Power production simulation and optimization configuration method, device and product based on numerical simulation

Abstract

The present application discloses a method, device and product for power production simulation and optimal configuration based on numerical simulation, relating to the field of new energy technologies. The method includes: constructing an investment cost evolution model according to relevant historical data and future planning data; constructing a capacity evolution sequence generation model according to the unit capacity investment cost evolution prediction data output by the investment cost evolution model; generating multiple operation scenarios of the power system according to relevant historical data; constructing a time-series production simulation model of the power system with hydrogen long-duration energy storage according to the given capacity evolution sequence output by the capacity evolution sequence generation model and the multiple operation scenarios; constructing a data-model hybrid drive model according to the capacity evolution sequence generation model and the time-series production simulation model of the power system; constructing a simulation model for the low-carbon evolution path of the power system; and obtaining the low-carbon evolution path of the power system considering hydrogen long-duration energy storage according to the simulation model for the low-carbon evolution path of the power system. The present application improves the utilization rate of new energy.

Description

Technical Field

[0001] The present application relates to the field of new energy technologies, and particularly to a simulation method, device, medium and product for the low-carbon evolution path of a power system. Background Art

[0002] Under the guidance of the dual-carbon goal, the current power system is developing towards a clean and low-carbon direction. The power system should minimize the construction of coal-fired power units and replace the output of traditional power units with clean energy units, which will exacerbate the dilemma of unstable power supply. At the same time, the large-scale grid connection of a high proportion of new energy will bring more severe new energy consumption problems. To improve the utilization rate of new energy, long-term energy storage represented by hydrogen energy systems has excellent prospects and plays an important role in breaking the new energy consumption dilemma and accelerating the low-carbon transformation of the power system. Therefore, in the process of the power system developing towards a system integrating electricity, hydrogen, and carbon, how to balance the system economy, low-carbon nature, and cleanliness and realize the simulation of the evolution path driven by multi-dimensional factors of the power system is the key issue concerned by the present application. Summary of the Invention

[0003] The purpose of the present application is to provide a simulation method, device, medium and product for the low-carbon evolution path of a power system, which can improve the utilization rate of new energy while balancing the system economy, low-carbon nature, and cleanliness in the process of the power system developing towards a system integrating electricity, hydrogen, and carbon.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a simulation method for the low-carbon evolution path of a power system, including:

[0006] Obtaining relevant historical data, future planning data, the upper limit of the constructible scale of equipment corresponding to each type of energy, the currently constructed equipment scale, and the planned retirement time of the target area; the future planning data includes the future planned capacity of the installed capacity of each type of energy and the future planned R & D investment; the relevant historical data includes the installed capacity, R & D investment, and unit capacity investment cost of each type of energy over the years;

[0007] Constructing an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate based on the relevant historical data and the future planning data;

[0008] Constructing a capacity evolution sequence generation model with the sum of the investment cost and the maintenance cost minimized as the objective function and the equipment capacity connection constraint and the capacity limit constraint as the constraint conditions based on the unit capacity investment cost evolution prediction data output by the investment cost evolution model, the upper limit of the constructible scale of equipment corresponding to each type of energy, the currently constructed equipment scale, and the planned retirement time;

[0009] Based on the relevant historical data, use the k-means clustering algorithm to generate multiple operation scenarios of the power system;

[0010] Based on the given capacity evolution sequence output by the capacity evolution sequence generation model and the multiple operation scenarios of the power system, construct a time-series production simulation model of a power system with hydrogen long-duration energy storage, with carbon emission limit constraints and clean energy electricity proportion constraints as the core constraints and minimizing the sum of operation cost and carbon trading cost as the objective function;

[0011] Based on the capacity evolution sequence generation model and the time-series production simulation model of the power system with hydrogen long-duration energy storage, use a convolutional neural network - bidirectional gated recurrent unit - attention mechanism to construct a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system; the data-model hybrid-driven model takes the capacity evolution sequence output by the capacity evolution sequence generation model as the input and the corresponding marginal cost of unit electricity proportion and marginal cost of unit carbon quantity limit as the output;

[0012] Based on the marginal cost of unit electricity proportion, the marginal cost of unit carbon quantity limit, the minimization of the sum of investment cost and maintenance cost, and the minimization of the sum of operation cost and carbon trading cost, construct a simulation model of the low-carbon evolution path of a power system considering hydrogen long-duration energy storage, which involves economic cost driving factors, clean energy electricity proportion driving factors, and carbon emission limit driving factors;

[0013] Based on the simulation model of the low-carbon evolution path of the power system, with the goal of minimizing the comprehensive benefit cost, propose a two-layer solution framework including a capacity layer and an operation layer. With the goal of minimizing the comprehensive benefit cost, the capacity layer uses the PID search algorithm, and the operation layer uses an improved differential evolution algorithm to obtain the low-carbon evolution path of the power system considering hydrogen long-duration energy storage.

[0014] Optionally, based on the relevant historical data and the future planning data, and based on a two-factor learning curve model, considering the uncertainty of the technological innovation breakthrough rate at the same time, construct an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate.

[0015] Optionally, the investment cost evolution model is:

[0016] α y =α0A y B y ;

[0017]

[0018] where α yis the investment cost per unit capacity in the y-th year; α0 is the initial investment cost per unit capacity; A y is the degree of decrease in the investment cost per unit capacity caused by the increase in the installed capacity scale in the y-th year; B y is the degree of decrease in the investment cost per unit capacity caused by the increase in R & D investment in the y-th year; N y is the cumulative installed capacity scale in the y-th year; N0 is the initial installed capacity scale; a is the learning rate index of the cumulative installed capacity scale; u y is a binary variable, taking 1 when a technological innovation breakthrough occurs, and conversely, taking 0 when no technological innovation breakthrough occurs; C y is the cumulative R & D investment in the y-th year; C0 is the initial R & D investment; b is the learning rate index of the cumulative R & D investment; B y-1 is the degree of decrease in the investment cost per unit capacity caused by the increase in R & D investment in the (y - 1)-th year.

