Electricity-hydrogen coupling system optimization operation method for promoting distributed new energy consumption

Abstract

The invention discloses an electricity-hydrogen coupling system optimization operation method for promoting distributed new energy consumption, and the method comprises the following steps: S1, constructing an electricity-hydrogen coupling system, and an energy management and cooperative control strategy of the electricity-hydrogen coupling system; s2, constructing an optimal scheduling model of the electricity-hydrogen coupling system to quantitatively realize the optimal decision of the strategy; and S3, carrying out optimization solution on the optimization scheduling model of the electricity-hydrogen coupling system by adopting a scheduling optimization strategy comprising a day-ahead rolling stage and an intra-day rolling stage to obtain an optimal scheduling scheme. According to the system, surplus electric power of intermittent power sources such as photovoltaic power sources is effectively absorbed through water electrolysis hydrogen production, stored hydrogen energy is fully utilized through hydrogen energy power generation, the new energy consumption rate is greatly increased, and clean energy waste is reduced. By optimizing the operation time sequence of the electric hydrogen equipment, the purposes of peak load shifting and electric charge expenditure reduction are achieved, the utilization of clean energy is improved, meanwhile, good economic benefits are achieved, and a new technical approach is provided for cost reduction and efficiency improvement of the park.

Description

Technical Field

[0001] This invention belongs to the technical field of electro-hydrogen coupling systems, specifically relating to an optimized operation method for electro-hydrogen coupling systems that promotes the consumption of distributed new energy sources. Background Technology

[0002] The essence of electro-hydrogen coupling is to introduce hydrogen energy as a medium into the energy system, forming an integrated energy system (IES) that optimizes multiple energy sources. Compared with traditional single-energy systems, integrated energy systems, through the complementarity and synergy of multiple energy sources such as electricity, heat, gas, and hydrogen, can improve the comprehensive utilization efficiency of renewable energy, reduce wind and solar curtailment, and enhance energy supply flexibility. This has become one of the effective technical approaches to solving the problems of new energy consumption and supply-demand balance. In particular, with the introduction of the electro-hydrogen coupling link, electricity and hydrogen energy can achieve bidirectional conversion: when there is a power surplus, the electricity is converted into hydrogen energy for storage through water electrolysis; when there is a power shortage, the hydrogen energy generation unit converts hydrogen energy into electricity to supplement it. This is equivalent to increasing the system's "virtual power generation" and "virtual load," significantly enhancing the peak-shaving capacity of the park's energy system. This electro-hydrogen synergy has attracted great attention in the fields of scientific research and engineering. In recent years, a large number of studies on the optimized scheduling of electro-hydrogen coupling systems have emerged, aiming to meet the load's energy supply needs while minimizing system operating costs and carbon emissions, and achieving efficient utilization of new energy. For example, some studies have constructed a two-level optimization model of source-load-storage to coordinate power-to-gas (P2G) and carbon trading mechanisms, improving the economy and low-carbon nature of integrated energy systems; others have built a multi-energy complementary framework of electricity-heat-hydrogen, achieving a balance between improving energy utilization efficiency and minimizing operating costs. These studies provide useful references and technical reserves for this invention.

[0003] Despite this, existing methods still have some shortcomings in practical industrial park applications: First, they lack an overall optimization strategy for distributed renewable energy consumption. Traditional scheduling often operates with each energy subsystem acting independently, failing to fully explore the synergistic potential between electricity and hydrogen, resulting in some power curtailment. Second, they do not fully consider the impact of forecast uncertainty on operational decisions. Many optimized scheduling models are calculated based on a single day-ahead scenario, and the resulting plans may not match the actual situation in real-time operation due to forecast deviations, requiring further adjustments. Third, the scheduling models are highly complex and lack real-time optimization capabilities. Electricity-hydrogen coupling systems involve discrete start-stop decisions and energy storage states across time scales, often requiring long computation times for solutions, making it difficult to apply them promptly to real-time control. Therefore, it is necessary to develop an optimized operation method for electricity-hydrogen coupling systems in distributed renewable energy scenarios in industrial parks, capable of coordinating renewable energy consumption and economic goals during the daily planning phase, and rapidly responding to fluctuations in the real-time phase, to overcome the aforementioned technical limitations. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides an optimized operation method for an electric-hydrogen coupling system that promotes the consumption of distributed new energy sources. This method solves the problems of existing methods lacking an overall optimization strategy for the consumption of distributed new energy sources, not fully considering the impact of prediction uncertainties on operational decisions, and having high scheduling model complexity and lacking real-time optimization capabilities.

[0005] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources, comprising the following steps: S1. Construct an electro-hydrogen coupling system and its energy management and coordinated control strategy; S2. Construct an optimal scheduling model for the electro-hydrogen coupling system to quantitatively achieve optimal strategy decision-making; S3. An optimization strategy involving two stages, namely day-ahead and intraday rolling, is adopted to optimize the scheduling model of the electric-hydrogen coupling system and obtain the optimal scheduling scheme.

[0006] Furthermore, in S1, the electro-hydrogen coupling system includes a photovoltaic power generation unit, an electrolysis water hydrogen production unit, a hydrogen storage unit, and a hydrogen energy power generation unit connected in sequence.

[0007] Furthermore: In S1, the energy management and coordinated control strategy is specifically as follows: Real-time monitoring of load power and photovoltaic power generation The trend of the change is used to determine whether there is a surplus of photovoltaic power. If so, the hydrogen production mode is started; if not, the hydrogen power generation mode is started first. The specific method for activating the hydrogen production mode is as follows: determine whether the hydrogen storage capacity of the hydrogen storage unit has reached its maximum capacity; if not, schedule the water electrolysis hydrogen production unit to operate at its maximum power. The system operates to generate hydrogen and store it in a hydrogen storage unit. If so, any excess electricity is transmitted via an inverter. The specific method for prioritizing the activation of hydrogen power generation mode is as follows: Determine if the hydrogen storage capacity of the hydrogen storage unit exceeds the minimum threshold. If so, dispatch the hydrogen power generation unit according to power output. If the system is not operational, it will compensate for the power deficit by generating electricity from hydrogen energy; otherwise, it will meet the power deficit through the external power grid.

