Efficient operation method of green hydrogen metallurgy comprehensive energy system

Abstract

The invention discloses an efficient operation method for a green hydrogen metallurgy comprehensive energy system, and the method comprises the steps: forming a probability interval of an uncertain variable through probability distribution fitting; secondly, a distributed robust optimization method is adopted to obtain an optimized operation scheme considering multiple operation targets, and a continuous convex envelope approximation strategy is applied to optimize the solution precision and the calculation efficiency. Under the background of coping with the uncertainty of energy supply, a large number of operation scenes are generated, the energy efficiency, flexibility and risk of the system are evaluated, and a dynamic association rule is proposed to support tradable energy blocks and carbon blocks in multi-market transactions. And further analyzing time sequence coupling characteristics of the energy block and the carbon block, researching a bidding quotation strategy and different operation modes, and exploring a self-adaptive transaction decision-making method based on multi-agent reinforcement learning. The invention aims to improve the safety, economy and flexibility of the green hydrogen metallurgy comprehensive energy system, provides a multi-market transaction support strategy, and provides theoretical and practical support for efficient utilization and sustainable development of green energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system operation optimization, and specifically relates to an efficient operation method of a green hydrogen metallurgical integrated energy system. Background Art

[0002] With the global emphasis on low-carbon development, green hydrogen metallurgical technology, as an important green steel production method, is developing rapidly. The green hydrogen metallurgical integrated energy system combines renewable energy with hydrogen energy to achieve a significant reduction in carbon emissions in the steel production process, becoming an important way to transform the energy structure and reduce carbon emissions. However, the high complexity and multi-energy-mass-flow coupling characteristics of the system have brought many technical challenges, especially in the flexible regulation and optimized operation of the system.

[0003] At present, the research on green hydrogen metallurgical integrated energy system has established a multi-time scale optimization planning and scheduling model, focusing on the regulation characteristics and flexible regulation capabilities of various flexible and adjustable resources in the system (such as grid access, renewable energy generation, electrolyzers, hydrogen storage devices, etc.). These models take economy as the main goal and balance the safety, economy and flexibility of the system through multi-objective optimization methods. In addition, existing research also explores the cross-link regulation characteristics in the process of multi-energy and mass flow transmission, such as the time delay problem in the transmission and conversion process, which provides certain theoretical support for the coordinated optimization operation of the system.

[0004] Although the existing collaborative optimization operation technology has improved the operating efficiency of the system to a certain extent, it still has significant shortcomings when facing a complex dynamic coupling environment. First, the dynamic coupling relationship between system energy efficiency, flexibility and risk has not been fully considered, especially under the fluctuation of a high proportion of renewable energy, energy efficiency, flexibility and risk constrain each other, and the existing model cannot effectively balance these factors. Secondly, with the increase in tradable categories such as green hydrogen, green electricity and carbon blocks, the existing modeling technology lacks in-depth research in dealing with the temporal dynamic coupling of tradable energy blocks and carbon blocks and the improvement of flexibility under market conditions. In addition, the dynamic correlation of the system in multi-market transactions and the optimal operation under multiple uncertainties have not been fully resolved. Therefore, the collaborative operation method for the green hydrogen metallurgical integrated energy system needs to be further improved to achieve efficient operation and flexible control of the system. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the efficient operation method of the green hydrogen metallurgical integrated energy system provided by the present invention solves the problem of insufficient operation efficiency of the existing related methods when dealing with the green hydrogen metallurgical integrated energy system with dynamic coupling, multiple uncertainties and complex constraints.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for efficiently operating a green hydrogen metallurgical integrated energy system, comprising the following steps:

[0007] S1. Based on the uncertainty variables in the green hydrogen metallurgical integrated energy system, a multi-objective optimization operation model for stochastic production simulation is constructed based on the distributed robust optimization method, and it is converted into a linear model using the continuous convex envelope approximation strategy;

[0008] S2. Based on the uncertainty of energy supply in the green hydrogen metallurgical integrated energy system, a large number of operation scenarios are generated through random production simulation, and an oblique decision tree model based on multi-indicator sparse weights and regularization is constructed according to the generated operation scenarios;

[0009] S3, using the constructed oblique decision tree model to extract the association rules between energy efficiency, flexibility and risk in the operation scenario data;

[0010] S4. Based on the extracted association rules between energy efficiency, flexibility and risk and the linear model obtained by transformation, the energy block and the carbon block in the green hydrogen metallurgical comprehensive energy are sequentially subjected to time series coupling analysis and confidence interval analysis, and then the coupling characteristics of the energy block and the carbon block are quantitatively inferred;

[0011] The energy block and carbon block represent the physical energy transmission and carbon emission quota in the green hydrogen metallurgical integrated energy system respectively;

[0012] S5. Analyze the optimal bidding strategies of energy blocks and carbon blocks in different markets in multi-market transactions through game theory models;

[0013] S6. Based on the optimal bidding strategy of energy blocks and carbon blocks, an adaptive trading auxiliary decision-making model based on multi-agent reinforcement learning is constructed, and the scheduling and trading strategies of energy blocks and carbon blocks are outputted to achieve efficient operation of the system.