[0019] Optionally, the objective function of the capacity evolution sequence generation model is:

[0020] minF1 = min(F inv + F main ) ;

[0021]

[0022] where F1 is the cost of the capacity evolution sequence generation model; F inv is the investment cost; F main is the maintenance cost; Ω kn is the set of newly added devices; Ω y is the set of planning years; is the investment cost per unit capacity of each type of energy device; P kn,y is the installed capacity of the newly added device in the y-th year; is the investment cost per unit energy of each type of energy device; E kn,y is the energy of the newly added device in the y-th year; F sa is the residual value of the non-retired devices within the planning period; Ω k is the set of online energy devices; ρ k is the salvage recovery coefficient; is the investment cost per unit capacity at the end of the planning period; P k,Y is the installed capacity at the end of the planning period; is the investment cost per unit energy at the end of the planning period; E k,Y is the energy at the end of the planning period; H k,Y is the remaining life of each type of energy device at the end of the planning period; T k is the service life of each type of energy device; Y0 is the time when the energy device starts to be put into use; is the unit capacity maintenance cost of each type of energy device; is the unit energy maintenance cost of each type of energy device; Y is the final year of the plan.

[0023] Optionally, the objective function of the power system time-series production simulation model with hydrogen-based long-duration energy storage is:

[0024] minF2 = min(F om +F co );

[0025]

[0026] where F2 is the cost of the time-series production simulation model; F om is the operating cost; F co is the carbon trading cost; Ω k is the set of online energy devices; Ω y is the set of planning years; Ω s is the set of operating scenarios; Ω t is the set of daily operating times; is the unit coal consumption cost; is the coal consumption; is the unit natural gas consumption cost; is the natural gas consumption; unit hydrogen consumption cost; is the hydrogen consumption; Δt is the time interval; is the start-up cost of thermal power units (including coal-fired and gas-fired); is the cost associated with outage; is the carbon trading price in the yth year; CO y is the total carbon trading volume in the yth year.

[0027] Optionally, according to the capacity evolution sequence generation model and the power system time-series production simulation model with hydrogen-based long-duration energy storage, a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system is constructed by using a convolutional neural network - bidirectional gated recurrent unit - attention mechanism, specifically including:

[0028] Generate a capacity evolution random sequence based on the capacity evolution sequence generation model by applying a random function;

[0029] Input the capacity evolution random sequence into the power system time-series production simulation model with hydrogen-based long-duration energy storage to obtain the minimized operating cost and carbon trading cost;

[0030] According to the minimized operating cost and carbon trading cost, calculate the corresponding marginal cost of unit electricity proportion and marginal cost of unit carbon limit by adjusting the proportion of clean energy electricity or carbon emission limit;

[0031] Taking the capacity evolution random sequence as the input and the corresponding marginal cost of unit electricity quantity ratio and the marginal cost of unit carbon quantity limit as the output, the convolutional neural network-bidirectional gated recurrent unit-attention mechanism is trained to obtain a data-model hybrid driving model with the ability to measure the cleanliness and low-carbon nature of the system.

[0032] Optionally, the simulation model of the low-carbon evolution path of the power system considering long-term hydrogen energy storage is:

[0033] V = ω F V F + ω new V new + ω co V co ;

[0034] where V is the comprehensive benefit cost value; ω F is the weight coefficient driven by the economic cost; V F is the benefit cost value driven by the economic cost; ω new is the weight coefficient driven by the proportion of clean energy electricity; V new is the benefit cost value driven by the proportion of clean energy electricity; ω co is the weight coefficient driven by the carbon emission limit; V co is the benefit cost value driven by the carbon emission limit.

[0035] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the low-carbon evolution path simulation method of the power system described in any one of the above.

[0036] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the low-carbon evolution path simulation method of the power system described in any one of the above are implemented.

[0037] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the low-carbon evolution path simulation method of the power system described in any one of the above are implemented.

[0038] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0039] The present application provides a simulation method, device, medium and product for the low-carbon evolution path of a power system. First, historical data related to energy costs and future development levels in a target region are obtained; an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate is constructed; based on the historical data related to energy costs, a differential evolution algorithm is used to solve the model parameters, and then the investment cost per unit capacity of energy is predicted, and the upper limit of the constructible scale of various types of equipment in the target region and the currently constructed equipment scale and planned retirement time are obtained. A capacity evolution sequence generation model is constructed with the sum of the investment cost and the maintenance cost minimized as the objective function, and the constraint conditions are the equipment capacity connection constraint and the capacity limit constraint. Then, historical data of the actual operation of the electrical load and the output of wind power and photovoltaic power in the target region are obtained, and the k-means clustering algorithm is used to generate basic operation scenarios; with the minimization of the sum of the operation cost and the carbon trading cost as the objective, a time-series production simulation model of a power system with hydrogen-based long-duration energy storage is constructed with the carbon emission limit constraint and the clean energy electricity proportion constraint as the core constraints. The objective function of this model is to minimize the sum of the operation cost and the carbon trading cost, and the constraint conditions are mainly composed of the electrical energy module constraint, the hydrogen energy module constraint, the carbon quantity module constraint and the driving factor constraint. Finally, based on the capacity evolution sequence generation model and the time-series production simulation model of the power system with hydrogen-based long-duration energy storage, a data-model hybrid driving model with the ability to measure the cleanliness and low-carbon nature of the system is constructed; further integrating the economic cost driving factor, the clean energy electricity proportion driving factor and the carbon emission limit driving factor, a simulation model and a solution framework for the low-carbon evolution path of a power system considering hydrogen-based long-duration energy storage are constructed. For the established model, a hierarchical optimization idea is adopted, and a two-layer solution framework is proposed. With the minimization of the comprehensive benefit cost as the objective, the PID search algorithm is used in the capacity layer, and the improved differential evolution algorithm is used in the operation layer to output the evolution result, forming a low-carbon evolution path of a power system considering hydrogen-based long-duration energy storage. Through the simulation results of the low-carbon evolution path of a power system considering hydrogen-based long-duration energy storage, the utilization rate of new energy can be improved when weighing the economy, low-carbon nature and cleanliness of the system during the development process of the power system towards a system integrating multiple factors of electricity-hydrogen-carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic framework diagram of a simulation method for the low-carbon evolution path of a power system considering hydrogen-based long-duration energy storage provided by an embodiment of the present application;

[0042] Figure 2 Schematic diagram of the specific implementation process of a simulation method for the low-carbon evolution path of a power system provided in an embodiment of the present application;

[0043] Figure 3 Schematic diagram of the process of a simulation method for the low-carbon evolution path of a power system provided in an embodiment of the present application;

[0044] Figure 4 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0045] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0046] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0047] The simulation method for the low-carbon evolution path of the power system provided in the embodiment of the present application, as Figures 1 to 3 shown, includes:

[0048] Step S1: Obtain the relevant historical data, future planning data, upper limit of the constructible scale of equipment corresponding to each type of energy, currently constructed equipment scale, and planned retirement time of the target area; the future planning data includes the future planned capacity of the installed capacity of each type of energy and the future planned R & D investment; the relevant historical data includes the installed capacity, R & D investment, and unit capacity investment cost of each type of energy over the years.

