An industrial user electric-carbon-green certificate collaborative benefit optimization method under a multi-coupling mechanism

Abstract

The present application relates to the technical field of power system optimal operation and energy market coordination, in particular to a kind of industrial user electric-car-green certificate coordinated income optimization method under multi-coupling mechanism, in view of the problems such as poor data adaptability of existing technology hierarchical optimization, green certificate and CCER repeated accounting, insufficient algorithm accuracy and the like, the present application sequentially completes multi-source data standardization pretreatment, three market income modeling, integrated adaptive coordinated optimization model construction, improved particle swarm algorithm solution, optimal solution multidimensional verification and parameter calibration, and finally outputs dynamic transaction scheme;The present application realizes three market global coordinated optimization, avoids accounting deviation and policy risk, can adapt to market dynamic fluctuation, effectively improves the comprehensive income of industrial user, and has good practicality and application prospect.

Description

Technical Field

[0001] This invention relates to the field of power system optimization operation and energy market coordination technology, specifically to a method for optimizing the coordinated benefits of electricity, carbon and green certificates for industrial users under a multi-coupling mechanism. Background Technology

[0002] The deep integration and linkage of the electricity market, carbon market, and green certificate market have made participation in multi-market transactions a core way for industrial users to reduce operating costs, fulfill their carbon emission reduction responsibilities, and improve operating profits. Currently, when industrial users optimize transactions in these three markets, they generally adopt a traditional hierarchical optimization model, designing and calculating each market transaction strategy independently, without establishing a unified collaborative optimization system.

[0003] In this model, market decisions are fragmented, achieving only local optima in a single market and failing to coordinate overall interests, thus hindering the full realization of the revenue potential from multi-market collaboration. Furthermore, the raw data from multiple markets encompasses various types of data, including electricity load, renewable energy output, and different transaction prices, with significant differences in data volume and dimensions. Existing technologies lack standardized preprocessing steps, resulting in poor data adaptability and potential deviations in revenue calculations, directly impacting the accuracy of optimization results.

[0004] At the transaction accounting level, existing solutions do not accurately quantify the energy loss during the energy storage charging and discharging process, nor do they set mutual exclusion constraints between green certificates and CCERs. In actual operation, the two types of indicators are frequently calculated repeatedly, which violates market regulatory rules and brings significant compliance risks to industrial users. In terms of optimization algorithms, traditional solution algorithms have weak optimization capabilities, are prone to getting trapped in local optima during the iteration process, and have slow convergence speed, further reducing the optimization quality.

[0005] Furthermore, existing technologies lack robust result verification and parameter calibration mechanisms, resulting in insufficient model resilience and poor adaptability to dynamic changes in market prices, renewable energy output, and electricity load. Moreover, the various business processes are loosely integrated, failing to form a complete operational loop, making it difficult to directly implement optimization strategies to guide actual operations. These combined issues render existing technologies inadequate for adapting to the complex and ever-changing market environment and increasingly stringent policy requirements, necessitating a new, integrated optimization solution for the industry. Summary of the Invention

[0006] To address the shortcomings of existing multi-market optimization technologies for industrial users, such as hierarchical design, redundant calculation of indicators, low algorithm accuracy, and insufficient model robustness, this invention provides a collaborative benefit optimization method for electricity, carbon, and green certificates. This method achieves integrated collaborative optimization of the three markets, eliminates the problem of redundant calculation, improves the model's environmental adaptability, and maximizes the overall benefits for users.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism includes:

[0009] Multi-source data acquisition and standardization processing: Collect multi-dimensional raw data from industrial user load, new energy output, electricity market, carbon market, and green certificate market, and use the Z-score standardization algorithm to perform unified scale transformation on all raw data to construct a multi-market coupled dataset;

[0010] Electricity-Carbon-Green Certificate Three-Market Revenue Modeling: Based on a standardized coupled dataset, independent revenue calculation models for the electricity market, carbon market, and green certificate market are constructed respectively, and the total revenue of the multiple markets is obtained by summing them up.

[0011] Construct an integrated optimization model: With the goal of maximizing net revenue from multiple markets, a global objective function is constructed. Based on the total revenue from multiple markets, various costs are integrated, and an adaptive dynamic adjustment algorithm is introduced to dynamically regulate market weights. An energy storage loss accounting rule is added, and a collaborative constraint system including new energy output, energy storage operation, and mutual exclusion between green certificates and CCERs is established to form an integrated optimization model.

[0012] Solving the integrated optimization model: An improved particle swarm optimization algorithm is used to iteratively solve the integrated optimization model. The algorithm performance is optimized by using an inertia weight reduction strategy. The parameters of the integrated optimization model are dynamically corrected by combining real-time data. The optimal solution is output after the convergence condition is met.

[0013] Optimal solution verification and parameter calibration: Feasibility, optimality and robustness of the optimal solution output by the integrated optimization model are verified in three dimensions. The adaptive dynamic adjustment coefficient and inertia weight are iteratively calibrated through parameter iterative calibration algorithm to control the deviation between the output of the integrated optimization model and the actual industrial operation data within the set threshold.

[0014] Optimization output and dynamic adjustment: Based on the verified optimal solution, three optimization schemes are output: electricity market electricity declaration, carbon market quota and CCER allocation, and green certificate market holding and trading. The scheme parameters are adjusted in real time in combination with market changes and data fluctuations.

[0015] Furthermore, the multi-dimensional raw data includes:

[0016] The data includes three types of operational data: actual electricity load of industrial users, wind power output, and photovoltaic power output; two types of electricity price data: medium- and long-term contract prices and spot prices in the electricity market; two types of carbon trading data: carbon quota prices and CCER prices in the carbon market; and one type of green certificate data: green certificate trading prices in the green certificate market.

[0017] Furthermore, the specific steps in the electricity-carbon-green certificate three-market revenue modeling process include:

[0018] Three market revenue models are established: the electricity market combines medium- and long-term green electricity contracts with spot price deviation trading, calculating revenue based on contracted electricity volume, actual load, and corresponding electricity price; the carbon market relies on the carbon emission reduction logic formed by green electricity replacing thermal power, combining carbon quotas, CCER trading, and fixed parameters related to carbon emissions to calculate revenue; and the green certificate market integrates green certificate trading revenue and green electricity subsidies, calculating corresponding revenue based on green certificate sales volume and effective green electricity consumption.