[0014] Furthermore, the step S1 includes the following sub-steps:

[0015] S11. Statistically analyze the historical operation data in the green hydrogen metallurgical integrated energy system, perform probability distribution fitting on the uncertainty variables therein, and characterize the uncertainty variables as corresponding uncertainty sets based on the distances between different probability distributions; the uncertainty variables include power supply fluctuations and market price fluctuations;

[0016] S12. Based on the multiple uncertainty conditions of the uncertainty set, a multi-objective optimization operation model for random production simulation is constructed using the distributed robust optimization method;

[0017] S13. A continuous convex envelope approximation strategy is used to transform the multi-objective optimization operation model with nonlinear constraints into a linear model and solve it to obtain a high-value optimal solution.

[0018] Furthermore, in step S12, the multi-objective optimization operation model for implementing random production simulation is expressed as:

[0019]

[0020] In the formula, x is the decision variable of the system, ξ is the uncertainty variable, f(x,ξ) is the profit and loss function of the system corresponding to the optimization target, and θ represents the robustness parameter, which is used to control the trade-off between the desirability and conservatism of the system. Indicates based on The expected function of represents the uncertainty set, P ξ represents the probability distribution of the uncertainty variable ξ, represents the reference distribution obtained by fitting the operational history data;

[0021] The constraints of the multi-objective optimization operation model are the extended resource-task network of the green hydrogen metallurgical integrated energy system.

[0022] Furthermore, the step S2 includes the following sub-steps:

[0023] S21. Power supply fluctuations and market price fluctuations are considered as uncertainty variables in the operation scenario;

[0024] S22, randomly generate uncertainty variables contained in different operation scenarios, and take maximizing the system's operating efficiency and minimizing risks as the optimization goal of the operation scenarios, thereby generating a large number of operation scenarios;

[0025] S23, analyzing the generated operation scenarios, extracting the main features that affect the key indicators of system operation, and constructing an oblique decision tree based on multi-indicator sparse weights;

[0026] S24. Regularize the sparse weights in the oblique decision tree to obtain an oblique decision tree model.

[0027] Furthermore, in step S22, the optimization target of the operation scenario is expressed as:

[0028]

[0029] In the formula, x s represents the decision variable of running scenario s, f(x s ,ξ s ,η s ) represents the system’s operating performance under operating scenario s, R(x s ,ξs ,η s ) represents the system risk, η s and s They represent the power supply fluctuation and market price fluctuation in the operating scenario s respectively, λ represents the weight coefficient for balancing system performance and risk, s∈S, and S represents the operating scenario set.

[0030] Furthermore, in step S24, the objective function E(q) of the sparse weight regularization in the oblique decision tree is expressed as:

[0031]

[0032] In the formula, represents weight entropy, l1 and l2 represent regularization parameters that control the degree of sparsity, |q| and ||q|| represent sparse regularization terms, and W i (q) is expressed as the weight function associated with sample i, H i (q) represents the indicator or function related to sample i, N represents the number of samples, and i∈I represents the sample index.

[0033] Furthermore, in step S4, the energy block and the carbon block are sequentially subjected to time-series coupling analysis through a linear coupling relationship, which is expressed as:

[0034] C(t)=αE(t)+β+ε t

[0035] Where (C(t)) represents the carbon block, (E(t)) represents the energy block, α and β represent the coupling coefficients between energy and carbon emissions, respectively, and ε t represents the error term.

[0036] Furthermore, the step S5 is specifically as follows:

[0037] The optimal bidding strategy of market participants is described through a game theory model. When the bids in each market meet the Nash equilibrium condition, the optimal bidding strategy of energy blocks and carbon blocks in different markets is obtained by optimizing the utility function that represents the total revenue of the energy market and the carbon market.

[0038] Among them, the Nash equilibrium condition is expressed as:

[0039]

[0040] Among them, U m (b m ,b -m ) represents the utility function of market m under other market bidding bm, which is expressed as:

[0041]

[0042] Where b m Indicates price, b -m Indicates other prices, E m (b m ) represents the potential field m and b m The expected utility associated with this, C m (b m ) represents the relationship between market m and b m The relevant cost, p m Represents the price of market m.

