An Optimization Scheduling Method Based on Electricity-Carbon Synergy and Source-Load Low-Carbon Interaction

Abstract

This invention discloses an optimized scheduling method based on power-carbon synergy and source-load low-carbon interaction, comprising: analyzing the interaction relationship between sources and loads, and constructing a power-carbon synergy source-load low-carbon interaction operation system; based on the power-carbon synergy source-load low-carbon interaction operation system, constructing a source-load low-carbon interaction demand response optimization scheduling model; the source-load low-carbon interaction demand response scheduling model includes an optimal economic optimization scheduling model on the source side and a low-carbon coupled price demand response optimization model on the user side; solving the source-load low-carbon interaction demand response optimization scheduling model 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. This invention establishes a power-carbon synergy mechanism, transforming source-side carbon information into a user-perceptible power-carbon coupled price signal, achieving reasonable sharing of carbon costs on the user side; improving the system's low-carbon performance and economy, reducing the overall carbon emissions of the system, and taking into account both new energy consumption and load peak-valley difference optimization.

Description

Technical Field

[0001] This invention relates to the field of power system optimization technology, and in particular to an optimized scheduling method based on power carbon synergy and source-load low-carbon interaction. Background Technology

[0002] Driven by the "dual carbon" goals, building a low-carbon power system has become an important research direction. my country is focusing on building an electricity-carbon integrated market. Currently, most research on low-carbon operation of the power system focuses on the source side. However, the power system is a "source-following-load" system, and users are the consumers of electricity, influencing the system's carbon emissions. Emission reduction in the power industry involves the electricity carbon market mechanism and operational strategies. Therefore, it is crucial to study how to fully tap the emission reduction potential on the user side, make users bear the responsibility for carbon emissions, and formulate user-side pricing strategies in low-carbon demand response.

[0003] While existing source-side carbon trading technologies can reduce system carbon emissions to some extent, such as combining participation in carbon trading markets with tiered carbon trading, and adjusting generator power generation plans by having the source side participate in carbon market trading, they mostly focus on source-side unit scheduling optimization. They cannot effectively transform source-side carbon information on the user side into price signals that users can perceive, and user-side electricity consumption adjustments are also difficult to be fed back to source-side unit power generation plan optimization in real time. As a result, a complete source-load low-carbon interaction closed loop has not been built, which means that the system cannot achieve effective source-load coordinated carbon reduction, and the overall low-carbon capability of the system needs to be explored.

[0004] Existing technologies for low-carbon demand response strategies on the user side mostly rely on dynamic carbon emission factors and time-of-use pricing as guiding signals. Some technologies use dynamic carbon emission factors as carbon signals to guide users to adjust their electricity consumption time, and use electricity price signals to guide the orderly charging and discharging of electric vehicles. Regarding electricity carbon pricing methods, existing technologies divide electricity price periods based on peak and off-peak times, and all user nodes use a fixed carbon tax for carbon pricing. However, two problems exist:

[0005] 1) Users only receive carbon signals or electricity price signals and do not bear the carbon costs associated with electricity consumption. The guidance of a single signal is insufficient, and it is impossible to intuitively perceive the correlation between electricity consumption behavior and carbon emissions, which reduces users' enthusiasm for low-carbon electricity consumption.

[0006] 2) The electricity-carbon coupling pricing scheme does not consider the division of electricity price periods from a carbon perspective, and adopts a fixed carbon tax price. It cannot reflect the different electricity carbon costs caused by the difference in electricity sources (thermal power / new energy) among different user nodes. It is difficult to accurately guide users to shift electricity consumption to low-carbon periods, and it cannot guarantee the pricing fairness of users at different nodes. Summary of the Invention

[0007] Technical Objective: To address the weakness of weak correlation between electricity and carbon in existing user-side pricing strategies, this invention discloses an optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction. It establishes an electricity-carbon interaction mechanism based on source-load synergy, transforming source-side carbon information into a user-perceptible electricity-carbon coupled price signal, thereby achieving a reasonable sharing of carbon costs on the user side. This enhances the system's low-carbon performance and economic efficiency, reduces overall system carbon emissions, and balances renewable energy consumption with load peak-valley difference optimization.

