This represents the potential photovoltaic installed capacity of the i-th type of generator set in year t; The system adequacy constraint is shown in formula (27): （27） in: This represents the confidence coefficient for the capacity of coal-fired power generation in year t. This represents the confidence coefficient for the capacity of wind power generation in year t. This represents the confidence coefficient for the capacity of the photovoltaic power generation in year t. This represents the confidence coefficient for the capacity of nuclear power sources in year t. This represents the confidence coefficient for the capacity of hydropower in year t. This represents the confidence coefficient for the capacity of the gas-fired power source in year t. This represents the confidence coefficient for the capacity of biomass power generation in year t. This represents the total installed capacity of coal-fired power plants of the i-th type of generator unit in year t. This represents the total installed nuclear power capacity of the i-th type of generator unit in year t. This represents the total installed hydropower capacity of the i-th type of generator unit in year t. This represents the total installed capacity of gas-fired power generation for the i-th type of generator set in year t. This represents the total installed capacity of biomass generator sets of type i in year t. The operational standby constraints are shown in formula (28): （28） in, This represents the upper limit coefficient of the energy storage output of the i-th type of generator unit in the h-th hour of year t. This represents the capacity of the i-th type of generator set in year t. This represents the energy storage capacity occupied by the i-th type of generator unit in the h-th hour of year t. This represents the prediction error coefficient for the base load of the i-th type of generator set at hour h in year t. This represents the base load for the h-th hour of year t. This represents the prediction error coefficient for wind power generation of the i-th type of generator unit in the h-th hour of year t. This represents the prediction error coefficient for photovoltaic power generation of the i-th type of generator set in the h-th hour of year t; The chemical energy storage constraints are shown in equations (29)-(34): （29） （30） （31） （32） （33） （34） in: This represents the energy storage status of the j-th type of energy storage device in the h-th hour of year t. This represents the energy storage status of the energy storage device of type j in year t at hour h=0. Indicates the initial energy storage status. This represents the energy storage status of the energy storage device of type j in the (h+1)th hour of year t. This represents the charging efficiency of the j-th type of energy storage. This represents the discharge efficiency of the j-th type of energy storage. This represents the energy loss rate of the j-th type of energy storage. This represents the energy storage charging of the j-th type of energy storage device in the h-th hour of year t. This represents the energy storage discharge of the j-th type of energy storage device in the h-th hour of year t. This represents the lower limit coefficient for the j-th type of energy storage. This represents the upper limit coefficient for the j-th type of energy storage. This represents the energy storage capacity of the j-th type of energy storage in year t. This represents the number of charge / discharge limits for the j-th type of energy storage in year t; 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 equations (35) and (36): （35） （36） in, This represents the lower limit of the discharge power of a hydropower station for the j-th type of energy storage in the h-th hour of year t. This represents the upper limit of the discharge power of a hydropower station of type j energy storage in the h-th hour of year t. This represents the water storage capacity of the physical energy storage power station reservoir in the h-th hour of year t. This represents the minimum energy storage value of the physical energy storage power station in year t. This represents the power generation efficiency of the physical energy storage turbine in year t. The physical energy storage charging constraints are shown in equations (37) and (38): （37） （38） in, This represents the lower limit of the charging power of the physical energy storage power station in the h-th hour of year t. This represents the upper limit of the charging power of the physical energy storage power station in the h-th hour of year t. This represents the maximum energy storage value of the physical energy storage power station in year t. This 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 hour h in year t. As shown in formula (39): （39） in, This represents the water storage capacity of the physical energy storage power station reservoir at time h in year t+1. The 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): （40） The installed capacity constraints are shown in formulas (41)-(45): （41） （42） （43） （44） （45） in, Indicates the capacity of the wind turbine unit. Indicates the capacity of the photovoltaic unit. Indicates the capacity of the nuclear power unit. Indicates the capacity of the hydropower unit. Indicates the capacity of the gas turbine generator set. This represents the upper limit of wind power installed capacity in year t. This represents the upper limit of photovoltaic installed capacity in year t. This represents the upper limit of nuclear power installed capacity in year t. This represents the upper limit of hydropower installed capacity in year t. This represents the upper limit of gas-fired power installed capacity in year t.

4. The multi-grid collaborative operation optimization system for a high-resolution power system according to claim 3, characterized in that, The power balance extended constraints are shown in equations (46)-(48): （46） （47） （48） in, This represents the original load in the h-th hour of year t. This represents the amount of wind and solar power curtailment for the i-th type of generator unit in the h-th hour of the t-th year. Indicates different degrees of electrical energy substitution; The extended constraint for power balance is shown in formula (49): （49） in, Indicates the amount of electricity wasted. This represents the original electricity consumption in year t.

5. The multi-grid collaborative operation optimization system for a high-resolution power system according to claim 4, characterized in that, In the aforementioned 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.

6. The multi-grid collaborative operation optimization system for a high-resolution power system according to claim 5, characterized in that, The power balance constraints of a large power grid are shown in equations (55)-(55): （55） （56） （57） in, This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in the large power grid during the h-th hour of the t-th year. This represents the output load of the microgrid in the h-th hour of year t. This represents the output load of the large power grid in the h-th hour of year t. This represents the load of the large power grid in the h-th hour of year t. The power balance constraints of a large power grid under different energy substitution rates are shown in formula (58): （58） The power balance constraints of a microgrid are shown in equations (59)-(61): （59） （60） （61） in, This represents the amount of wind and solar power curtailment generated by the i-th type of generator unit in a microgrid during the h-th hour of the t-th year. This represents the microgrid load at hour h in year t; The power balance constraints of microgrids under different energy substitution rates are shown in Equation (62): （62） The power balance constraint of a large power grid is shown in formula (63): （63） in, This represents the power generation of the i-th type of generator set in year t. This represents the energy storage discharge of the i-th type of generator set in year t. This represents the energy storage charging amount of the i-th type of generator set in year t, where ESG represents energy storage discharging and ESS represents energy storage charging. This indicates the amount of electricity abandoned by the large power grid. Indicates the output of the large power grid. Indicates the input of the microgrid. This indicates the electricity demand of the large power grid. This indicates the amount of electricity wasted by the microgrid. This indicates the electricity demand of the microgrid; The power balance constraints of a large power grid under different power substitution rates are shown in formula (64): （64） The power balance constraint of the microgrid is shown in formula (65): （65） in, This indicates the amount of electricity wasted by the microgrid. This indicates the electricity demand of the microgrid; The energy balance constraints of microgrids under different energy substitution rates are shown in formula (66): （66）。

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