[0043] Furthermore, in step S6, the adaptive transaction auxiliary decision-making model learns and optimizes the transaction strategies of energy blocks and carbon blocks by simulating the interactive behaviors of multiple agents in the market, so as to adapt to different market changes and output the scheduling and transaction strategies of energy blocks and carbon blocks;

[0044] The optimization goal of the adaptive trading assisted decision-making model is to maximize the cumulative reward of each intelligent agent through the scheduling and trading strategies of the output energy blocks and carbon blocks. The intelligent agents include electricity trading agents, carbon emission rights trading agents and green hydrogen trading agents.

[0045] The beneficial effects of the present invention are:

[0046] (1) The present invention provides an efficient operation method for a green hydrogen metallurgical integrated energy system. Aiming at the power fluctuations and market information uncertainty caused by renewable energy generation in the green hydrogen metallurgical integrated energy system, a characterization method for power supply and market uncertainty sets is studied and constructed, and the probability interval of uncertain variables is formed by probability distribution fitting. Secondly, based on these uncertainties, multi-objective optimization problems are studied, and distributed robust optimization is used to balance the expectation and conservatism, while solving the nonlinear constraints of the multi-energy mass flow transmission network, and improving the solution accuracy and efficiency through the continuous convex envelope approximation strategy. Finally, a large number of operation scenarios are generated, the energy efficiency, flexibility and risk of the system are evaluated, and dynamic association rules are proposed to support the management of energy blocks and carbon blocks in multi-market transactions. At the same time, an adaptive transaction decision method based on multi-agent reinforcement learning is explored. The present invention aims to improve the safety, economy and flexibility of the green hydrogen metallurgical integrated energy system, propose a multi-market transaction support strategy, and provide theoretical and practical support for the efficient utilization and sustainable development of green energy.

[0047] (2) This paper proposes to construct the probability interval of uncertain variables in the green hydrogen metallurgical integrated energy system through probability distribution fitting, and adopts the distributed robust optimization method to balance the safety, economy and flexibility goals of the system. At the same time, in view of the nonlinear constraints of the multi-energy and mass flow transmission network, the continuous convex envelope approximation strategy is innovatively applied to improve the accuracy and computational efficiency of the optimization solution.

[0048] (3) The present invention innovatively proposes a method for extracting association rules between energy efficiency, flexibility and risk based on large-scale operation scenarios. By deeply mining the multi-dimensional operation data of the green hydrogen metallurgical integrated energy system, the complex dynamic correlation between the energy efficiency, flexibility and risk of the system in different scenarios is extracted. This method is based on data-driven analysis and uses technical tools such as decision trees and regression models to deeply reveal the interactions and influence paths between system performance indicators. This innovation provides accurate decision-making support for the system's optimized operation and scheduling strategies, and effectively improves the system's overall performance and risk management capabilities in the face of uncertainty and complex market environments.

[0049] (4) This invention proposes an analysis method for the time series coupling characteristics of energy blocks and carbon blocks in the green hydrogen metallurgical integrated energy system, and designs an adaptive trading decision method through multi-agent reinforcement learning technology. This innovation provides decision support for the optimization of energy blocks and carbon blocks in multi-market transactions, effectively improving the flexibility and control efficiency of the system in a complex and changing market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of the efficient operation method of the green hydrogen metallurgical integrated energy system provided by the present invention.

[0051] Figure 2 A block diagram of the principle of random production simulation taking into account multiple uncertainties in power supply and market provided by the present invention.

[0052] Figure 3 A block diagram of the principle of dynamic quantitative analysis of energy efficiency-flexibility-risk based on the oblique decision tree model provided by the present invention.

[0053] Figure 4 A block diagram of the flexible and efficient operating principles based on multi-market transactions of energy blocks and carbon blocks provided by the present invention. DETAILED DESCRIPTION

[0054] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0055] The embodiment of the present invention provides a method for efficiently operating a green hydrogen metallurgical integrated energy system, such as Figure 1 As shown, the following steps are included:

[0056] S1. Based on the uncertainty variables in the green hydrogen metallurgical integrated energy system, a multi-objective optimization operation model for stochastic production simulation is constructed based on the distributed robust optimization method, and it is converted into a linear model using the continuous convex envelope approximation strategy;

[0057] S2. Based on the uncertainty of energy supply in the green hydrogen metallurgical integrated energy system, a large number of operation scenarios are generated through random production simulation, and an oblique decision tree model based on multi-indicator sparse weights and regularization is constructed according to the generated operation scenarios;

[0058] S3, using the constructed oblique decision tree model to extract the association rules between energy efficiency, flexibility and risk in the operation scenario data;