[0049] Specifically, based on the relevant historical data and the future planning data, a learning curve model with two factors is used, and considering the uncertainty of the technological innovation breakthrough rate, an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate is constructed.

[0050] Step S2: Construct an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate according to the relevant historical data and the future planning data.

[0051] In practical applications, first, obtain the relevant historical data of the energy cost in a certain area, including the installed capacity scale, R & D investment, and unit capacity investment cost of each type of energy over the years; obtain the future development of the installed capacity scale of each type of energy in a certain area and the future R & D investment intensity.

[0052] Secondly, part of the reason for the decline in the investment cost per unit capacity of various types of energy is the result of experience accumulation, and the experience accumulation is regarded as the result of the growth of the energy installed capacity scale; another part of the reason is the result of R & D accumulation, and the R & D accumulation is regarded as the result of the growth of the R & D investment in the energy industry. Based on the above historical data, a two-factor learning curve model is used, and considering the uncertainty of the technological innovation breakthrough rate, an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate is constructed. The specific model is as follows:

[0053] α y = α0A y B y (1)

[0054]

[0055] In formula (1): A y is the degree of decline in the investment cost per unit capacity caused by the growth of the installed capacity scale in the y-th year, B y is the degree of decline in the investment cost per unit capacity caused by the growth of the R & D investment in the y-th year, α0 is the initial investment cost per unit capacity, and α y is the investment cost per unit capacity in the y-th year. In formula (2): N y is the cumulative installed capacity scale in the y-th year, N0 is the initial installed capacity scale, and a is the learning rate index of the cumulative installed capacity scale. In formula (3): C y is the cumulative R & D investment in the y-th year, C0 is the initial R & D investment, and b is the learning rate index of the cumulative R & D investment; u y is a binary variable, which takes 1 when technological innovation breaks through, and vice versa, takes 0 when technological innovation does not break through.

[0056] When the R & D investment accumulates to a certain extent and the technological breakthrough rate reaches the target, the benefit of the R & D investment B y in reducing the cost per unit capacity begins to play a role. A relational expression between the technological breakthrough rate and the equivalent R & D cost is established, which can generally be characterized by a piecewise linear function; at the same time, an envelope uncertainty model is used to describe the uncertain relationship between the technological breakthrough rate and the R & D cost; further considering the time benefit of the R & D cost, the specific model is as follows:

[0057]

[0058]

[0059] In formula (4): is the equivalent cumulative R & D investment, is the technological innovation breakthrough rate under equivalent cumulative R & D investment. In Equation (5): σ is the boundary index characterizing the uncertainty of the technological innovation breakthrough rate, and π y is the actual technological innovation breakthrough rate considering uncertainty. In Equation (6): θ is the time benefit parameter of the R & D cost, and c y is the R & D investment in the newly added part in the y-th year; is the equivalent cumulative R & D investment in the (y + 1)-th year.

[0060] Finally, based on historical data, with the goal of minimizing variance, the differential evolution algorithm is used to solve the model parameters a, b, θ, and σ. According to the future development of the installed capacity scale of various types of energy and the data of R & D investment intensity, the investment cost per unit capacity of energy is predicted and used as the equipment cost data of the capacity evolution sequence generation model.

[0061] Step S3: Based on the predicted data of the investment cost per unit capacity evolution output by the investment cost evolution model, the upper limit of the constructible scale of the equipment corresponding to each type of energy, the currently installed equipment scale, and the planned retirement time, a capacity evolution sequence generation model is constructed with the sum of the investment cost and the maintenance cost minimized as the objective function and the equipment capacity connection constraint and the capacity limit constraint as the constraint conditions.

[0062] In practical applications, first, obtain the upper limit of the constructible scale of various types of equipment in a certain region, as well as the currently installed equipment scale and the planned retirement time.

[0063] Secondly, construct a capacity evolution sequence generation model with the sum of the investment cost and the maintenance cost minimized as the objective function and the equipment capacity connection constraint and the capacity limit constraint as the constraint conditions. In addition to the historical data in Step S1, the model input data also includes the equipment cost data in Step S2.

[0064] Objective function:

[0065] min F1 = min(F inv + F main )(9)

[0066]

[0067] In Equation (9): F1 is the cost of the capacity evolution sequence generation model, mainly including the investment cost F inv and the maintenance cost F main . In Equation (10): is the investment cost per unit capacity of various types of energy devices, is the investment cost per unit energy of various types of energy devices, P kn,y and E kn,y are the installed capacity and energy of the newly added devices in the y-th year, and Fsa is the residual value of the devices not retired within the planning period. In formula (11): ρ k is the salvage recovery coefficient, and are the unit capacity investment cost and unit energy investment cost at the end of the planning period, P k,Y and E k,Y are the installed capacity and energy at the end of the planning period, H k,Y is the remaining life of each type of energy device at the end of the planning period, T k is the service life of each type of energy device. In formula (12): Y0 is the time when the energy device starts to be put into use. In formula (13): and are the unit capacity maintenance cost and unit energy maintenance cost of each type of energy device.

[0068] The constraint conditions include the equipment capacity connection constraint and the equipment capacity limit constraint. Among them, the equipment capacity connection constraint is:

[0069] The initial equipment will be taken out of service after reaching its operation period. At the same time, the energy devices installed in a certain stage will continue to operate in the subsequent stages and will be taken out of service after reaching their service life.

[0070] P k,y = P k,y-1 + P kn,y - P kr,y (14)

[0071]

[0072] In formulas (14) to (15): P k,y is the installed capacity online in the yth year, P k,y-1 is the installed capacity online in the (y - 1)th year; P kn,y is the newly added installed capacity at the beginning of the yth year, P kr,y is the installed capacity retired at the beginning of the yth year; is the installed capacity online in the (y - T) k th year.

[0073] The equipment capacity limit constraint is:

[0074] B k,y ≤ P k,y ≤ A k,y (16)

[0075] In formula (16): B k,y and A k,y are respectively the lower limit and upper limit of the installed capacity of the energy device in the yth year.

[0076] Step S4: According to the relevant historical data, use the k-means clustering algorithm to generate multiple operation scenarios of the power system.