[0008] The beneficial effects of the above-mentioned further scheme are as follows: Based on the energy management and collaborative control strategy, the electro-hydrogen coupling system realizes automatic response to different operating scenarios: during periods of high photovoltaic output and low load, it maximizes the conversion of electrical energy into hydrogen energy for storage, and during periods of low photovoltaic output and high load, it prioritizes the use of previously stored hydrogen energy for power generation, so as to reduce dependence on the external power grid.

[0009] Furthermore: In S2, the objective function of the optimization scheduling model for the electro-hydrogen coupling system F The specific expression is: In the formula, For the power purchase capacity, For electricity sales capacity, The hydrogen production power of the water electrolysis hydrogen production unit. The power output of the hydrogen energy power generation unit, This refers to the power that has been abandoned. For electricity purchase price, For electricity sales price, , and All are penalty coefficients. t For time.

[0010] The beneficial effects of the above-mentioned further solutions are as follows: Based on the operating mechanism of the electro-hydrogen coupling system, this invention further proposes an optimized scheduling model to quantitatively achieve the optimal decision-making of the above control strategy. The scheduling optimization aims to reduce the total operating cost and improve the utilization rate of new energy sources. Taking into account both equipment operating constraints and energy conservation constraints, a mathematical model of the park's energy system including the electro-hydrogen coupling link is established, and advanced algorithms are used to solve for the optimal operating plans of the water electrolysis hydrogen production unit, hydrogen power generation unit, etc.

[0011] Furthermore: In S2, the constraints of the optimal scheduling model for the electro-hydrogen coupling system include power balance constraints, equipment operation constraints, hydrogen storage dynamic constraints, hydrogen storage capacity boundaries, and mutual exclusion between purchasing and selling electricity; The specific expression for the power balance constraint is as follows: In the formula, Photovoltaic power generation capacity, For load power; The specific expression for the equipment operation constraints is as follows: In the formula, This represents the maximum hydrogen production power of the water electrolysis hydrogen production unit. This represents the maximum power output of the hydrogen energy power generation unit. and All are start / stop status variables. ; The specific expression for the dynamic constraints on hydrogen storage is as follows: In the formula, The hydrogen production power of the water electrolysis hydrogen production unit. For the efficiency of hydrogen power generation units, For the duration of the period, For time t Hydrogen energy storage, For time t+ 1 hydrogen energy storage; The specific expression for the hydrogen storage capacity boundary is: In the formula, For maximum hydrogen energy storage; The specific expression for the mutual exclusion of electricity purchase and sale is: In the formula, For mutually exclusive indicator variables, , M It is a sufficiently large constant.

[0012] Furthermore, S3 includes the following sub-steps: S31. Based on the predicted values ​​of photovoltaic power generation and load power, perform day-ahead scheduling optimization to obtain the planned output power of each device for each time period of the next day; S32. Monitor the load power and photovoltaic power generation in real time during each period of the next day, and optimize and adjust the planned output power of each device according to the preset rolling window to obtain the optimal scheduling scheme.

[0013] The beneficial effects of the above-mentioned further scheme are as follows: This invention employs a two-stage optimization method—day-ahead and intraday—to predict the optimal scheduling scheme. In the day-ahead stage, a mixed-integer linear programming model is used to generate the planned output power of each device for the entire next day. In the intraday stage, this invention uses a "rolling optimization" approach to address prediction deviations: first, a preliminary plan is obtained by solving the day-ahead optimized scheduling problem based on the day-ahead predicted data; then, during actual operation, the latest photovoltaic and load monitoring and prediction data are used at short intervals to continuously update the optimization calculations for subsequent periods, thereby correcting deviations in the original plan. Through this "plan-re-optimization" closed-loop control method, the adaptability of the scheduling scheme to real-time changes is significantly improved, avoiding supply-demand imbalances caused by a single fixed plan failing to match reality.

[0014] Furthermore, S31 includes the following sub-steps: S311. Collect meteorological data and production plan information for the following day to form a quantile forecast set for photovoltaic power generation and load power. , as well as the photovoltaic power generation and load power predicted recently; S312. Constructing a safe output reference based on quantile prediction sets ; In the formula, For the first t Baseline forecast of time quantiles, For the first t Lower predicted value of time period quantiles For the first t Upper predicted value of the time quantile. k For safety margin coefficient, ; S313. Based on the safe output reference, a mixed integer linear programming model is established. The input of the mixed integer linear programming model includes the day-ahead forecast of photovoltaic power generation and load power. The decision variables of the mixed integer linear programming model include power purchase, power sale and hydrogen storage energy. The mixed integer linear programming model takes the optimization scheduling model of the electric-hydrogen coupling system with the minimum daily operating cost as the optimization objective function, and satisfies the power balance constraint, equipment operation constraint, hydrogen storage dynamic constraint, hydrogen storage capacity boundary and power purchase / sale mutual exclusion. S314. Solve the objective function of minimizing the total daily operating cost using a mixed-integer linear programming model to obtain the planned output power of each device for each time period of the next day, including the hydrogen production power of the water electrolysis hydrogen production unit, the power generation power of the hydrogen power generation unit, the power purchased, the power sold, the power abandoned, the start-stop state variables, and the hydrogen storage energy.

