A synergistic optimization method for a wind-solar-hydrogen storage integrated energy system

Abstract

This invention belongs to the field of integrated energy systems, specifically relating to a collaborative optimization method for a wind-solar-hydrogen-storage integrated energy system, aiming to solve the problem of the inability to dynamically optimize the allocation of environmental rights and interests in green electricity. The method includes: acquiring operational status information; establishing a dynamic decision-making model for environmental rights and interests in green electricity, calculating the marginal revenue of surplus green electricity between the green certificate selling path and the hydrogen production, storage, and emission reduction path based on real-time carbon prices, green certificate prices, and hydrogen prices, and outputting flow direction decision instructions; constructing a multi-timescale collaborative scheduling strategy, configuring lithium batteries as resources for minute-level power fluctuation mitigation, and configuring electrolyzers and hydrogen storage tanks as hour-level long-cycle energy time-shifting resources; executing multi-timescale energy management scheduling, and outputting an operational scheduling scheme. This invention also constructs a planning-operation two-layer optimization framework and a feedback correction mechanism, and uses an improved multi-objective particle swarm optimization algorithm for solution.

Description

Technical Field

[0001] This invention relates to the field of integrated energy systems, and more particularly to a synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system. Background Technology

[0002] Integrated Energy Systems (IES), by integrating wind, solar, hydrogen, and diverse energy storage devices to achieve multi-energy complementarity and cascade utilization, have become an important technological path for improving renewable energy absorption capacity and promoting the low-carbon transformation of energy systems. Currently, research on the optimal scheduling of IES mainly focuses on two dimensions: low-carbon operation and market-based trading. Regarding low-carbon operation, technologies such as tiered carbon trading mechanisms and multi-energy demand response have been widely introduced to reduce system carbon emissions and improve wind and solar energy absorption rates. In terms of market-based trading, green certificate trading mechanisms are used to incentivize renewable energy generation. Some studies further couple carbon trading with green certificate trading to improve the overall system benefits through joint trading mechanisms. For example, Chinese invention patent CN115271171B discloses a method for optimizing the coordinated operation of cross-border integrated energy systems considering carbon-green certificate joint trading. By constructing a coordinated optimization model for carbon-green certificate joint trading, it achieves low-carbon economic scheduling of the system in cross-border scenarios. In addition, related studies have proposed a low-carbon optimization scheduling method for integrated energy systems that couples green certificates with tiered carbon trading mechanisms (such as CN120952368A), as well as a day-ahead optimization scheduling method for integrated energy systems with carbon-green certificates that takes into account electricity-to-gas conversion (such as CN118761525B).

[0003] However, the aforementioned existing technologies still have the following shortcomings: First, existing carbon-green certificate joint trading mechanisms mostly use static coupling or linear superposition to describe the value of environmental rights, failing to dynamically decide the marginal benefit trade-off between the two paths of "profiting from selling green certificates" and "hydrogen production, energy storage, and emission reduction" based on real-time fluctuations in carbon prices, green certificate prices, and hydrogen energy price signals. This makes it difficult for the system to lock in the optimal compliant profit path in complex market environments. Second, existing integrated energy system energy storage dispatch schemes mostly focus on single-time-scale adjustment of batteries or thermal storage tanks, lacking a cross-time-scale collaborative dispatch mechanism for the high-frequency response of lithium batteries and the long-term energy transfer of hydrogen energy. Especially under extreme operating conditions of "source-load misalignment" caused by the high proportion of renewable energy access, the system's flexibility in adjustment is insufficient. Third, most existing optimization dispatch models adopt a single-layer planning architecture. There is a lack of a closed-loop feedback correction mechanism between the equipment capacity configuration of the planning layer and the operation strategy of the dispatch layer, making it difficult to achieve the global optimization of the system's full life-cycle economics and operational economics. Summary of the Invention

[0004] The purpose of this invention is to provide a collaborative optimization method for a wind-solar-hydrogen-storage integrated energy system, aiming to solve the technical problems in the existing technology, such as the inability to dynamically optimize the allocation of green electricity environmental rights, the lack of cross-timescale collaborative scheduling of energy storage systems, and the disconnect between planning and operation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: This application provides a collaborative optimization method for a wind-solar-hydrogen-storage integrated energy system, comprising: S1, acquiring the operating status information of the wind-solar-hydrogen-storage integrated energy system, including wind power output, photovoltaic power output, electricity load demand, heat load demand, real-time carbon trading price, real-time green certificate trading price, and real-time hydrogen market price; S2, establishing a dynamic decision-making model for green electricity environmental rights, configured to calculate the marginal benefit comparison between the path of selling green certificates and the path of hydrogen production, energy storage, and emission reduction based on the real-time carbon trading price, real-time green certificate trading price, and real-time hydrogen market price obtained in step S1, and outputting a real-time flow decision instruction for the surplus green electricity, wherein the marginal benefit is calculated based on the real-time carbon trading price, real-time green certificate trading price, and real-time hydrogen market price obtained in step S1. The revenue is dynamically adjusted based on the energy storage status correction coefficient, the heat load demand correction coefficient, and the carbon emission reduction target gap correction coefficient; S3, construct a multi-timescale collaborative scheduling strategy, which includes configuring the lithium battery energy storage unit as a minute-level power fluctuation smoothing resource, and configuring the electrolyzer hydrogen production unit and hydrogen storage tank unit as an hour-level long-cycle energy time-shifting resource, and establishing minute-level power compensation constraints for the lithium battery energy storage unit and hour-level energy scheduling constraints for the hydrogen storage tank unit; S4, based on the real-time flow decision instructions of the surplus green electricity output in step S2 and the multi-timescale collaborative scheduling strategy established in step S3, execute multi-timescale energy management scheduling and output the system operation scheduling scheme.

[0006] In step S2, the marginal revenue comparison of the green electricity environmental rights dynamic decision-making model is performed as follows: Calculate the expected revenue of the green certificate selling path, which is determined by the remaining green electricity power, the green certificate base price, the green certificate time premium coefficient, renewable energy subsidies, green electricity premium, and the implicit carbon emission reduction corresponding to the surplus green electricity; calculate the expected revenue of the hydrogen production and energy storage emission reduction path, which is determined by the amount of hydrogen sold, the total amount of hydrogen produced, the hydrogen market price, the natural gas price, the hydrogen production subsidy, the equivalent amount of natural gas saved by the combustion or power generation of the cogeneration unit, and the carbon emission reduction of hydrogen production; compare the expected revenue of the green certificate selling path with the expected revenue of the hydrogen production and energy storage emission reduction path. If the expected revenue of the green certificate selling path is greater than or equal to the expected revenue of the hydrogen production and energy storage emission reduction path, then output the green certificate selling decision instruction; otherwise, output the hydrogen production decision instruction.

[0007] In step S3, the minute-level power compensation constraint condition of the lithium battery energy storage unit is configured as follows: when the deviation between the actual output power of the wind and solar turbine and the basic output power within a 15-minute time scale exceeds the preset fluctuation response threshold, the lithium battery energy storage unit is activated to perform power compensation, and the compensation power of the lithium battery energy storage unit does not exceed its maximum charging and discharging power.

[0008] In step S3, the hourly energy dispatch constraints of the hydrogen storage tank unit are configured as follows: the electrolyzer hydrogen production unit converts surplus green electricity into hydrogen and stores it in the hydrogen storage tank unit. During peak electricity price periods, the hydrogen storage tank unit releases hydrogen to the hydrogen-blended cogeneration unit for power generation feedback or hydrogen-blended heating. The state of charge of the hydrogen storage tank unit is maintained within the safe operating range between the upper and lower limits.

[0009] Before step S1, a two-layer optimization framework for planning and operation is constructed. This framework includes: Step S01: The upper planning layer aims to maximize the net present value over the entire life cycle. Under the constraints of investment budget and total carbon emissions, it determines the optimal configuration scheme for the installed capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units. Step S02: The lower scheduling layer uses the optimal configuration scheme output by the upper planning layer as boundary conditions and aims to minimize the daily net operating cost. It then executes the multi-timescale energy management and scheduling steps from S1 to S4. Step S03: The lower scheduling layer feeds back the actual carbon emission reduction, equipment equivalent loss rate, and actual revenue generated during its actual operation to the upper planning layer. The upper planning layer dynamically adjusts the annual carbon emission reduction target, equipment configuration capacity, and environmental rights decision threshold based on the feedback information.

[0010] The feedback correction in step S03 is performed as follows: the actual carbon emission reduction output by the lower scheduling layer is fed back to the upper planning layer to correct the annual carbon emission reduction target; based on the equipment equivalent loss rate output by the lower scheduling layer, the configuration capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units is iteratively corrected; when the actual benefit output by the lower scheduling layer deviates from the expected benefit output by the upper planning layer, the environmental rights decision threshold is adjusted to correct the benefit deviation.

[0011] The planning-running two-layer optimization framework is solved using an improved multi-objective particle swarm optimization algorithm. The improved multi-objective particle swarm optimization algorithm includes: an adaptive inertia weight adjustment strategy to balance the algorithm's global search capability and local search capability; an elite file storage and non-dominated solution crowding distance selection mechanism to store non-dominated solutions and maintain the diversity of the solution set; and a carbon emission reduction-oriented forced perturbation strategy to apply random perturbation to the positions of some particles when the carbon emission reduction completion rate is lower than a preset threshold.