[0008] Technical solution: To achieve the above technical objectives, the present invention adopts the following technical solution.

[0009] An optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction, characterized in that the method includes:

[0010] Analyze the interaction between power sources and loads, and construct a power-carbon synergistic, low-carbon interactive operation system.

[0011] 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.

[0012] 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.

[0013] Beneficial effects: This invention establishes a source-load coordinated electricity-carbon interaction mechanism, transforming source-side carbon information into user-perceptible electricity-carbon coupled price signals, thereby achieving reasonable sharing of user-side carbon costs and reducing the overall carbon potential level of the system. This invention improves the system's low-carbon performance and economy, reduces the overall carbon emissions of the system, and takes into account both the consumption of new energy sources and the optimization of load peak-valley differences. Attached Figure Description

[0014] Figure 1 This diagram illustrates the framework of a source-load low-carbon interactive operation system with electric carbon synergy, according to an embodiment of the present invention.

[0015] Figure 2 This diagram illustrates the equality of carbon valence integral areas according to an embodiment of the present invention.

[0016] Figure 3 This illustrates a low-carbon demand response optimization scheduling model framework according to an embodiment of the present invention;

[0017] Figure 4 This diagram illustrates the solution flowchart for the optimized scheduling model according to an embodiment of the present invention.

[0018] Figure 5 This diagram illustrates the improved IEEE 30-node system topology according to an embodiment of the present invention.

[0019] Figure 6 The wind power and system load curves of an embodiment of the present invention are shown;

[0020] Figure 7 This illustrates the node carbon potential distribution in a scenario system according to an embodiment of the present invention.

[0021] Figure 8 A flowchart illustrating a method according to an embodiment of the present invention is shown. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0023] Example

[0024] As attached Figure 1 and attached Figure 8 As shown in the figure, an optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction in this embodiment includes the following steps:

[0025] S1. Analyze the interaction between power sources and loads, and construct a power-carbon synergistic low-carbon interactive operation system.

[0026] The process of constructing a source-load low-carbon interactive operation system with synergistic electricity and carbon emissions includes:

[0027] S11. Construct a source-load low-carbon interactive operation system framework for electricity-carbon synergy, including the source side, carbon trading market and user side;

[0028] Since the source moves with the load, both the source and load sides jointly influence the system's carbon potential level, determining the overall carbon emissions. The two are inextricably linked. Therefore, this invention designs a source-load low-carbon interactive operation system framework with synergistic electricity-carbon integration, as follows: Figure 1 As shown, low-carbon interaction between the source and user sides is promoted through electro-carbon coupling;

[0029] On the source side, including wind turbines and thermal power units, the power generation costs and carbon trading costs are considered during the low-carbon economy dispatch process, and the power generation plan of the units is scheduled. At the same time, the carbon information of the units is input into the system for carbon emission accounting and carbon potential calculation, and the relevant results, together with carbon quotas, affect the carbon trading market.

[0030] The carbon trading market serves two purposes: firstly, it receives information such as carbon emission accounting from the source side; secondly, it outputs nodal carbon potential, thereby influencing carbon signal quantification and pricing strategies. Carbon signal quantification and pricing strategies include carbon time period segmentation, electricity-carbon coupling pricing, and user satisfaction assessment, and the electricity-carbon coupling price is transmitted to the user side.

[0031] 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.

[0032] 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.

[0033] 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:

[0034] (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:

[0035] ,

[0036] 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.

[0037] (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:

[0038] ,

[0039] in, This represents the actual total carbon emissions at the source. For the first i Carbon emission intensity coefficient of each thermal power unit.

[0040] (3) The actual carbon trading volume can be derived from the source-side carbon quota and the actual carbon emissions. :

[0041] ,

[0042] (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:

[0043] ,

[0044] 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.

[0045] S13. Electric-carbon coupling pricing based on nodal carbon potential;

[0046] 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:

[0047] (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:

[0048] ,

[0049] in, for j Node at t Carbon potential at any given moment; For the direction of active power j The set of routes to the terminal points; 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.

[0050] (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:

[0051] ,

[0052] 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.

[0053] 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:

[0054] ,

[0055] 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 .

[0056] (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.