[0077] In practical applications, obtain the historical data of the actual operation of the electrical load and the output of wind power and photovoltaic power in a certain area. For the historical data of the output of wind power and photovoltaic power and the electrical load data, divide the historical data into four parts: spring, summer, autumn, and winter according to the weather characteristics of the region. Taking one day as a cluster, use the k-means clustering algorithm to cluster the multi-day data within each quarter into one cluster, and obtain four basic scenarios of the power system operation. Based on this, the predicted data of the electrical load for future years can also be obtained according to the electricity quantity growth ratio.

[0078] Step S5: According to the given capacity evolution sequence generated by the capacity evolution sequence generation model and the multiple operation scenarios of the power system, construct a time-series production simulation model of a power system with hydrogen-based long-duration energy storage, with carbon emission limit constraints and clean energy electricity proportion constraints as the core constraints, and minimizing the sum of operation costs and carbon trading costs as the objective function.

[0079] In practical applications, under the given capacity evolution sequence, construct a time-series production simulation model of a power system with hydrogen-based long-duration energy storage with carbon emission limit constraints and clean energy electricity proportion constraints as the core constraints. The objective function of this model is to minimize the sum of operation costs and carbon trading costs, and the constraint conditions mainly consist of power module constraints, hydrogen energy module constraints, carbon quantity module constraints, and driving factor constraints.

[0080] The objective function of the model is:

[0081] min F2 = min(F om + F co ) (17)

[0082]

[0083] In Equation (17): F2 is the cost of the time-series production simulation model, mainly including the operation cost F om and the carbon trading cost F co . In Equation (18): and are the coal consumption, natural gas consumption, and hydrogen consumption respectively; and are the unit coal consumption cost, unit natural gas consumption cost, and unit hydrogen consumption cost respectively; and are the start-up cost and shut-down cost of thermal power units (including coal-fired and gas-fired). In Equation (19): is the carbon trading price in the y-th year, CO yis the total carbon trading volume in the y-th year. In addition, Ω k is the set of online energy devices, Ω kn is the set of newly added devices, Ω kr is the set of retired devices, Ω y is the set of planned years, Ω s is the set of operation scenarios, Ω t is the set of daily operation times. Δt is the time interval. Nine types of energy devices are considered in this model, Ω k-g is the set of online energy devices of thermal power units, consisting of coal-fired thermal power units Ω k-coal and gas-fired thermal power units Ω k-gas constitute, Ω k-pv is the set of online energy devices of photovoltaic units, Ω k-wind is the set of online energy devices of wind turbine units, Ω k-ess is the set of online energy devices of electrochemical energy storage devices, Ω k-el is the set of online energy devices of electrolyzer devices, Ω k-fc is the set of online energy devices of fuel cells, Ω k-ht is the set of online energy devices of hydrogen storage tanks and Ω k-ccs is the set of online energy devices of carbon capture and storage devices.

[0084] The constraints of the electric energy module in the model include the operation constraints of electrochemical energy storage, the operation constraints of coal-fired units and gas-fired units, and the operation constraints of wind turbine units and photovoltaic units.

[0085] Operation constraints of electrochemical energy storage:

[0086]

[0087]

[0088] In Equation (20): is the external output power of the electrochemical energy storage, which is composed of the charging power of the electrochemical energy storage and the discharging power of the electrochemical energy storage constitutes. Equation (21) is the power upper and lower limit constraint of the electrochemical energy storage, and are the lower and upper limits of the charging and discharging power of the electrochemical energy storage. In Equation (22): is the state of charge of the electrochemical energy storage at the t-th moment, is the rated energy of the electrochemical energy storage, η ess,in and η ess,out are the charging efficiency and discharging efficiency of the electrochemical energy storage; is the state of charge of the electrochemical energy storage at the (t - 1)-th moment; k, y, s, t - 1 respectively represent the k-th device, the y-th year, the s-th operation scenario and the (t - 1)-th moment; tN is the end moment; t0 is the initial moment. In formula (23): and are the minimum state of charge and the maximum state of charge of the electrochemical energy storage. In formula (24): and are the state of charge at the initial moment and the state of charge at the end moment of the electrochemical energy storage device within the daily scheduling cycle.

[0089] Operating constraints of coal-fired / gas-fired thermal power units:

[0090]

[0091]

[0092]

[0093]

[0094] In formulas (25) to (26): and are the output electric powers of the coal-fired thermal power unit and the gas-fired thermal power unit, and are the unit consumption output power conversion coefficients of the coal-fired thermal power unit and the gas-fired thermal power unit, is the input coal quantity of the coal-fired thermal power unit; is the input natural gas quantity of the gas-fired thermal power unit. In formulas (27) to (30): u k,y,s,t-1 is the on / off state of the thermal power unit at the (t - 1)th moment; is the start-up cost of the thermal power unit at the tth moment; is the shutdown cost of the thermal power unit at the tth moment; u k,y,s,t is the on / off state of the thermal power unit at the tth moment, TS and TO are the minimum shutdown time and the minimum operation time of the thermal power unit; U k and D k are the single start-up cost and the single shutdown cost of the thermal power unit. In formulas (31) to (33): is the output power of the thermal power unit at the tth moment, is the installed capacity of the thermal power unit, is the output power of the thermal power unit at the (t - 1)th moment; and are the minimum technical output and the maximum technical output of the thermal power unit, r k,y and d k,y are the upward ramp rate and the downward ramp rate of the thermal power unit.

[0095] Operating constraints of wind power / solar photovoltaic units:

[0096]

[0097]

[0098] In formulas (34) to (35): and are the actual output electric powers of the wind turbine and the photovoltaic unit, and are the predicted electric powers of the wind turbine and the photovoltaic unit, and are the installed capacities of the wind turbine and the photovoltaic unit.

[0099] The hydrogen energy storage system has the same energy storage function as other energy storage devices such as batteries. When the output power of wind and light is greater than the load demand, the electrolyzer consumes the surplus electric energy, generates hydrogen by electrolyzing water, and stores the hydrogen in the hydrogen storage tank, which is equivalent to increasing the electric load. When the output power of wind and light is less than the load demand, the fuel cell generates electric energy to meet the load demand, improving the reliability of the system. The hydrogen energy module constraints include the electrolyzer operation model, the fuel cell operation model, and the hydrogen storage tank operation model.

[0100] Electrolyzer operation model:

[0101]

[0102] In formula (36): is the hydrogen output of the electrolyzer device, is the input electric power of the electrolyzer device, is the electro-hydrogen conversion coefficient of the electrolyzer device. In formula (37): is the maximum input electric power of the electrolyzer device, which depends on the installed capacity of the electrolyzer device and the situation of the hydrogen storage tank, is the maximum hydrogen storage capacity of the hydrogen storage tank, is the actual hydrogen storage situation of the hydrogen storage tank. In formula (39): is the maximum state of charge of the hydrogen storage tank, is the rated energy of the hydrogen storage tank.