[0012] The integrated energy system of wind, solar, hydrogen and energy storage also includes a tiered carbon trading mechanism model. The tiered carbon trading mechanism model is configured to gradually reduce carbon emissions and carbon trading costs by setting tiered carbon prices. The carbon trading cost is determined by the difference between the total carbon quota and the actual carbon emissions, the carbon trading benchmark price and the tiered price growth rate.

[0013] The integrated wind, solar, hydrogen, and energy storage system also includes a green certificate trading mechanism model. The green certificate trading mechanism model is configured such that when the number of green certificates obtained by the system from consuming non-hydro renewable energy exceeds the system's green certificate quota, the system sells green certificates to generate profit; otherwise, it purchases green certificates to meet the system's green certificate quota. The green certificate trading cost is determined by the green certificate market selling price or purchase price and the difference between the number of green certificates and the green certificate quota.

[0014] The system operation scheduling schemes output in step S4 include: electric power balance scheduling scheme, thermal power balance scheduling scheme, hydrogen energy balance scheduling scheme and natural gas balance scheduling scheme; among which, the electric power balance scheduling scheme is jointly determined by the power generation of wind and solar turbines, the power generation of cogeneration units, the discharge power of lithium batteries, the charging power of lithium batteries, the power purchased by the grid, the power of electrical load, the power consumption of electrolyzers and the surplus green electricity of the system.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention establishes a dynamic decision-making model for the environmental rights and interests of green electricity. Based on real-time carbon trading prices, green certificate trading prices, and hydrogen energy market prices, it dynamically calculates the marginal revenue of surplus green electricity between the "selling green certificates path" and the "hydrogen production, energy storage, and emission reduction path." It also introduces multiple correction coefficients such as energy storage status, heat load demand, and carbon emission reduction target gap, enabling the system to accurately decide the direction of green electricity flow based on real-time fluctuations in market signals. This avoids double measurement or value loss of environmental rights and maximizes the economic conversion efficiency of environmental externalities.

[0016] 2. This invention configures the lithium battery energy storage unit as a resource for mitigating minute-level power fluctuations, and the electrolyzer hydrogen production unit and hydrogen storage tank unit as long-cycle energy time-shifting resources on an hourly basis, forming a spatiotemporally complementary architecture of "minute-level lithium battery power mitigation - hourly hydrogen energy storage and generation". The lithium battery is responsible for high-frequency response to mitigate instantaneous fluctuations in wind and solar power, while the hydrogen energy storage is responsible for cross-period energy transport to resolve source-load misalignment. The two complement each other in parallel and relay on a time scale, significantly improving the system's flexible adjustment capability and renewable energy consumption level under extreme operating conditions.

[0017] 3. This invention constructs a two-layer "planning-operation" optimization framework. The upper layer determines equipment capacity allocation with the goal of maximizing the net present value over the entire life cycle, while the lower layer executes multi-timescale scheduling with the goal of minimizing daily operating net cost. Through a triple feedback mechanism of carbon emission reduction feedback, equipment capacity decay correction, and revenue deviation correction, it achieves closed-loop iterative optimization between layers. This mechanism solves the problem of the disconnect between the planning and scheduling layers in existing technologies, enabling the system to dynamically adapt to equipment aging, market fluctuations, and carbon emission reduction requirements during long-term operation, achieving global optimization of both life-cycle and operational economics.

[0018] 4. This invention employs an improved multi-objective particle swarm optimization algorithm. It balances global search and local exploitation through adaptive inertia weights and maintains solution set diversity through elite files and non-dominated solution crowding distance selection. In particular, it designs a carbon reduction-oriented forced perturbation strategy for the low-carbon scheduling problem. When the carbon reduction completion rate is lower than the threshold, random perturbation is applied to the particles, which effectively prevents the algorithm from converging to local extrema too early and improves the solution quality of multi-objective optimization problems under complex constraints. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an IES system architecture provided in an embodiment of this application; Figure 2 This is a flowchart of a collaborative optimization method for a wind-solar-hydrogen-storage integrated energy system provided in an embodiment of this application; Figure 3 This is a schematic diagram of parameter setting provided in an embodiment of this application; Figure 4 This is a schematic diagram of wind and solar resource prediction and multivariate load curve provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating the time-of-use pricing and pricing standards provided in an embodiment of this application; Figure 6 This is a schematic diagram of an algorithm convergence process and feedback correction mechanism provided in an embodiment of this application; Figure 7 This is a schematic diagram of an optimized configuration result and key performance indicators provided in an embodiment of this application; Figure 8 This is a schematic diagram of the SOC change curve of an energy storage system provided in an embodiment of this application; Figure 9 This is a schematic diagram of the power balance scheduling result of a typical day in a system provided in an embodiment of this application; Figure 10 This is a comparison chart of comprehensive benefits provided in an embodiment of this application; Figure 11 This is a schematic diagram of an electricity price sensitivity analysis provided in an embodiment of this application; Figure 12 This is a schematic diagram of an auxiliary market price sensitivity analysis provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] This application provides a synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system, applied to an integrated energy system (IES). The IES encompasses all physical components—source, grid, load, and storage—with wind turbines and photovoltaic units as the core energy supply terminals, achieving cascaded energy utilization through multi-energy flow coupling. For example,... Figure 1 As shown, at the operational level, the system introduces hydrogen co-firing technology to optimize the traditional "heat-driven power generation" operation mode of CHP units. Specifically, the lithium battery energy storage unit serves as a flexible resource, undertaking the function of smoothing out minute-level power fluctuations; surplus green electricity is prioritized to drive the electrolyzer hydrogen production unit to produce hydrogen, and the produced hydrogen is transferred over a long period of time through the hydrogen storage tank unit; during peak electricity price periods or when the system power is insufficient, the green hydrogen stored in the hydrogen storage tank unit is used to generate electricity or co-firing hydrogen for heat supply through the CHP unit, thereby reducing the system's carbon emission intensity while ensuring stable thermal and power output.

[0022] Reference Figure 2 This application provides a method for the synergistic optimization of a wind-solar-hydrogen-storage integrated energy system, the method comprising: S1. Obtain the operating status information of the integrated wind, solar, hydrogen and storage energy system. The operating status information includes wind power output, photovoltaic power output, electricity load demand, heat load demand, real-time carbon trading price, real-time green certificate trading price and real-time hydrogen market price.

[0023] The step S1 precedes the construction of a planning-running two-layer optimization framework, which includes: Step S01: The upper planning layer aims to maximize the net present value (NPV) over the entire life cycle. Under the constraints of investment budget and total carbon emissions, it decides on the optimal configuration scheme for the installed capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units.

[0024] The optimal configuration scheme output by the upper planning layer is passed to the lower scheduling layer as boundary conditions. Specifically, the upper planning layer uses the configuration parameters such as the installed capacity and energy storage capacity of each device as fixed inputs to constrain the operation and scheduling range of the lower scheduling layer.

[0025] For example, the objective function of the upper planning layer is expressed as: (1) Among them, the initial net investment of the system The initial configuration cost of each piece of equipment is determined after discounting it based on the investment subsidy rate: (2) No. Net cash flow for the year Determined by multiplying the annualized benchmark return and annual operating cost by the average annual benefit decay factor: (3) in, This represents the initial net investment of the system. The benchmark discount rate; Project operation cycle (years); For the first Total annual revenue and operating costs; The residual value of the equipment; For investment subsidy rate; These are the initial configuration costs for wind turbine generator sets, photovoltaic generator sets, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units, respectively. For the first Net cash flow for the year; The annualized benchmark return and annual operating cost; This represents the annual benefit attenuation coefficient.

[0026] The upper-level planning layer must meet the following constraints when optimizing the configuration: I. Equipment Capacity Constraints -- The configuration capacity of each piece of equipment should be between its minimum and maximum allowable values, expressed as follows (4): in and They represent the first The lower and upper limits of the configuration capacity of this type of device. .

[0027] II. Investment Constraints – The system's cumulative annual carbon emission reduction must not be lower than the annual total carbon emission reduction control limit, expressed as: (5) in, This is the maximum investment limit for the project.

[0028] III. Carbon Emission Reduction Constraints – The system's cumulative annual carbon emission reduction must not be lower than the annual total carbon emission reduction control limit, expressed as: (6) in, For the first The system's daily carbon emission reduction. This is the annual carbon emission reduction control limit.

[0029] IV. Renewable Energy Consumption Constraints – The system's renewable energy consumption ratio shall not be lower than the minimum consumption ratio stipulated by policy, expressed as: (7) in, , Time periods The wind power output and photovoltaic power output, For time period The power of wind and solar power curtailment This refers to the minimum renewable energy consumption ratio stipulated by policy.

[0030] Step S02: The lower-level scheduling layer uses the optimal configuration scheme output by the upper-level planning layer as the boundary condition and aims to minimize the daily net operating cost, and executes the multi-timescale energy management scheduling steps from S1 to S4.

