Energy dispatch methods, systems, equipment, and media for carbon trading and green electricity trading

Abstract

This invention discloses an energy dispatching method, system, equipment, and medium for carbon trading and green electricity trading, specifically relating to the field of energy dispatching technology. The method includes: acquiring three types of core data from a target region; discretizing the corresponding data according to a preset minimum time unit, calculating the theoretical maximum green electricity consumption for each time period, and generating spatiotemporal carbon asset package data; calculating the coupling degree index for each time period and completing time period type labeling identification, generating an enhanced dataset; constructing a multi-objective optimization dispatching model with the dual objectives of maximizing carbon emission reduction benefits and minimizing comprehensive energy costs, introducing a coupling degree correction factor to obtain the optimal dispatching curve; and finally generating and issuing corresponding dispatching instructions to complete closed-loop iteration. This invention achieves the spatiotemporal binding of the physical attributes of green electricity, contractual ownership, and carbon asset value, improving the compliance and executability of dispatching schemes, and balancing corporate carbon emission reduction benefits with energy cost optimization.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and more specifically, to energy dispatching methods, systems, equipment, and media for carbon trading and green electricity trading. Background Technology

[0002] With the continued deepening and advancement of the "dual carbon" goals, the national carbon emission trading market has completed multiple compliance cycles, gradually expanding its coverage from the power generation industry to high-energy-consuming industries such as steel, non-ferrous metals, chemicals, and building materials. Carbon emission compliance management has become a rigid constraint on the production and operation of relevant enterprises. Simultaneously, the national green electricity trading market system is constantly improving, achieving coordinated development of routine intra-provincial trading and cross-provincial / regional special trading. The assessment of renewable energy power consumption responsibility weights has also become a core requirement for enterprise energy management. Against this backdrop, the energy management model of high-energy-consuming enterprises has shifted from the traditional "supply guarantee as the primary focus, cost as a secondary consideration" to a multi-objective coordinated management approach encompassing "power security and supply guarantee, carbon compliance, green electricity consumption achievement, and a balance between cost control and carbon revenue mining." This has created an urgent industry demand for refined energy dispatching technologies adapted to the linkage rules of the carbon market and the green electricity market.

[0003] Existing energy dispatch technologies for "dual-carbon" scenarios can be mainly divided into three categories, and all of them have obvious limitations in practical applications: The first type of technology focuses on the source-load supply and demand matching optimization on the grid side or enterprise side, and only incorporates carbon price and green electricity consumption responsibility weight as fixed boundary conditions into the model. It does not incorporate the time constraints, ownership attributes, and settlement and write-off rules of the green electricity trading contracts signed by enterprises into the core dispatch logic. As a result, the actual green electricity consumption period in the generated dispatch scheme does not match the power generation period agreed in the contract. This not only makes it impossible to complete the compliant write-off of green electricity trading contracts, but may also lead to the enterprise failing to meet the green electricity consumption responsibility weight assessment, and even the compliance risk of "green electricity has been consumed but cannot offset carbon emissions". The second type of technology optimizes trading strategies solely for the carbon market or green electricity market. These strategies are often based on historical price data, annual quotas, and annual consumption targets to formulate medium- to long-term trading strategies. They fail to link these strategies with the daily electricity load curves of enterprises and the daily power generation forecasts of regional renewable energy sources in a spatiotemporal dimension. This prevents the breakdown of macro-level trading strategies into executable, minute-level energy dispatch instructions, resulting in a complete disconnect between the trading strategies and actual energy dispatch, and the cost optimization objectives set by the strategies cannot be achieved. The third type of technology attempts to construct multi-objective optimization dispatch models, but these often focus on minimizing overall energy costs as the core optimization objective, failing to incorporate the carbon emission reduction benefits of green electricity consumption into the core optimization dimension and ignoring the carbon asset attributes of green electricity. Some bi-objective optimization methods also fail to consider the differences in contract constraints across different time periods, applying uniform weight settings to all time periods throughout the dispatch cycle. This results in the Pareto optimal solution being unexecutable during periods with strict contract constraints, leading to a severe lack of robustness and executability in the dispatch scheme.

[0004] Furthermore, there are still subtle differences in the rules connecting the current carbon market and the green electricity market. The ownership determination of green electricity trading across provinces and regions, and the timing requirements for green electricity consumption and carbon emission deduction, all place higher demands on the precision of spatiotemporal matching in energy dispatch. Existing technologies cannot meet this dispatch requirement that balances compliance and economic efficiency. There is an urgent need to develop an energy dispatch method that can achieve spatiotemporal binding of the physical attributes of green electricity, contract ownership, and carbon asset value, and adapt to the rules of the dual market linkage. Summary of the Invention

[0005] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide an energy dispatching method, system, equipment, and medium for carbon trading and green electricity trading. The following solutions address the technical problems mentioned in the background art, such as the mismatch between green electricity consumption and contract ownership, insufficient feasibility of dispatching schemes, and the inability to balance carbon emission reduction benefits with comprehensive energy costs.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an energy dispatch method for carbon trading and green electricity trading, comprising S1: acquiring real-time carbon quota price data from the carbon trading market, real-time green electricity premium data from the green electricity trading market, and real-time operation data of the power system;

[0007] S2: Based on the real-time carbon quota price data, the real-time green electricity premium data, and the system marginal carbon intensity, construct a carbon-certificate dynamic equivalent mapping function, wherein the system marginal carbon intensity is dynamically calculated based on the real-time operation data of the power system;

[0008] S3: Based on the carbon emission flow theory, calculate the real-time carbon emission factor of each load node in the power grid, and establish a scheduling coordination shadow price signal between the source-side carbon cost and the load-side green electricity revenue according to the green electricity trading contract of the load-side entity.

