Optimized scheduling method based on electricity-carbon cooperation and source-load low-carbon interaction

Abstract

The invention discloses an optimized scheduling method based on electricity-carbon cooperation and source-load low-carbon interaction, and the method comprises the steps: analyzing the interaction relation between sources and loads, and constructing an electricity-carbon cooperation source-load low-carbon interaction operation system; the method comprises the following steps: constructing a source-load low-carbon interaction demand response optimization scheduling model based on an electricity-carbon collaborative source-load low-carbon interaction operation system; the source-load low-carbon interactive demand response scheduling model comprises an optimal economic optimization scheduling model at a source side and a low-carbon coupling price demand response optimization model at a user side; and solving the source-load low-carbon interactive demand response optimization scheduling model to obtain the optimal output of the source side and the scheduling result of the user side, and obtaining various costs and carbon emissions of the user side. According to the invention, a source-load coordinated electricity-carbon interaction mechanism is established, source-side carbon information is converted into an electricity-carbon coupling price signal which can be sensed by a user, and reasonable sharing of user-side carbon cost is realized; the low-carbon property and economical efficiency of the system are improved, the overall carbon emission of the system is reduced, and new energy consumption and load peak-valley difference optimization are both considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimization, in particular to an optimal scheduling method based on electricity-carbon coordination and source-load low-carbon interaction. BACKGROUND

[0002] Under the promotion of the "double carbon" goal, building a low-carbon power system has become an important research direction. China is striving to build an electricity-carbon integration market. At present, in the research of low-carbon operation of power systems, most of them are carried out from the source side. However, the power system is a "source following load" system, and the user side is the user of electric energy, which affects the system carbon emission. The emission reduction of the power industry involves the electricity-carbon market mechanism and operation strategy. Therefore, it is very important to study how to fully tap the emission reduction potential of the user side, make the user bear the responsibility of carbon emission, and develop user-side pricing strategies in low-carbon demand response.

[0003] Although the existing source-side carbon trading technology can reduce system carbon emission to a certain extent, such as the combination of participating in the carbon trading market and ladder carbon trading, by allowing the source side to participate in carbon market trading to adjust the unit generation plan, it mainly focuses on the optimization of source-side unit dispatching. The carbon information of the source side cannot be effectively converted into a price signal perceived by the user side, and the user-side power adjustment is also difficult to be fed back to the source-side unit generation plan optimization in real time. A complete source-load low-carbon interaction closed loop is not constructed, which leads to the fact that the system cannot realize effective source-load collaborative carbon reduction, and the overall low-carbon capability of the system needs to be tapped.

[0004] The existing technology in the user-side low-carbon demand response strategy mainly uses dynamic carbon emission factors and time-of-use electricity prices as guiding signals. Some technologies use dynamic carbon emission factors as carbon signals to guide users to adjust the electricity consumption time, and use electricity price signals to guide the orderly charging and discharging of electric vehicles. In the electricity-carbon pricing method, the existing technology divides the electricity price period according to the peak and valley, and uses a fixed carbon tax for carbon pricing for all user nodes. However, there are two problems: 1) The user only receives the carbon signal or the electricity price signal, does not bear the carbon cost accompanying the electricity consumption, the single signal guiding is insufficient, and the user cannot intuitively perceive the correlation between the electricity consumption behavior and the carbon emission, which reduces the enthusiasm of the user for low-carbon electricity consumption; 2) The electricity-carbon coupled pricing scheme does not consider the division of electricity price period from the carbon perspective, and uses a fixed carbon tax price, which cannot reflect the difference in carbon cost of electricity consumption caused by the difference in electricity source (thermal power / new energy) of different user nodes. It is difficult to accurately guide the user to shift electricity consumption to low-carbon period, and it is also difficult to ensure the pricing fairness of different node users. SUMMARY

[0005] Technical purposes: In view of the defects of weak correlation between user side pricing strategy and carbon in the prior art, the application discloses an optimal scheduling method based on electric-carbon cooperation and source-load low-carbon interaction, establishes an electric-carbon interaction mechanism of source-load cooperation, converts source side carbon information into an electric-carbon coupling price signal perceivable by users, realizes reasonable sharing of carbon cost on the user side, improves the low-carbon property and economy of the system, reduces the overall carbon emission of the system, and considers new energy consumption and load peak-valley difference optimization.