[0031] For example, multi-timescale energy management and scheduling includes: minute-level scheduling that uses lithium battery energy storage units to smooth out power fluctuations of wind and solar power units within a 15-minute timescale, and hour-level scheduling that uses electrolyzer hydrogen production units and hydrogen storage tank units to achieve long-cycle energy time shifting, including surplus green electricity to produce hydrogen, hydrogen storage, and hydrogen-blended cogeneration units for power generation feedback or hydrogen-blended heating during peak electricity price periods.

[0032] For example, the lower-level scheduling layer aims to minimize the total net cost of daily operations, and its objective function is expressed as: (8) The specific expressions for each cost are as follows: Operating costs (electricity, gas, and maintenance costs): (9) The cost of curtailing wind and solar power: (10) Demand response compensation costs: (11) in, Total net operating cost for the day; Costs include electricity and gas purchases, as well as operation and maintenance. The cost of penalizing the abandonment of wind and solar power; The total transaction cost under the carbon-green certificate synergy mechanism; To compensate for costs in response to demand; For time period The power purchased by the power grid; For time period Natural gas consumption of cogeneration units; For time period Time-of-use electricity pricing; For natural gas prices; Time period Equipment maintenance costs; The unit is the penalty coefficient for wind and solar power curtailment; For time period The amount of wind and solar power curtailed; For the first Transferable and reducible compensation prices for similar loads; , For the first Load type during time period The amount of load that can be transferred and the amount of load that can be reduced.

[0033] S03. The lower-level scheduling layer feeds back the actual carbon emission reduction, equipment equivalent loss rate and actual benefits generated during its actual operation to the upper-level planning layer. The upper-level planning layer dynamically adjusts the annual carbon emission reduction target, equipment configuration capacity and environmental rights decision threshold based on the feedback information.

[0034] The feedback correction in step S03 is performed as follows: the actual carbon emission reduction output by the lower scheduling layer is fed back to the upper planning layer to correct the annual carbon emission reduction target; based on the equipment equivalent loss rate output by the lower scheduling layer, the configuration capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units is iteratively corrected; when the actual benefit output by the lower scheduling layer deviates from the expected benefit output by the upper planning layer, the environmental rights decision threshold is adjusted to correct the benefit deviation.

[0035] To achieve dynamic coordination between the upper planning layer and the lower scheduling layer, this embodiment designs a feedback correction mechanism. This mechanism dynamically corrects the decision parameters of the upper planning layer based on data generated by the actual operation of the lower scheduling layer, specifically including the following three aspects: (1) Carbon emission reduction target revision -- carbon emission reduction generated by the actual operation of the lower-level scheduling layer This feedback is sent to the higher-level planning layer to revise the annual carbon emission reduction targets, thereby strengthening emission reduction constraints. The revision formula is: (12) in, The revised annual carbon emission reduction target; The revised annual carbon emission reduction target; This represents the actual carbon emission reductions fed back from the lower-level scheduling layer. This is the carbon target feedback coefficient, used to control the correction magnitude. The formula means: when the actual carbon emission reduction... Below the target value hour, Positive, the corrected target If the actual carbon emission reduction exceeds the original target, the emission reduction requirements should be raised; conversely, if the actual carbon emission reduction exceeds the target value, the target should be appropriately lowered to avoid over-investment.

[0036] (2) Equipment Capacity Attenuation Correction – Based on the actual operating conditions fed back from the lower-level scheduling layer, and considering the equipment's cycle life loss, the configured capacity of each piece of equipment is iteratively corrected. The correction formula is: (13) in, For the revised version The capacity of this type of equipment; To correct the previous one The capacity of this type of equipment; For the first The iterative equivalent loss rate of the device reflects the capacity decay caused by each iteration. This represents the number of iterations.

[0037] (3) Environmental rights decision threshold correction -- the actual benefit output by the current scheduling layer Deviating from the expected returns of the upper-level planning layer At the same time, adjust the environmental rights decision-making threshold. To correct for the profit bias. The corrected formula is: (14) in, The revised environmental rights decision-making threshold; The environmental rights decision-making threshold before the revision; This is the economic feedback coefficient, used to control the adjustment range; The expected benefits from the upper-level planning layer; This represents the actual benefit fed back from the lower-level scheduling layer. The formula means: when the actual benefit... Lower than expected returns hour, Positive, corrected threshold If the actual return is greater than the original threshold, the dynamic decision-making model will be more inclined to choose the hydrogen production route (because the return of the hydrogen production route needs to be multiplied by a larger threshold to be compared with the green certificate sales route, which actually lowers the comparison threshold for hydrogen production); conversely, when the actual return is higher than expected, the threshold will be appropriately lowered to adjust the decision preference.

[0038] The aforementioned triple feedback correction mechanism, through multiple rounds of iterative operation, continuously brings the decision parameters of the upper planning layer closer to the optimal values ​​of actual operation, thereby achieving dynamic coordination and global optimization between planning and operation.

[0039] Through the aforementioned closed-loop feedback mechanism, the upper planning layer and the lower scheduling layer undergo multiple iterations. In each iteration, the upper planning layer dynamically adjusts the configuration scheme and decision parameters based on feedback information, while the lower scheduling layer re-executes multi-timescale energy management scheduling under the updated boundary conditions. After multiple iterations, the system gradually converges to the globally optimal solution that achieves synergistic optimization of economic efficiency and low carbon emissions.

[0040] As one possible implementation, the planning-running two-layer optimization framework employs an improved multi-objective particle swarm optimization algorithm for solving the problem. The improved multi-objective particle swarm optimization algorithm includes: an adaptive inertia weight adjustment strategy to balance the algorithm's global search capability and local search capability; an elite archive storage and non-dominated solution crowding distance selection mechanism to store non-dominated solutions and maintain the diversity of the solution set; and a carbon reduction-oriented forced perturbation strategy to apply random perturbations to the positions of some particles when the carbon reduction completion rate is lower than a preset threshold.

[0041] The standard Particle Swarm Optimization (PSO) algorithm finds the optimal solution by simulating the random foraging behavior of a flock of birds. Its basic principle is as follows: in an artificially created flock of birds, ignoring the flock's mass, volume, and individual differences, the flock is abstracted as a swarm of particles, and food is abstracted as fitness. Utilizing the information interaction between particles and between particles and the flock, the algorithm determines the next movement direction by tracking the flock's optimal fitness and the particle's own historical optimal fitness, iterating continuously to find the optimal fitness.

[0042] Let the first The particle in the first The velocity and position at the next iteration are respectively and Then the formulas for updating the particle's velocity and position are: (15) in, For the first During the nth iteration The velocity of each particle; For the first During the nth iteration The position of each particle; The inertial weight is used to control the effect of the particle's previous velocity on its current velocity; These are learning factors, used to adjust the step size of the particle learning towards its individual optimal position and the global optimal position, respectively. For distribution in Random numbers within the range are used to increase the randomness of the search; For the first The particle in the first The optimal position of the individual at the next iteration; For the first The algorithm uses the aforementioned update formula to continuously move the particle swarm in the solution space, gradually converging to the optimal solution. However, the standard PSO algorithm is prone to getting trapped in local optima and has limited convergence accuracy when dealing with complex optimization problems involving high dimensions, multiple constraints, and multiple objectives. Therefore, this invention proposes an improved multi-objective particle swarm optimization algorithm based on the standard PSO algorithm.

[0043] To address the problem that standard particle swarm optimization algorithms are prone to getting trapped in local optima, this embodiment proposes the following improvement strategy to form an improved multi-objective particle swarm optimization algorithm (MOPSO).

[0044] To balance the algorithm's global search capability with its local exploitation capability, an adaptive inertia weight strategy is adopted. Inertia weight. It decays dynamically with the number of iterations, and its update formula is: (16) in, For the first Inertia weights in the next iteration; This represents the maximum value (upper limit) of the inertia weight. Minimum (lower limit) of inertia weight; This represents the current iteration number; This represents the maximum number of iterations. This strategy allows the algorithm to maintain a large inertia weight in the early stages of iteration to enhance global search capabilities; in the later stages of iteration, the inertia weight gradually decreases to strengthen local fine-grained search, thereby improving convergence accuracy.

[0045] An Elite Archive is established to store non-dominated solutions, and a crowding distance selection mechanism is introduced to maintain the diversity of the solution set. (Crowding distance) The calculation formula is as follows (17): in, For the first The crowding distance of each solution; For the first The objective function is solved The function value at that location; The number of objective functions; For the first The objective function is found to have a maximum and a minimum value in the current elite archive. During the maintenance of the elite archive, non-dominated solutions with larger crowding distances are preferentially retained, i.e., solutions located in sparser regions of the solution space, thereby maintaining a uniform distribution of the Pareto front and avoiding excessive concentration of the solution set in a certain region.