[0019] The combined revenue from the three markets is used to obtain the total revenue from electricity, carbon, and green certificates, providing a target for subsequent optimization.

[0020] Furthermore, the step of constructing the integrated optimization model specifically includes:

[0021] Construct a global objective function: Based on the total revenue of the three major markets, and superimposed on the costs of new energy power generation, energy storage operation, carbon quotas and green certificate loss penalties, the core optimization objective is to maximize the net revenue of the multiple markets, thus clarifying the overall optimization direction of the model.

[0022] An adaptive dynamic adjustment algorithm is added: Based on fuzzy control logic, the deviation between real-time data and initial data of market, load and new energy output is calculated, and the profit weight of each market is dynamically updated in combination with the adjustment coefficient. This responds to external operating condition fluctuations, balances transaction revenue and operational risks, and enables the model to have dynamic adaptation capabilities.

[0023] Accurately calculate energy storage energy loss: Based on the energy storage charging and discharging power, operating efficiency and time interval, the energy loss of energy storage charging and discharging in each period is quantitatively calculated; and the electricity after deducting the loss is used as the effective green electricity statistical base to eliminate the problem of duplicate accounting of green certificates and CCERs, and ensure the rigor of data and revenue calculation.

[0024] Establish a multi-dimensional collaborative constraint system: set hard constraints in combination with equipment operation specifications and market policy requirements: limit the output range of wind power and photovoltaic to ensure normal equipment operation; constrain the state of charge and time-series change of energy storage to ensure energy storage safety; set mutual exclusion constraints between green certificates and CCERs to conform to industry rules and ensure that the optimization results are legal and feasible.

[0025] Furthermore, the solution steps for the integrated optimization model specifically include:

[0026] Complete the initialization of algorithm parameters, use various transactions and allocation decisions in the electricity, carbon, and green certificate markets as particle position parameters, use the global objective function value as particle fitness, and configure acceleration factors, random numbers, population and iteration basic parameters to build the foundation for algorithm operation;

[0027] The algorithm continuously updates particle velocity and position, and searches for feasible decision solutions in the solution space based on the iterative motion of the particles. It introduces a linearly decreasing inertial weight strategy. In the early stage of the algorithm, the inertial weight is larger, focusing on global exploration and expanding the optimization range. In the later stage, the inertial weight is gradually reduced, shifting to local fine search, improving the optimization accuracy and effectively improving the shortcomings of traditional algorithms.

[0028] A dual-iteration convergence criterion is set up, and the calculation is terminated when the change in the objective function value reaches a minimum threshold or the number of iterations reaches a set upper limit. The entire solution process is synchronously linked with an adaptive dynamic adjustment algorithm to correct the parameters of the integrated optimization model in real time to adapt to fluctuations in the market, load and new energy output, and finally output the optimal solution.

[0029] Furthermore, the optimal solution verification and parameter calibration steps specifically include:

[0030] Conduct 3D verification:

[0031] Feasibility verification involves statistically analyzing the percentage of optimal solutions that satisfy various constraints to determine whether the results meet the hard requirements of equipment operation and market policies, thus confirming that the solution can be implemented.

[0032] Optimality verification involves changing multiple sets of initial conditions and repeatedly solving the problem, comparing the differences in profit data, avoiding the problem of the algorithm getting trapped in local optima, and verifying whether the result is the global optimum.

[0033] Robustness verification involves recalculating the original data, such as load, renewable energy output, and market prices, after applying random perturbations. Based on the fluctuation range of revenue, the model's ability to withstand changes in operating conditions is tested.

[0034] Based on this, the gradient descent algorithm is used to iteratively calibrate the adaptive dynamic adjustment coefficients and inertia weights, continuously reducing the deviation between the model calculation results and actual industrial operation data, and keeping the error within the specified range.

[0035] Furthermore, the optimization result output and dynamic adjustment steps specifically include:

[0036] Based on the validated optimal solution, three types of market-based transaction optimization solutions that can be directly implemented are output, while a long-term dynamic adjustment mechanism is established to adapt to long-term market and policy changes:

[0037] Electricity market optimization plan: Determine the optimal medium- and long-term green electricity contract volume, formulate a time-of-use trading adjustment strategy for the deviation of the spot market volume, smooth out the deviation between load and contract volume, and maximize electricity market revenue;

[0038] Carbon market optimization plan: Determine the optimal carbon allowance holding, formulate CCER time-sharing and scenario-based trading strategies, accurately calculate green electricity emission reductions, avoid carbon allowance penalty risks, and achieve optimal carbon market returns;

[0039] Green certificate market optimization plan: Determine the optimal scale of green certificate holdings, formulate a green certificate trading plan, and combine it with green electricity subsidy benefits to maximize the returns of the green certificate market while meeting policy assessment requirements;

[0040] Industrial users and electricity sales companies will implement the above plan, respond in real time to market prices, renewable energy output, and load fluctuations, dynamically adjust the plan parameters, and continuously maintain the maximum synergistic benefits of multiple markets including electricity, carbon, and green certificates.

[0041] Furthermore, based on long-term green electricity contracts for industrial users, and by using the spot market to smooth out the discrepancy between actual electricity load and contracted electricity volume, a power market revenue model is constructed, as shown in the following formula:

[0042]

[0043] In the formula: For industrial users, green electricity is subject to medium- and long-term contracted electricity volume. This represents the actual power load of industrial users. This refers to the price of medium- to long-term contracts; This is the spot price; The deviation in electricity volume in the spot market is represented by positive values ​​for spot electricity purchases and negative values ​​for spot electricity sales.