[0015] Furthermore, S32 includes the following sub-steps: S321. Set the scroll window length Operating safety lower limit Operating safety limit and the time period to be predicted The system collects data on load power, photovoltaic power generation, electricity purchase price, and electricity sales price for the time period to be predicted. In the formula, This is a lower limit proportional buffer, with a value range of 0.05 to 0.20. As a safety buffer for the upper limit, The adjustment parameters for the upper limit safety buffer, , For the future q Equivalent hydrogen demand for each time period q For the forward-looking period number; In the formula, for Power consumption during a given time period for Photovoltaic power generation during specific time periods; S322, Based on the current time t Collected load power Photovoltaic power generation Electricity purchase price and electricity sales price Generate based on scrolling window Short-term predicted load power and photovoltaic power generation ; S323, Fixed scrolling window The time period has been implemented historically, based on short-term forecasts of load power. and photovoltaic power generation ,by The optimization window is constructed based on the time period and the planned output power of each device in each time period of the next day is used as the initial value for warm-up of the variables within the window. The set of power reserve is then calculated. and hydrogen content retention balance ,in, ; In the formula, This indicates that the hydrogen production unit using water electrolysis has a power margin. This indicates the power reserve of the hydrogen power generation unit. and For power retention ratio, This indicates the rated maximum power of the water electrolysis hydrogen production unit. This indicates the rated maximum power of the hydrogen power generation unit. This is the conversion factor from interval width to buffer size. This indicates taking the non-negative part. This is an approximation of the width of the net load forecast interval. For the first The upper predicted value of the load power quantile for the time period. For the first Lower predicted value of the load power quantile for the time period. For the first The upper predicted value of the quantile of photovoltaic power generation during the time period. For the first Lower edge prediction of the quantile of photovoltaic power generation during the time period; S324. Solve the mixed-integer linear programming model using the optimization window to obtain the current time.t Hydrogen production power of water electrolysis hydrogen production unit Hydrogen power generation unit power output Power purchased Electricity sales capacity and time t+ 1 hydrogen energy storage ; S325, in response to This reduces the power output of the hydrogen energy power generation unit. and increase power purchase capacity. ; in response to This reduces the hydrogen production power of the water electrolysis hydrogen production unit. and increase electricity sales capacity. or abandoned power ; S326. Determine the current time t Has the target time been exceeded? T If not, then let Return to S321; if yes, then the time period to be predicted is obtained. The planned output power of each unit, including the hydrogen production power of the water electrolysis hydrogen production unit. Hydrogen power generation unit power output Power purchased Electricity sales capacity abandoned power Start-stop state variables and hydrogen energy storage.

[0016] The beneficial effects of the above-mentioned further solutions are as follows: Through intraday rolling optimization, it can compensate for deficiencies caused by forecast errors in the daily plan, respond promptly to sudden weather changes, and achieve closed-loop optimized control. When actual photovoltaic output is lower than predicted, intraday optimization will increase hydrogen power generation or grid power purchases to meet the load; conversely, when photovoltaic output exceeds prediction, hydrogen production capacity will be increased or the output of hydrogen power generation units will be reduced to absorb excess power. Ultimately, after multiple intraday adjustments, the system operation is closer to the actual optimal state, ensuring both supply and demand balance and maximizing the utilization of clean energy.

[0017] The beneficial effects of this invention are as follows: (1) Significantly improve the utilization rate of new energy and reduce the waste of clean energy: Compared with the traditional scheduling scheme without hydrogen storage, this invention effectively absorbs the surplus power of intermittent power sources such as photovoltaics by producing hydrogen through water electrolysis, and makes full use of the stored hydrogen energy through hydrogen power generation. According to the simulation results, the utilization rate of new energy in the park has been significantly improved after adopting this method, and the amount of wind and solar power curtailment has been reduced to almost zero.

[0018] (2) Significantly Reduced Overall Energy Costs in the Industrial Park: This invention optimizes the operation sequence of the hydrogen electrolysis equipment, achieving peak shaving and valley filling, and reducing electricity costs. During peak hours when electricity prices are high, the purchase of electricity from external sources is minimized, prioritizing the use of the hydrogen power generation unit to output the park's own hydrogen-converted electricity. During off-peak hours when electricity prices are low, the water electrolysis hydrogen production unit is fully utilized to convert inexpensive electricity into hydrogen for storage and later use. In this way, the time distribution of electricity purchased by the park is redistributed, with more electricity being purchased during lower-priced periods. According to economic calculations, compared to the baseline scenario without this invention, the park's daily electricity costs can be reduced by approximately 10% to 20% (depending on the peak-valley price difference and photovoltaic output). Therefore, this method achieves good economic benefits while improving the utilization of clean energy, providing a new technical approach for cost reduction and efficiency improvement in industrial parks.

[0019] (3) Improved power grid operation and electricity safety: This invention enables the park to achieve greater energy self-sufficiency and balance, reducing reliance on the main power grid during peak electricity consumption periods, thereby alleviating pressure on the distribution network and improving overall operational stability. Hydrogen energy storage is equivalent to adding a long-term, large-scale "buffer." When the power grid experiences faults or power rationing, the park can still use hydrogen-hydrogen power generation units to provide power independently for short periods, enhancing energy supply flexibility and emergency response capabilities. In addition, through the synergy of electricity and hydrogen, this method reduces the need for start-up and shutdown of thermal power units and deep regulation during traditional peak shaving, which is conducive to the safe and stable operation of the power system and energy conservation and emission reduction. It can be considered that this invention organically combines distributed energy consumption with the improvement of power supply reliability in the park, which has positive significance for building an active power distribution and consumption model under a new power system.

[0020] (4) Strong versatility and scalability: This method is designed for typical industrial park scenarios, but its approach is applicable to various energy systems that include renewable energy and hydrogen energy utilization. For example, in microgrids with distributed wind power, the architecture of wind power to produce hydrogen, hydrogen energy storage, and hydrogen energy generation can be used to improve wind energy utilization. In regional integrated energy systems, waste heat utilization and hydrogen synthesis of other fuels (such as methanol and ammonia) can be further combined to improve the overall energy utilization efficiency. Other types of energy storage (such as electrochemical energy storage) and adjustable loads can also be included in the model to participate in optimized scheduling, achieving more complete multi-energy coordinated control. Therefore, this invention has good scalability and compatibility, and can provide a reference for the operation optimization of various new energy high-proportion energy internet systems. Attached Figure Description

[0021] Figure 1 This is a flowchart of an optimized operation method for an electric-hydrogen coupling system to promote the consumption of distributed new energy sources, according to the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0023] like Figure 1 As shown, in one embodiment of the present invention, an optimized operation method for an electric-hydrogen coupling system that promotes the consumption of distributed new energy sources includes the following steps: S1. Construct an electro-hydrogen coupling system and its energy management and coordinated control strategy; S2. Construct an optimal scheduling model for the electro-hydrogen coupling system to quantitatively achieve optimal strategy decision-making; S3. An optimization strategy involving two stages, namely day-ahead and intraday rolling, is adopted to optimize the scheduling model of the electric-hydrogen coupling system and obtain the optimal scheduling scheme.