[0046] To prevent the algorithm from prematurely converging to local optima, when the carbon reduction completion rate falls below a preset threshold, random perturbations are applied to the positions of some particles. The forced perturbation strategy is expressed as: (18) in, For the first During the nth iteration The position of each particle; For the first During the nth iteration The position of each particle; The amplitude of the disturbance controls the intensity of the random disturbance. For random noise function, generate Random numbers within the interval; This is the carbon emission reduction threshold; a disturbance is triggered when the carbon emission reduction completion rate falls below this value. This represents the current actual carbon emission reduction. The target is the amount of carbon emission reduction. When the carbon emission reduction is insufficient, this strategy uses random perturbation to make particles jump out of the current local optimum, thereby expanding the search space and enhancing the algorithm's ability to find the global optimum.

[0047] To adapt to the optimization characteristics of the improved MOPSO algorithm, the objective of minimizing system operating cost is transformed into solving the fitness function maximization problem. Taking into account economic, environmental, and technical factors, the following multi-objective fitness function is constructed: (19) The specific expressions for each sub-indicator are as follows (20): Constraint penalty function term The values ​​are determined segmented according to the carbon emission reduction completion rate as shown in formula (21): in, These are the weighting coefficients for economic, environmental, and technical indicators. The constraint penalty function term is used to impose penalties on solutions that do not meet carbon emission reduction requirements; These include economic indicators, environmental indicators, and technical indicators. As the normalized benchmark value for economic indicators, take ; The lowest internal rate of return; For electrolysis efficiency; This refers to the number of cycles of a lithium battery. This is the cyclic penalty coefficient.

[0048] This fitness function transforms a multi-objective optimization problem into a single-objective maximization problem through weighted summation, while introducing a penalty function term to ensure that the solution satisfies the carbon emission reduction constraint.

[0049] Based on carbon emission reduction completion rate Dynamically adjust the weight of economic indicators and environmental indicator weights The algorithm is guided to focus on different optimization objectives at different stages, as shown in equation (22): in, To assess the completion rate of carbon emission reduction, This represents the current actual carbon emission reduction. The target is carbon emission reduction.

[0050] This strategy assigns higher weight to environmental indicators when carbon emission reduction completion is low. When the carbon emission reduction rate is relatively high, the incentive algorithm prioritizes searching for solutions that can improve carbon emission reduction capabilities; when the carbon emission reduction completion rate is high, the weight of economic indicators is appropriately increased. (Increase), guiding the algorithm to seek a balance between economy and low carbon emissions.

[0051] The aforementioned improvement strategies work synergistically to enable the improved MOPSO algorithm to effectively avoid local optima, quickly converge to the global optimum, and ensure the multi-objective balance of the solution set when solving the bi-level optimization model of this invention.

[0052] S2. Establish a dynamic decision-making model for the environmental rights and interests of green electricity. The dynamic decision-making model for the environmental rights and interests of green electricity is configured to calculate the marginal revenue comparison results between the green certificate selling path and the hydrogen production, energy storage and emission reduction path based on the real-time carbon trading price, real-time green certificate trading price and real-time hydrogen energy market price obtained in step S1, and output the real-time flow decision instructions of surplus green electricity. The marginal revenue is dynamically adjusted according to the energy storage status correction coefficient, the heat load demand correction coefficient and the carbon emission reduction target gap correction coefficient.

[0053] In step S2, the marginal revenue comparison of the green electricity environmental rights dynamic decision-making model is performed as follows: Calculate the expected revenue of the green certificate selling path, which is determined by the remaining green electricity power, the green certificate base price, the green certificate time premium coefficient, renewable energy subsidies, green electricity premium, and the implicit carbon emission reduction corresponding to the surplus green electricity; calculate the expected revenue of the hydrogen production and energy storage emission reduction path, which is determined by the amount of hydrogen sold, the total amount of hydrogen produced, the hydrogen market price, the natural gas price, the hydrogen production subsidy, the equivalent amount of natural gas saved by the combustion or power generation of the cogeneration unit, and the carbon emission reduction of hydrogen production; compare the expected revenue of the green certificate selling path with the expected revenue of the hydrogen production and energy storage emission reduction path. If the expected revenue of the green certificate selling path is greater than or equal to the expected revenue of the hydrogen production and energy storage emission reduction path, then output the green certificate selling decision instruction; otherwise, output the hydrogen production decision instruction.

[0054] In some embodiments, the green electricity environmental rights dynamic decision-making model calculates the expected returns of the "selling green certificates path" and the "hydrogen production, energy storage, and emission reduction path" based on the real-time flow of surplus green electricity, and compares them using dynamic decision thresholds to output the optimal real-time flow decision instruction. When the system decides to use surplus green electricity for selling green certificates, the expected return of this path is calculated according to the following formula: (twenty three) Among them, the implicit carbon emission reductions corresponding to surplus green electricity Calculate using the following formula: (twenty four) in, For time period Expected returns from selling green certificates; For time period The remaining green power (i.e., surplus green power). For time period The base price of a green certificate; For time period The green certificate time-period premium coefficient is used to reflect the fluctuation of the market value of green certificates in different time periods; Subsidies for renewable energy; Premium for green electricity; For time period The implicit carbon emission reductions corresponding to surplus green electricity; It serves as the benchmark price for carbon trading; Carbon emission factors from purchasing electricity from the grid; For time intervals.

[0055] The revenue from this path comes from three parts: first, the direct revenue from the sale of green certificates (the base price of green certificates multiplied by the time-period premium factor); second, renewable energy subsidies and green electricity premiums; and third, the revenue from the carbon emission reductions implied by surplus green electricity in the carbon market.

[0056] When the system decides to use surplus green electricity for hydrogen production and energy storage, the expected returns of this path are calculated using the following formula: (25) Among them, carbon emission reduction from hydrogen production It consists of three parts: carbon emission reduction from power grid substitution, carbon emission reduction from gray hydrogen substitution, and carbon emission reduction from natural gas substitution, specifically expressed as follows: (26) (27) (28) (29) in, For time period Expected benefits of hydrogen production, energy storage, and emission reduction pathways; For time period The amount of hydrogen sold externally; For time period Total hydrogen production; For time period The market price of hydrogen; For natural gas prices; Subsidies for hydrogen production; For time period The equivalent amount of natural gas saved by cogeneration units from combustion or power generation due to the use of hydrogen instead of natural gas; For time period The carbon emission reduction from hydrogen production; To reduce carbon emissions by using surplus green electricity to produce hydrogen instead of purchasing electricity from the grid; Carbon emission reductions resulting from replacing traditional gray hydrogen with green hydrogen; carbon emission reductions resulting from replacing natural gas with green hydrogen; For time period The power consumption of the electrolyzer for hydrogen production; For time period The power consumption of the electrolyzer for hydrogen production; This serves as a benchmark for carbon emissions from traditional gray hydrogen. To take into account the carbon emission factor of gas consumption in cogeneration units after hydrogen co-firing; This is the efficiency correction factor for combined heat and power units due to hydrogen addition.

[0057] The benefits of this approach come from four parts: first, the revenue from selling hydrogen; second, the cost savings from saving natural gas; third, hydrogen production subsidies; and fourth, the revenue from carbon emission reductions resulting from the hydrogen production process in the carbon market.

[0058] Based on the expected returns calculated from the two paths mentioned above, the system compares the results using a dynamic decision threshold and outputs a real-time decision instruction on the flow of surplus green electricity. The dynamic decision model is represented as follows: (30) Among them, dynamic decision threshold Determined based on the product of a base threshold and multiple correction coefficients: (31) in, For time period The decision variable has a value of 1 indicating that the path of selling green certificates is chosen, and a value of 2 indicates that the path of hydrogen production, energy storage and emission reduction is chosen. This is a dynamic decision threshold used to balance the marginal benefits of two paths. The base threshold is the preset initial comparison benchmark. This is the energy storage state correction factor, based on the time period. hydrogen storage tank status This determination reflects the adjustment of hydrogen production pathway priority based on the remaining capacity of the hydrogen storage tank; This is a correction factor for heat load demand, based on the time period. Heat load demand This determines the adjustment of heat demand to the hydrogen-doped CHP operation requirements; This is a correction factor for the carbon emission reduction target gap, based on the time period. The deviation between the cumulative carbon emissions and the target value This determination reflects the adjustment of decision-making due to pressure to meet carbon emission reduction obligations.

[0059] Specifically, the expected returns from selling green certificates. The expected benefits of a hydrogen production, energy storage, and emission reduction pathway multiplied by a dynamic decision threshold At that time, system decision variables That is, choosing to use surplus green electricity for the sale of green certificates; conversely, when the expected revenue of the hydrogen production, energy storage, and emission reduction path multiplied by the dynamic decision threshold is greater than or equal to the expected revenue of the green certificate sale path, the system decision variable... This means choosing to use surplus green electricity for hydrogen production, energy storage, and emission reduction.

[0060] Dynamic decision threshold The introduction of this feature allows the model to dynamically adjust the benchmark for comparing the two paths based on the real-time operating status of the system (remaining capacity of the hydrogen storage tank, heat load demand level, and progress in achieving carbon reduction targets), avoiding the pursuit of economic benefits at the expense of system physical constraints and low-carbon compliance requirements. For example, when the hydrogen storage tank capacity is sufficient, the energy storage state correction coefficient... A smaller value lowers the comparison threshold for hydrogen production pathways and encourages hydrogen production; when the carbon emission reduction gap is large, the carbon emission reduction target gap correction coefficient... A larger value increases the weight of the hydrogen production pathway's revenue, incentivizing the system to achieve its carbon targets through hydrogen production and emission reduction.