[0009] S4: Based on the carbon-certificate dynamic equivalent mapping function and the scheduling coordination shadow price signal, a scheduling optimization model is constructed with the goal of minimizing the conditional risk value of the total system cost. The scheduling optimization model includes power balance constraints, unit operation constraints, total carbon quota constraints, and green electricity consumption responsibility weight constraints.

[0010] S5: Solve the scheduling optimization model to generate scheduling instructions, which include source-side unit output plans, load-side green electricity consumption plans, and reserved trading positions in the carbon market and green electricity market.

[0011] Energy dispatch systems for carbon trading and green electricity trading include:

[0012] Data acquisition module: used to collect green electricity generation forecast curves and enterprise load forecast curves in the target area, enterprise carbon quota balance and real-time carbon price data, and to analyze enterprise green electricity trading contracts to extract contract electricity volume, price and power generation period parameters;

[0013] Time series processing module: used to discretize the collected time series data according to the preset minimum time unit, calculate the theoretical maximum green electricity consumption of each time segment, bind physical power generation capacity, enterprise electricity demand, and contract ownership in the corresponding time period, and generate standardized spatiotemporal carbon asset package data.

[0014] Data augmentation module: used to calculate the time-segment coupling index for each time segment, complete the time-segment type labeling based on the coupling index and source-load matching relationship, supplement the corresponding feature fields on the basis of spatiotemporal carbon asset package data, and generate an augmented dataset;

[0015] The optimization solution module is used to construct a dual-objective optimization scheduling model that maximizes carbon emission reduction benefits and minimizes comprehensive energy costs based on the enhanced dataset. After introducing a coupling degree correction factor as a scheduling reliability weight, the model is solved to generate the optimal scheduling curve covering the entire scheduling cycle.

[0016] The scheduling execution module is used to generate and issue energy dispatch instructions, carbon asset management instructions, and green electricity contract execution instructions based on the optimal scheduling curve.

[0017] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the energy dispatch method for carbon trading and green electricity trading.

[0018] A computer storage medium storing a computer program, which, when executed by a processor, implements the energy dispatching method for carbon trading and green electricity trading.

[0019] The technical effects and advantages of this invention are as follows:

[0020] 1. This invention achieves deep linkage between the trading rules of the two markets and the refined energy dispatching of enterprises by connecting the data links of the carbon trading market, the green electricity trading market and the enterprise energy dispatching system. It breaks down the macro trading strategy into implementable time-sharing dispatching instructions, thus solving the industry pain point of the disconnect between trading strategy and actual energy dispatching.

[0021] 2. This invention binds and matches the physical power generation capacity of green electricity, the electricity demand of enterprises, and the ownership of green electricity contracts in the same period, eliminating invalid green electricity quotas without legal ownership or exceeding supply and demand capacity. This fundamentally avoids the compliance risks of mismatch between green electricity consumption and contractual agreements and the inability of green electricity to offset carbon emissions, and ensures the compliance of the entire dispatch process.

[0022] 3. This invention quantifies the degree of constraint of contract ownership on green electricity consumption by constructing a time-period coupling index, completes the labeling identification of time periods with different constraint types, sets differentiated scheduling rules for different types of time periods, and introduces a coupling degree correction factor as a scheduling reliability weight, which effectively improves the actual executability and scenario robustness of the scheduling scheme.

[0023] 4. This invention breaks through the technical limitations of traditional single cost optimization by constructing a dual-objective optimization scheduling model that maximizes carbon emission reduction benefits and minimizes comprehensive energy costs. It simultaneously realizes the synergistic optimization of enterprise carbon emission reduction benefit mining and comprehensive energy cost control, and can flexibly adapt to the different operational strategy needs of enterprises. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0025] Figure 2 This is a schematic diagram of the process for acquiring spatiotemporal carbon asset package data according to the present invention;

[0026] Figure 3 This is a schematic diagram of the time period type identification and enhanced dataset generation process of the present invention;

[0027] Figure 4 This is a schematic diagram of the instruction issuance process of the present invention;

[0028] Figure 5 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] As attached Figures 1 to 4 The energy dispatch methods for carbon trading and green electricity trading shown include:

[0031] S1: Obtain the green electricity generation forecast curve and enterprise load forecast curve of the target area as the first set of data; obtain the enterprise carbon quota balance and real-time carbon price as the second set of data; analyze the enterprise green electricity trading contract to extract the contract electricity volume, contract price and contract power generation period as the third set of data;