[0024] The main objectives of this invention are as follows: 1. Improve the absorption rate of new energy sources and reduce wind and solar power curtailment losses: By configuring water electrolysis hydrogen production and hydrogen power generation devices within the park, surplus electricity from distributed power sources such as photovoltaics during the day is converted into hydrogen for storage. This achieves energy transfer from periods of oversupply to periods of undersupply, minimizing the waste of renewable energy due to momentary surplus. At night or when sunlight is insufficient, the previously stored hydrogen is used to generate electricity, replacing some of the electricity purchased from the external grid. This strategy effectively improves the self-sufficiency rate of clean energy within the park, achieving the goal of enhancing the absorption level of new energy sources.

[0025] 2. Reduce energy costs in the industrial park and promote economic operation: By optimizing the start-up, shutdown, and power output of the water electrolysis hydrogen production unit and the hydrogen power generation unit, hydrogen production and storage can be maximized during off-peak hours when electricity prices are low, while power generation can be achieved during peak hours when electricity prices are high, thus reducing electricity costs through peak shaving and valley filling. Simultaneously, the purchase of electricity from the external grid during high-price periods is minimized, with greater utilization of inexpensive off-peak electricity or zero-marginal-cost photovoltaic power. The optimization model incorporates an economic cost objective function, comprehensively considering factors such as electricity purchase and sales costs, equipment operation and maintenance costs, and penalties for power curtailment, ensuring that the proposed solution achieves the lowest overall operating cost while improving the utilization of new energy sources. Case studies show that compared to the baseline scenario without this optimization method, the method of this invention can significantly reduce the overall energy costs of the industrial park, demonstrating outstanding economic benefits.

[0026] 3. Enhanced System Flexibility and Reliability: This invention fully utilizes the advantages of hydrogen energy storage—large capacity and long continuous power supply time—providing additional buffering and regulation capabilities for the park's power system. When encountering continuous rainy weather or other extreme conditions that cause a significant drop in photovoltaic output, the hydrogen energy storage system can serve as a backup power source, continuously supplying power through the hydrogen power generation unit, thus improving the system's power supply reliability. In cases of sudden increases in solar radiation or grid failures, the water electrolysis hydrogen production unit can also quickly absorb excess power, alleviating pressure on the distribution network. Through advanced control strategies, the various modules work in synergy, overcoming the limitations of traditional single grid dispatching methods, enabling the system to have stronger adaptability and robustness to changing supply and demand conditions. In summary, the method of this invention is superior to existing traditional solutions in terms of new energy consumption and utilization, operational economy, and energy supply security.

[0027] In S1, the electro-hydrogen coupling system includes a photovoltaic power generation unit, an electrolysis water hydrogen production unit, a hydrogen storage unit, and a hydrogen energy power generation unit connected in sequence. In this embodiment, the electro-hydrogen coupling system is designed for typical industrial park energy consumption scenarios. The photovoltaic power generation unit serves as the energy supply side of the park, providing clean electricity to the system. The park load represents the electricity demand that needs to be met, including electricity for production equipment and office / residential use. The power system connects all elements through the park's distribution network bus. Photovoltaic power is prioritized for supplying local park loads. When photovoltaic output exceeds load demand, surplus electricity is converted into hydrogen through a water electrolysis unit and stored in a hydrogen storage unit. Conversely, when photovoltaic output is insufficient and there is surplus hydrogen in the hydrogen storage unit, the hydrogen power generation unit is activated to convert hydrogen into electricity to supplement power supply. If there is still a power shortage, electricity is purchased from the external grid to meet the park load. If there is surplus photovoltaic power and the hydrogen storage capacity is full, excess electricity can be fed back to the external grid via inverter connection or is forcibly discarded. Through these steps, a flexible and adaptable two-way energy flow architecture between electricity and hydrogen is formed, enabling efficient transfer and utilization of energy across different times and spaces.

[0028] In S1, the energy management and collaborative control strategies are as follows: Real-time monitoring of load power and photovoltaic power generation The trend of the change is used to determine whether there is a surplus of photovoltaic power. If so, the hydrogen production mode is started; if not, the hydrogen power generation mode is started first. The specific method for activating the hydrogen production mode is as follows: determine whether the hydrogen storage capacity of the hydrogen storage unit has reached its maximum capacity; if not, schedule the water electrolysis hydrogen production unit to operate at its maximum power. The system operates to generate hydrogen and store it in a hydrogen storage unit. If so, any excess electricity is transmitted via an inverter. The specific method for prioritizing the activation of hydrogen power generation mode is as follows: Determine if the hydrogen storage capacity of the hydrogen storage unit exceeds the minimum threshold. If so, dispatch the hydrogen power generation unit according to power output. If the system is not operational, it will compensate for the power deficit by generating electricity from hydrogen energy; otherwise, it will meet the power deficit through the external power grid.

[0029] In this embodiment, based on energy management and collaborative control strategies, the electro-hydrogen coupling system achieves automatic response to different operating scenarios: during periods of high photovoltaic output and low load, it maximizes the conversion of electrical energy into hydrogen for storage; during periods of low photovoltaic output and high load, it prioritizes the use of previously stored hydrogen for power generation, thereby reducing dependence on the external power grid. It can be seen that electro-hydrogen coupling utilizes renewable energy sources that were originally unusable simultaneously through the "hydrogen storage-hydrogen release" process, significantly improving the local consumption rate of new energy; it also plays a role in peak shaving and valley filling, making the load curve of the park's electricity purchase from the grid flatter, reducing the grid's peak-shaving pressure and the park's energy costs. This invention achieves efficient connection between photovoltaic power generation, grid power supply, and hydrogen storage and utilization through energy management and collaborative control strategies. The control process is executed cyclically in each scheduling time slot (e.g., 15 minutes or 1 hour), continuously adjusting the operating status of the water electrolysis hydrogen production unit and the hydrogen power generation unit according to the dynamic changes in photovoltaic power and load, achieving dynamic balance and collaborative optimization between electricity and hydrogen energy.