[0061] The aforementioned dynamic decision-making model for green electricity environmental rights works in conjunction with the aforementioned carbon-green certificate synergy mechanism, tiered carbon trading mechanism model, and green certificate trading mechanism model to jointly achieve real-time optimized scheduling of surplus green electricity in the integrated energy system of wind, solar, hydrogen, and energy storage.

[0062] S3. Construct a multi-timescale collaborative scheduling strategy. The multi-timescale collaborative scheduling strategy includes configuring the lithium battery energy storage unit as a minute-level power fluctuation smoothing resource, and configuring the electrolyzer hydrogen production unit and hydrogen storage tank unit as hour-level long-cycle energy time-shifting resources. It also establishes minute-level power compensation constraints for the lithium battery energy storage unit and hour-level energy scheduling constraints for the hydrogen storage tank unit.

[0063] The heat output of a combined heat and power unit For the thermal efficiency of combined heat and power units, It has the lowest calorific value of hydrogen.

[0064] In step S3, the minute-level power compensation constraint condition of the lithium battery energy storage unit is configured as follows: when the deviation between the actual output power of the wind and solar turbine and the basic output power within a 15-minute time scale exceeds the preset fluctuation response threshold, the lithium battery energy storage unit is activated to perform power compensation, and the compensation power of the lithium battery energy storage unit does not exceed its maximum charging and discharging power.

[0065] In step S3, the hourly energy dispatch constraints of the hydrogen storage tank unit are configured as follows: the electrolyzer hydrogen production unit converts surplus green electricity into hydrogen and stores it in the hydrogen storage tank unit. During peak electricity price periods, the hydrogen storage tank unit releases hydrogen to the hydrogen-blended cogeneration unit for power generation feedback or hydrogen-blended heating. The state of charge of the hydrogen storage tank unit is maintained within the safe operating range between the upper and lower limits.

[0066] For example, hourly hydrogen energy dispatch constraints include electrolyzer operation constraints, hydrogen storage tank constraints, and CHP hydrogen consumption constraints. The hydrogen production capacity, power range, and ramp rate of the electrolyzer hydrogen production unit should satisfy the following equation (32): in, For time period hydrogen production For electrolysis efficiency, The electro-hydrogen conversion coefficient, For the power of the electrolytic cell, Configure capacity for the electrolytic cell. For climbing speed limit.

[0067] The state of charge and hydrogen storage balance of the hydrogen storage tank should satisfy the following equation (33): in, For time period The state of charge of the hydrogen storage tank, For time period Total hydrogen consumption flowing to cogeneration units and the market, For the capacity of the hydrogen storage tank, and These represent the lower and upper limits of the state of charge of the hydrogen storage tank, respectively.

[0068] When cogeneration units co-fire hydrogen, the amount of hydrogen co-fired should meet the co-firing ratio limit and the hydrogen supply capacity limit of the hydrogen storage tank should meet the following formula (34): in, For time period The amount of hydrogen co-fired in a combined heat and power unit This is the maximum hydrogen blending ratio for combined heat and power units (taken as 1 in pure hydrogen power generation mode). For time period The heat output of a combined heat and power unit For the thermal efficiency of combined heat and power units, It has the lowest calorific value of hydrogen.

[0069] In the embodiments provided in this application, to smooth out power fluctuations of wind and solar turbines over a 15-minute timescale, the lithium battery energy storage unit performs rapid power compensation. Specifically, it considers four 15-minute sub-periods within each hour (i.e., The lithium battery responds based on the deviation between the actual power output of the wind and solar power and the power output of the foundation.

[0070] Let the first Within the last hour The fluctuation of wind and solar power over 15 minutes is The actual output of renewable energy With basic renewable energy output The relationship between the wind and solar power fluctuations The absolute value exceeds the preset fluctuation response threshold. At that time, the lithium battery starts with power compensation, and its compensation power is... The following rules determine the representation as shown in equation (35). in, This refers to the maximum charge and discharge power of the lithium battery. When the fluctuation exceeds the threshold, the lithium battery compensates by a power level not exceeding its maximum power, with the compensation amount being the smaller of the absolute value of the fluctuation and the maximum power. When the fluctuation does not exceed the threshold, the lithium battery does not compensate.

[0071] Furthermore, the state of charge (SOC) of a lithium battery evolves over time, taking into account charge and discharge efficiency. Afterwards, its update formula and the state of charge of the lithium battery should be maintained within the safe operating range as follows (36): in, For time period Actual renewable energy output and base renewable energy output; For the first Within the last hour The fluctuation of wind and solar power over 15 minutes; For the first Within the last hour The compensation power of a lithium battery for 15 minutes (positive value indicates discharge compensation, negative value indicates charge absorption). This is the fluctuation response threshold; compensation is initiated when the absolute value of the fluctuation exceeds this value. This refers to the maximum charge and discharge power of the lithium battery. For time period State of charge of lithium batteries; The configuration capacity of the lithium battery; The duration of each 15-minute sub-period (0.25 hours); and These represent the lower and upper limits of the state of charge (SOC) of lithium batteries.

[0072] For example, the system operation needs to meet the real-time balance constraints of electrical power, thermal power, hydrogen energy, and natural gas, as detailed below: (1) Power balance constraint -- Power balance requires that the output of each power source be equal to that of each electrical load, which is expressed as: (37) in, For time period Wind power output and photovoltaic power output; For time period The total power generation capacity of the combined heat and power unit and hydrogen fuel cell; For time period The discharge power and charging power of the lithium battery; For time period The power purchased by the power grid; For time period The electrical load power; For time period The power consumption of the electrolyzer for hydrogen production; For time period The system surplus green power (refers to the portion of wind and solar power output that still has a surplus after meeting the electricity load and the consumption of electrolyzers).

[0073] (2) Heat power balance constraint -- The heat power balance requirement is that the heat output of the cogeneration unit, after deducting the heat network transmission loss, must meet the heat load demand: (38) in, For time period The thermal output power of the cogeneration unit; For time period Power loss during heat network transmission; For time period The power required for heat load.

[0074] (3) Hydrogen energy balance constraint -- The hydrogen energy balance requirement is that the hydrogen production capacity of the electrolyzer is equal to the sum of the consumption of each hydrogen-using link and the change in the hydrogen storage tank: (39) in, For time period The hydrogen production of the electrolyzer; For time period The amount of hydrogen co-fired in the cogeneration unit; For time period Hydrogen load demand; For time period The amount of hydrogen sold to the market; For time period Changes in the amount of hydrogen in the hydrogen storage tank (positive values ​​indicate an increase, negative values ​​indicate a decrease).

[0075] (4) Natural gas balance constraint -- Natural gas balance requires that the net purchase of natural gas equals the natural gas consumption of the cogeneration unit: (40) in, For time period Net purchases of natural gas (in power units). For time period Natural gas consumption of a combined heat and power (CHP) unit (converted to power units).

[0076] S4. Based on the real-time flow decision instructions of surplus green electricity output in step S2 and the multi-time-scale collaborative scheduling strategy established in step S3, execute multi-time-scale energy management scheduling and output the system operation scheduling scheme.

[0077] The system operation scheduling schemes output in step S4 include: electric power balance scheduling scheme, thermal power balance scheduling scheme, hydrogen energy balance scheduling scheme and natural gas balance scheduling scheme; among which, the electric power balance scheduling scheme is jointly determined by the power generation of wind and solar turbines, the power generation of cogeneration units, the discharge power of lithium batteries, the charging power of lithium batteries, the power purchased by the grid, the power of electrical load, the power consumption of electrolyzers and the surplus green electricity of the system.

[0078] As a possible implementation method, the integrated energy system of wind, solar, hydrogen and storage also includes a tiered carbon trading mechanism model. The tiered carbon trading mechanism model is configured to gradually reduce carbon emissions and carbon trading costs by setting tiered carbon prices. The carbon trading cost is determined by the difference between the total carbon quota and the actual carbon emissions, the carbon trading benchmark price and the tiered price growth rate.

[0079] Carbon trading mechanisms are mainly divided into two types: traditional carbon trading and tiered carbon trading. Traditional carbon trading calculates carbon trading costs using a fixed carbon price; tiered carbon trading, on the other hand, uses economic means to incentivize the system to gradually reduce carbon emissions and carbon trading costs by setting tiered carbon prices that increase with carbon emissions. This application's embodiment adopts a tiered carbon trading mechanism, and the specific implementation of this mechanism model is described in detail below.

[0080] First, calculate the total carbon allowance for the system. The total carbon allowance is determined based on the carbon allowance coefficients for electricity purchased per unit, gas purchased by cogeneration units, and gas consumption on the load side, combined with the grid's electricity purchase power, the natural gas consumption of cogeneration units, and the gas consumption power on the load side. Specifically, it is expressed as follows: (41) in, This refers to the total amount of carbon allowances. Carbon allowance coefficient per unit of electricity purchased; Carbon quota coefficient for unit gas purchase by cogeneration units; This is the carbon quota coefficient per unit of gas used on the load side; For time period The power purchased by the power grid; For time period Natural gas consumption of cogeneration units; For time period The load-side gas consumption power; is the time interval; T is the total number of time periods.