[0059] S4. Based on the extracted association rules between energy efficiency, flexibility and risk and the linear model obtained by transformation, the energy block and the carbon block in the green hydrogen metallurgical comprehensive energy are sequentially subjected to time series coupling analysis and confidence interval analysis, and then the coupling characteristics of the energy block and the carbon block are quantitatively inferred;

[0060] The energy block and carbon block represent the physical energy transmission and carbon emission quota in the green hydrogen metallurgical integrated energy system respectively;

[0061] S5. Analyze the optimal bidding strategies of energy blocks and carbon blocks in different markets in multi-market transactions through game theory models;

[0062] S6. Based on the optimal bidding strategy of energy blocks and carbon blocks, an adaptive trading auxiliary decision-making model based on multi-agent reinforcement learning is constructed, and the scheduling and trading strategies of energy blocks and carbon blocks are outputted to achieve efficient operation of the system.

[0063] Step S1 of the embodiment of the present invention includes the following sub-steps:

[0064] S11. Statistically analyze the historical operation data in the green hydrogen metallurgical integrated energy system, perform probability distribution fitting on the uncertainty variables therein, and characterize the uncertainty variables as corresponding uncertainty sets based on the distances between different probability distributions; the uncertainty variables include power supply fluctuations and market price fluctuations;

[0065] S12. Based on the multiple uncertainty conditions of the uncertainty set, a multi-objective optimization operation model for random production simulation is constructed using the distributed robust optimization method;

[0066] S13. A continuous convex envelope approximation strategy is used to transform the multi-objective optimization operation model with nonlinear constraints into a linear model and solve it to obtain a high-value optimal solution.

[0067] In step S11 of this embodiment, during the operation of the green hydrogen metallurgical integrated energy system, power supply fluctuations and market price changes have significant uncertainties. In order to effectively deal with these uncertainties, this embodiment proposes an uncertainty set characterization method based on probability distribution fitting.

[0068] Specifically, by statistically analyzing historical operating data, the uncertainty of power supply fluctuations (such as fluctuations in renewable energy power generation output) and market price changes (such as fluctuations in electricity spot market prices) are modeled. Probability distribution fitting technology is used to generate probability intervals for multiple uncertain variables in power supply and market, which can effectively characterize multiple possible operating states and provide a data basis for subsequent optimization and scheduling.

[0069] In an example of this embodiment, when describing the uncertainty of power supply and market, the probability distribution fitting method is first used to generate the uncertainty set. The power supply fluctuation (such as the fluctuation of new energy power generation) and the market price fluctuation are set as random variables ξ, and their probability distribution is P ξ . Assume that there is historical data {ξ1,ξ2,........,ξ N}, the probability distribution of these variables can be estimated by kernel density estimation or other probability distribution fitting methods.

[0070] For the characterization of uncertainty sets, Wasserstein distance is used to measure the distance between different probability distributions. Wasserstein distance W is defined as:

[0071]

[0072] Among them, P1 and P2 are two probability distributions, and ∏(P1, P2) represents the joint distribution set of the two distributions. Through this distance, the differences in probability distributions in different power supply and market environments can be described.

[0073] In this embodiment, in order to characterize the uncertainty set, the distribution set of uncertainty variables is defined as:

[0074]

[0075] in, is the reference distribution fitted by historical data, and ρ is the Wasserstein radius, which indicates the degree of uncertainty that the system can accept.

[0076] In step S12 of this embodiment, considering the impact of power supply and market uncertainties on system operation, the distributionally robust optimization (DRO) method is introduced to achieve optimization of random production simulation. In this process, robustness parameters are introduced through the optimization model, so that the system can balance the expectancy and conservatism of operation under different power supply and market environments. Specifically, the optimization objectives cover the safety, economy and flexibility of system operation. In a random environment, distributionally robust optimization can not only provide a stable and reliable production strategy for the green hydrogen metallurgical system, but also minimize operational risks.

[0077] Specifically, in this embodiment, under multiple uncertainties, the distributionally robust optimization (DRO) method is used. Considering the stochastic production optimization problem of the green hydrogen metallurgical system, the objective function can be expressed as the worst-case expectation under the uncertainty set:

[0078]

[0079] Where x represents the decision variable of the system (for example, power generation scheduling, market transaction volume, etc.), ξ represents the uncertainty variable (such as power supply fluctuation, market price, etc.), and f(x,ξ) is the profit and loss function of the system (including indicators such as economy, safety, and flexibility).