[0103] Fuel cell operation model:

[0104]

[0105] In formula (40): is the output electric power of the fuel cell, is the input hydrogen consumption of the fuel cell, is the hydrogen-electric conversion coefficient of the fuel cell. In formula (41): is the maximum output electric power of the fuel cell, which depends on the fuel cell installed capacity and the case of the hydrogen storage tank is the minimum hydrogen storage capacity of the hydrogen storage tank. In Equation (43): is the minimum state of charge of the hydrogen storage tank.

[0106] Hydrogen storage tank operation model:

[0107]

[0108]

[0109] Equation (44) is the relational expression when the hydrogen storage tank stores hydrogen. is the hydrogen storage capacity of the hydrogen storage tank at the (t - 1)th moment; is the output hydrogen volume of the electrolyzer device at the (t - 1)th moment; Δt is the time interval; is the hydrogen consumption at the (t - 1)th moment; Equation (45) is the relational expression when the hydrogen storage tank releases hydrogen. is the charge-discharge efficiency of the hydrogen storage tank. is the maximum hydrogen storage capacity of the hydrogen storage tank. In Equation (47): and are the initial state of charge and the final state of charge of the hydrogen storage tank during the annual scheduling period. s0 is the initial scenario of the hydrogen storage tank operation; s N is the final scenario of the hydrogen storage tank operation; In order to ensure that the hydrogen energy storage system can work continuously and effectively, it should be ensured that the state of charge of the hydrogen storage tank at the beginning and end of the scheduling period is equal.

[0110] The carbon module consists of the carbon trading cost and the carbon capture and storage (CCS) device.

[0111] The carbon trading cost has been described by Equation (19). Carbon trading is essentially a trading mechanism to achieve carbon emission reduction by buying and selling carbon emission allowances.

[0112] The process of the CCS device capturing carbon dioxide emitted by thermal power units is as follows, where the set g includes coal-fired and gas-fired thermal power units.

[0113]

[0114]

[0115] In Equation (48): is the energy consumption of the thermal power unit, is the carbon emission per unit energy consumption, is the carbon emission generated by the thermal power unit. In Equation (49): is the capture efficiency of the CCS device, is the actual capture volume of the CCS device, is the dissipation during the capture process of the CCS device. In Equations (50) to (51): is the actual power consumption of the CCS device, is the installed capacity of the CCS device, is the power consumption coefficient of the CCS device.

[0116] The driving factor constraints consist of the clean energy electricity proportion constraint, the carbon emission limit constraint, and the power balance constraint.

[0117] Clean energy electricity proportion constraint:

[0118]

[0119] In Equation (52), is the electrical load in the region, is the minimum clean energy electricity proportion parameter.

[0120] Carbon emission limit constraint: For the carbon emission limit constraint, there are mainly two paths. The first is to steadily limit it year by year, and the other is the total amount limit.

[0121]

[0122] X y ≤X base σ y (54)

[0123]

[0124] In Equations (53) to (55): X y is the equivalent carbon emission of the region in the yth year, X base is the basic carbon emission per year, σ y is the carbon emission limit parameter in the yth year, X base,a is the total basic carbon emission during the planning period, σ a is the total carbon emission limit parameter during the planning period.

[0125] Power balance constraint: At any time in any scenario, the sum of the power outputs of the power sources should be equal to the total electrical load.

[0126]

[0127] Finally, redefine the objective function according to the weights of the basic scenarios, and the objective function is re-expressed as:

[0128]

[0129] Equation (57): τ s is the weight coefficient of each operation scenario, and are the operating cost and carbon trading cost in the sth scenario, respectively, and F s is the sum of the operating cost and carbon trading cost in the sth scenario.

[0130] Step S6: Generate a capacity evolution sequence generation model and a time-series production simulation model of the hydrogen-based long-duration energy storage power system, and use a convolutional neural network-bidirectional gated recurrent unit-attention mechanism to construct a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system; the data-model hybrid-driven model takes the capacity evolution sequence output by the capacity evolution sequence generation model as input and the marginal cost of unit electricity proportion and the marginal cost of unit carbon quantity limit corresponding thereto as output.

[0131] S6 specifically includes:

[0132] Step S61: Generate a capacity evolution random sequence based on the capacity evolution sequence generation model by applying a random function.

[0133] Step S62: Input the capacity evolution random sequence into the time-series production simulation model of the hydrogen-based long-duration energy storage power system to obtain the minimized operating cost and carbon trading cost.

[0134] Step S63: According to the minimized operating cost and carbon trading cost, calculate the corresponding marginal cost of unit electricity proportion and the marginal cost of unit carbon quantity limit by adjusting the proportion of clean energy electricity or the carbon emission limit.

[0135] Step S64: Use the capacity evolution random sequence as input and the corresponding marginal cost of unit electricity proportion and the marginal cost of unit carbon quantity limit as output to train the convolutional neural network-bidirectional gated recurrent unit-attention mechanism to obtain a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system.

[0136] In practical applications, first, the proportion of clean energy electricity and the carbon emission limit are used as specific indicators to measure the cleanliness and low-carbon nature of the hydrogen-based long-duration energy storage power system. Based on the expected path of the proportion of clean energy electricity and the expected annual carbon emission limit path, by appropriately increasing the proportion of clean energy electricity and reducing the carbon emission limit, the relationship between the economic cost and the path is studied.

[0137]

[0138] In Equations (58) to (59): ΔF2 is the change in the operating cost and carbon trading cost, is the change in the proportion of clean energy electricity, Δσ y is the change in the carbon emission limit, is the marginal cost of unit electricity proportion, is the marginal cost of carbon quantity limit per unit.

[0139] Secondly, based on the capacity evolution sequence generation model and the time-series production simulation model of the power system with hydrogen-based long-duration energy storage, a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system is constructed using a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention Mechanism (CNN-BiGRU-AM). The CNN-BiGRU-AM model helps to extract the hidden spatio-temporal features in the capacity evolution sequence data. The attention mechanism guides the model to prioritize the relevant input data features, enhancing the robustness of the model.