[0030] Based on the operating mechanism of the electro-hydrogen coupling system, this invention further proposes an optimized scheduling model to quantitatively achieve the optimal decision-making of the aforementioned control strategy. The scheduling optimization aims to reduce total operating costs and improve the utilization rate of new energy sources. Taking into account both equipment operating constraints and energy conservation constraints, a mathematical model of the park's energy system including the electro-hydrogen coupling link is established, and advanced algorithms are used to solve for the optimal operating plans of the water electrolysis hydrogen production unit, hydrogen power generation unit, etc. In S2, the objective function of the optimized scheduling model of the electro-hydrogen coupling system is... F The specific expression is: In the formula, For the power purchase capacity, For electricity sales capacity, The hydrogen production power of the water electrolysis hydrogen production unit. The power output of the hydrogen energy power generation unit, This refers to the power that has been abandoned. For electricity purchase price, For electricity sales price, , and All are penalty coefficients. t For time. This invention achieves this through reasonable setting. Much higher than the price of electricity, By using peak electricity prices of 1.5–3 times, the model is made to ensure that it tends to utilize hydrogen storage rather than curtailed electricity, thereby internalizing the requirement to increase the consumption of new energy sources into the optimization objective.

[0031] In this embodiment, the optimization scheduling model of the electro-hydrogen coupling system takes minimizing the total daily operating cost as the optimization objective function. The total cost consists of the following components: 1) Electricity purchase cost: the amount of electricity purchased from the external grid in each time period multiplied by the time-of-use electricity price. The cumulative cost of electricity production is calculated as follows: 1) Cumulative cost of electricity production; minus the revenue from selling surplus electricity; 2) Hydrogen production and consumption costs: Hydrogen production through water electrolysis consumes raw materials such as water and incurs equipment depreciation, which can be approximated as an operating cost item proportional to the hydrogen production power. The cost of hydrogen consumption for hydrogen power generation is also considered (if hydrogen is regarded as an internal energy currency, its cost can reflect the electricity consumption for producing hydrogen); 3) Penalty cost for curtailment of renewable energy: To quantify the losses from curtailing wind and solar power, a higher virtual cost coefficient can be set. If there is surplus photovoltaic power that is not utilized, a penalty fee will be incurred, thereby prompting the model to prioritize the use of renewable energy during optimization. The objective function is formed by the weighted summation of the above cost elements. F .

[0032] In S2, the constraints of the optimal scheduling model of the electro-hydrogen coupling system include power balance constraints, equipment operation constraints, hydrogen storage dynamic constraints, hydrogen storage capacity boundaries, and mutual exclusion of power purchase / sale. The specific expression for the power balance constraint is as follows: In the formula, Photovoltaic power generation capacity, The load power; the power balance constraint ensures the instantaneous conservation of energy.

[0033] The specific expression for the equipment operation constraints is as follows: In the formula, This represents the maximum hydrogen production power of the water electrolysis hydrogen production unit. This represents the maximum power output of the hydrogen energy power generation unit. and All are start / stop status variables. In this embodiment, This indicates that the water electrolysis hydrogen production unit has started. This indicates that the water electrolysis hydrogen production unit is shut down. This indicates that the hydrogen power generation unit has started. This indicates that the hydrogen power generation unit is off.

[0034] The specific expression for the dynamic constraints on hydrogen storage is as follows: In the formula, The hydrogen production power of the water electrolysis hydrogen production unit. For the efficiency of the hydrogen power generation unit, the recommended range is used in this embodiment: , , For the duration of the period, For time t Hydrogen energy storage, For time t+ 1 hydrogen energy storage; The specific expression for the hydrogen storage capacity boundary is: In the formula, To maximize hydrogen storage energy; in this embodiment, the hydrogen storage dynamic constraint describes the state evolution of the hydrogen storage unit.

[0035] The specific expression for the mutual exclusion of electricity purchase and sale is: In the formula, For mutually exclusive indicator variables, , M To ensure that the constant is sufficiently large, this embodiment adopts a method of setting it according to the actual situation.

[0036] Based on the above constraints, this embodiment establishes a mixed-integer linear programming (MILP) model to describe the intraday optimal scheduling problem of the electro-hydrogen coupling system. This model comprehensively considers the sources (photovoltaics), loads, and storage (hydrogen tanks), characterizing the process of electro-hydrogen energy conversion and storage. Its solution yields the optimal power output plan for each time period within the entire scheduling cycle, including photovoltaic power utilization, operating power of hydrogen production and hydrogen power generation units, and grid exchange power. It is worth noting that the optimization solution fully considers the impact of the forecast uncertainties of photovoltaic power output and load on scheduling decisions.

[0037] S3 includes the following steps: S31. Based on the predicted values ​​of photovoltaic power generation and load power, perform day-ahead scheduling optimization to obtain the planned output power of each device for each time period of the next day; S32. Monitor the load power and photovoltaic power generation in real time during each period of the next day, and optimize and adjust the planned output power of each device according to the preset rolling window to obtain the optimal scheduling scheme.

[0038] In this embodiment, the present invention employs a two-stage optimization method—day-ahead and intraday—to predict the optimal scheduling scheme. In the day-ahead stage, a mixed-integer linear programming (MILP) model is used to generate the planned output power of each device for the entire next day. In the intraday stage, the present invention uses a "rolling optimization (model predictive control, MPC)" approach to address prediction deviations: first, a preliminary plan is obtained by solving the day-ahead optimized scheduling problem based on the day-ahead predicted data; then, during actual operation, the optimization calculations for subsequent periods are updated every short time interval (e.g., hourly) using the latest photovoltaic and load monitoring and prediction data, thereby correcting deviations in the original plan. This "plan-re-optimization" closed-loop control method significantly improves the adaptability of the scheduling scheme to real-time changes, avoiding supply-demand imbalances caused by a single fixed plan failing to match reality.