[0006] Technical scheme: In order to achieve the above technical purposes, the application adopts the following technical scheme.

[0007] An optimal scheduling method based on electric-carbon cooperation and source-load low-carbon interaction, characterized in that the method comprises: analyzing the interaction relationship between source and load, and constructing an electric-carbon cooperative source-load low-carbon interaction operation system; based on the electric-carbon cooperative source-load low-carbon interaction operation system, constructing a source-load low-carbon interaction demand response optimal scheduling model; the source-load low-carbon interaction demand response scheduling model comprises an optimal economic optimal scheduling model of the source side and a low-carbon coupling price demand response optimal model of the user side; solving the source-load low-carbon interaction demand response optimal scheduling model to obtain the optimal output of the source side, the scheduling result of the user side, and the cost and carbon emission of each item of the user side.

[0008] Advantages: The application establishes an electric-carbon interaction mechanism of source-load cooperation, converts source side carbon information into an electric-carbon coupling price signal perceivable by users, realizes reasonable sharing of carbon cost on the user side, and reduces the overall carbon potential level of the system; the application improves the low-carbon property and economy of the system, reduces the overall carbon emission of the system, and considers new energy consumption and load peak-valley difference optimization. Brief description of drawings

[0009] Figure 1 The electric-carbon cooperative source-load low-carbon interaction operation system framework schematic diagram of the embodiment of the application is shown; Figure 2 The carbon price integral area equal schematic diagram of the embodiment of the application is shown; Figure 3 The low-carbon demand response optimal scheduling model framework of the embodiment of the application is shown; Figure 4 The optimal scheduling model solving flowchart of the embodiment of the application is shown; Figure 5 The improved IEEE30 node system topology diagram of the embodiment of the application is shown; Figure 6 The wind power and system load curve of the embodiment of the application is shown; Figure 7 The scene system node carbon potential distribution of the embodiment of the application is shown; Figure 8 A flow chart of a method of an embodiment of the application is shown. DETAILED DESCRIPTION

[0010] In order to enable persons skilled in the art to better understand the schemes of the present application, the technical schemes in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present application.

[0011] EMBODIMENT As shown in the accompanying drawings Figure 1 and the accompanying drawings Figure 8 , an optimal scheduling method based on electric-carbon coordination and source-load low-carbon interaction of the present embodiment includes the following steps: S1, analyzing the interaction relationship between the source and the load, and constructing an electric-carbon coordinated source-load low-carbon interaction operation system; The process of constructing the electric-carbon coordinated source-load low-carbon interaction operation system includes: S11, constructing an electric-carbon coordinated source-load low-carbon interaction operation system framework, including a source side, a carbon trading market and a user side; Due to the source following the load, the source and the load on both sides jointly affect the system carbon potential level, determine the overall carbon emission, and are inseparable from each other. Therefore, the present application designs an electric-carbon coordinated source-load low-carbon interaction operation system framework as shown in the accompanying drawings Figure 1 , through electric-carbon coupling, to promote the low-carbon interaction between the source side and the user side; The source side includes wind turbine generators and thermal power generators, which participate in the low-carbon economic dispatching process considering the power generation cost and the carbon trading cost, and perform unit power generation plan scheduling. At the same time, the unit carbon information is input into the system for carbon emission accounting and carbon potential calculation, and the related results and the carbon quota jointly act on the carbon trading market.