[0057] 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:

[0058] ,

[0059] 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.

[0060] The membership function analysis of electricity price periods and carbon time periods is used as follows:

[0061] ,

[0062] in, , These represent the maximum and minimum values ​​of the system's average carbon emission factor.

[0063] After obtaining the carbon time period membership value, the carbon time period attribution calculation model is as follows:

[0064] ,

[0065] in, Represented as t Carbon time period attribute function corresponding to the time period; , , These are divided into high-carbon, medium-carbon, and low-carbon periods; , This is the membership determination value for carbon time periods.

[0066] Prices are assigned according to different carbon periods:

[0067] ,

[0068] in, for t The time-of-use electricity price corresponds to the carbon emission period; , and These represent the prices corresponding to high, medium, and low carbon periods, respectively; among which... .

[0069] The difference in node coupling prices established in this invention is due to the different carbon potentials of different nodes. The carbon tax (yuan / tCO2) and node carbon potential (tCO2 / MW) are correlated and transformed into an energy price (yuan / MW) coupled with the electricity price. This is expressed as the cost per unit of electricity consumed being both the electricity cost and the carbon cost. The calculation formula for the node electricity-carbon coupling price includes:

[0070] ,

[0071] in, for j Node at t Coupling price for different time periods.

[0072] S2. Based on the source-load low-carbon interactive operation system of electricity-carbon synergy, construct a source-load low-carbon interactive demand response optimization scheduling model; 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.

[0073] The framework of the source-load low-carbon interactive demand response optimization scheduling model is as follows: Figure 3 As shown, there is a supply and demand balance between the source side and the user side. Under the condition of satisfying various constraints, the source side aims to minimize the power generation cost and carbon trading cost, optimizes the output of each unit, and transmits the price information to the user side according to the established pricing strategy. Various users respond to low-carbon demand. The user side aims to minimize the demand electricity cost and EV discharge subsidy cost, and obtains the optimized load.

[0074] The optimal economic optimization model on the source side includes:

[0075] The objective function is to minimize the power generation cost and carbon trading cost F. The formula for calculating the objective function is:

[0076] ,

[0077] 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.

[0078] The constraints include:

[0079] System active power balance constraints:

[0080] ,

[0081] in, This is the set of nodes where the load resides;

[0082] Thermal power unit output constraints:

[0083] ,

[0084] 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.

[0085] Thermal power unit ramping constraints:

[0086] ,

[0087] in, and The first i The uphill and downhill gradient rates of the thermal power units.

[0088] Thermal power unit start-up and shutdown time constraints:

[0089] ,

[0090] in, and The first i One thermal power unit t- 1. Run or stop time at any given moment; and The first i The minimum start-up and shutdown time for each thermal power unit.

[0091] Cost constraints for starting and stopping thermal power units:

[0092] ,

[0093] in, and The first i The cost of a single start-up and shutdown of a thermal power unit.

[0094] Wind turbine output constraints:

[0095] ,

[0096] Line power constraints:

[0097] ,

[0098] in, , These represent the minimum and maximum current power of the line, For the line t Constant flow power.

[0099] The low-carbon coupled price demand response optimization model on the user side includes:

[0100] 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:

[0101] ,

[0102] 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.

[0103] The constraints include:

[0104] User-side power balance constraints:

[0105] ,

[0106] 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.

[0107] EV charging and discharging power constraints:

[0108] ,

[0109] in, and These represent the maximum charging and discharging power of the EV, respectively.

[0110] EV charge / discharge state constraints:

[0111] ,

[0112] 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.

[0113] 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:

[0114] ,

[0115] 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.

[0116] Other load response constraints:

[0117] 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:

[0118] ,

[0119] Elasticity coefficient Calculate as follows:

[0120] ,

[0121] in, and They are the j-th nodes. The original load and load variation for the time period; and They are the j-th nodes. The change in electricity price during a given period and the change in electricity price after coupling with carbon dioxide; where, when When the elasticity is 0, it is the self-elasticity coefficient; otherwise, it is the mutual elasticity coefficient.