[0039] S31 includes the following steps: S311. Collect meteorological data and production plan information for the following day to form a quantile forecast set for photovoltaic power generation and load power. And the photovoltaic power generation capacity predicted recently. and load power Loading electricity purchase price Electricity sales price and initial hydrogen storage ; S312. Constructing a safe output reference based on quantile prediction sets ; In the formula, For the first t Baseline forecast of time quantiles, For the first t Lower predicted value of time period quantiles For the first t The upper limit of the time-period quantile forecast, where the quantile forecasts include the quantile forecasts for photovoltaic power generation and load power. k For safety margin coefficient, ; In this embodiment, , and Quantile prediction set of photovoltaic power generation or load power quantile prediction set Take the value from; S313. Establish a mixed-integer linear programming model based on the safe output reference. The input of the mixed-integer linear programming model includes the day-ahead forecast of photovoltaic power generation. and load power The decision variables in the mixed-integer linear programming model include electricity purchase power. Electricity sales power The mixed-integer linear programming model for hydrogen energy storage uses the optimization scheduling model of the electro-hydrogen coupling system with the minimum daily total operating cost as the optimization objective function, satisfying power balance constraints, equipment operation constraints, hydrogen storage dynamic constraints, hydrogen storage capacity boundaries, and mutual exclusion of power purchase / sale. S314. Solve the objective function of minimizing the total daily operating cost using a mixed-integer linear programming model to obtain the planned output power of each device for each time period of the following day, including the hydrogen production power of the water electrolysis hydrogen production unit. Hydrogen power generation unit power output Power purchased Electricity sales power abandoned power Start-stop state variables and hydrogen energy storage.

[0040] In this embodiment, intraday rolling optimization can compensate for deficiencies caused by forecast errors in the daily plan, promptly address sudden weather changes, and achieve closed-loop optimized control. It is important to emphasize that intraday rolling optimization uses the planned output power of each device for each time period of the following day as an initial reference, but it further optimizes the allocation of output for units such as the water electrolysis hydrogen production unit and the hydrogen power generation unit based on real-time conditions. For example, when the actual photovoltaic output is lower than the forecast, intraday optimization increases hydrogen power generation or grid power purchases to meet the load; conversely, when the photovoltaic output exceeds the forecast, it increases hydrogen production power or reduces the output of the hydrogen power generation unit to absorb the excess power. Ultimately, after multiple intraday adjustments, the system operates closer to the actual optimal state, ensuring both supply and demand balance and maximizing the utilization of clean energy.

[0041] To meet the real-time requirements of intraday rolling optimization, the algorithm design of this invention only targets the remaining time period that has not yet been executed during each re-optimization, greatly reducing the problem size. Furthermore, heuristic algorithms combined with model prediction can be used to further accelerate the process, for example, using the solution from the previous rolling cycle as the initial value for the next cycle, or pre-calculating policy curves for typical scenarios offline to provide online reference.

[0042] S32 includes the following steps: S321. Set the scroll window length Operating safety lower limit Operating safety limit and the time period to be predicted The system collects data on load power, photovoltaic power generation, electricity purchase price, and electricity sales price for the time period to be predicted. In the formula, This is a lower limit proportional buffer, dimensionless, with a value range of 0.05 to 0.20. As a safety buffer for the upper limit, The adjustment parameters for the upper limit safety buffer, , For the future q Equivalent hydrogen demand for each time period q For the forward-looking period number; S322, Based on the current time t Collected load power Photovoltaic power generation Electricity purchase price and electricity sales price Generate based on scrolling window Short-term predicted load power and photovoltaic power generation ; S323, Fixed scrolling window The time period has been implemented historically, based on short-term forecasts of load power. and photovoltaic power generation ,by The optimization window is constructed based on the time period and the planned output power of each device in each time period of the next day is used as the initial value for warm-up of the variables within the window. The set of power reserve is then calculated. and hydrogen content retention balance ,in, ; In the formula, This indicates that the hydrogen production unit using water electrolysis has a power margin. This indicates the power reserve of the hydrogen power generation unit, which is used to control the lower limit of the power of the water electrolysis hydrogen production unit and the hydrogen power generation unit. and The rated upper limit is reserved for the water electrolysis hydrogen production unit and the hydrogen power generation unit respectively, so as to ensure that when encountering problems such as short-term load overhaul, grid connection restrictions, and ramp-up continuity, there is room for temporary adjustment or smoothing, so as not to frequently default or start and stop. and The power retention ratio is typically set between 0.03 and 0.10. and The rated maximum power of the equipment, This indicates the rated maximum power of the water electrolysis hydrogen production unit. This indicates the rated maximum power of the hydrogen power generation unit. As an uncertainty buffer, the width of the prediction interval is converted into the amount of additional hydrogen that needs to be retained. This is the conversion factor from interval width to buffer size, with a value ranging from 0.3 to 0.7. This indicates taking the non-negative part. This is an approximation of the width of the net load forecast interval. For the first The upper predicted value of the load power quantile for the time period. For the first Lower predicted value of the load power quantile for the time period. For the first The upper predicted value of the quantile of photovoltaic power generation during the time period. For the first Lower edge prediction of the quantile of photovoltaic power generation during the time period; In the formula, for Power consumption during a given time period for Photovoltaic power generation during specific time periods; S324. Solve the mixed-integer linear programming model using the optimization window to obtain the current time. t Hydrogen production power of water electrolysis hydrogen production unit Hydrogen power generation unit power output Power purchased Electricity sales power and time t+ 1 hydrogen energy storage ; S325, in response to This reduces the power output of the hydrogen energy power generation unit. and increase power purchase capacity. ; in response This reduces the hydrogen production power of the water electrolysis hydrogen production unit. and increase electricity sales capacity. or abandoned power ; S326. Determine the current time t Has the target time been exceeded? T If not, then let Return to S321; if yes, then the time period to be predicted is obtained. The planned output power of each unit, including the hydrogen production power of the water electrolysis hydrogen production unit. Hydrogen power generation unit power output Power purchased Electricity sales power abandoned power Start-stop state variables and hydrogen energy storage.