[0012] The carbon trading market is used to receive the carbon emission accounting information of the source side, on the other hand, to output the node carbon potential, and then to affect the carbon signal quantification and pricing strategy. The carbon signal quantification and pricing strategy includes carbon time period division, electric-carbon coupling price, and user satisfaction evaluation, and will pass the electric-carbon coupling price to the user side.

[0013] The user side includes EV charging and discharging users and other load users: Because electric vehicles (EVs) not only have the unique attribute of discharging, but also can be dispatched more flexibly compared to other loads, this invention divides the user-side load into EV charging and discharging users and other load users. The user side will consider electricity costs and discharging subsidy costs based on the electricity carbon coupling price to implement a low-carbon response, with EVs charging and discharging in an orderly manner and other loads adjusting their electricity usage time. The electricity demand on the user side will, in turn, influence the generator units on the source side to adjust their power generation plans, forming an interactive closed loop between source and load to achieve low-carbon and economical operation of the entire power system.

[0014] S12. Utilize the baseline method at the source side to conduct carbon trading on a daily basis and establish a tiered reward and punishment carbon trading model. This invention involves source-side participation in carbon market trading, allocating and trading free carbon allowances on a daily basis within a scheduling cycle, and assigning free carbon allowances to source-side units using a baseline method. The specific steps are as follows: (1) Calculate the total free carbon allowance on the source side during the dispatch cycle based on the output of thermal power units, the actual output of wind power units, and the free carbon allowance coefficient. The formula is as follows: , in, All carbon allowances on the source side are provided free of charge during the scheduling period. Free carbon from the source side Quota coefficient; T One scheduling cycle; The number of thermal power units; For the first i One thermal power unit t Efforts during a specific time period; This refers to the number of wind turbine units; For the first z wind turbine units t Actual output value over a given time period.

[0015] (2) Since the carbon emissions generated by wind turbines are very small and negligible, it is assumed that all carbon emissions on the source side come from thermal power units. Therefore, the formula for calculating the total carbon emissions during the source-side dispatch cycle is: , in, This represents the actual total carbon emissions at the source. For the first i Carbon emission intensity coefficient of each thermal power unit.

[0016] (3) The actual carbon trading volume can be derived from the source-side carbon quota and the actual carbon emissions. : , (4) Divide the carbon trading volume into intervals and use a tiered carbon trading price to calculate costs. When the actual emissions exceed the allowance, purchase carbon emission credits at a tiered price increase (penalty); when the actual emissions are below the allowance, sell the remaining allowances at a tiered price increase (reward). The formula is as follows: , in, For source-side carbon trading costs; The base carbon trading price; Length of carbon emissions; As a reward for the price growth coefficient; To penalize the price growth rate.

[0017] S13. Electric-carbon coupling pricing based on nodal carbon potential; Based on carbon emission theory, a nodal carbon potential calculation model is constructed. Combining unit consumption characteristics, a carbon price considering the rate of change of nodal carbon potential is proposed. A regional carbon emission average factor is incorporated to classify carbon-oriented electricity price periods, and a nodal load marginal electricity-carbon coupling pricing strategy is formulated. Carbon emissions from the source-side power generation are closely related to user electricity demand, which is the decisive factor in carbon emissions. To hold users accountable for their electricity consumption carbon responsibilities, nodal carbon potential can directly reflect the carbon emissions associated with each unit of electricity consumption at a node. Therefore, the carbon emissions of user nodes can be quantified through nodal carbon potential, which also provides an important basis for the rational allocation of low-carbon emission reduction targets in the load region. To enable users to respond to low-carbon emission reduction targets, electricity-carbon information is transmitted from the source side to the load side by coupling price signals. The specific steps are as follows: (1) Based on the carbon emission flow theory, the node carbon potential is calculated according to the proportional sharing principle, reflecting the carbon emissions associated with each unit of electricity consumption at the node. The formula is as follows: , in, for j Node at t Carbon potential at any given moment; For the direction of active power j The set of routes ending at the terminal point; For nodes m exist t Carbon potential at any given moment; For generator i With nodes j The set that is joined; for t time m Node flow j The active power of the line at the node.