[0044] Based on the carbon emission reduction mechanism of replacing thermal power with green electricity, and combining the dual benefits of carbon quota trading and CCER trading, a carbon market revenue model is constructed, as shown in the following formula:

[0045]

[0046] In the formula: To replace thermal power generation with new energy sources; fixed parameters Tons of standard coal / MWh; fixed parameters tons of CO2 per ton of standard coal; For carbon quota prices; CCER trading volume; The price for CCER transactions;

[0047] By integrating the revenue from green certificate trading and the revenue from green electricity subsidies, a green certificate market revenue model is constructed, as shown in the following formula:

[0048]

[0049] In the formula: The number of green certificates sold to industrial users; The price at which green certificates are traded; Effective green electricity consumption for industrial users; The standard for green electricity subsidies for organizations.

[0050] Furthermore, the energy storage loss accounting rules specifically include:

[0051] To prevent duplicate accounting of green certificates and CCERs, and to accurately calculate energy losses during the charging and discharging process of energy storage, the effective green electricity accounting base is calculated by subtracting energy storage energy losses from the actual power generation of new energy sources. The formula for energy storage energy loss is as follows:

[0052]

[0053] In the formula: for Energy loss during constant energy storage; , They are respectively It can store and charge / discharge power at any time; , These are the energy storage charging and discharging efficiencies, respectively. For time intervals.

[0054] A system for implementing the synergistic benefit optimization method of industrial users' electricity-carbon-green certificates under the aforementioned multi-coupling mechanism, comprising:

[0055] The data acquisition standardization module is used to collect raw data from multiple markets, users, and new energy sources, perform Z-score standardization processing, and construct a standardized coupled dataset.

[0056] The three-market revenue modeling module is used to build revenue calculation models for the electricity, carbon, and green certificate markets, and to summarize the total basic revenue of multiple markets.

[0057] The integrated optimization modeling module is used to construct a multi-market collaborative optimization model that includes adaptive weight regulation, energy storage loss accounting, and multi-dimensional constraints.

[0058] The iterative solution module is used to iteratively solve the optimization model using an improved particle swarm optimization algorithm and output the optimal solution.

[0059] The verification and calibration module is used to perform three-dimensional verification of the optimal solution and iterative parameter calibration to optimize model accuracy.

[0060] The optimization results execution module is used to output optimization operation plans for the three major markets and realize the dynamic adaptive adjustment and implementation of the plans.

[0061] The present invention has the following beneficial effects:

[0062] This invention abandons the traditional hierarchical optimization model and incorporates the decision variables, constraints, and profit targets of the electricity, carbon, and green certificate markets into a unified optimization framework. It achieves global collaborative optimization across multiple markets, breaks the limitations of local optima in a single market, and fully releases the profit potential brought about by the linkage of the three major markets.

[0063] This invention establishes precise calculation rules for energy storage energy loss and sets mutually exclusive constraints between green certificates and CCERs. It completely solves the problem of double-counting of the two types of indicators from the perspective of calculation logic, so that the optimization results comply with current market policies and regulatory requirements, and effectively reduce the compliance risks of industrial users participating in multi-market transactions.

[0064] This invention employs the Z-score algorithm to standardize multi-source data, eliminating computational interference caused by data dimensions. Simultaneously, it introduces an inertial weight reduction strategy to improve the particle swarm optimization algorithm, overcoming the drawbacks of traditional algorithms that are prone to getting trapped in local optima and have slow convergence speed, thus significantly improving the overall accuracy of modeling and optimization.

[0065] This invention comprehensively verifies the optimization results from three dimensions: feasibility, optimality, and robustness. It also uses the gradient descent algorithm to complete the iterative calibration of core parameters, which greatly enhances the model's ability to resist disturbances. The model can stably adapt to the dynamic fluctuations of market prices, new energy output, and electricity load, and has stronger environmental adaptability.

[0066] This invention establishes a closed-loop system covering the entire process from data acquisition, modeling and solving, verification and calibration to the implementation of the solution. The output of the power, carbon, and green certificate-specific optimization solutions can directly guide industrial users and power sales companies in carrying out actual operations. It is highly practical and has broad engineering application prospects. Attached Figure Description

[0067] Figure 1 This is a flowchart of a method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism, as proposed in this invention. Detailed Implementation

[0068] The present invention will be further described in detail below with reference to specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Example 1:

[0070] A method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism includes:

[0071] Multi-source data acquisition and standardization processing: Collect multi-dimensional raw data from industrial user load, new energy output, electricity market, carbon market, and green certificate market, and use the Z-score standardization algorithm to perform unified scale transformation on all raw data to construct a multi-market coupled dataset;

[0072] Electricity-Carbon-Green Certificate Three-Market Revenue Modeling: Based on a standardized coupled dataset, independent revenue calculation models for the electricity market, carbon market, and green certificate market are constructed respectively, and the total revenue of the multiple markets is obtained by summing them up.

[0073] Construct an integrated optimization model: With the goal of maximizing net revenue from multiple markets, a global objective function is constructed. Based on the total revenue from multiple markets, various costs are integrated, and an adaptive dynamic adjustment algorithm is introduced to dynamically regulate market weights. An energy storage loss accounting rule is added, and a collaborative constraint system including new energy output, energy storage operation, and mutual exclusion between green certificates and CCERs is established to form an integrated optimization model.

[0074] Solving the integrated optimization model: An improved particle swarm optimization algorithm is used to iteratively solve the integrated optimization model. The algorithm performance is optimized by using an inertia weight reduction strategy. The parameters of the integrated optimization model are dynamically corrected by combining real-time data. The optimal solution is output after the convergence condition is met.

[0075] Optimal solution verification and parameter calibration: Feasibility, optimality and robustness of the optimal solution output by the integrated optimization model are verified in three dimensions. The adaptive dynamic adjustment coefficient and inertia weight are iteratively calibrated through parameter iterative calibration algorithm to control the deviation between the output of the integrated optimization model and the actual industrial operation data within the set threshold.

[0076] Optimization output and dynamic adjustment: Based on the verified optimal solution, three optimization schemes are output: electricity market electricity declaration, carbon market quota and CCER allocation, and green certificate market holding and trading. The scheme parameters are adjusted in real time in combination with market changes and data fluctuations.