[0081] Secondly, the actual carbon emissions of the system are calculated. The actual carbon emissions are determined based on the carbon emission factors of electricity purchased from the grid, gas consumption of the combined heat and power (CHP) unit after considering hydrogen co-firing, and gas consumption on the load side, minus the carbon emission reductions corresponding to green certificates. Specifically, it is expressed as follows: (42) in, This refers to actual carbon emissions; Carbon emission factors from purchasing electricity from the grid; To take into account the carbon emission factor of gas consumption in cogeneration units after hydrogen co-firing; The carbon emission factor for gas consumed on the load side; The carbon emission reduction amount discounted by green certificates represents the carbon emission reduction credit amount corresponding to the green certificate exchange.

[0082] The tiered carbon trading cost is calculated using different carbon prices depending on the range in which the difference between actual carbon emissions and total carbon allowances falls. Specifically, it is expressed as formula (43): in, The cost of carbon trading occurs when the system's actual carbon emissions are lower than the carbon allowance. A negative value indicates that the system can profit by selling remaining carbon allowances; It serves as the benchmark price for carbon trading; , The carbon price growth rate is tiered and satisfies δ1>δ2>0, meaning the higher the carbon emissions exceed the limit, the higher the carbon price growth rate. This represents the length of the carbon emission range, used to divide different tiers of emission levels.

[0083] Specifically, the carbon trading cost model operates according to the following tiered rules: (1) When the actual carbon emissions Less than or equal to the total carbon quota At that time, the system's carbon emissions are within the quota, and the carbon trading cost is based on the benchmark price. The difference between the actual carbon emissions and the carbon allowance is multiplied to calculate the revenue. If the difference is negative, it means that the system can sell the remaining carbon allowances to generate revenue.

[0084] (2) When the actual carbon emissions In When emissions exceed the first tier, carbon trading costs are based on the benchmark price. Multiply by the excess amount for calculation.

[0085] (3) When the actual carbon emissions In During this period, carbon emissions enter the second tier, and the carbon trading price is increased by the growth rate based on the benchmark price. That is, the carbon price is .

[0086] (4) When the actual carbon emissions Greater than At that time, carbon emissions enter the third tier, and the carbon trading price is increased by the growth rate based on the benchmark price. ( ), that is, carbon price is To incentivize deeper emissions reductions through stronger economic penalties.

[0087] The aforementioned tiered carbon trading mechanism model works in conjunction with the dynamic decision-making model for green electricity environmental rights in step S2. Specifically, when calculating the expected benefits of the hydrogen production and energy storage emission reduction path, the carbon emission reduction from hydrogen production is... Incorporating this carbon reduction into the carbon emission reduction accounting system can lower the system's actual carbon emissions. This allows the system to fall into a lower tier in the tiered carbon trading system, reducing carbon trading costs and even generating revenue from carbon allowance sales. Simultaneously, green certificates discount carbon emission reductions. The environmental rights are directly deducted in formula (42), reflecting the principle of "uniqueness" of environmental rights - the environmental attributes of the same unit of green electricity cannot be repeatedly measured between the sale of green certificates and carbon emission deduction.

[0088] Through the joint optimization of the aforementioned tiered carbon trading mechanism and the dynamic decision-making model for green electricity environmental rights, the system can dynamically weigh the combined benefits of the two paths of "profiting from selling green certificates" and "hydrogen production for emission reduction" based on real-time carbon price signals, thereby achieving the optimal synergy between low carbon emissions and economic efficiency.

[0089] As one possible implementation, the integrated wind-solar-hydrogen-storage energy system also includes a green certificate trading mechanism model. The green certificate trading mechanism model is configured such that when the number of green certificates obtained by the system from consuming non-hydro renewable energy exceeds the system's green certificate quota, the system sells green certificates to generate profit; otherwise, it purchases green certificates to meet the system's green certificate quota. The green certificate trading cost is determined by the green certificate market selling price or purchase price and the difference between the number of green certificates and the green certificate quota.

[0090] The basic principle of the green certificate trading mechanism is as follows: Systems that consume electricity generated from non-hydro renewable energy sources (such as wind and solar power) can obtain green electricity certificates (referred to as "green certificates"). When the number of green certificates obtained by the system exceeds the green certificate quota it is obligated to fulfill, the system can sell the excess green certificates in the green certificate market to generate revenue; conversely, when the number of green certificates obtained by the system is insufficient to meet the quota, the system needs to purchase green certificates to fulfill the quota requirements. Through this mechanism, the goals of promoting and regulating the system's green electricity consumption are achieved.

[0091] In the embodiments provided in this application, the cost model for Integrated Energy Systems (IES) participating in green certificate trading is expressed as follows: (44) Green certificate quota It is determined by the product of the load-side green certificate quota coefficient and the electrical load, specifically expressed as follows: (45) Number of green certificates generated by the system It is determined by the product of the wind power green certificate quantification coefficient and the wind and solar power output used for grid connection and sale, specifically expressed as follows: (46) in, For green certificate transaction costs, when A negative value indicates the revenue the system receives from green certificate transactions; The market price for green certificates, i.e. the price the system receives when selling green certificates; The market price for green certificates is the price paid when the system purchases green certificates. The number of green certificates generated by wind and solar turbines participating in the electricity market; This refers to the number of green certificate quotas for the system. This is the load-side green certificate quota coefficient, used to characterize the proportion of green certificate quota that a unit of electrical load needs to bear; This is the quantification coefficient for wind power green certificates, used to characterize the number of green certificates that can be obtained per unit of wind and solar power connected to the grid; For time period The electrical load power; For time period Wind and solar power output used for grid-connected electricity sales; is the time interval; T is the total number of time periods.

[0092] Specifically, when the system generates a number of green certificates Greater than the system's green certificate quota At that time, the system will exceed the limit. Green certificates sold at a price Selling green certificates on the market incurs transaction costs. A negative value indicates that the system has gained revenue. This applies to the number of green certificates generated by the system. Less than or equal to the number of green certificates in the system At that time, the system needs to purchase the insufficient portion. The green certificate, and at the purchase price Payment costs, at this time A positive value or zero indicates that the system bears the cost of purchasing green certificates.

[0093] The aforementioned green certificate trading mechanism model works in conjunction with the dynamic decision-making model for green electricity environmental rights in step S2 above: when the system has surplus green electricity, the dynamic decision-making model compares the marginal revenue of the two paths, "selling green certificates" and "hydrogen production and energy storage," based on real-time market price signals. The expected revenue from the green certificate selling path comes from the green certificate trading revenue described in this model. Through this coordination, the system can maximize the value of environmental rights while meeting green certificate quota requirements.

[0094] In this embodiment, the integrated wind-solar-hydrogen-storage energy system also establishes a carbon-green certificate synergy mechanism. This mechanism, by constructing an "energy-environment" coupled trading system, breaks down trading barriers in a single carbon market or a single green certificate market, thereby maximizing the value of environmental rights. This mechanism strictly adheres to the principle of "uniqueness" in the allocation of environmental rights, imposing physical exclusivity constraints on the environmental attributes of the same unit of green electricity, preventing double measurement between green certificate sales and carbon emission deductions. In other words, the environmental rights corresponding to the same unit of green electricity cannot simultaneously obtain double benefits or double deductions in both the green certificate trading market and the carbon trading market.

[0095] The system simultaneously constructs a multi-dimensional carbon emission reduction accounting system, which covers the following three emission reduction paths: a green electricity substitution path, using green electricity generated by wind and solar power units to replace grid-purchased electricity, reducing indirect carbon emissions from grid-purchased electricity; a green hydrogen substitution path, using green hydrogen produced by electrolyzers to replace gray hydrogen produced by traditional fossil fuels, reducing carbon emissions in the hydrogen production process; and a CHP (carbon dioxide and hydrogen blending) low-carbon operation path, blending green hydrogen into combined heat and power (CHP) units for combustion, replacing some natural gas consumption and reducing the carbon emission intensity of unit operation. Based on these, the system establishes a price-responsive dynamic decision-making mechanism. Based on the fluctuation signals of real-time green certificate trading prices, hydrogen market prices, and carbon trading prices, the mechanism uses an optimization algorithm to dynamically weigh the marginal benefits of the following two paths: The first path involves selling surplus green electricity to the grid, obtaining corresponding green certificates, and then selling them in the green certificate market for profit; the second path involves using surplus green electricity to produce hydrogen in an electrolyzer, profiting from the sale of hydrogen, while reducing carbon emissions through methods such as replacing gray hydrogen with green hydrogen and blending CHP with hydrogen, thereby obtaining carbon emission reduction benefits or reducing carbon trading costs in the carbon trading market.

[0096] The dynamic decision-making mechanism compares the marginal returns of the two paths mentioned above based on real-time market price signals, and dynamically adjusts the energy flow direction of surplus green electricity to ensure that the system always chooses the operating strategy that maximizes overall returns. Based on the above logic, the total transaction cost of the carbon-green certificate synergy mechanism is defined as the sum of the green certificate transaction cost and the carbon transaction cost, specifically expressed as: (47) in, The total transaction cost under the carbon-green certificate synergy mechanism; For green certificate transaction costs; This refers to the cost of carbon trading.