[0080] In order to further consider the safety and flexibility of system operation, the extended resource-task network of the green hydrogen metallurgical integrated energy system is introduced as a model constraint condition, so that the system can achieve the optimization effect under a certain robustness level, and the robustness parameter θ is introduced to express the multi-objective optimization operation model for random production simulation as follows:

[0081]

[0082] In the formula, x is the decision variable of the system, ξ is the uncertainty variable, f(x,ξ) is the profit and loss function of the system corresponding to the optimization target, and θ represents the robustness parameter, which is used to control the trade-off between the desirability and conservatism of the system. Indicates based on The expected function of represents the uncertainty set, P ξ represents the probability distribution of the uncertainty variable ξ, represents the reference distribution obtained by fitting the operational history data;

[0083] In step S1 of the embodiment of the present invention, in view of the complexity and nonlinear constraints of the multi-energy mass flow transmission network in the system, a continuous envelope approximation strategy is proposed in this embodiment. Since nonlinear problems in system operation are usually difficult to solve directly, the approximation strategy can be used to convert complex nonlinear problems into linear problems, thereby improving the accuracy and efficiency of the solution. Through the continuous convex envelope technology, it is possible to ensure that a high-value optimal solution is quickly found under limited computing resources, so as to improve the operating efficiency of the system when dealing with multiple uncertainties.

[0084] Specifically, in an example of this embodiment, the objective function f(x) in the original problem is assumed to be nonlinear and difficult to solve directly. The problem can be simplified by finding its convex hull approximation. Assuming f(x) is a nonlinear function, its k The linear approximation at is:

[0085]

[0086] Through this linearization method, the original problem is approximated as a linear programming (LP) problem, so that high-quality solutions can be obtained under limited computing resources. In the optimization solution process, this strategy can be used to improve the computational efficiency and ensure that the accuracy of the solution meets the system's operating requirements.

[0087] like Figure 2 As shown, the principle of stochastic production simulation of the green hydrogen metallurgical integrated energy system taking into account multiple uncertainties in power supply and market in the above step S1 is demonstrated.

[0088] Step S2 of the embodiment of the present invention includes the following sub-steps:

[0089] S21. Power supply fluctuations and market price fluctuations are considered as uncertainty variables in the operation scenario;

[0090] S22, randomly generate uncertainty variables contained in different operation scenarios, and take maximizing the system's operating efficiency and minimizing risks as the optimization goal of the operation scenarios, thereby generating a large number of operation scenarios;

[0091] S23, analyzing the generated operation scenarios, extracting the main features that affect the key indicators of system operation, and constructing an oblique decision tree based on multi-indicator sparse weights;

[0092] S24. Regularize the sparse weights in the oblique decision tree to obtain an oblique decision tree model.

[0093] In step S22 of this embodiment, based on the implementation process of step S1 above, in order to comprehensively evaluate the operating performance of the green hydrogen metallurgical integrated energy system under asynchronous deterministic conditions, a large number of different operating scenarios are generated through random production simulation. By simulating a large number of possible power supply and market environments, the energy efficiency, flexibility and risk characteristics of the system under various operating conditions can be captured. These scenario data not only provide a solid foundation for subsequent optimization and decision-making, but also provide a comprehensive risk assessment and flexibility analysis tool for system operation.

[0094] Specifically, power supply fluctuation ξ and market price fluctuation η are set as uncertainty variables, and different scenario sets S are constructed. Each scenario s∈S contains variables (ξ s ,η s ) is generated by random sampling or Monte Carlo simulation. For each scenario, the optimization goal is to maximize the operating efficiency of the system while minimizing the risk; based on this, in step S22 of this embodiment, the optimization goal of the operating scenario is expressed as:

[0095]

[0096] In the formula, x s represents the decision variable of running scenario s, f(x s ,ξ s ,η s ) represents the system’s operating performance under operating scenario s, R(x s ,ξ s ,η s ) represents the system risk, η s and s They represent the power supply fluctuation and market price fluctuation in the operating scenario s respectively, λ represents the weight coefficient for balancing system performance and risk, s∈S, and S represents the operating scenario set.

[0097] In step S23 of this embodiment, based on a large number of operation scenarios, an oblique decision tree based on multi-indicator sparse weights is proposed to dynamically quantify the energy efficiency, flexibility and risk of the system. The oblique decision tree extracts the main features that affect the key indicators of system operation by introducing sparse weights, and controls the complexity of the model through regularization methods to avoid overfitting. The model can not only efficiently process multidimensional data, but also dynamically identify the intrinsic connection between various indicators in system operation, and provide high-precision quantitative analysis for multi-objective optimization problems in uncertain environments.

[0098] Specifically, the oblique decision tree is a decision tree based on linear segmentation, which is used to process multi-dimensional, continuous data. Assume that the linear segmentation function of the kth node in the decision tree is:

[0099]

[0100] Among them, x is the input feature vector (such as energy efficiency, flexibility, risk and other indicators), ω k is the sparse weight vector of the kth node of the oblique decision tree, b k is the bias term.