[0077] The multi-dimensional raw data includes:

[0078] The data includes three types of operational data: actual electricity load of industrial users, wind power output, and photovoltaic power output; two types of electricity price data: medium- and long-term contract prices and spot prices in the electricity market; two types of carbon trading data: carbon quota prices and CCER prices in the carbon market; and one type of green certificate data: green certificate trading prices in the green certificate market.

[0079] The specific steps in modeling the returns of the electricity-carbon-green certificate three-market system include:

[0080] Three market revenue models are established: the electricity market combines medium- and long-term green electricity contracts with spot price deviation trading, calculating revenue based on contracted electricity volume, actual load, and corresponding electricity price; the carbon market relies on the carbon emission reduction logic formed by green electricity replacing thermal power, combining carbon quotas, CCER trading, and fixed parameters related to carbon emissions to calculate revenue; and the green certificate market integrates green certificate trading revenue and green electricity subsidies, calculating corresponding revenue based on green certificate sales volume and effective green electricity consumption.

[0081] The combined revenue from the three markets is used to obtain the total revenue from electricity, carbon, and green certificates, providing a target for subsequent optimization.

[0082] The specific steps for constructing an integrated optimization model include:

[0083] Construct a global objective function: Based on the total revenue of the three major markets, and superimposed on the costs of new energy power generation, energy storage operation, carbon quotas and green certificate loss penalties, the core optimization objective is to maximize the net revenue of the multiple markets, thus clarifying the overall optimization direction of the model.

[0084] An adaptive dynamic adjustment algorithm is added: Based on fuzzy control logic, the deviation between real-time data and initial data of market, load and new energy output is calculated, and the profit weight of each market is dynamically updated in combination with the adjustment coefficient. This responds to external operating condition fluctuations, balances transaction revenue and operational risks, and enables the model to have dynamic adaptation capabilities.

[0085] Accurately calculate energy storage energy loss: Based on the energy storage charging and discharging power, operating efficiency and time interval, the energy loss of energy storage charging and discharging in each period is quantitatively calculated; and the electricity after deducting the loss is used as the effective green electricity statistical base to eliminate the problem of duplicate accounting of green certificates and CCERs, and ensure the rigor of data and revenue calculation.

[0086] Establish a multi-dimensional collaborative constraint system: set hard constraints in combination with equipment operation specifications and market policy requirements: limit the output range of wind power and photovoltaic to ensure normal equipment operation; constrain the state of charge and time-series change of energy storage to ensure energy storage safety; set mutual exclusion constraints between green certificates and CCERs to conform to industry rules and ensure that the optimization results are legal and feasible.

[0087] The specific steps for solving the integrated optimization model include:

[0088] Complete the initialization of algorithm parameters, use various transactions and allocation decisions in the electricity, carbon, and green certificate markets as particle position parameters, use the global objective function value as particle fitness, and configure acceleration factors, random numbers, population and iteration basic parameters to build the foundation for algorithm operation;

[0089] The algorithm continuously updates particle velocity and position, and searches for feasible decision solutions in the solution space based on the iterative motion of the particles. It introduces a linearly decreasing inertial weight strategy. In the early stage of the algorithm, the inertial weight is larger, focusing on global exploration and expanding the optimization range. In the later stage, the inertial weight is gradually reduced, shifting to local fine search, improving the optimization accuracy and effectively improving the shortcomings of traditional algorithms.

[0090] A dual-iteration convergence criterion is set up, and the calculation is terminated when the change in the objective function value reaches a minimum threshold or the number of iterations reaches a set upper limit. The entire solution process is synchronously linked with an adaptive dynamic adjustment algorithm to correct the parameters of the integrated optimization model in real time to adapt to fluctuations in the market, load and new energy output, and finally output the optimal solution.

[0091] The specific steps for optimal solution verification and parameter calibration include:

[0092] Conduct 3D verification:

[0093] Feasibility verification involves statistically analyzing the percentage of optimal solutions that satisfy various constraints to determine whether the results meet the hard requirements of equipment operation and market policies, thus confirming that the solution can be implemented.

[0094] Optimality verification involves changing multiple sets of initial conditions and repeatedly solving the problem, comparing the differences in profit data, avoiding the problem of the algorithm getting trapped in local optima, and verifying whether the result is the global optimum.

[0095] Robustness verification involves recalculating the original data, such as load, renewable energy output, and market prices, after applying random perturbations. Based on the fluctuation range of revenue, the model's ability to withstand changes in operating conditions is tested.

[0096] Based on this, the gradient descent algorithm is used to iteratively calibrate the adaptive dynamic adjustment coefficients and inertia weights, continuously reducing the deviation between the model calculation results and actual industrial operation data, and keeping the error within the specified range.

[0097] The optimization results output and dynamic adjustment steps specifically include:

[0098] Based on the validated optimal solution, three types of market-based transaction optimization solutions that can be directly implemented are output, while a long-term dynamic adjustment mechanism is established to adapt to long-term market and policy changes:

[0099] Electricity market optimization plan: Determine the optimal medium- and long-term green electricity contract volume, formulate a time-of-use trading adjustment strategy for the deviation of the spot market volume, smooth out the deviation between load and contract volume, and maximize electricity market revenue;

[0100] Carbon market optimization plan: Determine the optimal carbon allowance holding, formulate CCER time-sharing and scenario-based trading strategies, accurately calculate green electricity emission reductions, avoid carbon allowance penalty risks, and achieve optimal carbon market returns;

[0101] Green certificate market optimization plan: Determine the optimal scale of green certificate holdings, formulate a green certificate trading plan, and combine it with green electricity subsidy benefits to maximize the returns of the green certificate market while meeting policy assessment requirements;

[0102] Industrial users and electricity sales companies will implement the above plan, respond in real time to market prices, renewable energy output, and load fluctuations, dynamically adjust the plan parameters, and continuously maintain the maximum synergistic benefits of multiple markets including electricity, carbon, and green certificates.