[0097] The aforementioned carbon-green certificate synergy mechanism is integrated with the dynamic decision-making model for green electricity environmental rights in step S2. Specifically, in the marginal revenue comparison in step S2, the expected revenue from selling green certificates already implicitly includes the revenue from green certificate transactions (corresponding to...). The expected returns when the value is negative), while the expected returns from the hydrogen production, energy storage, and emission reduction pathway implicitly include reducing carbon trading costs through carbon emission reduction or obtaining revenue from the sale of carbon allowances (corresponding to...). (The portion of revenue when the value is negative). The dynamic decision-making model compares the combined marginal revenue of the two paths and outputs real-time decision instructions on the flow of surplus green electricity, thereby maximizing the environmental rights value under the carbon-green certificate synergy mechanism.

[0098] Through the aforementioned carbon-green certificate synergy mechanism, the system can flexibly adjust its operating strategy based on real-time market price signals, while adhering to the principle of the uniqueness of environmental rights, thereby achieving optimal synergy between low carbon emissions and economic efficiency.

[0099] To verify the effectiveness of the method provided in this application, this embodiment uses a wind-solar-hydrogen-storage integrated energy system in a certain region as the application object for simulation experiments. The system operating cycle is set to 25 years, and typical summer day data is selected to verify the system's regulation capability under extreme conditions. Parameter settings comprehensively consider the cost reduction trend in the industry chain and actual engineering costs, selecting the overall system cost under the EPC general contracting model as the benchmark. The main equipment parameters and carbon emission accounting benchmarks are as follows: Figure 3 As shown.

[0100] Reference Figure 4 The park exhibits a significant "source-load reverse distribution" characteristic: the overlapping of wind and solar power output at midday creates a large power surplus, which is compounded by the low electricity load and process heat demand, resulting in a severe spatiotemporal misalignment between source and load. To address this extreme "large source, small load" condition, the system initiates a multi-timescale collaborative scheduling strategy. Specifically, it employs a collaborative mechanism where the electrolyzer hydrogen production unit serves as an hourly long-cycle energy time-shifting resource to absorb surplus green electricity, and the lithium battery energy storage unit serves as a minute-level power fluctuation smoothing resource to provide short-term support. This is combined with the "electricity-driven heat" operation mode of the hydrogen-blended cogeneration unit, thereby resolving the source-load supply-demand contradiction while achieving optimal system economy and reliability.

[0101] To fully guide peak shaving and valley filling through the coordinated efforts of power generation, load, and storage, the system adopts a time-of-use pricing mechanism for electricity purchases. Specific time periods and pricing standards are as follows: Figure 5 As shown. An improved MOPSO algorithm is used for two-level collaborative optimization. The algorithm's convergence process and feedback correction mechanism are as follows. Figure 6 As shown. Figure 6As shown, the average fitness curve of the population exhibits a significant "step-like leap" characteristic: in the early stages of iteration, due to the strong penalty function constraint, the fitness oscillates at a low level; by the 50th generation, the fitness achieves a leap, indicating that the algorithm successfully guides the population out of the infeasible region using the feedback correction mechanism and the carbon emission reduction-oriented forced perturbation strategy. The global optimal fitness curve also undergoes a qualitative change in the 50th generation, and after subsequent fine-tuning, the optimal fitness value of 40.81 is finally locked in the 594th generation, fully verifying the strong ability of the improved multi-objective particle swarm optimization algorithm to escape local optima.

[0102] Figure 7 The listed optimal configuration schemes correspond to the following at the algorithm level: Figure 6 The globally optimal fitness value at the convergence point is 40.81. This fitness score specifically reflects the following characteristics: photovoltaic generators and wind turbines form a synergistic complementarity; a multi-scale energy storage system composed of lithium battery energy storage units and hydrogen storage tank units accurately shifts midday energy surplus to fill the evening peak power gap. Under this configuration, the system exhibits a highly competitive net present value over its entire life cycle and an ideal dynamic payback period, resulting in significant economic benefits. Simultaneously, the multi-timescale collaborative scheduling strategy significantly improves the renewable energy absorption level under extreme operating conditions and significantly reduces the unit carbon emission reduction cost, verifying that the method of this invention can effectively transform environmental externalities into internal benefits, maximizing the comprehensive value of the system's economic efficiency and low-carbon performance.

[0103] For example, the SOC change curve of an energy storage system is as follows: Figure 8 As shown, this visually illustrates the spatiotemporal complementary mechanism of "short-cycle arbitrage of lithium batteries - long-cycle storage of hydrogen energy." (Refer to...) Figure 8 The lithium battery energy storage unit exhibits a high-frequency response characteristic during the day: it utilizes wind power for rapid full charging during off-peak hours at night, strategically maintains "high-level standby" during daytime parity periods to avoid ineffective cycles and reduce lifespan losses, and only discharges intensively during the evening peak hours to support the power gap, precisely achieving system arbitrage of "low storage and high output." The hydrogen storage tank unit undertakes the functions of tiered absorption and long-term peak shifting. In terms of charging sequence, the hydrogen storage tank unit and the lithium battery energy storage unit form a parallel and complementary relay: before the lithium battery energy storage unit approaches saturation, the electrolyzer hydrogen production unit starts to absorb surplus green electricity from the system to produce hydrogen, which is stored in the hydrogen storage tank unit and maintained at a high level in the afternoon; as photovoltaic output declines and load increases, the hydrogen storage tank unit starts a long-cycle energy release mode, cooperating with the hydrogen-blended cogeneration unit to continuously discharge and provide heat by leveraging its thermoelectric advantages, working together with the lithium battery energy storage unit to support the evening peak power gap, thus achieving perfect complementarity between the "short-term intraday smoothing" of the lithium battery energy storage unit and the "cross-period energy transport" of the hydrogen storage tank unit.

[0104] The results of the power balance scheduling of the system on a typical day (1:00-24:00) are as follows: Figure 9 As shown, refer to Figure 9 Figure (a) shows that the power balance exhibits significant multi-timescale coordinated response characteristics. During the off-peak hours at night, the system utilizes surplus wind power to centrally charge the lithium battery energy storage units, and during the transition period, it adopts a parallel strategy of "charging lithium battery energy storage units + producing hydrogen from electrolyzer hydrogen production units" to achieve energy relay. During the daytime high-output wind and solar power periods, the system implements a parallel strategy of "producing hydrogen + selling electricity" to maximize the absorption of surplus green electricity. During the evening peak hours, the lithium battery energy storage units and hydrogen power generation units (including hydrogen-blended cogeneration units) provide the main support through coordinated discharge. Only during peak hours, due to equipment physical constraints and lifespan economic penalties, is the system flexibly introduced from the grid for power purchase. This verifies the optimal operating strategy of the system under the premise of ensuring regulation margin.

[0105] Reference Figure 9 Figure (b) shows that the heat power balance effectively covers the dual peak heat loads of morning and evening, while also reflecting the electrothermal coupling operation characteristics of the hydrogen-blended cogeneration unit. This unit needs to respond to the electrical load in real time, and its associated heat generation slightly exceeds the low summer demand throughout the day, resulting in a continuous small heat surplus. This characteristic objectively reflects the thermodynamic constraints under the "strong electricity, weak heat" operating condition and verifies the reliability of the system's heating supply.

[0106] To verify the superiority of the method of this invention, this embodiment constructs four typical operating scenarios for comparison based on the current state of the industry: Scenario 1 (Ghosting Mode): The system is not equipped with energy storage facilities, and surplus green electricity is forced to be abandoned and does not participate in green certificate trading or hydrogen production and energy storage. This serves as an economic control group.

[0107] Scenario 2 (Full Green Certificate Mode): Simulating mainstream grid-connected power plants, all surplus green electricity is used to issue green certificates, and revenue is generated solely through grid-connected electricity sales and environmental premiums (green certificate trading).

[0108] Scenario 3 (Full Hydrogen Production Mode): Simulating a rigid consumption project, following the "electricity-hydrogen" unidirectional conversion path, surplus green electricity is preferentially converted into hydrogen to ensure hydrogen production.

[0109] Scenario 4 (Dynamic Collaborative Mode): Applying the multi-timescale collaborative scheduling strategy and green electricity environmental rights dynamic decision-making model provided by this invention, dynamic optimization and flexible switching are performed among multiple paths such as direct electricity sales, green certificate trading, and hydrogen production and energy storage based on real-time marginal revenue.

[0110] Its overall benefits are, for example Figure 10 As shown, by Figure 10As can be seen, in scenario 4 using the method of this invention, the daily revenue reaches 207,000 yuan, the net present value over the entire life cycle is 464.942 million yuan, the internal rate of return is 18.09%, the dynamic payback period is 7.32 years, the renewable energy integration rate reaches 76.5%, the annual carbon emission reduction is 95,942 tons of CO2 equivalent, and the unit carbon emission reduction cost is 27.69 yuan / ton of CO2 equivalent. Compared with the other three scenarios, the method of this invention has significant advantages in both economic and environmental aspects.