[0032] It should be specifically noted that the first set of data was acquired as follows: A stable data transmission link was established using encrypted communication through a dedicated data interface opened by the power grid dispatching system. The green electricity generation forecast curve (denoted as G(t), where t is a time node and the minimum sampling unit is 15 minutes) and the enterprise load forecast curve (denoted as L(t), where the time dimension of t perfectly matches G(t)) for the next 24 hours were collected from the target area. Both types of curves used a 15-minute minimum time sampling unit to form continuous time-series data nodes. G(t) includes the total predicted power generation of renewable energy sources such as wind power and photovoltaics within the area (unit: MW). Each time segment T... n The corresponding predicted green electricity generation G(T) n The value is calculated by converting power to a 15-minute duration, with units of MWh. L(t) is the enterprise's predicted power consumption curve (unit: MW), generated by the grid side in conjunction with the enterprise's historical power consumption patterns and production plans; each time segment T... n Corresponding forecast enterprise load L(T) nThe value is calculated by converting power consumption to a 15-minute duration, and the unit is MWh.

[0033] The second set of data is obtained as follows: After identity authentication is completed through the official API interface of the regional carbon exchange, the real-time data of the enterprise's carbon quota account is read, and the available carbon quota balance that has not been frozen or written off during the current compliance period is extracted (denoted as C). q (Unit: tons of CO2 equivalent), data accuracy is accurate to tons of carbon dioxide equivalent; synchronously collects the real-time transaction price of carbon emission allowances on the carbon exchange (denoted as P). c (Unit: Yuan / ton CO2 equivalent) and the best buy and sell quotes are updated at fixed intervals of 5 minutes during trading hours, and the most recent valid price is used during non-trading hours.

[0034] The third set of data is obtained by: connecting to the green electricity trading platform's contract management system, retrieving the green electricity trading contracts that the company has signed and that are within the performance period but have not yet been executed, performing structured parsing of the contract text, and extracting the total electricity volume agreed upon in the contract (denoted as Q). c (Unit: MWh) and remaining available power (denoted as Q) cr (Unit: MWh), Contract Settlement Price (denoted as P) g (Unit: Yuan / MWh), contractually agreed power generation dates and daily power generation periods (denoted as T) c (The format is [start time, end time], and the time granularity is the same as the first set of data). Contracts spanning different time periods are split and labeled according to natural time intervals, retaining the original agreement information such as contract ownership and time period constraints, without modifying the core content of contract electricity and price.

[0035] S2: The first set of data and the third set of data are discretized according to the preset minimum time unit to form multiple continuous time segments; for each time segment, the predicted green electricity generation, predicted enterprise load, and contract-locked green electricity are extracted for the corresponding time period, and the theoretical maximum green electricity consumption of the time segment is calculated. The physical power generation capacity, enterprise electricity demand, and contract ownership are bound to the corresponding time period to generate spatiotemporal carbon asset package data.

[0036] It should be specifically explained that the specific process of the time-series discretization is as follows: taking 15 minutes as a unified preset minimum time unit and the next 24 hours as a complete scheduling cycle, the green electricity generation forecast curve G(t) and the enterprise load forecast curve L(t) in the first set of data, and the contract generation period T in the third set of data are processed. c Remaining contracted electricity volume Q crUnified time alignment and slicing are performed; time segments T1, T2, ..., T3 are continuously divided according to chronological order, resulting in non-overlapping and non-omissionable time segments, with a total of n=96 segments, fully covering the 24-hour scheduling cycle; during the slicing process, the timestamps of all data are synchronized and verified to ensure that each time segment T... n The corresponding G(t), L(t) and contract period T c Complete alignment, with no time sequence offsets, data misalignments, or time period overlaps. After the segmentation is completed, a unique and continuous time sequence number is assigned to each time segment.

[0037] It should be further explained that, for each time segment, the predicted green electricity generation, predicted enterprise load, and contracted green electricity volume for that period are extracted to calculate the theoretical maximum green electricity consumption for that time segment. The specific process is as follows:

[0038] For each time segment T n Three core indicators were extracted from the segmented data: the predicted green electricity generation G(T) during this period. n ), taken from G(t) at T n The corresponding forecast value for the time period reflects the region's green electricity physical supply capacity; the forecast enterprise load L(T) during this time period n ), taken from L(t) in T n The corresponding forecast value for the time period reflects the upper limit of the enterprise's actual electricity demand; the contracted green electricity volume C(T) within this time period n ), from the remaining contracted electricity Q cr According to the contract period T c The green electricity quota obtained is the amount of green electricity that the company has the legal right to use during this period. Green electricity exceeding the contract period is not included in this indicator.

[0039] After completing the three data extractions, the theoretical maximum green energy consumption M(T) for this time segment was calculated. n The calculation formula is: M(T) n ) = min(G(T) n ), L (T n ), C(T) n This calculation simultaneously constrains the upper limit of green electricity supply, the upper limit of electricity demand, and the upper limit of legal ownership. It forcibly binds physical power generation capacity, enterprise electricity demand, and contractual ownership within the same time period, retaining only the effective green electricity scale that can be actually consumed and has legal ownership, and eliminating invalid green electricity quotas that do not have contractual rights, exceed power generation capacity, or exceed electricity load.