[0018] (2) Based on the characteristics of the unit consumption equation and considering the rate of change of node carbon potential, the node carbon price is determined using a quadratic function of node carbon potential. A carbon price that is positively correlated with node carbon potential is established, meaning the higher the carbon potential, the higher the price, and vice versa. The expression for the carbon price determined by node carbon potential is: , in, for j Node at t Carbon price at any given time, in units of ; a , b These are the coefficients of the quadratic and linear terms of the nodal carbon price, respectively. Their coefficients are determined by the carbon price set by the fixed carbon tax, using the principle that the average carbon price remains unchanged, i.e., the integral areas of the carbon prices of the two terms are equal.

[0019] A schematic diagram showing the equal areas of carbon valence integrals is shown below. Figure 2 Transforming both into mathematical problems and Integrating both is as follows: , in, Let S1 be the maximum carbon potential at the node; r is the fixed carbon tax. Based on the principle that the integral areas of carbon price are equal, let S1 equal S2, and calculate the coefficients of the quadratic and linear terms of the nodal carbon price. a , b .

[0020] (3) Carbon-guided electricity pricing is divided into time periods: electricity prices are appropriately increased during high-carbon periods, reduced during low-carbon periods, and kept stable during medium-carbon periods.

[0021] Using the system's average carbon emission factor as the classification standard, and combining it with fuzzy membership functions to determine high-carbon, medium-carbon, and low-carbon time periods, the formula for calculating the system's average carbon emission factor is as follows: , in, z It represents the set of all user nodes contained in the system; for j node t Load size during a given time period.

[0022] The membership function analysis of electricity price periods and carbon time periods is used as follows: , in, , These represent the maximum and minimum values ​​of the system's average carbon emission factor.

[0023] After obtaining the carbon time period membership value, the carbon time period attribution calculation model is as follows: , wherein, is t the corresponding carbon period attribute function under the time period; , , are high, medium and low carbon periods respectively; , is a carbon period membership degree determination value.

[0024] corresponding to different carbon periods are given prices: , wherein, is t the time-of-use electricity price corresponding to the carbon period at the moment; , and are the corresponding prices under high, medium and low carbon periods respectively; wherein .

[0025] The difference in the node coupling price prepared by the application is caused by the different carbon potentials of different nodes. The association of carbon tax yuan / tCO2 and node carbon potential tCO2 / MW is converted into energy price yuan / MW and electricity price coupling, which is expressed as the cost of consuming a unit of electricity, which is the electricity cost and the carbon cost. The calculation formula of the node electricity-carbon coupling price includes: , wherein, is j the coupling price of the node in t the time period.

[0026] S2, based on the source-load low-carbon interactive operation system of electricity-carbon coordination, a source-load low-carbon interactive demand response optimization scheduling model is constructed. The source-load low-carbon interactive demand response optimization scheduling model includes an optimal economic optimization scheduling model on the source side and a low-carbon coupling price demand response optimization model on the user side.

[0027] The framework of the source-load low-carbon interactive demand response optimization scheduling model is shown in Figure 3 , there is a supply-demand balance relationship between the source side and the user side. The source side optimizes the output of each unit under the condition of meeting each constraint, with the minimum generation cost and carbon trading cost as the target, and according to the established pricing strategy, the price information is transmitted to the user side. Various user low-carbon demand response, the user side obtains the optimized load with the minimum demand electricity cost and EV discharge subsidy cost as the target.

[0028] wherein, the optimal economic optimization model of the source side includes: with the minimum generation cost and carbon trading cost F as the objective function, the objective function calculation formula is: , in, , , , These are the costs of generating electricity from thermal power plants, photovoltaic power generation, wind curtailment, and carbon trading. , These are the coal consumption cost and start-up / shutdown cost of thermal power units, respectively. , , For the first i The coal consumption cost coefficient for each thermal power unit is set according to the actual situation. and The respective i Start-up and shutdown costs of a single thermal power unit; This is the cost coefficient for wind power generation; This is the wind curtailment penalty coefficient; For the first z wind turbine units t The predicted output values ​​for the time period were generated using the Latin difference cubic sampling method.