[0111] To quantify the impact of market fluctuations on the decision-making results of the green electricity environmental rights dynamic decision-making model described in this invention, this paper selects four key variables—time-of-use electricity price, carbon trading price, green certificate trading price, and hydrogen market price—for sensitivity analysis. For example, refer to... Figure 11 The system's revenue exhibits a significant "threshold-triggered" characteristic in its sensitivity to electricity prices. When the electricity price exceeds 0.47 yuan / kWh, the system's total lifecycle net present value and daily operating net revenue curves show a steep increase. This threshold corresponds to the break-even point of the energy storage system's (including lithium battery energy storage units and hydrogen storage tank units) total lifecycle costs. Once the market electricity price covers the energy storage cost, the control strategy determined by the green electricity environmental rights dynamic decision-making model undergoes a qualitative change: the algorithm switches from a "conservative stabilization" mode to an "extreme arbitrage" mode, actively increasing the cycle frequency of lithium battery energy storage units to capture time-of-use electricity price differences. This mechanism mutation triggered by price signals verifies the sensitivity of the improved multi-objective particle swarm optimization algorithm in capturing high-value market opportunities.

[0112] For example, refer to Figure 12 ,like Figure 12 As shown in Figures (a)-(c), price fluctuations in different markets reveal differentiated decision-making response mechanisms. The decision-making proportion of carbon trading prices exhibits a step-like upward trend, indicating that high carbon price signals can significantly correct economic weights, incentivizing the system to tilt towards a deep decarbonization model and obtain environmental premiums through hydrogen substitution. Green certificate trading prices, however, show a reverse reversal in decision-making: as green certificate trading prices rise, accompanied by a step increase in the system's total life-cycle net present value, the hydrogen production proportion actually decreases. This reveals the opportunity cost game between green certificates and hydrogen production—when the green certificate premium surpasses the marginal benefit of the hydrogen production, energy storage, and emission reduction path, the dynamic decision-making model for green electricity environmental rights automatically switches strategies, prioritizing the sale of green certificates (i.e., selling green electricity rights). Fluctuations in hydrogen market prices reflect resource-dominated characteristics: although the system's total life-cycle net present value increases linearly with hydrogen prices, the hydrogen production proportion remains locked throughout the process. This indicates that hydrogen production has reached the physical limit determined by the surplus of wind and solar power (output of wind and solar units minus electricity load and losses). Simply increasing prices can only bring about a net profit increase and cannot break through the rigid physical constraints of resources.

[0113] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0114] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system, characterized in that, include: S1. Obtain the operational status information of the integrated wind-solar-hydrogen-storage energy system. This operational status information includes wind power output, solar power output, electricity load demand, heat load demand, real-time carbon trading price, real-time green certificate trading price, and real-time hydrogen market price. S2. Establish a dynamic decision-making model for green electricity environmental rights. This model is configured to calculate the marginal revenue comparison between the green certificate selling path and the hydrogen production, storage, and emission reduction path for surplus green electricity based on the real-time carbon trading price, green certificate trading price, and hydrogen market price obtained in step S1. It then outputs a real-time flow decision instruction for surplus green electricity. The marginal revenue is determined based on the energy storage status correction coefficient. The heat load demand correction coefficient and the carbon emission reduction target gap correction coefficient are dynamically corrected; S3, a multi-time-scale collaborative scheduling strategy is constructed, which includes configuring the lithium battery energy storage unit as a minute-level power fluctuation smoothing resource, and configuring the electrolyzer hydrogen production unit and the hydrogen storage tank unit as an hour-level long-cycle energy time-shifting resource, and establishing minute-level power compensation constraints for the lithium battery energy storage unit and hour-level energy scheduling constraints for the hydrogen storage tank unit; S4, based on the real-time flow decision instruction of the surplus green electricity output in step S2 and the multi-time-scale collaborative scheduling strategy established in step S3, multi-time-scale energy management scheduling is executed, and the system operation scheduling scheme is output.

2. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, In step S2, the marginal revenue comparison of the green electricity environmental rights dynamic decision-making model is performed as follows: Calculate the expected revenue of the green certificate selling path, which is determined by the remaining green electricity power, the green certificate base price, the green certificate time premium coefficient, renewable energy subsidies, green electricity premium, and the implicit carbon emission reduction corresponding to surplus green electricity; calculate the expected revenue of the hydrogen production and energy storage emission reduction path, which is determined by the amount of hydrogen sold, the total amount of hydrogen produced, the hydrogen market price, the natural gas price, the hydrogen production subsidy, the equivalent amount of natural gas saved by the combustion or power generation of the cogeneration unit, and the carbon emission reduction of hydrogen production; compare the expected revenue of the green certificate selling path with the expected revenue of the hydrogen production and energy storage emission reduction path. If the expected revenue of the green certificate selling path is greater than or equal to the expected revenue of the hydrogen production and energy storage emission reduction path, then output a green certificate selling decision instruction; otherwise, output a hydrogen production decision instruction.

3. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, In step S3, the minute-level power compensation constraint condition of the lithium battery energy storage unit is configured as follows: when the deviation between the actual output power of the wind and solar turbine and the basic output power within a 15-minute time scale exceeds the preset fluctuation response threshold, the lithium battery energy storage unit is activated to perform power compensation, and the compensation power of the lithium battery energy storage unit does not exceed its maximum charging and discharging power.

4. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, In step S3, the hourly energy dispatch constraints of the hydrogen storage tank unit are configured as follows: the electrolyzer hydrogen production unit converts surplus green electricity into hydrogen and stores it in the hydrogen storage tank unit. During peak electricity price periods, the hydrogen storage tank unit releases hydrogen to the hydrogen-blended cogeneration unit for power generation feedback or hydrogen-blended heating. The state of charge of the hydrogen storage tank unit is maintained within the safe operating range between the upper and lower limits.

5. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, Before step S1, a planning-operation dual-layer optimization framework is constructed. This framework includes: Step S01: The upper planning layer, with the goal of maximizing the net present value over the entire life cycle, determines the optimal configuration scheme for the installed capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units, under the constraints of investment budget and total carbon emissions; Step S02: The lower scheduling layer, using the optimal configuration scheme output by the upper planning layer as boundary conditions, executes the multi-timescale energy management and scheduling steps described in steps S1 to S4 with the goal of minimizing the daily net operating cost; Step S03: The lower scheduling layer feeds back the actual carbon emission reduction, equipment equivalent loss rate, and actual revenue generated during its actual operation to the upper planning layer. The upper planning layer dynamically adjusts the annual carbon emission reduction target, equipment configuration capacity, and environmental rights decision threshold based on the feedback information.

6. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 5, characterized in that, The feedback correction in step S03 is performed as follows: the actual carbon emission reduction output by the lower scheduling layer is fed back to the upper planning layer to correct the annual carbon emission reduction target; based on the equipment equivalent loss rate output by the lower scheduling layer, the configuration capacity of wind turbine generators, photovoltaic generators, lithium battery energy storage units, electrolyzer hydrogen production units, and hydrogen storage tank units is iteratively corrected; when the actual benefit output by the lower scheduling layer deviates from the expected benefit output by the upper planning layer, the environmental rights decision threshold is adjusted to correct the benefit deviation.

7. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 5, characterized in that, The planning-running two-layer optimization framework is solved using an improved multi-objective particle swarm optimization algorithm. The improved multi-objective particle swarm optimization algorithm includes: an adaptive inertia weight adjustment strategy to balance the algorithm's global search capability and local search capability; an elite file storage and non-dominated solution crowding distance selection mechanism to store non-dominated solutions and maintain the diversity of the solution set; and a carbon emission reduction-oriented forced perturbation strategy to apply random perturbations to the positions of some particles when the carbon emission reduction completion rate is lower than a preset threshold.

8. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, The integrated energy system of wind, solar, hydrogen and energy storage also includes a tiered carbon trading mechanism model. The tiered carbon trading mechanism model is configured to gradually reduce carbon emissions and carbon trading costs by setting tiered carbon prices. The carbon trading cost is determined by the difference between the total carbon quota and the actual carbon emissions, the carbon trading benchmark price and the tiered price growth rate.

9. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, The integrated wind, solar, hydrogen, and energy storage system also includes a green certificate trading mechanism model. The green certificate trading mechanism model is configured such that when the number of green certificates obtained by the system from consuming non-hydro renewable energy exceeds the system's green certificate quota, the system sells green certificates for profit; otherwise, it purchases green certificates to meet the system's green certificate quota. The green certificate trading cost is determined by the green certificate market selling price or purchase price and the difference between the number of green certificates and the green certificate quota.

10. The synergistic optimization method for a wind-solar-hydrogen-storage integrated energy system according to claim 1, characterized in that, The system operation scheduling schemes output in step S4 include: electric power balance scheduling scheme, thermal power balance scheduling scheme, hydrogen energy balance scheduling scheme and natural gas balance scheduling scheme; wherein the electric power balance scheduling scheme is jointly determined by the power generation of wind and solar turbines, the power generation of cogeneration units, the discharge power of lithium batteries, the charging power of lithium batteries, the power purchased by the grid, the power of electrical load, the power consumption of electrolyzers and the surplus green electricity of the system.

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