[0040] It should be further explained that the generation process of the spatiotemporal carbon asset package data is as follows:

[0041] Using time segment Tn as the unique index field, the theoretical maximum green energy consumption M(T) corresponding to each time segment is determined. n ), corresponding contract settlement unit price P g Perform standardized structured encapsulation; if a single time segment T n For multiple green electricity contracts within their performance periods, P g The weighted average of the remaining available electricity for each contract as a percentage of the total locked green electricity for that period is calculated using the following formula: Where m is T n The number of contracts corresponding to the time period, C i (T) n For the i-th contract in T n Locked green electricity during the period, P gi Let be the settlement unit price of the i-th contract.

[0042] S3: Receive the spatiotemporal carbon asset package data, calculate the time period coupling index corresponding to each time segment, and based on the time period coupling index, the matching relationship between predicted green electricity generation and predicted enterprise load, perform time period type labeling identification on each time segment to generate an enhanced dataset;

[0043] It should be specifically noted that the calculation process of the time-period coupling index is as follows:

[0044] For each time segment T n For the calculation unit, retrieve the theoretical maximum green energy absorption capacity M(T) corresponding to this time period. n ), predicted green electricity generation G (T) n ) and forecast enterprise load L (T n A time-series coupling index calculation model is constructed. This index is used to quantify the degree of constraint of green energy ownership on time-series absorption. The calculation formula is as follows: In the formula, K(T) n ) is T n The coupling index for the time period ranges from 0 to 1; min(G(T) n ), L (T n M(T) represents the theoretically maximum amount of green electricity that can be absorbed during this period, without considering contractual ownership constraints; n ) represents the actual usable green electricity quota after the superimposed contractual ownership constraints; when K(T) n When K(T) = 1, it means that the contractual rights are sufficient and there are no restrictions on the consumption of green electricity; when K(T) = 1, it means that the contractual rights are sufficient and there are no restrictions on the consumption of green electricity. n When min(G(T) < 1, it indicates insufficient contracted electricity volume, and ownership constraints directly limit the scale of green electricity consumption; when min(G(T) < 1, it indicates insufficient contracted electricity volume, and ownership constraints directly limit the scale of green electricity consumption. n ), L (T n When K(T) = 0 (i.e., there is no green electricity supply or no enterprise electricity load during this period), there is no possibility of green electricity consumption during this period, and there are no ownership constraints. The default value is K(T).n =1, and does not participate in subsequent scheduling priority sorting and penalty calculation.

[0045] It should be further explained that the specific process of the time period type labeling identification is as follows:

[0046] Based on the coupling index K(T) n ) and G(T n ), L (T) n The size relationship of each time segment T is determined by priority. n Constraints are categorized into three types and labeled accordingly:

[0047] First priority: If K(T) n If the value is less than 1, the period is marked as a period of ownership constraint, indicating that the contracted green electricity volume is insufficient during this period. Regardless of the matching between green electricity supply and load demand, the consumption of green electricity is limited by contract ownership.

[0048] Second priority (non-ownership constraint period, i.e., K(T)) n =1): If G(T) n )≥L(T n If the green electricity supply is sufficient during this period, then the green electricity consumption limit is determined by the enterprise's electricity load.

[0049] The third priority (non-ownership constraint period, i.e., K(T)) n =1): If G(T) n ) <L(T n If the green electricity shortage occurs, the period will be marked as a green electricity shortage period, indicating that the green electricity generation capacity is insufficient to meet the electricity demand of enterprises and that they need to rely on thermal power and carbon quota compliance.

[0050] Simultaneously, for the period of ownership constraint, calculate the green electricity ownership deficit D(T) for that period. n The calculation formula is: D(T) n ) = min(G(T) n ), L (T n ))-C(T n ).

[0051] It should be further explained that the process of generating the augmented dataset is as follows: using time segment T n As a unique index, a new coupling index K(T) is added to the existing spatiotemporal carbon asset package data. n ), Time Period Type Label, Ownership Shortage D (T) n The dataset consists of three types of fields, forming a standardized enhanced dataset. Each record in the dataset contains complete time-series information, available green electricity quota, cost parameters, coupling indicators, and time period type, with no redundant fields or invalid data.

[0052] S4: Based on the enhanced dataset, a multi-objective optimization scheduling model is constructed with the dual objectives of maximizing carbon emission reduction benefits and minimizing comprehensive energy costs; a coupling degree correction factor is introduced into the multi-objective optimization scheduling model as a scheduling reliability weight, and the multi-objective optimization scheduling model is solved to generate the optimal scheduling curve covering each time segment;

[0053] It should be specifically noted that the construction process of the multi-objective optimization scheduling model is as follows:

[0054] First, the core decision variables of the model are identified. Based on the augmented dataset and the collected data, two types of core decision variables are defined as follows:

[0055] Core decision variable 1: Each time segment T n Actual green electricity dispatch usage M act (T) n (Unit: MWh), representing the actual amount of green electricity consumed by enterprises during this period and used to offset carbon emissions, satisfying the non-negative constraint and not exceeding the theoretical maximum green electricity consumption for the corresponding period, i.e., 0 ≤ MWh. act (T) n )≤M(T n This ensures that the use of green electricity complies with the triple constraints of physical supply, electricity demand, and contractual ownership.