[0029] The constraints include: System active power balance constraints: , in, This is the set of nodes where the load resides; Thermal power unit output constraints: , in, For the start-stop state variables of thermal power units; and For the first i The maximum and minimum output of the thermal power units.

[0030] Thermal power unit ramping constraints: , in, and The first i The uphill and downhill gradient rates of the thermal power units.

[0031] Thermal power unit start-up and shutdown time constraints: , in, and The first i One thermal power unit t- 1. Run or stop time at any given moment; and The firsti The minimum start-up and shutdown time for each thermal power unit.

[0032] Cost constraints for starting and stopping thermal power units: , in, and The first i The cost of a single start-up and shutdown of a thermal power unit.

[0033] Wind turbine output constraints: , Line power constraints: , in, , These represent the minimum and maximum current-carrying power of the line, For the line t Constant flow power.

[0034] The low-carbon coupled price demand response optimization model on the user side includes: On the user side, the electricity-carbon coupling price serves as an incentive, subsidizing EV discharge to guide users' low-carbon demand response, based on the electricity purchase cost under the electricity-carbon coupling price. and EV discharge subsidy costs Minimize is the objective, and the objective function is: , in, for j node t Electricity purchased during a specific time period; for j The number of schedulable EVs per node; The unit discharge subsidy coefficient for EVs; For the first n EVs in t Discharge power during a given period.

[0035] The constraints include: User-side power balance constraints: , in, For the first n EVs in t Charging power during a given period; for j node t Price changes in low-carbon response load over a given period.

[0036] EV charging and discharging power constraints: , in, and These represent the maximum charging and discharging power of the EV, respectively.

[0037] EV charge / discharge state constraints: , in, and For the first n EVs in t The charging and discharging state during a given period is a 0-1 variable; EVs connected to the grid have three states: charging, discharging, and idle. When an EV is charging... The value is 1, when the EV is discharging. The value is 1, and both are 0 when the device is idle.

[0038] To prevent EVs from over-discharging and reducing battery life, and to meet the power requirements of owners when offline, there are EV battery state constraints: , in, and The first n The battery level of the EV when it is connected to the charger and the battery level when it is disconnected from the charger; and The first n The charging and discharging efficiency of an EV; and The first n EVs during the period t and time period t- 1. Battery state of charge; This refers to the rated capacity of the EV battery. and The first n The minimum and maximum states of charge of an EV.

[0039] Other load response constraints: To quantify the response of electricity-carbon coupling prices to loads other than EV flexible dispatch, the electricity-price elasticity coefficient matrix H is used to calculate the response of other loads. The specific calculation model is as follows: , Elasticity coefficient Calculate as follows: , in, and They are the j-th nodes. The original load and load variation for the time period; and The jth node The original electricity price and the electricity price change amount after the electricity-carbon coupling of the time period; wherein, when The self-elasticity coefficient, and vice versa, the mutual-elasticity coefficient.