[0103] Based on long-term green electricity contracts for industrial users, and by using the spot market to smooth out the discrepancy between actual electricity load and contracted electricity volume, a power market revenue model is constructed, as shown in the following formula:

[0104]

[0105] In the formula: For industrial users, green electricity is subject to medium- and long-term contracted electricity volume. This represents the actual power load of industrial users. This refers to the price of medium- to long-term contracts; This is the spot price; The deviation in electricity volume in the spot market is represented by positive values ​​for spot electricity purchases and negative values ​​for spot electricity sales.

[0106] Based on the carbon emission reduction mechanism of replacing thermal power with green electricity, and combining the dual benefits of carbon quota trading and CCER trading, a carbon market revenue model is constructed, as shown in the following formula:

[0107]

[0108] In the formula: To replace thermal power generation with new energy sources; fixed parameters Tons of standard coal / MWh; fixed parameters tons of CO2 per ton of standard coal; For carbon quota prices; CCER trading volume; The price for CCER transactions;

[0109] By integrating the revenue from green certificate trading and the revenue from green electricity subsidies, a green certificate market revenue model is constructed, as shown in the following formula:

[0110]

[0111] In the formula: The number of green certificates sold to industrial users; The price at which green certificates are traded; Effective green electricity consumption for industrial users; The standard for green electricity subsidies for organizations.

[0112] The specific rules for calculating energy loss in energy storage include:

[0113] To prevent duplicate accounting of green certificates and CCERs, and to accurately calculate energy losses during the charging and discharging process of energy storage, the effective green electricity accounting base is calculated by subtracting energy storage energy losses from the actual power generation of new energy sources. The formula for energy storage energy loss is as follows:

[0114]

[0115] In the formula: for Energy loss during constant energy storage; , They are respectively It can store and charge / discharge power at any time; , These are the energy storage charging and discharging efficiencies, respectively. For time intervals.

[0116] Example 2:

[0117] A system for implementing a method to optimize the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism, comprising:

[0118] The data acquisition standardization module is used to collect raw data from multiple markets, users, and new energy sources, perform Z-score standardization processing, and construct a standardized coupled dataset.

[0119] The three-market revenue modeling module is used to build revenue calculation models for the electricity, carbon, and green certificate markets, and to summarize the total basic revenue of multiple markets.

[0120] The integrated optimization modeling module is used to construct a multi-market collaborative optimization model that includes adaptive weight regulation, energy storage loss accounting, and multi-dimensional constraints.

[0121] The iterative solution module is used to iteratively solve the optimization model using an improved particle swarm optimization algorithm and output the optimal solution.

[0122] The verification and calibration module is used to perform three-dimensional verification of the optimal solution and iterative parameter calibration to optimize model accuracy.

[0123] The optimization results execution module is used to output optimization operation plans for the three major markets and realize the dynamic adaptive adjustment and implementation of the plans.

[0124] Application example:

[0125] A method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism, the specific steps of which are as follows:

[0126] Step 1: Multi-source coupled data acquisition and standardized preprocessing

[0127] It collects multi-dimensional original operation and market transaction data of industrial users' electricity, carbon and green certificates, covering three types of operation data: actual electricity load, wind power output and photovoltaic power output of industrial users; two types of electricity price data: long-term contract price and spot price in the electricity market; two types of carbon trading data: carbon quota price and CCER price in the carbon market; and one type of green certificate data: green certificate trading price in the green certificate market.

[0128] For raw data of different magnitudes and dimensions, the Z-score normalization algorithm is used to implement a unified scale transformation, eliminating the interference of data dimension differences on the accuracy of subsequent modeling and solution, and constructing a standardized multi-market coupled dataset. The Z-score normalization calculation formula is as follows:

[0129]

[0130] In the formula: Any type of raw collected data; This represents the sample mean of the original data for the corresponding category. This represents the sample standard deviation of the original data for the corresponding category.

[0131] After processing by this algorithm, all data are uniformly converted into standard data with a mean of 0 and a variance of 1, ensuring that all types of data meet the accuracy requirements for subsequent profit modeling and optimization.

[0132] Step 2: Modeling the Coupling Returns of the Electricity-Carbon-Green Certificates Three Markets

[0133] Based on the standardized multi-market coupled dataset from step one, independent revenue calculation models for the electricity market, carbon market, and green certificate market are constructed respectively. These models are then overlaid and aggregated to obtain a total revenue model for the multi-markets, providing core objective inputs for subsequent integrated optimization.

[0134] 2.1 Electricity Market Revenue Model

[0135] Based on long-term green electricity contracts for industrial users, and by using the spot market to smooth out the discrepancy between actual electricity load and contracted electricity volume, a power market revenue model is constructed, as shown in the following formula:

[0136]

[0137] In the formula: For industrial users, green electricity is subject to medium- and long-term contracted electricity volume. This represents the actual power load of industrial users. This refers to the price of medium- to long-term contracts; This is the spot price; This represents the deviation in electricity volume in the spot market; positive values ​​indicate spot electricity purchases, while negative values ​​indicate spot electricity sales.

[0138] 2.2 Carbon Market Revenue Model

[0139] Based on the carbon emission reduction mechanism of replacing thermal power with green electricity, and combining the dual benefits of carbon quota trading and CCER trading, a carbon market revenue model is constructed, as shown in the following formula:

[0140]

[0141] In the formula: To replace thermal power generation with new energy sources; fixed parameters Tons of standard coal / MWh (standard coal consumption per unit of electricity); fixed parameters Tons of CO2 / ton of standard coal (carbon emission factor from standard coal combustion); For carbon quota prices; CCER trading volume; This refers to the CCER trading price.

[0142] 2.3 Green Certificate Market Return Model

[0143] By integrating the revenue from green certificate trading and the revenue from green electricity subsidies, a green certificate market revenue model is constructed, as shown in the following formula:

[0144]

[0145] In the formula: The number of green certificates sold to industrial users; The price at which green certificates are traded; Effective green electricity consumption for industrial users; The standard for green electricity subsidies for organizations.