[0056] Core decision variable 2: Each time segment T n Actual carbon quota usage C act (T) n (Unit: tons of CO2 equivalent), representing the amount of carbon allowance used to offset carbon emissions from thermal power plants during this period, satisfying the non-negative constraint, i.e., C act (T) n ≥0, to ensure compliance in the use of carbon allowances.

[0057] Based on the above decision variables, a bi-objective optimization function is constructed to maximize carbon emission reduction benefits and minimize comprehensive energy costs, as detailed below:

[0058] The first objective function (maximizing carbon emission reduction benefits): calculates the total benefit by multiplying the carbon emission reduction corresponding to green electricity consumption by the real-time carbon price, and introduces the thermal power baseline emission factor EF. thermal (Unit: tons of CO2 equivalent / MWh, preset as an industry-standard value, representing the carbon emissions generated per MWh of thermal power generation) and green electricity emission factor EF green (Unit: tons of CO2 equivalent / MWh, preset to an industry-approved value close to 0, representing the carbon emissions generated per MWh of green electricity generated), the objective function expression is: In the formula, F1 represents the total carbon emission reduction benefit. For a single time segment Tn The carbon emission reduction corresponding to the consumption of green electricity, multiplied by the real-time carbon price P c The carbon emission reduction benefits for a single time period are obtained by summing over 96 time segments. The core logic is to maximize the benefits brought by carbon emission reduction by increasing the actual scale of green electricity consumption.

[0059] Second objective function (minimizing overall energy cost): The objective function expression is as follows: In the formula, M act (T) n )·P g The cost of using green electricity during this period; (L(T) n )-M act (T) n ))·P f For a single time segment T n The cost of using thermal power (unit: yuan), of which L (T n )-M act (T) n For the electricity load gap of enterprises that cannot be covered by green electricity, thermal power will supplement the power supply. f Preset unit cost of electricity supply for thermal power (industry benchmark, unit: yuan / MWh); C act (T) n )·P c For a single time segment T n The cost of carbon allowance usage (unit: yuan) is used to offset carbon emissions generated from thermal power generation, ensuring corporate carbon emission compliance. It is calculated by multiplying the actual carbon allowance usage by the real-time carbon price P. c get.

[0060] In addition, considering the time period type, coupling index, and constraints of the augmented dataset, the model constraints are set as follows:

[0061] Green electricity use constraints: for each time segment T n Actual green electricity dispatch volume M act (T) n )≤M(T n That is, the actual usage must not exceed the theoretical maximum green energy consumption M (T) for that period. n Avoid using green electricity beyond supply, demand, or contractual limits;

[0062] Carbon quota constraint: Total carbon quota usage over the entire scheduling cycle This means that the amount of carbon allowances used must not exceed the enterprise's available carbon allowance balance C. q To avoid exceeding the quota and thus violating contractual obligations;

[0063] Carbon emission credit constraints for thermal power plants: The amount of carbon allowance used in each time period Tn must meet the carbon emission credit requirements for thermal power plants, i.e., C act (T)n )≥(L(T) n -M act (T) n ))·EF thermal Ensure that all carbon emissions generated by thermal power generation are offset through carbon allowances, thus complying with carbon compliance requirements;

[0064] Time Period Constraints: Constraints are set specifically based on the time period type, as follows:

[0065] During periods of load constraint (sufficient green electricity supply): encourage full consumption of green electricity, and constrain M act (T) n ) = M(T n (This will allow for full utilization of green electricity resources and reduce the use of thermal power and carbon quota consumption.)

[0066] During periods of green energy shortage (insufficient green energy supply): prioritize the full use of available green energy, constraining M. act (T) n ) = M(T n The shortfall will be supplemented by thermal power, which will be matched with the carbon quota deduction requirements to control the scale of thermal power use.

[0067] During the period of ownership constraints (when contracted electricity volume is insufficient): Green electricity use is controlled according to the green electricity volume locked in the contract, and the basic green electricity use constraints are strictly enforced to avoid exceeding the contract for green electricity use, which would lead to ownership violations.

[0068] It should be further explained that the process and function of introducing the coupling correction factor are as follows:

[0069] To address the issue of low executability of scheduling schemes during periods of low coupling (with severe ownership constraints), a coupling correction factor β is introduced (with a preset value range of 0.1~0.3, which can be dynamically adjusted according to the actual scheduling needs of the enterprise and the strictness of contract constraints). This factor modifies the coupling index K (T... n As a scheduling reliability weight, it is incorporated into a bi-objective optimization function to impose penalties on green electricity scheduling during periods of low coupling, thereby improving the actual executability of the scheduling scheme.