[0040] S3, as shown in the source load low-carbon interaction demand response optimization scheduling model is solved, the source side optimal output, user side scheduling results, and the user side each cost and carbon emissions are obtained; the specific solving steps are as follows: Figure 4 S31, first input each unit parameter, load and wind power prediction data, etc.; S32, in the optimal economic optimization scheduling model of the source side, taking the most economical operation of the source side as the scheduling target, the source side optimal output and the system branch direct current flow are solved; S33, based on the carbon emission flow theory, the node carbon potential and the regional carbon emission average factor are calculated, the node carbon price is obtained, and the time-of-use electricity price period is divided, and the electricity-carbon coupling price is obtained and transmitted to the user side; S34, in the low-carbon coupling price demand response optimization model of the user side, taking the minimum of the user side power cost and the EV discharge subsidy cost as the objective function, the EV is optimally scheduled under the low-carbon price, and other loads are also responded to the low-carbon price demand response; S35, then the optimized load condition is solved, it is judged whether the node load changes, if the node load changes, return to S32 and execute to S35 in turn, and the related scheduling is operated in combination with the electricity-carbon coupling price; if the node load does not change, the optimization iteration is ended, and the user side each cost and carbon emissions are output. The present application firstly analyzes the interaction between source and load, constructs the source and load low-carbon interaction operation system of electricity-carbon cooperation, secondly, uses the baseline method for carbon trading in the source side in a day unit, establishes the step-type reward and punishment carbon trading model; then, based on the carbon emission theory, the node carbon potential calculation model is constructed, the carbon price considering the node carbon potential change rate is proposed combined with the unit consumption characteristics, the regional carbon emission average factor is integrated to divide the carbon-oriented electricity price period, and the node load marginal electricity-carbon coupling pricing strategy is developed; finally, the source and load low-carbon interaction demand response optimization model based on the electricity-carbon coupling price is constructed, the source side is the optimal economic scheduling model, and the user side is the low-carbon coupling price demand response model, and the optimization model is solved to obtain the scheduling result.

[0041]

[0042] ​To evaluate the feasibility of the pricing method, this invention also proposes an evaluation method that analyzes overall user satisfaction from two perspectives: user satisfaction with electricity usage habits and satisfaction with electricity costs. User satisfaction with electricity usage habits refers to the period when users do not participate in demand response and use electricity according to their original usage plans, at which point user satisfaction is highest. User satisfaction with electricity costs is determined after the addition of carbon pricing to the original electricity price, when users adjust their electricity usage habits to minimize the increase in electricity costs. In this invention, both user satisfaction with electricity usage habits and satisfaction with electricity costs are calculated on a regional user basis, with a maximum value of 1. The formulas for calculating regional user satisfaction with electricity usage habits and satisfaction with electricity costs are as follows: , in, Satisfaction with electricity usage habits; Satisfaction with electricity costs; This is the set of nodes where the load resides; and Before and after changes in electricity usage habits j node t Load during a given time period; and They are respectively j node t Electricity prices before and after the change in time of day.

[0043] The formula for user electricity satisfaction Q is as follows: , Where A is the satisfaction coefficient for electricity usage habits, A∈[0,1]; 1-A is the satisfaction coefficient for electricity costs, Q habit Q cost ∈[0,1], that is, Q∈[0,1].

[0044] Simulation verification: This invention compares the above technical solutions with existing technical solutions in different scenarios: Scenario 1: Time-of-use electricity pricing and fixed carbon tax, electricity and carbon are not coupled, and loads do not participate in demand response.

[0045] Scenario 2: Time-of-use electricity pricing and fixed carbon tax, with electricity-carbon coupling signals guiding loads to participate in demand response, and EVs charging and discharging in an orderly manner.

[0046] Scenario 3: The method proposed in this invention guides the load to participate in demand response through electro-carbon coupling signals, enabling orderly charging and discharging of EVs.

[0047] It has the following technical effects: This invention employs an improved IEEE 30-bus system simulation, in which wind turbines are introduced at 13 nodes. The system topology is shown below. Figure 5As shown, the numbers 1 to 30 are 30 nodes of IEEE, and the corresponding nodes and lines are marked in the topology graph; new energy units are introduced in this verification process, which is closer to the real environment, and energy units can be introduced at any node. In order to build a new type of power system containing new energy, and it is more conducive to verify the effectiveness of the method. The parameters of thermal power units are as shown in Table 1, the Latin difference cube sampling method is used to generate wind power prediction, and the system load and wind power prediction curve are shown in Figure 6 , the wind power generation cost coefficient is 100 yuan / MW, the wind power penalty coefficient is 300 yuan / MW, the carbon tax is 150 yuan / ton, the carbon emission interval length is 500t, the reward price growth coefficient is 0.2, the penalty price growth coefficient is 0.25. The EV discharge subsidy is 100 yuan / MW, the EV considered in the application is of the same type, and the EV parameters are as shown in Table 2, and the Monte Carlo prediction is used to predict the uncertain parameters such as access time and departure time. It is set that 24 and 26 nodes have 500 and 200 schedulable EVs respectively. The scheduling time is 24h per day, the scheduling time interval is 1h, and the MATLAB calls the cplex solver to solve.