[0101] In step S24 of this embodiment, in order to ensure the sparsity of the model and avoid overfitting, regularization is introduced, and the objective function E(q) of the sparse weight regularization in the oblique decision tree is expressed as:

[0102]

[0103] In the formula, represents weight entropy, l1 and l2 represent regularization parameters that control the degree of sparsity, |q| and ||q|| represent sparse regularization terms, and W i (q) is expressed as the weight function associated with sample i, H i (q) represents the indicator or function related to sample i, N represents the number of samples, and i∈I represents the sample index.

[0104] In this embodiment, through regularization processing, the model can automatically select the most important features and remove variables that have little impact on system operation, thereby avoiding overfitting of complex models.

[0105] In step S3 of the embodiment of the present invention, a data-driven method for extracting rules for energy efficiency, flexibility and risk association is proposed by using large-scale data of operation scenarios. The complex dynamic relationship between energy efficiency, flexibility and risk is extracted by mining data mining technology. The rule extraction process is based on technologies such as decision trees and regression analysis, which can deeply explore the correlation between various performance indicators in the system and provide a reference for the system's optimal scheduling and operation strategy. This method provides theoretical support for the efficient operation of integrated energy systems in the face of uncertainty by analyzing the energy efficiency performance and risk characteristics under different operation scenarios.

[0106] Specifically, in this embodiment, based on the operation scenario data, data mining technology is used to extract the association rules between energy efficiency, flexibility and risk. Specifically, the above-mentioned oblique decision tree model is used to identify the association between variables. Assume that the association between the target variable y (such as energy efficiency) and the input feature x can be expressed by a regression model as follows:

[0107]

[0108] Among them, β iis the regression coefficient, and ε is the error term. Through decision tree analysis, the significant relationship between energy efficiency, flexibility, risk and other indicators in different operating scenarios can be identified, providing a basis for system optimization and operation strategy.

[0109] In optimal scheduling, energy efficiency, flexibility and risk are usually set as multi-objective optimization problems. A typical multi-objective optimization model is:

[0110] min f(E,F,R)=w1E+w2F+w3R(10)

[0111] Among them, w1, w2 and w3 are the weights of energy efficiency, flexibility and risk, respectively, indicating their importance in system optimization.

[0112] By limiting the constraints, we can ensure that the optimization results meet the system requirements:

[0113] subject to:E≥E min F≥F min R≥R max (11)

[0114] This will achieve the system optimization goals of maximizing energy efficiency, improving flexibility and minimizing risks.

[0115] like Figure 3 As shown, the green hydrogen metallurgical integrated energy system in step S2 and step S3 described above is based on the principle of dynamic quantitative analysis of energy efficiency-flexibility-risk of the oblique decision tree.

[0116] In step S4 of the embodiment of the present invention, a time-series coupling analysis method is proposed for energy blocks and carbon blocks in multi-market transactions, and a quantitative evaluation is performed through confidence inference. The energy block and the carbon block represent the transmission of physical energy and the changes in carbon emission quotas in the integrated energy system, respectively, and their transactions in the market are highly correlated. Through the coupling analysis model, the interaction between energy and carbon is studied, especially its transmission and trading patterns in different time periods. This embodiment also proposes an inference method based on confidence intervals to quantify the coupling characteristics of energy blocks and carbon blocks under different market conditions, and provide quantitative analysis results for system optimization.

[0117] Specifically, in step S4 of this embodiment, at different time periods t, energy and carbon emissions are correlated. The energy block and the carbon block are sequentially subjected to time series coupling analysis through a linear coupling relationship, which is expressed as:

[0118] C(t)=αE(t)+β+ε t (12)

[0119] Where (C(t)) represents the carbon block, (E(t)) represents the energy block, α and β represent the coupling coefficients between energy and carbon emissions, respectively, and ε t represents the error term.

[0120] Furthermore, in order to evaluate the confidence of this coupling relationship, a confidence interval is constructed. Assuming that the energy and carbon emissions under a set of samples satisfy the normal distribution, the confidence interval can be expressed as:

[0121]

[0122] in, is the estimated coupling coefficient, is the quantile of the standard normal distribution, σ α is the estimated standard error, and n is the sample size.

[0123] In this embodiment, the coupling characteristics between the energy block and the carbon block can be quantitatively inferred through confidence interval analysis.