[0070] The specific approach is to add a coupling penalty term to the original bi-objective optimization function. The modified bi-objective optimization function is as follows, and the modification logic closely aligns with the physical meaning of the coupling index and is tightly integrated with the coupling calculation logic:

[0071] ;

[0072] ;

[0073] Correction logic explanation: K(T) n The value range of K(T) is 0~1. nThe higher the value of β, the looser the contractual ownership constraints during that period, and the higher the reliability of green power dispatch. The penalty term β·(1-K(T)) n ))·M act (T) n )·P g The weaker the effect of K(T), the higher the priority of green electricity dispatch; conversely, the stronger the effect of K(T), the higher the priority of green electricity dispatch. n The lower the value of 1-K (T), the more severe the ownership constraint. n The larger the value of M, the stronger the penalty effect, which directly increases the overall energy cost during that period, thereby reducing the cost of M during that period. act (T) n The scheduling priority is set to avoid scheduling schemes failing to be implemented due to contract constraints.

[0074] It should be further explained that the solution process of the multi-objective optimization scheduling model and the generation process of the optimal scheduling curve are as follows:

[0075] Algorithm selection: The NSGA-Ⅲ algorithm (Non-dominated sorting genetic algorithm Ⅲ) is used to solve the modified bi-objective optimization model.

[0076] Algorithm parameter initialization: Set the core algorithm parameters to ensure solution accuracy and efficiency. Specific parameters are as follows: preset iteration count of 1000, population size of 200, crossover probability of 0.8, mutation probability of 0.05. Simultaneously, determine the convergence condition for the Pareto optimal solution set—the error between the optimal solutions of two adjacent iterations is less than 10. -4 This ensures the accuracy of the solution results.

[0077] Population initialization and fitness evaluation: based on decision variables (M act (T) n C act (T) n Within the constraints of the given time frame, an initial population is randomly generated. Each individual in the population corresponds to a complete scheduling scheme covering 96 time segments (including M for each time segment). act (T) n ) and C act (T) n Substitute each individual in the population into the modified biobjective function. and The fitness value is calculated, and in conjunction with the model constraints, invalid individuals that do not meet the constraints (such as exceeding green electricity quotas or carbon quotas) are removed, while valid and feasible individuals are retained.

[0078] Iterative solution and convergence: Perform non-dominated sorting on the effective population individuals to select Pareto optimal individuals. Retain the individual with the best diversity in the solution set through crowding calculation as the parent of the next generation population. Perform crossover and mutation operations on the parent population to generate offspring population to ensure population diversity. Repeat the above fitness evaluation, non-dominated sorting, crossover and mutation steps until the preset number of iterations or convergence conditions are reached, and output the final Pareto optimal solution set.

[0079] Optimal Solution Selection and Optimal Scheduling Curve Generation: From the final Pareto optimal solution set, combined with the company's actual operational needs (prioritizing the balance between carbon emission reduction benefits and comprehensive energy costs, while ensuring compliance), a unique optimal solution is selected; the time segments T corresponding to the optimal solution are then... n M act (T) n (Actual green electricity consumption), C act (T) n (Actual carbon allowance usage), by time segment T1 to T 96 The order of events is arranged to generate the optimal scheduling curve covering the entire scheduling cycle (24 hours).

[0080] S5: Based on the optimal scheduling curve, generate corresponding energy consumption scheduling instructions, carbon asset management instructions, and green electricity contract execution instructions, and issue them for execution.

[0081] It should be specifically explained that, based on the optimal scheduling curve, corresponding energy consumption scheduling instructions, carbon asset management instructions, and green electricity contract execution instructions are generated and issued for execution. The specific process is as follows:

[0082] Energy scheduling instructions: Based on each time segment T in the optimal scheduling curve... n The corresponding actual green energy consumption M act (T) n Based on the core data generation criteria, combined with the time period type label and the predicted enterprise load L(T) for that period, the system generates the data. n ), generating structured control instructions one by one; the timing number T is explicitly marked in the instructions. n Planned green electricity consumption M act (T) n Thermal power supplementary power supply L(Tn)-M act (T) n The load regulation strategy includes differentiated execution rules for different time periods: during load-constrained periods, the directive mandates full consumption of green electricity and the closure of redundant thermal power circuits; during periods of green electricity shortage, the directive limits the scale of green electricity use, automatically supplements thermal power supply, and simultaneously links carbon quota usage; during periods of ownership constraints, the directive strictly limits green electricity consumption to no more than the contracted locked-in amount C(T). nAfter the command is generated, it is converted into a communication protocol format that the energy management system (EMS) can recognize, and then sent to the enterprise's power supply and distribution control system through an encrypted interface. This is used to switch power sources and allocate power loads in real time, and to complete the energy-side scheduling execution.

[0083] Carbon asset management directive: Based on the actual carbon allowance usage C in the optimal scheduling curve. act (T) n ), thermal power base emission factor EF thermal Generate compliant carbon compliance directives, the content of which includes time-series fragments T. n The carbon emissions corresponding to thermal power generation, the number of carbon allowances to be locked and cancelled, and the enterprise's carbon allowance account information; at the same time, the remaining available allowances are calculated based on the total carbon allowance usage. Risk warnings are added for periods approaching the quota limit; after the instruction is generated, it is packaged according to the carbon exchange interface specification and pushed to the enterprise carbon asset management system and carbon trading filing platform to complete the pre-locking and deduction filing of the corresponding carbon quota, so as to avoid cost risks caused by insufficient quota or price fluctuations during the compliance period.