[0122] S3, such as Figure 4 As shown, 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. The specific solution steps are as follows:

[0123] S31. First, input the parameters of each unit, load, and wind power forecast data, etc.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 execute S35 in sequence to perform relevant scheduling operations in conjunction with the electricity-carbon coupling price. If the node load has not changed, the optimization iteration ends and the user-side costs and carbon emissions are output.

[0128] This invention first analyzes the interaction between power sources and loads, constructing a low-carbon interactive operation system for power generation and carbon emissions. Second, it utilizes a baseline method on the source side to conduct carbon trading on a daily basis, establishing a tiered reward and punishment carbon trading model. Next, based on carbon emission theory, it constructs a node carbon potential calculation model, and, combined with unit consumption characteristics, proposes a carbon price that considers the rate of change of node carbon potential. It incorporates regional carbon emission average factors to divide carbon-oriented electricity price periods, formulating a marginal power-carbon coupling pricing strategy for node loads. Finally, it constructs a low-carbon interactive demand response optimization model for power generation and loads based on power-carbon coupling prices. The source side uses an optimal economic dispatch model, while the user side uses a low-carbon coupling price demand response model. The optimization model is then solved to obtain the dispatch results.

[0129] 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:

[0130] ,

[0131] 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.

[0132] The formula for user electricity satisfaction Q is as follows:

[0133] ,

[0134] 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].

[0135] Simulation verification:

[0136] This invention compares the above technical solutions with existing technical solutions in different scenarios:

[0137] 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.

[0138] 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.

[0139] 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.

[0140] It has the following technical effects:

[0141] 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 5 As shown, numbers 1 to 30 represent the 30 nodes of the IEEE network, and the corresponding nodes and lines are marked in the topology diagram. This verification process incorporates new energy generating units, which more closely resembles the real-world environment. Furthermore, energy generating units can be introduced at any node, aiming to build a new power system incorporating new energy sources and better verify the effectiveness of the method. Thermal power unit parameters are shown in Table 1. Wind power forecasts were generated using the Latin difference cubic sampling method. System load and wind power forecast curves are shown in [Table 1]. Figure 6 The wind turbine generation cost coefficient is 100 yuan / MW, the wind curtailment penalty coefficient is 300 yuan / MW, the carbon tax is 150 yuan / ton, the carbon emission range length is 500t, the incentive price growth coefficient is 0.2, and the penalty price growth coefficient is 0.25. The EV discharge subsidy is 100 yuan / MW. The EVs in this invention are considered to be of the same model, and the EV parameters are shown in Table 2. Uncertain parameters such as access time and departure time are predicted using Monte Carlo methods. Nodes 24 and 26 are set to have 500 and 200 dispatchable EVs, respectively. The dispatch time is 24 hours a day, the dispatch interval is 1 hour, and the solution is obtained using the cplex solver called by MATLAB.

[0142] Table 1 Parameter Table of Thermal Power Units

[0143]

[0144] Table 2 EV Parameter Table

[0145]

[0146] Based on the optimized unit output and system load, the carbon potential of each node under the technology of this invention in Scenario 3 is as follows: Figure 7 As shown in the diagram, due to lower electricity consumption between 01:00 and 06:00, the proportion of thermal power output is relatively low, resulting in a relatively low overall node carbon potential for the system. Conversely, from 18:00 to 22:00, higher electricity consumption leads to a relatively higher overall node carbon potential. Node 1, connected only to a generator with no load, consistently has a carbon potential equal to the generator's carbon emission intensity. Node 13, connected to a wind turbine, has a negligible carbon emission, resulting in a consistently zero node carbon potential. Nodes 12-20 have lower loads and are closer to wind turbines, with almost all their electricity coming from wind turbines, resulting in relatively low carbon potentials. Other nodes, located close to thermal power units, receive most of their electricity, leading to higher node carbon potentials.

[0147] In the embodiments of this application, the terms "first" and "second" (if they exist) are used only as name identifiers and do not represent the order of first and second.