[0146] 2.4 Multi-market total return model

[0147] By combining the returns from the three types of markets, the total multi-market coupled return is obtained, as shown in the following formula:

[0148]

[0149] Step 3: Construct a multi-market integrated adaptive collaborative optimization model

[0150] Abandoning the traditional hierarchical optimization model, this approach sets electricity market electricity declaration, carbon quota / CCER allocation, and green certificate holding and trading strategies as core decision variables to construct a unified optimization framework. It integrates revenue, operating costs, and penalty costs to establish a global objective function, embeds an adaptive dynamic adjustment algorithm, adds precise accounting rules for energy storage losses and multi-dimensional collaborative constraints, and achieves integrated collaborative optimization across multiple markets.

[0151] 3.1 Global Optimization Objective Function

[0152] With maximizing net returns from multiple markets as the core objective, and deducting the costs of renewable energy generation, energy storage operation, insufficient carbon allowances, and insufficient green certificates, an adaptive objective function is constructed, as shown in the following formula:

[0153]

[0154] In the formula: Cost of new energy power generation; For energy storage operating costs; Penalties for insufficient carbon allowances; The cost of penalties for insufficient green certificates.

[0155] 3.2 Adaptive Dynamic Adjustment Algorithm

[0156] A fuzzy control algorithm is employed to dynamically adapt to market fluctuations, renewable energy output, and load changes. It collects real-time market data and analyzes the deviation between the data and the initial input data, dynamically adjusting the profit weights of the three markets to balance returns and trading risks. The data deviation calculation formula is as follows:

[0157]

[0158] In the formula: For real-time market data; Use the initial input data for the model.

[0159] Formula for dynamic adjustment of return weights:

[0160]

[0161] In the formula: For the first Initial market return weights; The adjustment coefficient ranges from 0.05 to 0.1 and can be adaptively fine-tuned according to market conditions. The market return weight is dynamically adjusted.

[0162] 3.3 Rules for Calculating Energy Losses from Energy Storage

[0163] To prevent duplicate accounting of green certificates and CCERs, and to accurately calculate energy losses during the charging and discharging process of energy storage, the effective green electricity accounting base is calculated by subtracting energy storage energy losses from the actual power generation of new energy sources. The formula for energy storage energy loss is as follows:

[0164]

[0165] In the formula: for Energy loss during constant energy storage; , They are respectively It can store and charge / discharge power at any time; , These are the energy storage charging and discharging efficiencies, respectively. For time intervals.

[0166] 3.4 Core Coordination and Constraint System

[0167] A multi-constraint system adapted to policies and actual operation is constructed to ensure the compliance and feasibility of the optimization results. The specific constraints are as follows:

[0168] (1) New energy output constraints: Limit wind power and photovoltaic output to the reasonable operating range of the equipment, the formula is as follows: , ;

[0169] (2) Energy storage operation constraints: To ensure the safe and stable operation of energy storage, the state of charge interval and time sequence iteration relationship are constrained, and the formula is as follows: , ;

[0170] (3) Mutual exclusion constraint between green certificates and CCER: Follow policy rules to prevent double accounting, the formula is as follows .

[0171] Step 4: Adaptive solution of the model based on the improved particle swarm optimization algorithm

[0172] An improved particle swarm optimization algorithm (IPSO) is used to iteratively solve the integrated optimization model. An inertial weight reduction strategy is introduced to solve the problems of traditional algorithms being prone to getting trapped in local optima and having slow convergence speed, and outputs a multi-market collaborative optimal decision solution.

[0173] 4.1 Algorithm Parameter Initialization

[0174] Electricity market electricity demand reporting, carbon quota / CCER allocation, and green certificate trading strategies are used as particle position parameters, and the global objective function value is used as the particle fitness value; an acceleration factor is set. random numbers The value range is [0,1], which is used to initialize the particle population and iteration parameters.

[0175] 4.2 Particle Iterative Update Mechanism

[0176] (1) Velocity update formula:

[0177]

[0178] In the formula: For inertial weights, Let be the local optimal position of the i-th particle. Let k be the globally optimal position for the entire particle population, and k be the current iteration number. Let be the velocity of the i-th particle in the k-th iteration along the d-th dimension. Let be the position of the i-th particle in the k-th iteration along the d-th dimension.

[0179] (2) Position update formula:

[0180]

[0181] In the formula: Let be the position of the i-th particle in the (k+1)-th iteration along the d-th dimension, corresponding to the value of the decision variable in the three-market collaborative optimization.

[0182] 4.3 Adaptive Decreasing Strategy for Inertia Weight

[0183] A linearly decreasing inertia weight is used to achieve global exploration in the early stage and precise local optimization in the later stage of the algorithm. The formula is as follows:

[0184]

[0185] Where: Maximum inertia weight Minimum inertia weight Maximum number of iterations , This represents the current iteration number.

[0186] 4.4 Iterative Convergence Criteria

[0187] The iteration stops and the optimal solution is output when any of the following conditions are met: (1) the relative rate of change of the objective function value. Second, the number of iterations reaches the maximum of 1000. During the solution process, the adaptive algorithm in step three is synchronized to adjust model parameters in real time and adapt to dynamic market fluctuations.

[0188] Step 5: Multidimensional verification of the optimal solution and iterative calibration of key parameters

[0189] The algorithm's output global optimal solution is verified in three dimensions: feasibility, optimality, and robustness. At the same time, the key parameters of the model are calibrated through gradient iteration algorithm to ensure the model's accuracy and practicality.

[0190] 5.1 Feasibility Verification

[0191] The compliance of the optimal solution is calculated by checking the constraint satisfaction degree, using the following formula: ,in, The number of core collaborative constraints satisfied by the solution results in step four. The total number of core collaborative constraints set for step three, when When the optimal solution satisfies all operational and policy constraints, the result is feasible.

[0192] 5.2 Optimality Verification

[0193] Select 3 to 5 different initial particle populations and solve the problem repeatedly. Compare the total reward results of each group. If the difference in reward among multiple groups is less than 5%, the output result is determined to be the global optimal solution, thus avoiding the problem of local optima.

[0194] 5.3 Robustness Verification

[0195] Apply ±10% random disturbances to the original load, renewable energy output, and market price data, resolve the model, and determine the disturbance resilience by the rate of change in revenue. The formula is as follows: ,in, The total revenue after data perturbation. The total revenue before data perturbation, when At that time, the robustness of the model was determined to be up to standard.