[0048] Table 1 Thermal power unit parameter table

[0049] Table 2 EV parameter table

[0050] According to the optimized unit output and system load, the carbon potential of each node under the application technology in scenario 3 is as shown in Figure 7 . As shown, the electricity consumption is small from 01:00 to 06:00, the thermal power output accounts for a low proportion, and the overall node carbon potential of the system is relatively low, while the electricity consumption is relatively high from 18:00 to 22:00, and the overall node carbon potential of the system is relatively high. The carbon potential of node 1 is always equal to the carbon emission intensity of the generator because it is only connected to the generator without load, and the carbon potential of node 13 is always 0 because the carbon emission of the wind power unit is small and can be ignored. The electricity load of nodes 12-20 is small, and the distance from the wind power unit is relatively close, and the electricity basically comes from the wind power unit, so the carbon potential is relatively low. The other nodes are relatively close to the thermal power unit, and most of the electricity comes from the thermal power unit, so the node carbon potential is high.

[0051] The "first", "second" (if any) in the names mentioned in the embodiments of the application are only used for name identification, and do not represent the first and second in order.

[0052] From the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above-mentioned example methods can be implemented by means of software and a general hardware platform. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product. The computer software product can be stored in a storage medium, and the storage medium can be various types of memories, such as random access memory (RAM), read only memory (ROM), flash memory, etc., such as read only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0053] The above description is only the preferred embodiments of the present application, and it should be pointed out that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. An optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction, characterized in that the method... include: Analyze the interaction between power sources and loads, and construct a power-carbon synergistic, low-carbon interactive operation system. Based on the source-load low-carbon interactive operation system of electricity-carbon synergy, a source-load low-carbon interactive demand response optimization scheduling model is constructed. The source-load low-carbon interactive demand response scheduling model includes the optimal economic optimization scheduling model on the source side and the low-carbon coupled price demand response optimization model on the user side. The source-load low-carbon interactive demand response optimization scheduling model is solved to obtain the optimal output on the source side, the scheduling results on the user side, and the various costs and carbon emissions on the user side.

2. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 1, characterized in that, The process of constructing a source-load low-carbon interactive operation system with synergistic electricity and carbon emissions includes: A framework for a source-load low-carbon interactive operation system with coordinated electricity and carbon emissions is constructed, including the source side, the carbon trading market, and the user side. The source side includes wind turbines and thermal power units. The carbon trading market is used to receive carbon emission accounting information from the source side and output node carbon potential. The user side load includes EV charging and discharging users and other load users. Carbon trading is conducted on a daily basis using the baseline method at the source side, establishing a tiered reward and punishment carbon trading model. Electric-carbon coupling pricing based on nodal carbon potential.

3. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 2, characterized in that: The process of establishing a tiered reward and punishment carbon trading model includes: Calculate all free carbon allowances on the source side within the dispatch cycle based on the output of thermal power units, the actual output of wind power units, and the free carbon allowance coefficient. Calculate the total carbon emissions during the source-side scheduling cycle; The actual carbon trading volume is derived from the source-side carbon quota and the actual carbon emissions. The system divides emissions into zones based on actual carbon trading volume and uses a tiered carbon trading price to calculate costs. When actual emissions exceed the allowance, carbon emission credits are purchased at a tiered price as a penalty; when actual emissions are below the allowance, the remaining allowances are sold at a tiered price as a reward.

4. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 3, characterized in that: Using a tiered carbon trading price system, the formula for calculating source-side carbon trading costs includes: ， in, For source-side carbon trading costs; The base carbon trading price; Length of carbon emissions; As a reward for the price growth coefficient; To penalize the price growth rate, This represents the actual carbon trading volume.

5. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 2, characterized in that: Electric-carbon coupling pricing based on nodal carbon potential includes the following process: Based on the carbon emission flow theory, the node carbon potential is calculated according to the principle of proportional sharing. Based on the characteristics of the unit consumption equation, considering the rate of change of node carbon potential, the node carbon price is determined using a quadratic function of node carbon potential, and a carbon price that is positively correlated with node carbon potential is formulated. Carbon-guided electricity pricing is divided into time periods: electricity prices are increased during high-carbon periods, decreased during low-carbon periods, and kept stable during medium-carbon periods. This is coupled with carbon prices to obtain the nodal carbon-coupled electricity price.

6. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 5, characterized in that: Carbon tax is positively correlated with carbon price at node carbon potential, and the calculation formula includes: ， in, for j Node at t Carbon price at any given moment for j Node at t Carbon potential at any given moment; a , b These are the coefficients of the quadratic and linear terms of the nodal carbon price, respectively. Their coefficients are determined by the carbon price set by the fixed carbon tax, using the principle that the average carbon price remains unchanged, i.e., the integral areas of the carbon prices of the two terms are equal.

7. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 5, characterized in that: The formula for calculating the cost of nodal electrocarbon coupling includes: ， in, for j Node at t Coupling price for different time periods; for t The time-of-use electricity price corresponds to the carbon emission period; for j Node at t The carbon price at any given moment.

8. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 1, characterized in that: The optimal economic optimization model on the source side includes: minimizing power generation cost and carbon trading cost as the objective function, with constraints including: system active power balance constraint, thermal power unit output constraint, thermal power unit ramping constraint, thermal power unit start-up and shutdown time constraint, thermal power unit start-up and shutdown cost constraint, wind turbine output constraint, and line power constraint; the low-carbon coupled price demand response optimization model on the user side includes: minimizing electricity purchase cost and EV discharge subsidy cost under the electricity-carbon coupled price as the objective function, with constraints including: user-side power balance constraint, EV charging and discharging power constraint, EV charging and discharging state constraint, EV battery state constraint, and other load response constraints.

9. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 1, characterized in that: Solving the source-load low-carbon interactive demand response optimization scheduling model includes: S31. Input the parameters of each unit, load and wind power forecast data; S32. In the optimal economic scheduling model on the source side, with the most economical operation on the source side as the scheduling objective, solve for the optimal power output on the source side and the DC power flow of the system branches. S33. Based on the carbon emission flow theory, calculate the node carbon potential and the regional carbon emission average factor, obtain the node carbon price, divide the time-of-use electricity price period, and obtain the electricity-carbon coupling price to transmit it to the user side. S34. In the low-carbon coupled price demand response optimization model on the user side, the objective function is to minimize the user side electricity cost and EV discharge subsidy cost. The EV is scheduled for low-carbon price optimization, while other loads respond to low-carbon price demand response. S35. Subsequently, the optimized load situation is obtained by solving the problem. It is determined whether the node load has changed. If the node load has changed, return to S32 and then proceed to S35 in sequence. If the node load has not changed, the optimization iteration ends and the user-side costs and carbon emissions are output.

10. The optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction according to claim 1, characterized in that: After solving the source-load low-carbon interactive demand response optimization scheduling model, the process also includes calculating user electricity satisfaction to evaluate the optimization scheduling results. User electricity satisfaction includes user satisfaction with electricity usage habits and satisfaction with electricity costs. Formula for calculating user electricity satisfaction Q include: ， Where A represents the satisfaction rate coefficient for electricity usage habits. Satisfaction with electricity usage habits; Satisfaction with electricity costs.

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