[0084] Green electricity contract execution instructions: based on the planned green electricity consumption M for each time period act (T) n This serves as the basis for contract write-off, combined with the corresponding green electricity contract number and contract period T. c Remaining available power Q cr Generate contract execution instructions; the instructions specify the timing segment T. n The amount of contracted electricity to be written off, the corresponding contract number, and the remaining available contracted electricity must be specified to ensure that the written-off amount does not exceed the contracted green electricity C (T) for that period. n The instructions are generated and uploaded according to the data format of the green electricity trading platform. After receiving the instructions, the platform deducts the contracted electricity in real time and marks the status, completing the one-to-one correspondence and cancellation of dispatched green electricity and contract ownership, and ensuring the compliance of green electricity use ownership.

[0085] As attached Figure 5 The energy dispatch system for carbon trading and green electricity trading shown includes: a data acquisition module, a time series processing module, a data augmentation module, an optimization solution module, and a dispatch execution module;

[0086] The data acquisition module is used to collect the green electricity generation forecast curve and enterprise load forecast curve of the target area, the enterprise carbon quota balance and real-time carbon price data, and at the same time, it analyzes the enterprise green electricity trading contract to extract the contract electricity volume, price and power generation period parameters.

[0087] The time series processing module is used to discretize the collected time series data according to a preset minimum time unit, calculate the theoretical maximum green electricity consumption of each time segment, bind physical power generation capacity, enterprise electricity demand, and contract ownership in the corresponding time period, and generate standardized spatiotemporal carbon asset package data.

[0088] The data augmentation module is used to calculate the time-segment coupling index for each time segment, complete the time-segment type labeling based on the coupling index and the source-load matching relationship, supplement the corresponding feature fields on the basis of the spatiotemporal carbon asset package data, and generate an augmented dataset.

[0089] The optimization solution module is used to construct a dual-objective optimization scheduling model based on the enhanced dataset, which maximizes carbon emission reduction benefits and minimizes comprehensive energy costs. After introducing a coupling degree correction factor as a scheduling reliability weight, the model is solved to generate the optimal scheduling curve covering the entire scheduling cycle.

[0090] The scheduling execution module is used to generate and issue energy consumption scheduling instructions, carbon asset management instructions, and green electricity contract execution instructions based on the optimal scheduling curve.

[0091] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor;

[0092] It should be specifically noted that the electronic device includes at least one processor, at least one memory communicatively connected to the processor, and a communication bus; wherein, the processor and the memory communicate and interact with each other through the communication bus; the memory stores a computer program that can be executed by the processor, and when the processor calls the computer program, it can execute all the steps of the energy dispatching method for carbon trading and green electricity trading;

[0093] It should be further explained that the electronic device also includes an input device and an output device. The input device is connected to the processor and is used to receive user-inputted custom parameters such as the scheduling cycle, the value range of the coupling degree correction factor, and the enterprise scheduling priority configuration. The output device is connected to the processor and is used to display visualized data such as the generated optimal scheduling curve, scheduling execution results, carbon emission reduction benefit calculation results, and energy cost statistics results. The electronic device can be an independent industrial control computer, the core server of an enterprise energy management system, a cloud server cluster, or an embedded industrial control terminal. This embodiment does not limit the specific form of the electronic device, as long as it can perform the scheduling operation described in this embodiment.

[0094] A computer storage medium storing a computer program, which, when executed by a processor, implements all steps of an energy dispatching method for carbon trading and green electricity trading.

[0095] It should be specifically noted that the computer-readable storage medium is a non-transitory storage medium with a physical storage carrier; when the computer program is executed by the processor, it can fully realize the entire process of the aforementioned energy dispatching method, complete the enterprise energy dispatching optimization under the linkage of carbon trading and green electricity trading, and at the same time ensure the compliance and feasibility of the dispatching plan, and achieve synergistic optimization of enterprise carbon emission reduction benefits and comprehensive energy costs.

[0096] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0097] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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. An energy dispatch method for carbon trading and green electricity trading, characterized in that, include: S1: Obtain the green electricity generation forecast curve and the enterprise load forecast curve of the target area as the first set of data; The second set of data is obtained by acquiring the enterprise's carbon quota balance and real-time carbon price; the third set of data is obtained by analyzing the enterprise's green electricity trading contracts to extract the contracted electricity volume, contracted price, and contracted power generation period. S2: The first set of data and the third set of data are discretized according to the preset minimum time unit to form multiple continuous time segments; for each time segment, the predicted green electricity generation, predicted enterprise load, and contract-locked green electricity are extracted for the corresponding time period, and the theoretical maximum green electricity consumption of the time segment is calculated. The physical power generation capacity, enterprise electricity demand, and contract ownership are bound to the corresponding time period to generate spatiotemporal carbon asset package data. S3: Receive the spatiotemporal carbon asset package data, calculate the time period coupling index corresponding to each time segment, and based on the time period coupling index, the matching relationship between predicted green electricity generation and predicted enterprise load, perform time period type labeling identification on each time segment to generate an enhanced dataset; S4: Based on the enhanced dataset, a multi-objective optimization scheduling model is constructed with the dual objectives of maximizing carbon emission reduction benefits and minimizing comprehensive energy costs; a coupling degree correction factor is introduced into the multi-objective optimization scheduling model as a scheduling reliability weight, and the multi-objective optimization scheduling model is solved to generate the optimal scheduling curve covering each time segment; S5: Based on the optimal scheduling curve, generate corresponding energy consumption scheduling instructions, carbon asset management instructions, and green electricity contract execution instructions, and issue them for execution.

2. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The process of time-series discretization involves using 15 minutes as a unified preset minimum time unit and the next 24 hours as a complete scheduling cycle. The green electricity generation forecast curve and enterprise load forecast curve in the first set of data, as well as the contract generation period and contract power volume in the third set of data, are time-series aligned and sliced. Continuous time segments are obtained in chronological order, with a total of 96 segments.

3. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The spatiotemporal carbon asset package data is obtained by extracting the predicted green electricity generation, predicted enterprise load, and contract-locked green electricity for each time segment, calculating the theoretical maximum green electricity consumption for that time segment, binding the physical power generation capacity, enterprise electricity demand, and contract ownership to the corresponding time segment, and then using the time segment as a unique index to standardize and structure the theoretical maximum green electricity consumption and corresponding contract price for each time segment.

4. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The time-period coupling index is obtained by taking each time segment as a calculation unit, retrieving the theoretical maximum green energy consumption, predicted green energy generation, and predicted enterprise load corresponding to that time segment, and calculating the ratio of the theoretical maximum green energy consumption to the theoretical upper limit of green energy consumption without considering contractual ownership constraints. The theoretical upper limit of green energy consumption without considering contractual ownership constraints is the minimum value between the predicted green energy generation and the predicted enterprise load for the corresponding time segment.

5. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The process of labeling and identifying time periods involves using each time segment as an identification unit. Based on the time period coupling index, the matching relationship between predicted green electricity generation and predicted enterprise load, labeling is completed according to a preset priority. The first priority is time segments with a time period coupling index less than 1, which are marked as ownership constraint time periods. The second priority is time segments with a time period coupling index equal to 1 and predicted green electricity generation greater than or equal to predicted enterprise load, which are marked as load constraint time periods. The third priority is time segments with a time period coupling index equal to 1 and predicted green electricity generation less than predicted enterprise load, which are marked as green electricity shortage time periods.

6. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The multi-objective optimization scheduling model is constructed based on the augmented dataset. First, the core decision variables of the model are defined, including the actual green electricity scheduling usage and the actual carbon quota usage for each time segment. Then, with the dual objectives of maximizing carbon emission reduction benefits and minimizing comprehensive energy costs, corresponding optimization functions are constructed. Subsequently, the constraints of the model are set, including green electricity usage constraints, carbon quota constraints, thermal power carbon emission deduction constraints, and time-segmented differentiated constraints. Finally, the coupling degree correction factor is introduced as a scheduling reliability weight, and a corresponding penalty term is added to the optimization function, thus completing the construction of the multi-objective optimization scheduling model.

7. The energy dispatch method for carbon trading and green electricity trading according to claim 1, characterized in that: The optimal scheduling curve is obtained by iteratively solving the constructed multi-objective optimization scheduling model to obtain a Pareto optimal solution set. The unique optimal solution is then selected from the Pareto optimal solution set based on the actual operational needs of the enterprise. The actual green electricity scheduling usage and carbon quota usage for each time segment corresponding to the optimal solution are arranged in chronological order of the time segments to generate an optimal scheduling curve covering the entire scheduling cycle.

8. An energy dispatch system for carbon trading and green electricity trading, characterized in that, include: Data acquisition module: used to collect green electricity generation forecast curves and enterprise load forecast curves in the target area, enterprise carbon quota balance and real-time carbon price data, and to analyze enterprise green electricity trading contracts to extract contract electricity volume, price and power generation period parameters; Time series processing module: used to discretize the collected time series data according to the preset minimum time unit, calculate the theoretical maximum green electricity consumption of each time segment, bind physical power generation capacity, enterprise electricity demand, and contract ownership in the corresponding time period, and generate standardized spatiotemporal carbon asset package data. Data augmentation module: used to calculate the time-segment coupling index for each time segment, complete the time-segment type labeling based on the coupling index and source-load matching relationship, supplement the corresponding feature fields on the basis of spatiotemporal carbon asset package data, and generate an augmented dataset; The optimization solution module is used to construct a dual-objective optimization scheduling model that maximizes carbon emission reduction benefits and minimizes comprehensive energy costs based on the enhanced dataset. After introducing a coupling degree correction factor as a scheduling reliability weight, the model is solved to generate the optimal scheduling curve covering the entire scheduling cycle. The scheduling execution module is used to generate and issue energy dispatch instructions, carbon asset management instructions, and green electricity contract execution instructions based on the optimal scheduling curve.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the energy dispatching method for carbon trading and green electricity trading as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the energy dispatching method for carbon trading and green electricity trading as described in any one of claims 1 to 7.

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