[0140] The specific process is as follows: First step, continuously generate random capacity evolution sequences based on the capacity evolution sequence generation model using a random function; Second step, input the random capacity evolution sequences into the time-series production simulation model of the power system with hydrogen-based long-duration energy storage to obtain the minimized operating cost and carbon trading cost, and further appropriately increase the proportion of clean energy electricity or reduce the carbon emission limit to calculate the marginal cost of electricity proportion per unit and the marginal cost of carbon quantity limit per unit; Third step, construct a large dataset, input the random capacity evolution sequences, the marginal cost of electricity proportion per unit, and the marginal cost of carbon quantity limit per unit into the CNN-BiGRU-AM neural network model, and continuously train; Fourth step, after training, input the capacity evolution sequence into the CNN-BiGRU-AM model, and the corresponding marginal cost of electricity proportion per unit and the marginal cost of carbon quantity limit per unit can be quickly obtained.

[0141] Step S7: According to the marginal cost of electricity proportion per unit and the marginal cost of carbon quantity limit per unit, the sum of the minimized investment cost and maintenance cost, and the sum of the minimized operating cost and carbon trading cost, construct a simulation model for the low-carbon evolution path of the power system considering hydrogen-based long-duration energy storage, which involves economic cost driving factors, clean energy electricity proportion driving factors, and carbon emission limit driving factors.

[0142] In practical applications, considering economic cost driving factors, clean energy electricity proportion driving factors, and carbon emission limit driving factors, construct corresponding driving models.

[0143] Construct an economic cost driving model:

[0144]

[0145] v + (F s )=-(F0 - F s - F1), F0 > F s + F1 (61)

[0146] v - (F s )=ζ(F s+F1 - F0), F s +F1 ≥ F0 (62)

[0147] In equations (60) to (62), is the economic cost probability coefficient; F s is the sum of the operating cost and the carbon trading cost in the s-th scenario; in step S3, the minimized investment cost and maintenance cost are output, and in step S6, the minimized operating cost and carbon trading cost are output based on the expected clean energy electricity proportion path and the expected annual carbon emission limit path. F0 is the sum of the above minimized investment cost and maintenance cost, and the minimized operating cost and carbon trading cost. ζ is the driving coefficient.

[0148] Build a clean energy electricity proportion driving model and a carbon emission limit driving model.

[0149]

[0150] v + (γ y ) = -(γ y - γ y,0 ), γ y >γ y,0 (64)

[0151] v - (γ y ) = ζ(γ y,0 - γ y ), γ y,0 ≥γ y (65)

[0152] In equations (64) to (65): is the clean energy electricity proportion probability coefficient; γ y is the clean energy electricity proportion in the y-th year; γ y,0 is the clean energy electricity proportion reference parameter in the y-th year.

[0153]

[0154] v + (σ y ) = -(σ y,0 - σ y ), σ y,0 >σ y (67)

[0155] v - (σ y ) = ζ(σ y - σ y,0 ), σ y ≥σ y,0 (68)

[0156] In formulas (67) to (68): is the carbon emission limit probability coefficient; σ y is the carbon emission limit in the y-th year; σ y,0 is the reference parameter for the carbon emission limit in the y-th year.

[0157] Secondly, the objective function of the low-carbon evolution path simulation model of the power system considering long-duration hydrogen energy storage is expressed as:

[0158] V = ω F V F + ω new V new + ω co V co (69)

[0159] In formula (69): V is the comprehensive benefit-cost value, V F , V new and V co are the benefit-cost values under the drive of economic cost, the proportion of clean energy electricity, and carbon emission limit respectively, ω F , ω new and ω co are the weight coefficients under the drive of economic cost, the proportion of clean energy electricity, and carbon emission limit respectively.

[0160] Step S8: According to the low-carbon evolution path simulation model of the power system, with the goal of minimizing the comprehensive benefit-cost, a two-layer solution framework including a capacity layer and an operation layer is proposed. With the goal of minimizing the comprehensive benefit-cost, the capacity layer adopts a PID search algorithm, and the operation layer adopts an improved differential evolution algorithm to obtain the low-carbon evolution path of the power system considering long-duration hydrogen energy storage.

[0161] In practical applications, the low-carbon evolution path simulation model of the power system considering long-duration hydrogen energy storage performs relaxation transformation on the constraints of the proportion of clean energy electricity and carbon emission limit, that is, through the marginal cost per unit of electricity proportion and the marginal cost per unit of carbon limit, the constraints of the proportion of clean energy electricity and carbon emission limit are converted into a clean energy electricity proportion-driven model and a carbon emission limit-driven model, and the constraints are transferred to the objective function part to achieve the relaxation of the model. Therefore, the constraints of the model do not include these two, and other constraints are the same as those of the capacity evolution sequence generation model and the time-series production simulation model of the power system with long-duration hydrogen energy storage.

[0162] For the established simulation model of the low-carbon evolution path of the power system considering long-term hydrogen energy storage, a hierarchical optimization idea is adopted, and a two-layer solution framework is proposed. The hierarchical solution is carried out with the goal of minimizing the comprehensive benefit cost. The model can be divided into two parts: the capacity layer and the operation layer. The capacity layer uses the PID search algorithm, and the operation layer uses the improved differential evolution algorithm to solve.

[0163] In the capacity layer, a PID search algorithm is used for solving. The PID search algorithm is based on the incremental PID algorithm and converges the entire population to the optimal state by continuously adjusting the system deviation.

[0164] e k (t) = x * (t - 1) - x(t - 1) (70)

[0165] Δu(t) = K p r2[e k (t) - e k-1 (t)] + K i r3e k (t) + K d r4[e k (t) - 2e k-1 (t) + e k-2 (t)] (71)

[0166] x(t + 1) = x(t) + ξΔu(t) + (1 - ξ)o(t) (72)

[0167] In equations (70) to (72): e k (t) is the overall deviation at the t-th iteration, x(t) is the population state at the t-th iteration, x * (t) is the best state at the iteration number t, which is the state of the overall historical minimum; x * (t - 1) is the best state at the iteration number t - 1; x(t - 1) is the population state at the (t - 1)-th iteration, x(t + 1) is the population state at the (t + 1)-th iteration, e k-1 (t) is the overall deviation of the previous iteration when the iteration number is t, e k-2 (t) is the overall deviation of the two previous iterations when the iteration number is t, r2, r3, and r4 are random numbers, K p 、K i and K d are the proportional, integral, and differential coefficients respectively; Δu(t) is the output value of the PID adjustment, o(t) is the zero-output condition factor, and ξ is a random number.