[0148] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium. The memory can be various types of memory, such as random access memory, read-only memory, flash memory, etc., such as read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which can be a personal computer, server, or network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0149] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An optimized scheduling method based on electricity-carbon synergy and source-load low-carbon interaction, characterized in that the method... The application relates to a source-load low-carbon interactive operation system based on electricity-carbon coordination. The source-load low-carbon interactive operation system based on electricity-carbon coordination comprises a source-side, a carbon trading market and a user-side. The source-side comprises wind turbine generators and thermal power generators. The carbon trading market is used for receiving carbon emission information of the source-side and outputting node carbon potential. The user-side load comprises EV charging and discharging users and other load users. The source-side uses a baseline method to carry out carbon trading in a day unit. The source-side optimal economic optimization model comprises: taking the minimum of generation cost and carbon trading cost as an objective function, and the constraint conditions comprising: system active power balance constraint, thermal power generator output constraint, thermal power generator climbing constraint, thermal power generator start-stop time constraint, thermal power generator start-stop cost constraint, wind turbine generator output constraint and line power constraint. The user-side low-carbon coupled price demand response optimization model comprises: taking the minimum of power purchase cost and EV discharging subsidy cost under the electricity-carbon coupled price as an objective function, and the constraint conditions comprising: 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 amount constraint. The process of carrying out the source-load low-carbon interactive demand response optimization scheduling model solving comprises the following steps. S31, inputting each unit parameter, load and wind power prediction data; S32, in the source-side optimal economic optimization scheduling model, taking the most economical operation of the source-side as a scheduling target, solving the source-side optimal output and system branch direct current flow; S33, based on the carbon emission flow theory, calculating node carbon potential and regional carbon emission average factor, obtaining node carbon price and dividing time-of-use electricity price period, obtaining electricity-carbon coupled price and transmitting the same to the user-side; S34, in the user-side low-carbon coupled price demand response optimization model, taking the minimum of user-side power cost and EV discharging subsidy cost as an objective function, optimally scheduling the EV under the low-carbon price, and making other loads respond 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, it returns to S32 and executes to S35 in turn; if the node load does not change, the optimization iteration ends, and the user side cost and carbon emission are output.

2. The optimal scheduling method based on the synergy of electricity and carbon and the low-carbon interaction of source and load according to claim 1, characterized in that: The establishment process of the step reward and punishment carbon trading model includes: According to the thermal power unit output, the wind power unit actual output and the free carbon quota coefficient, the source side total free carbon quota in the dispatching period is calculated; The total carbon emission of the source side in the dispatching period is calculated; According to the source side carbon quota and the actual carbon emission, the actual carbon trading volume is obtained; According to the actual carbon trading volume, the interval is divided, the step type carbon trading price is used to calculate the cost, when the actual emission exceeds the quota, the carbon emission is purchased as the punishment; when the actual emission is lower than the quota, the remaining quota is sold as the reward.

3. The optimal scheduling method based on the synergy of electricity and carbon and the low-carbon interaction of source and load according to claim 2, characterized in that: The step type carbon trading price is used to calculate the cost, the calculation formula of the source side carbon trading cost includes: ， wherein, is a source-side carbon transaction cost; is a base carbon transaction price; is a carbon emission length; is a reward price increase coefficient; is a penalty price increase coefficient, is an actual carbon transaction amount.

4. The optimal scheduling method based on the synergy of electricity and carbon and the low-carbon interaction of source and load according to claim 1, characterized in that: The carbon tax is positively correlated with the node carbon potential, the calculation formula includes: ， Wherein, is j The carbon price of the node at t time, is j The carbon potential of the node at t time; a , b The quadratic term and the linear term coefficient of the carbon price of the node respectively, the coefficient is determined by the carbon price of the fixed carbon tax, and the coefficient is determined by the principle of constant carbon average price, that is, the integral area of the carbon price of the two is equal.

5. The method of claim 1, wherein the method is characterized by: The calculation formula of the node electric carbon coupling price 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.

6. The optimal scheduling method based on the synergy of electricity and carbon and the low-carbon interaction of source and load according to claim 1, characterized in that: After the source load low carbon interactive demand response optimization dispatching model is solved, the user electricity consumption satisfaction is calculated to evaluate the optimization dispatching result, the user electricity consumption satisfaction includes the user electricity consumption habit satisfaction and the electricity consumption cost satisfaction; The calculation formula of the user electricity consumption satisfaction Q includes: ​ ， Wherein, A is the electricity habit satisfaction proportion coefficient, is the electricity habit satisfaction; is the electricity cost satisfaction.

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