[0043] The beneficial effects of this invention are as follows: Significantly improving the renewable energy absorption rate and reducing clean energy waste: Compared with traditional scheduling schemes without hydrogen storage, this invention effectively absorbs surplus electricity from intermittent power sources such as photovoltaics through water electrolysis to produce hydrogen, and fully utilizes the stored hydrogen energy through hydrogen power generation. According to simulation results, the utilization rate of renewable energy in the park has significantly improved after adopting this method, and the amount of wind and solar power curtailment has almost decreased to zero. For example, in a certain case, the original scheme sent all the excess photovoltaic power output during the day back to the grid, but due to quota limitations, only 70% could be absorbed, and the remaining 30% was wasted; after introducing the hydrogen-electric optimization operation, 100% of the surplus photovoltaic power during the day is used for hydrogen production, and at night the hydrogen power generation unit converts all the hydrogen into electricity, realizing the full generation of renewable energy. It can be seen that this method fully utilizes the characteristics of large capacity and flexible time shift of hydrogen energy storage, so that renewable energy power generation is no longer constrained by the simultaneous absorption capacity of the load, effectively solving the problem of grid absorption of distributed renewable energy.

[0044] Significantly reducing overall energy costs in industrial parks: This invention optimizes the operation sequence of hydrogen electrolysis equipment, achieving peak shaving and valley filling, and reducing electricity costs. During peak hours when electricity prices are high, it minimizes the purchase of electricity from external sources, prioritizing the use of hydrogen power generation units to generate electricity from the park's own hydrogen production. Conversely, during off-peak hours when electricity prices are low, it fully utilizes water electrolysis to convert inexpensive electricity into hydrogen for storage and later use. In this way, the time distribution of electricity purchases in the park is redistributed, with more electricity being purchased during lower-price periods. Economic calculations show that compared to the baseline scenario without this invention, the park's daily electricity costs can be reduced by approximately 10% to 20% (depending on the peak-valley price difference and photovoltaic output). Therefore, this method achieves significant economic benefits while improving the utilization of clean energy, providing a new technical approach for cost reduction and efficiency improvement in industrial parks.

[0045] Improving power grid operation and electricity safety: This invention enables the industrial park to achieve greater energy self-sufficiency and balance, reducing reliance on the main power grid during peak electricity demand periods, thereby alleviating pressure on the distribution network and improving overall operational stability. Hydrogen energy storage acts as a large, long-term "buffer," allowing the park to still provide independent power for short periods using hydrogen-powered generators during grid failures or power curtailment, enhancing energy supply resilience and emergency response capabilities. Furthermore, through the synergy of electricity and hydrogen, this method reduces the need for start-up, shutdown, and deep regulation of thermal power units during traditional peak shaving, contributing to the safe and stable operation of the power system and energy conservation and emission reduction. In conclusion, this invention organically combines distributed energy consumption with improved power supply reliability in industrial parks, which is of positive significance for constructing a proactive power distribution and utilization model under a new power system.

[0046] High versatility and scalability: This method is designed for typical industrial park scenarios, but its approach is applicable to various energy systems that incorporate renewable energy and hydrogen utilization. For example, in microgrids with distributed wind power, a wind-to-hydrogen, hydrogen storage, and hydrogen-to-power architecture can be used to improve wind energy utilization. In regional integrated energy systems, waste heat utilization and hydrogen synthesis of other fuels (such as methanol and ammonia) can be further combined to improve overall energy efficiency. The model can also incorporate other types of energy storage (such as electrochemical energy storage) and adjustable loads for optimized scheduling, achieving more comprehensive multi-energy coordinated control. Therefore, this invention has good scalability and compatibility, and can provide a reference for the operational optimization of various energy internet systems with a high proportion of new energy sources.

[0047] In the description of this invention, it should be understood that the terms "center," "thickness," "upper," "lower," "horizontal," "top," "bottom," "inner," "outer," and "radial," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying the relative importance or the number of technical features implicitly specified. Therefore, a feature defined by "first," "second," and "third" may explicitly or implicitly include one or more of that feature.

Claims

1. A method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources, characterized in that, Includes the following steps: S1. Construct an electro-hydrogen coupling system and its energy management and coordinated control strategy; S2. Construct an optimal scheduling model for the electro-hydrogen coupling system to quantitatively achieve optimal strategy decision-making; S3. An optimization strategy involving two stages, namely day-ahead and intraday rolling, is adopted to optimize the scheduling model of the electric-hydrogen coupling system and obtain the optimal scheduling scheme.

2. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 1, characterized in that, In S1, the electro-hydrogen coupling system includes a photovoltaic power generation unit, an electrolysis water hydrogen production unit, a hydrogen storage unit, and a hydrogen energy power generation unit connected in sequence.

3. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 2, characterized in that, In S1, the energy management and collaborative control strategies are as follows: Real-time monitoring of load power and photovoltaic power generation The trend of the change is used to determine whether there is a surplus of photovoltaic power. If so, the hydrogen production mode is started; if not, the hydrogen power generation mode is started first. The specific method for activating the hydrogen production mode is as follows: determine whether the hydrogen storage capacity of the hydrogen storage unit has reached its maximum capacity; if not, schedule the water electrolysis hydrogen production unit to operate at its maximum power. The system operates to generate hydrogen and store it in a hydrogen storage unit. If so, any excess electricity is transmitted via an inverter. The specific method for prioritizing the activation of hydrogen power generation mode is as follows: Determine if the hydrogen storage capacity of the hydrogen storage unit exceeds the minimum threshold. If so, dispatch the hydrogen power generation unit according to power output. If the system is not operational, it will compensate for the power deficit by generating electricity from hydrogen energy; otherwise, it will meet the power deficit through the external power grid.

4. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 1, characterized in that, In S2, the objective function of the optimization scheduling model for the electro-hydrogen coupling system is... F The specific expression is: In the formula, For the power purchase capacity, For electricity sales capacity, The hydrogen production power of the water electrolysis hydrogen production unit. The power output of the hydrogen energy power generation unit, This refers to the power that has been abandoned. For electricity purchase price, For electricity sales price, , and All are penalty coefficients. t For time.

5. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 4, characterized in that, In S2, the constraints of the optimal scheduling model of the electro-hydrogen coupling system include power balance constraints, equipment operation constraints, hydrogen storage dynamic constraints, hydrogen storage capacity boundaries, and mutual exclusion of power purchase / sale. The specific expression for the power balance constraint is as follows: In the formula, Photovoltaic power generation capacity, For load power; The specific expression for the equipment operation constraints is as follows: In the formula, This represents the maximum hydrogen production power of the water electrolysis hydrogen production unit. This represents the maximum power output of the hydrogen energy power generation unit. and All are start / stop status variables. ; The specific expression for the dynamic constraints on hydrogen storage is as follows: In the formula, The hydrogen production power of the water electrolysis hydrogen production unit. For the efficiency of hydrogen power generation units, For the duration of the period, For time t Hydrogen energy storage, For time t+ 1 hydrogen energy storage; The specific expression for the hydrogen storage capacity boundary is: In the formula, For maximum hydrogen energy storage; The specific expression for the mutual exclusion of electricity purchase and sale is: In the formula, For mutually exclusive indicator variables, , M It is a sufficiently large constant.

6. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 5, characterized in that, S3 includes the following steps: S31. Based on the predicted values ​​of photovoltaic power generation and load power, perform day-ahead scheduling optimization to obtain the planned output power of each device for each time period of the next day; S32. Monitor the load power and photovoltaic power generation in real time during each period of the next day, and optimize and adjust the planned output power of each device according to the preset rolling window to obtain the optimal scheduling scheme.

7. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 6, characterized in that, S31 includes the following steps: S311. Collect meteorological data and production plan information for the following day to form a quantile forecast set for photovoltaic power generation and load power. , as well as the photovoltaic power generation and load power predicted recently; S312. Constructing a safe output reference based on quantile prediction sets ; In the formula, For the first t Baseline forecast of time quantiles, For the first t The lower bound predicted value of the time quantile. For the first t Upper predicted value of the time quantile. k For safety margin coefficient, ; S313. Based on the safe output reference, a mixed integer linear programming model is established. The input of the mixed integer linear programming model includes the day-ahead forecast of photovoltaic power generation and load power. The decision variables of the mixed integer linear programming model include power purchase, power sale and hydrogen storage energy. The mixed integer linear programming model takes the optimization scheduling model of the electric-hydrogen coupling system with the minimum daily operating cost as the optimization objective function, and satisfies the power balance constraint, equipment operation constraint, hydrogen storage dynamic constraint, hydrogen storage capacity boundary and power purchase / sale mutual exclusion. S314. Solve the objective function of minimizing the total daily operating cost using a mixed-integer linear programming model to obtain the planned output power of each device for each time period of the next day, including the hydrogen production power of the water electrolysis hydrogen production unit, the power generation power of the hydrogen power generation unit, the power purchased, the power sold, the power abandoned, the start-stop state variables, and the hydrogen storage energy.

8. The method for optimizing the operation of an electro-hydrogen coupling system to promote the consumption of distributed new energy sources according to claim 7, characterized in that, S32 includes the following steps: S321. Set the scroll window length Operating safety lower limit Operating safety limit and the time period to be predicted The system collects data on load power, photovoltaic power generation, electricity purchase price, and electricity sales price for the time period to be predicted. In the formula, This is a lower limit proportional buffer, with a value range of 0.05 to 0.

20. As a safety buffer for the upper limit, The adjustment parameters for the upper limit safety buffer, , For the future q Equivalent hydrogen demand for each time period q For the forward-looking period number; In the formula, for Power consumption during a given time period for Photovoltaic power generation during specific time periods; S322, Based on the current time t Collected load power Photovoltaic power generation Electricity purchase price and electricity sales price Generate based on scrolling window Short-term predicted load power and photovoltaic power generation ; S323, Fixed scrolling window The time period has been implemented historically, based on short-term forecasts of load power. and photovoltaic power generation ,by The optimization window is constructed based on the time period and the planned output power of each device in each time period of the next day is used as the initial value for warm-up of the variables within the window. The set of power reserve is then calculated. and hydrogen content retention margin ,in, ; In the formula, This indicates that the hydrogen production unit using water electrolysis has a power margin. This indicates the power reserve of the hydrogen power generation unit. and For power retention ratio, This indicates the rated maximum power of the water electrolysis hydrogen production unit. This indicates the rated maximum power of the hydrogen power generation unit. This is the conversion factor from interval width to buffer size. This indicates taking the non-negative part. This is an approximation of the width of the net load forecast interval. For the first The upper predicted value of the load power quantile for the time period. For the first Lower predicted value of load power quantile for the time period. For the first The upper predicted value of the quantile of photovoltaic power generation during the time period. For the first Lower edge prediction of the quantile of photovoltaic power generation during the time period; S324. Solve the mixed-integer linear programming model using the optimization window to obtain the current time. t Hydrogen production power of water electrolysis hydrogen production unit Hydrogen power generation unit power output Power purchased Electricity sales power and time t+ 1 hydrogen energy storage ; S325, in response to This reduces the power output of the hydrogen energy power generation unit. and increase power purchase capacity. ; in response to This reduces the hydrogen production power of the water electrolysis hydrogen production unit. and increase electricity sales capacity. or abandoned power ; S326. Determine the current time t Has the target time been exceeded? T If not, then let Return to S321; if yes, then the time period to be predicted is obtained. The planned output power of each unit, including the hydrogen production power of the water electrolysis hydrogen production unit. Hydrogen power generation unit power output Power purchased Electricity sales power abandoned power Start-stop state variables and hydrogen energy storage.

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