[0168] At the operation layer, an improved differential evolution algorithm is used for solving. The entire population is randomly divided into two sub-populations, and different mutation strategies are adopted respectively to accelerate the optimization speed of the algorithm and improve the optimization performance of the algorithm.

[0169]

[0170]

[0171] In equations (73) to (76): x i,G is the i-th individual of the population, G is the generation number of the population, v i,G is the individual of the population after mutation, and are three different random individuals in the population, x pbest,G is randomly selected from the top p individuals with excellent performance in the current population, F is the scaling factor; CR is the crossover probability, rand(j) is a random number, v i,j,G is the j-th element of the individual of the population after mutation, x i,j,G is the j-th element of the i-th population individual; u i,G is the individual of the population obtained after crossover, f(u i,G ) and f(x i,G ) are the fitness values of u i,G and x i,G respectively; u i,j,G is the j-th element of the individual of the population obtained after crossover; v i,j,G is the j-th element of the individual of the population after mutation; x i,G+1 is the i-th individual of the (G + 1)-th generation.

[0172] When performing the operation simulation part, first, the initial parameters are set according to the capacity configuration results transmitted from the capacity layer, the population is initialized, the individual fitness values are calculated, and the optimal value is recorded. Then, the entire population is randomly divided into two sub-populations, and different mutation strategies, namely equations (73) to (74), are adopted to perform corresponding mutations on each sub-population; crossover operations are performed according to equation (75); the trial individuals are compared with the target individuals according to equation (76), the optimal value is recorded, and it is judged whether the convergence accuracy is satisfied or the iteration number is reached. If the termination condition is not satisfied, the population is randomly divided again, mutation operations and crossover operations are performed again; when it stops, x i,G+1 is output, that is, the optimal result of the operation simulation part under the current capacity configuration of the energy device can be obtained.

[0173] Based on the above two algorithms, first, on the basis of the initial installed capacity of each type of energy device, the PID search algorithm is used to initialize the population of the installed capacity of the online energy device; next, the system error is calculated. At this time, when calculating the system error, the goal is to minimize the comprehensive benefit cost. According to the installed capacity of the online device, the investment cost and maintenance cost can be calculated. It is necessary to call the operation layer for time-series production simulation and return the optimal operation cost, carbon trading cost, proportion of clean energy electricity, and carbon emission limit of the operation layer; finally, the PID search algorithm makes the entire population converge to the optimal state by continuously adjusting the system capacity evolution sequence, and outputs the evolution scheme of the proportion of clean energy electricity path and carbon emission limit path.

[0174] The present application has the following innovative points:

[0175] (1) The investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate disclosed in the present application can predict the unit capacity investment cost of various types of typical equipment.

[0176] (2) The present application applies a data-model hybrid driving method based on a convolutional neural network - bidirectional gated recurrent unit - attention mechanism (CNN - BiGRU - AM) to achieve a fast measurement of the system's cleanliness and low carbon.

[0177] (3) The present application determines a low-carbon evolution path simulation model of a power system considering long-duration hydrogen energy storage under a capacity evolution sequence generation model and a time-series production simulation model of a power system with long-duration hydrogen energy storage, and relaxes the core constraints.

[0178] (4) The present application applies a PID search algorithm and an improved differential evolution algorithm to solve the low-carbon evolution path simulation model of the power system.

[0179] The present application has the following advantages:

[0180] (1) The present application constructs an energy device investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate. This model has an excellent fitting effect on the historical data of the investment cost and can improve the prediction accuracy of the unit capacity investment cost of the energy device.

[0181] (2) The present application constructs a data-model hybrid driving model with the ability to measure the cleanliness and low carbon of the system based on the CNN - BiGRU - AM neural network model, and can quickly obtain the equivalent benefits of cleanliness and low carbon under different capacity evolution sequences.

[0182] (3) This application constructs a capacity evolution sequence generation model and a time-series production simulation model of a power system with long-duration hydrogen energy storage, precisely models the parts of electric energy, hydrogen energy, and carbon content, and effectively reduces the operating scenarios, providing an effective means for the capacity evolution of energy devices in the power system, enhancing the model's solvability, and effectively reducing the economic cost of energy device configuration considering the role of long-duration hydrogen energy storage; further constructs a low-carbon evolution path simulation model of a power system considering long-duration hydrogen energy storage, providing a model basis for the optimization simulation of the evolution path.

[0183] (4) This application uses a two-layer solution framework of the PID search algorithm and the improved differential evolution algorithm to solve the low-carbon evolution path simulation model of a power system considering long-duration hydrogen energy storage, effectively improving the global search ability and convergence speed of the algorithm, and featuring high search efficiency and fast solution speed.

[0184] (5) This application provides an effective means for the evolution of the power system's power source structure and the arrangement of power source capacity construction, taking into account economy, low carbon, and cleanliness, and effectively improving the comprehensive benefits of the power system. The formed evolution path takes into account economy, low carbon, and cleanliness, provides a direction for the power system's power source construction, effectively reduces the capacity configuration cost, and improves the utilization rate of new energy and the degree of low-carbon and clean power system.

[0185] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the simulation data of the low-carbon evolution path of the power system. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for simulating the low-carbon evolution path of a power system.

[0186] Those skilled in the art can understand that Figure 4The structure shown is only a block diagram of some of the structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0187] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0188] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0190] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0191] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0192] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0193] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A simulation method for the low-carbon evolution path of a power system, characterized in that, The low-carbon evolution path simulation method of the power system includes: Obtaining the relevant historical data of the energy cost, future planning data, the upper limit of the constructible scale of the equipment corresponding to each type of energy, the currently constructed equipment scale and the planned retirement time in the target area; the future planning data includes the future planned capacity of the installed capacity of each type of energy and the future planned R & D investment; the relevant historical data includes the installed capacity, R & D investment and unit capacity investment cost of each type of energy over the years; Constructing an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate based on the relevant historical data and the future planning data; Constructing a capacity evolution sequence generation model with the sum of minimizing the investment cost and the maintenance cost as the objective function and the equipment capacity connection constraint and the capacity limit constraint as the constraint conditions according to the unit capacity investment cost evolution prediction data output by the investment cost evolution model, the upper limit of the constructible scale of the equipment corresponding to each type of energy, the currently constructed equipment scale and the planned retirement time; Generating multiple operation scenarios of the power system by using the k-means clustering algorithm according to the relevant historical data; Constructing a time-series production simulation model of the power system with hydrogen long-duration energy storage with the carbon emission limit constraint and the clean energy electricity proportion constraint as the core constraints and the sum of minimizing the operation cost and the carbon trading cost as the objective function according to the given capacity evolution sequence output by the capacity evolution sequence generation model and the multiple operation scenarios of the power system; Constructing a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system by using a convolutional neural network - bidirectional gated recurrent unit - attention mechanism according to the capacity evolution sequence generation model and the time-series production simulation model of the power system with hydrogen long-duration energy storage; the data-model hybrid-driven model takes the capacity evolution sequence output by the capacity evolution sequence generation model as the input and the marginal cost of the unit electricity proportion and the marginal cost of the unit carbon quantity limit as the output; Constructing a low-carbon evolution path simulation model of the power system considering hydrogen long-duration energy storage involving economic cost driving factors, clean energy electricity proportion driving factors and carbon emission limit driving factors according to the marginal cost of the unit electricity proportion, the marginal cost of the unit carbon quantity limit, the sum of minimizing the investment cost and the maintenance cost, and the sum of minimizing the operation cost and the carbon trading cost; According to the low-carbon evolution path simulation model of the power system, a two-layer solution framework including a capacity layer and an operation layer is proposed with the goal of minimizing the comprehensive benefit cost. With the goal of minimizing the comprehensive benefit cost, the capacity layer uses the PID search algorithm and the operation layer uses an improved differential evolution algorithm to obtain the low-carbon evolution path of the power system considering hydrogen long-duration energy storage.