[0124] In step S5 of the embodiment of the present invention, the coupling characteristics of the energy block and the carbon block are quantitatively inferred, and the bidding mechanism and market coupling characteristics between different markets are explored by conducting multi-market bidding coupling analysis. Based on this, step S5 in this embodiment is specifically as follows:

[0125] The optimal bidding strategy of market participants is described through a game theory model. When the bids in each market meet the Nash equilibrium condition, the optimal bidding strategy of energy blocks and carbon blocks in different markets is obtained by optimizing the utility function that represents the total revenue of the energy market and the carbon market.

[0126] Among them, the Nash equilibrium condition is expressed as:

[0127]

[0128] Among them, U m (b m ,b -m ) represents the utility function of market m under other market bidding bm, which is expressed as:

[0129]

[0130] Where b m Indicates price, b -m Indicates other prices, E m (b m ) represents the potential field m and b m The expected utility associated with this, C m (b m ) represents the relationship between market m and b mThe relevant cost, p m Represents the price of market m.

[0131] Through the above-mentioned optimization utility function, the bidding strategies of energy blocks and carbon blocks in different markets can be obtained, and the coupling analysis of energy and carbon markets can be realized.

[0132] In this embodiment, the above analysis not only helps to improve the market competitiveness of the integrated energy system, but also effectively promotes the coordinated development between the carbon market and the energy market, achieving a dual improvement in economic and environmental benefits.

[0133] In step S6 of the embodiment of the present invention, the adaptive trading auxiliary decision-making model learns and optimizes the trading strategies of energy blocks and carbon blocks by simulating the interactive behaviors of multiple agents in the market, so as to adapt to different market changes and output the scheduling and trading strategies of energy blocks and carbon blocks; the optimization goal of the adaptive trading auxiliary decision-making model is to maximize the cumulative return of each agent through the output scheduling and trading strategies of energy blocks and carbon blocks;

[0134] In this embodiment, the intelligent agents include an electricity trading intelligent agent, a carbon emission rights trading intelligent agent, and a green hydrogen trading intelligent agent obtained based on the timing analysis of carbon block and energy coupling.

[0135] Specifically, in order to improve the flexibility and adaptability of energy blocks and carbon blocks in market transactions, this embodiment introduces multi-agent reinforcement learning technology to involve an adaptive trading auxiliary decision-making model; in this model, the interaction between multiple agents is continuously improved through reinforcement learning, so that the system can adapt to different market changes and make the best decision in a complex trading environment. Through continuous strategy updates and optimization, this method can achieve efficient scheduling and trading of energy blocks and carbon blocks in a multi-market environment, and enhance the market responsiveness and profitability of the system.

[0136] For example, a multi-agent reinforcement learning (MARL) model is introduced. Assume that there are N agents, and the state of each agent i is s i (t), action is a i (t), the reward is γ i (t). The goal of reinforcement learning is to use the strategy π i Make each agent maximize its cumulative reward R i :

[0137]

[0138] Through continuous interaction between agents, the reinforcement learning system can adaptively adjust strategies to optimize the market transactions of energy blocks and carbon blocks. Ultimately, this multi-agent system can achieve optimal scheduling and trading decisions in complex market environments.

[0139] like Figure 4 As shown, the principle of flexible and efficient operation of the green hydrogen metallurgical integrated energy system based on multi-market transactions of energy blocks and carbon blocks in the above steps S4 to S6 is demonstrated.

[0140] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0141] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for efficiently operating a green hydrogen metallurgical integrated energy system, characterized in that: The following steps are involved: S1. Based on the uncertainty variables in the green hydrogen metallurgical integrated energy system, a multi-objective optimization operation model for stochastic production simulation is constructed based on the distributed robust optimization method, and it is converted into a linear model using the continuous convex envelope approximation strategy; S2. Based on the uncertainty of energy supply in the green hydrogen metallurgical integrated energy system, a large number of operation scenarios are generated through random production simulation, and an oblique decision tree model based on multi-indicator sparse weights and regularization is constructed according to the generated operation scenarios; S3, using the constructed oblique decision tree model to extract the association rules between energy efficiency, flexibility and risk in the operation scenario data; S4. Based on the extracted association rules between energy efficiency, flexibility and risk and the linear model obtained by transformation, the energy block and the carbon block in the green hydrogen metallurgical comprehensive energy are sequentially subjected to time series coupling analysis and confidence interval analysis, and then the coupling characteristics of the energy block and the carbon block are quantitatively inferred; The energy block and carbon block represent the physical energy transmission and carbon emission quota in the green hydrogen metallurgical integrated energy system respectively; S5. Analyze the optimal bidding strategies of energy blocks and carbon blocks in different markets in multi-market transactions through game theory models; S6. Based on the optimal bidding strategy of energy blocks and carbon blocks, an adaptive trading auxiliary decision-making model based on multi-agent reinforcement learning is constructed, and the scheduling and trading strategies of energy blocks and carbon blocks are outputted to achieve efficient operation of the system.

2. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 1 is characterized in that: The step S1 comprises the following sub-steps: S11. Statistically analyze the historical operation data in the green hydrogen metallurgical integrated energy system, perform probability distribution fitting on the uncertainty variables therein, and characterize the uncertainty variables as corresponding uncertainty sets based on the distances between different probability distributions; the uncertainty variables include power supply fluctuations and market price fluctuations; S12. Based on the multiple uncertainty conditions of the uncertainty set, a multi-objective optimization operation model for random production simulation is constructed using the distributed robust optimization method; S13. A continuous convex envelope approximation strategy is used to transform the multi-objective optimization operation model with nonlinear constraints into a linear model and solve it to obtain a high-value optimal solution.

3. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 2 is characterized in that: In step S12, the multi-objective optimization operation model for implementing random production simulation is expressed as: In the formula, x is the decision variable of the system, ξ is the uncertainty variable, f(x,ξ) is the profit and loss function of the system corresponding to the optimization target, and θ represents the robustness parameter, which is used to control the trade-off between the desirability and conservatism of the system. Indicates based on The expected function of represents the uncertainty set, P ξ represents the probability distribution of the uncertainty variable ξ, represents the reference distribution obtained by fitting the operational history data; The constraints of the multi-objective optimization operation model are the extended resource-task network of the green hydrogen metallurgical integrated energy system.

4. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 1 is characterized in that: The step S2 comprises the following sub-steps: S21. Power supply fluctuations and market price fluctuations are considered as uncertainty variables in the operation scenario; S22, randomly generate uncertainty variables contained in different operation scenarios, and take maximizing the system's operating efficiency and minimizing risks as the optimization goal of the operation scenarios, thereby generating a large number of operation scenarios; S23, analyzing the generated operation scenarios, extracting the main features that affect the key indicators of system operation, and constructing an oblique decision tree based on multi-indicator sparse weights; S24. Regularize the sparse weights in the oblique decision tree to obtain an oblique decision tree model.

5. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 4 is characterized in that: In step S22, the optimization target of the operation scenario is expressed as: In the formula, x s represents the decision variable of running scenario s, f(x s ,ξ s ,η s ) represents the system’s operating performance under operating scenario s, R(x s ,ξ s ,η s ) represents the system risk, η s and s They represent the power supply fluctuation and market price fluctuation in the operating scenario s respectively, λ represents the weight coefficient for balancing system performance and risk, s∈S, and S represents the operating scenario set.

6. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 4 is characterized in that: In step S24, the objective function E(q) of the sparse weight regularization in the oblique decision tree is expressed as: In the formula, represents weight entropy, l1 and l2 represent regularization parameters that control the degree of sparsity, |q| and ||q|| represent sparse regularization terms, and W i (q) is expressed as the weight function associated with sample i, H i (q) represents the indicator or function related to sample i, N represents the number of samples, and i∈I represents the sample index.

7. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 1 is characterized in that: In step S4, the energy block and the carbon block are sequentially subjected to time-series coupling analysis through a linear coupling relationship, which is expressed as: C(t)=αE(t)+β+ε t Where (C(t)) represents the carbon block, (E(t)) represents the energy block, α and β represent the coupling coefficients between energy and carbon emissions, respectively, and ε t represents the error term.

8. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 1 is characterized in that: The step S5 is specifically as follows: The optimal bidding strategy of market participants is described through a game theory model. When the bids in each market meet the Nash equilibrium condition, the optimal bidding strategy of energy blocks and carbon blocks in different markets is obtained by optimizing the utility function that represents the total revenue of the energy market and the carbon market. Among them, the Nash equilibrium condition is expressed as: subject to b m ≥0 Among them, U m (b m ,b -m ) represents the utility function of market m under other market bidding bm, which is expressed as: Where b m Indicates price, b -m Indicates other prices, E m (b m ) represents the potential field m and b m The relevant expected utility, C m (b m ) represents the relationship between market m and b m The associated costs, p m Represents the price of market m.

9. The efficient operation method of the green hydrogen metallurgical integrated energy system according to claim 1 is characterized in that: In step S6, the adaptive transaction auxiliary decision-making model learns and optimizes the transaction strategies of energy blocks and carbon blocks by simulating the interactive behaviors of multiple agents in the market, so as to adapt to different market changes and output the scheduling and transaction strategies of energy blocks and carbon blocks; The optimization goal of the adaptive trading assisted decision-making model is to maximize the cumulative reward of each intelligent agent through the scheduling and trading strategies of the output energy blocks and carbon blocks. The intelligent agents include electricity trading agents, carbon emission rights trading agents and green hydrogen trading agents.

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