[0196] 5.4 Iterative Calibration of Key Parameters

[0197] The gradient descent iterative calibration algorithm is used to optimize and calibrate core parameters such as the adaptive adjustment coefficient and the inertia weight range. The formula is as follows:

[0198]

[0199] In the formula: For the parameters to be calibrated, For learning rate, The gradient of the objective function is iterated. After iterative calibration, the deviation between the model output and actual industrial operating data is controlled within 3%.

[0200] Step Six: Outputting and Dynamically Adapting Multi-Market Collaborative Optimization Solutions

[0201] Based on the verified global optimal solution, three types of market-based transaction optimization solutions that can be directly implemented are output, while a long-term dynamic adjustment mechanism is established to adapt to long-term market and policy changes:

[0202] 1. Electricity Market Optimization Plan: Determine the optimal medium- and long-term green electricity contract volume, formulate a time-of-use trading adjustment strategy for the deviation of the spot market volume, smooth out the deviation between load and contract volume, and maximize electricity market revenue;

[0203] 2. Carbon Market Optimization Plan: Determine the optimal carbon allowance holding amount, formulate CCER time-sharing and scenario-based trading strategies, accurately calculate green electricity emission reductions, avoid carbon allowance penalty risks, and achieve optimal carbon market returns;

[0204] 3. Green Certificate Market Optimization Plan: Determine the optimal scale of green certificate holdings, formulate a green certificate trading plan, and combine it with green electricity subsidy benefits to maximize the returns of the green certificate market while meeting policy assessment requirements.

[0205] Industrial users and electricity sales companies will implement the above plan, respond in real time to market prices, renewable energy output, and load fluctuations, dynamically adjust the plan parameters, and continuously maintain the maximum synergistic benefits of multiple markets including electricity, carbon, and green certificates.

[0206] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism, characterized in that, include: Multi-source data acquisition and standardization processing: Collect multi-dimensional raw data from industrial user load, new energy output, electricity market, carbon market, and green certificate market, and use the Z-score standardization algorithm to perform unified scale transformation on all raw data to construct a multi-market coupled dataset; Electricity-Carbon-Green Certificate Three-Market Revenue Modeling: Based on a standardized coupled dataset, independent revenue calculation models for the electricity market, carbon market, and green certificate market are constructed separately, and the total revenue of the multiple markets is obtained by summing them up. Construct an integrated optimization model: With the goal of maximizing net revenue from multiple markets, a global objective function is constructed. Based on the total revenue from multiple markets, various costs are integrated, and an adaptive dynamic adjustment algorithm is introduced to dynamically regulate market weights. An energy storage loss accounting rule is added, and a collaborative constraint system including new energy output, energy storage operation, and mutual exclusion between green certificates and CCERs is established to form an integrated optimization model. Solving the integrated optimization model: An improved particle swarm optimization algorithm is used to iteratively solve the integrated optimization model. The algorithm performance is optimized by using an inertia weight reduction strategy. The parameters of the integrated optimization model are dynamically corrected by combining real-time data. The optimal solution is output after the convergence condition is met. Optimal solution verification and parameter calibration: Feasibility, optimality and robustness of the optimal solution output by the integrated optimization model are verified in three dimensions. The adaptive dynamic adjustment coefficient and inertia weight are iteratively calibrated through parameter iterative calibration algorithm to control the deviation between the output of the integrated optimization model and the actual industrial operation data within the set threshold. Optimization output and dynamic adjustment: Based on the verified optimal solution, three optimization schemes are output: electricity market electricity declaration, carbon market quota and CCER allocation, and green certificate market holding and trading. The scheme parameters are adjusted in real time in combination with market changes and data fluctuations.

2. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The multi-dimensional raw data includes: The data includes three types of operational data: actual electricity load of industrial users, wind power output, and photovoltaic power output; two types of electricity price data: medium- and long-term contract prices and spot prices in the electricity market; two types of carbon trading data: carbon quota prices and CCER prices in the carbon market; and one type of green certificate data: green certificate trading prices in the green certificate market.

3. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The specific steps for modeling the returns of the electricity-carbon-green certificate three-market system include: Three market revenue models are established: the electricity market combines medium- and long-term green electricity contracts with spot price deviation trading, calculating revenue based on contracted electricity volume, actual load, and corresponding electricity price; the carbon market relies on the carbon emission reduction logic formed by green electricity replacing thermal power, combining carbon quotas, CCER trading, and fixed parameters related to carbon emissions to calculate revenue; and the green certificate market integrates green certificate trading revenue and green electricity subsidies, calculating corresponding revenue based on green certificate sales volume and effective green electricity consumption. The combined revenue from the three markets is used to obtain the total revenue from electricity, carbon, and green certificates, providing a target for subsequent optimization.

4. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The steps for constructing the integrated optimization model specifically include: Construct a global objective function: Based on the total revenue of the three major markets, and superimposed on the costs of new energy power generation, energy storage operation, carbon quotas and green certificate loss penalties, the core optimization objective is to maximize the net revenue of the multiple markets, thus clarifying the overall optimization direction of the model. An adaptive dynamic adjustment algorithm is added: Based on fuzzy control logic, the deviation between real-time data and initial data of market, load and new energy output is calculated, and the profit weight of each market is dynamically updated in combination with the adjustment coefficient. This responds to external operating condition fluctuations, balances transaction revenue and operational risks, and enables the model to have dynamic adaptation capabilities. Accurately calculate energy storage energy loss: Based on the energy storage charging and discharging power, operating efficiency and time interval, the energy loss of energy storage charging and discharging in each period is quantitatively calculated; and the electricity after deducting the loss is used as the effective green electricity statistical base to eliminate the problem of duplicate accounting of green certificates and CCERs, and ensure the rigor of data and revenue calculation. Establish a multi-dimensional collaborative constraint system: set hard constraints in combination with equipment operation specifications and market policy requirements: limit the output range of wind power and photovoltaic to ensure normal equipment operation; constrain the state of charge and time-series change of energy storage to ensure energy storage safety; set mutual exclusion constraints between green certificates and CCERs to conform to industry rules and ensure that the optimization results are legal and feasible.

5. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The specific steps for solving the integrated optimization model include: Complete the initialization of algorithm parameters, use various transactions and allocation decisions in the electricity, carbon, and green certificate markets as particle position parameters, use the global objective function value as particle fitness, and configure acceleration factors, random numbers, population and iteration basic parameters to build the foundation for algorithm operation; The algorithm continuously updates particle velocity and position, and searches for feasible decision solutions in the solution space based on the iterative motion of the particles. It introduces a linearly decreasing inertial weight strategy. In the early stage of the algorithm, the inertial weight is larger, focusing on global exploration and expanding the optimization range. In the later stage, the inertial weight is gradually reduced, shifting to local fine search, improving the optimization accuracy and effectively improving the shortcomings of traditional algorithms. A dual-iteration convergence criterion is set up, and the calculation is terminated when the change in the objective function value reaches a minimum threshold or the number of iterations reaches a set upper limit. The entire solution process is synchronously linked with an adaptive dynamic adjustment algorithm to correct the parameters of the integrated optimization model in real time to adapt to fluctuations in the market, load and new energy output, and finally output the optimal solution.

6. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The optimal solution verification and parameter calibration steps specifically include: Conduct 3D verification: Feasibility verification involves statistically analyzing the percentage of optimal solutions that satisfy various constraints to determine whether the results meet the hard requirements of equipment operation and market policies, thus confirming that the solution can be implemented. Optimality verification involves changing multiple sets of initial conditions and solving the problem repeatedly, comparing the differences in profit data, avoiding the problem of the algorithm getting trapped in local optima, and verifying whether the result is the global optimum. Robustness verification involves recalculating the original data, such as load, renewable energy output, and market prices, after applying random perturbations. Based on the fluctuation range of revenue, the model's ability to withstand changes in operating conditions is tested. Based on this, the gradient descent algorithm is used to iteratively calibrate the adaptive dynamic adjustment coefficients and inertia weights, continuously reducing the deviation between the model calculation results and actual industrial operation data, and keeping the error within the specified range.

7. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The optimization result output and dynamic adjustment steps specifically include: Based on the validated optimal solution, three types of market-based transaction optimization solutions that can be directly implemented are output, while a long-term dynamic adjustment mechanism is established to adapt to long-term market and policy changes: Electricity market optimization plan: Determine the optimal medium- and long-term green electricity contract volume, formulate a time-of-use trading adjustment strategy for the deviation of the spot market volume, smooth out the deviation between load and contract volume, and maximize electricity market revenue; Carbon market optimization plan: Determine the optimal carbon allowance holding, formulate CCER time-sharing and scenario-based trading strategies, accurately calculate green electricity emission reductions, avoid carbon allowance penalty risks, and achieve optimal carbon market returns; Green certificate market optimization plan: Determine the optimal scale of green certificate holdings, formulate a green certificate trading plan, and combine it with green electricity subsidy benefits to maximize the returns of the green certificate market while meeting policy assessment requirements; Industrial users and electricity sales companies will implement the above plan, respond in real time to market prices, renewable energy output, and load fluctuations, dynamically adjust the plan parameters, and continuously maintain the maximum synergistic benefits of multiple markets including electricity, carbon, and green certificates.

8. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: Based on long-term green electricity contracts for industrial users, and by using the spot market to smooth out the discrepancy between actual electricity load and contracted electricity volume, a power market revenue model is constructed, as shown in the following formula: In the formula: For industrial users, green electricity is subject to medium- and long-term contract electricity volume. This represents the actual power load of industrial users. This refers to the price of medium- to long-term contracts; This is the spot price; The deviation in electricity volume in the spot market is represented by positive values ​​for spot electricity purchases and negative values ​​for spot electricity sales. Based on the carbon emission reduction mechanism of replacing thermal power with green electricity, and combining the dual benefits of carbon quota trading and CCER trading, a carbon market revenue model is constructed, as shown in the following formula: In the formula: To replace thermal power generation with new energy sources; fixed parameters Tons of standard coal / MWh; fixed parameters tons of CO2 per ton of standard coal; For carbon quota prices; CCER trading volume; The price for CCER transactions; By integrating the revenue from green certificate trading and the revenue from green electricity subsidies, a green certificate market revenue model is constructed, as shown in the following formula: In the formula: The number of green certificates sold to industrial users; The price at which green certificates are traded; Effective green electricity consumption for industrial users; The standard for green electricity subsidies for organizations.

9. The method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in claim 1, characterized in that: The specific rules for calculating energy storage loss include: To prevent duplicate accounting of green certificates and CCERs, and to accurately calculate energy losses during the charging and discharging process of energy storage, the effective green electricity accounting base is calculated by subtracting energy storage energy losses from the actual power generation of new energy sources. The formula for energy storage energy loss is as follows: In the formula: for Energy loss during constant energy storage; , They are respectively It can store and charge / discharge power at any time; , These are the energy storage charging and discharging efficiencies, respectively. For time intervals.

10. A system for implementing the method for optimizing the synergistic benefits of electricity, carbon, and green certificates for industrial users under a multi-coupling mechanism as described in any one of claims 1 to 9, characterized in that: include: The data acquisition standardization module is used to collect raw data from multiple markets, users, and new energy sources, perform Z-score standardization processing, and construct a standardized coupled dataset. The three-market revenue modeling module is used to build revenue calculation models for the electricity, carbon, and green certificate markets, and to summarize the total basic revenue of multiple markets. The integrated optimization modeling module is used to construct a multi-market collaborative optimization model that includes adaptive weight regulation, energy storage loss accounting, and multi-dimensional constraints. The iterative solution module is used to iteratively solve the optimization model using an improved particle swarm optimization algorithm and output the optimal solution. The verification and calibration module is used to perform three-dimensional verification of the optimal solution and iterative parameter calibration to optimize model accuracy. The optimization results execution module is used to output optimization operation plans for the three major markets and realize the dynamic adaptive adjustment and implementation of the plans.

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