2. The simulation method for the low-carbon evolution path of the power system according to claim 1, wherein Based on the two-factor learning curve model and considering the uncertainty of the technological innovation breakthrough rate at the same time, construct an investment cost evolution model considering the uncertainty of the technological innovation breakthrough rate according to the relevant historical data and the future planning data.

3. The simulation method for the low-carbon evolution path of the power system according to claim 1, wherein The investment cost evolution model is: α y = α0A y B y ; Among them, α y is the investment cost per unit capacity in the y-th year; α0 is the initial investment cost per unit capacity; A y is the degree of decrease in the investment cost per unit capacity caused by the increase in the installed capacity scale in the y-th year; B y is the degree of decrease in the investment cost per unit capacity caused by the increase in R & D investment in the y-th year; N y is the cumulative installed capacity scale in the y-th year; N0 is the initial installed capacity scale; a is the learning rate index of the cumulative installed capacity scale; u y is a binary variable, taking 1 when a technological innovation breakthrough occurs, and vice versa, taking 0 when there is no technological innovation breakthrough; C y is the cumulative R & D investment in the y-th year; C0 is the initial R & D investment; b is the learning rate index of the cumulative R & D investment; B y-1 is the degree of decrease in the investment cost per unit capacity caused by the increase in R & D investment in the (y - 1)-th year.

4. The simulation method for the low-carbon evolution path of the power system according to claim 1, wherein The objective function of the capacity evolution sequence generation model is: minF1 = min(F inv + F main ); Among them, F1 is the cost of the capacity evolution sequence generation model; F inv is the investment cost; F main is the maintenance cost; Ω kn is the set of newly added devices; Ω y is the set of planning years; is the unit capacity investment cost of each type of energy device; P kn,y is the installed capacity of the newly added device in the y-th year; is the unit energy investment cost of each type of energy device; E kn,y is the energy of the newly added device in the y-th year; F sa is the residual value of the non-retired devices within the planning period; Ω k is the set of on-line energy devices; ρ k is the salvage value recovery coefficient; is the unit capacity investment cost at the end of the planning period; P k,Y is the installed capacity at the end of the planning period; is the unit energy investment cost at the end of the planning period; E k,Y is the energy at the end of the planning period; H k,Y is the remaining life of each type of energy device at the end of the planning period; T k is the service life of each type of energy device; Y0 is the time when the energy device starts to be put into use; is the unit capacity maintenance cost of each type of energy device; is the unit energy maintenance cost of each type of energy device; Y is the end year of the planning period.

5. The simulation method for the low-carbon evolution path of the power system according to claim 1, characterized in that The objective function of the power system time-series production simulation model for hydrogen-based long-duration energy storage is as follows: minF2 = min(F om + F co ); Among them, F2 is the cost of the time-series production simulation model; F om is the operating cost; F co is the carbon trading cost; Ω k is the set of online energy devices; Ω y is the set of planning years; Ω s is the set of operating scenarios; Ω t is the set of daily operating times; is the cost per unit coal consumption; is the coal consumption; is the cost per unit natural gas consumption; is the natural gas consumption; is the cost per unit hydrogen consumption; is the hydrogen consumption; Δt is the time interval; is the start-up cost of the thermal power unit; is the cost associated with outage; is the carbon trading price in the y-th year; CO y is the total carbon trading volume in the y-th year.

6. The simulation method for the low-carbon evolution path of the power system according to claim 1, wherein, Based on the capacity evolution sequence generation model and the power system time-series production simulation model for hydrogen-based long-duration energy storage, a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system is constructed using a convolutional neural network - bidirectional gated recurrent unit - attention mechanism, specifically including: Generate a capacity evolution random sequence based on the capacity evolution sequence generation model using a random function; Input the capacity evolution random sequence into the power system time-series production simulation model for hydrogen-based long-duration energy storage to obtain the minimized operating cost and carbon trading cost; According to the minimized operating cost and carbon trading cost, calculate the corresponding marginal cost per unit electricity proportion and marginal cost per unit carbon quantity limit by adjusting the proportion of clean energy electricity or carbon emission limit; Using the capacity evolution random sequence as the input and the corresponding marginal cost per unit electricity proportion and marginal cost per unit carbon quantity limit as the output, train the convolutional neural network - bidirectional gated recurrent unit - attention mechanism to obtain a data-model hybrid-driven model with the ability to measure the cleanliness and low-carbon nature of the system.

7. The simulation method for the low-carbon evolution path of the power system according to claim 1, characterized in that The power system low-carbon evolution path simulation model considering hydrogen-based long-duration energy storage is as follows: V = ω F V F + ω new V new + ω co V co ; Among them, V is the comprehensive benefit-cost value; ω F is the weight coefficient driven by economic cost; V F is the benefit-cost value driven by economic cost; ω new is the weight coefficient driven by the proportion of clean energy electricity; V new is the benefit-cost value driven by the proportion of clean energy electricity; ω co is the weight coefficient driven by carbon emission limit; V co is the benefit-cost value driven by carbon emission limit.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the power system low-carbon evolution path simulation method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power system low-carbon evolution path simulation method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power system low-carbon evolution path simulation method according to any one of claims 1-7.

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