Comprehensive energy system low-carbon scheduling method and system considering comprehensive demand response

Abstract

The invention provides a comprehensive energy system low-carbon scheduling method and system considering comprehensive demand response, and relates to the technical field of comprehensive energy power system scheduling. Constructing a demand response optimization model based on a carbon emission flow theory to calculate a carbon emission flow; then calculating a dynamic carbon emission factor by using a carbon emission flow, guiding a user to respond to a demand response mechanism of an electricity price and a carbon signal by using the dynamic carbon emission factor, constructing a demand response model of an industrial user and a residential user, and performing simulation verification in an improved IEEE-39 node power system and a Belgian 20 node natural gas system; compared with the prior art, carbon emission in a scheduling period is reduced, carbon emission reduction is achieved from the source side and the load side, the renewable energy consumption capacity is greatly improved, the operation stability and multi-energy complementarity of the system are enhanced, and the reliability and flexibility of energy supply are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy power system dispatching, in particular to a method and system for low-carbon dispatching of integrated energy system considering integrated demand response. BACKGROUND

[0002] From the operation demand of the integrated energy system (IES), two core difficulties need to be broken through to realize the low-carbon economic dispatching of the IES: on the one hand, high proportion of renewable energy (such as wind power and photovoltaic) grid connection has become a trend, but the intermittency and volatility of such energy bring challenges to system stability, and it is urgent to improve the multi-energy complementary characteristics of the system through optimized dispatching to ensure reliable energy supply; on the other hand, with the continuous expansion of demand side resources, the demand response (DR) mechanism as an important means to activate the flexibility of the system, current researches have been carried out around the flexible load demand response, for example, a two-stage dispatching model of microgrid considering demand response is proposed, and a price-based demand response is introduced in the IES with carbon capture technology, although certain achievements have been made in improving energy utilization and economic efficiency, there are still obvious limitations: traditional demand response is mostly based on "TV corner", mainly relying on real-time electricity price or subsidy policy to guide users to adjust power consumption behavior, and only focuses on load distribution optimization, without fully associating carbon emission control demand; two, lack of mechanism for guiding load side to actively participate in system low-carbon operation from the perspective of carbon. SUMMARY

[0003] The main purpose of the present application is to provide a method and system for low-carbon dispatching of integrated energy system considering integrated demand response, which solves the technical problems of intermittent high proportion of renewable energy grid connection, insufficient system accommodation capacity, lack of carbon perspective, insufficient compatibility of dispatching scheme and insufficient stability of dispatching in the prior art.

[0004] To solve the above technical problems, the technical scheme adopted by the present application is: a method for low-carbon dispatching of integrated energy system considering integrated demand response, comprising the following steps: S1: constructing a demand response optimization model based on carbon emission flow theory, calculating carbon emission flow of each node in each period, the carbon emission flow including carbon flow rate, carbon flow density, branch carbon flow density and node carbon potential; S2: Obtain carbon emission flows and calculate dynamic carbon emission factors based on data provided by typical daily load curves. Then, use the dynamic carbon emission factors to guide users to respond to the demand response mechanism of electricity prices and carbon signals, so that users can shift their load from high carbon emission factor periods to low carbon emission factor periods, thereby achieving load regulation and carbon emission reduction. Specifically, according to the dynamic carbon emission factors, high-carbon, flat-carbon, and low-carbon electricity consumption times are divided. Electricity prices are increased during high-carbon times, decreased during low-carbon times, and unchanged during flat-carbon times. By using electricity prices, the load during high-carbon emission times is shifted to low-carbon emission times to reduce the carbon emissions of the distribution network. S3: Based on user characteristics, construct demand response models for industrial users and residential users respectively. The demand response model for industrial users aims at low scheduling costs and low carbon emissions, while the demand response model for residential users aims at electricity satisfaction and low carbon satisfaction. S4: The demand response model was simulated and validated in the improved IEEE-39 node power system and the Belgian 20 node natural gas system to obtain low-carbon dispatch results.

[0005] In the preferred embodiment, the carbon flux rate, carbon flux density, branch carbon flux density, and nodal carbon potential in S1 are calculated as follows: Carbon flow rate is the carbon emission corresponding to the energy flow through a network node or branch per unit time, expressed by the formula: (1); Carbon flux density represents the amount of carbon emissions per unit of electricity, and the formula is: (2); In the formula: P is the amount of electricity, and R is the amount of carbon emissions; The nodal carbon potential represents the equivalent carbon emissions on the generation side caused by a unit of electricity consumed at a node, and the formula is: (3); In the formula: For nodes carbon potential; For generator carbon emission intensity; Inject nodes into branches The power; For nodes generator ; output power; branch road i Power injected into the node; The number of branches for the injected power connected to this node.

[0006] In the preferred embodiment, in step S2, the dynamic carbon emission factor reflects the differences in carbon emissions resulting from user electricity consumption behavior at different time periods, and its mathematical model is as follows: (4); In the formula: In order to be in Carbon emission factor at any given moment; The set of nodes within the coverage area; For nodes The load at any given time; For nodes The magnitude of the carbon potential.

[0007] In the preferred embodiment, the load demand response in S3 is divided into two categories: price-based demand response based on dynamic carbon emission factors and incentive-based demand response based on carbon incentive mechanisms. Price-based demand response guides users to reduce load during high-carbon periods and increase electricity consumption during low-carbon periods based on the temporal changes of carbon emission factors. Incentive-based demand response guides users to actively adjust their load response behavior by setting reward prices under different carbon emission levels.

[0008] In the preferred embodiment, the objective function of the industrial user demand response model in step S3 is: (5); In the formula: , They are respectively t Industrial load electricity prices before and after demand response; , They are respectively t Industrial load before and after demand response; , These represent industrial load carbon emissions before and after demand response, respectively. Compensation for industrial demand response costs; The objective function of the residential user demand response model is: (6); In the formula: For demand response The absolute value of the change in residential load at any given time; Before demand response Resident load at any given time; , These represent the carbon emissions from residential loads before and after demand response; The equivalent carbon emissions from photovoltaic power absorbed by residential loads after demand response.

[0009] In the preferred embodiment, the demand response model further includes the following constraints: Power system operation constraints include node power balance constraints, phase angle constraints, unit output constraints, and transmission line constraints; The natural gas network constraints mainly consider the node supply-demand balance constraint, node pressure constraint, gas network pipeline constraint and gas source constraint, and the formulas are as follows: (7); (8); (9); (10); In the formula, is the outflow of natural gas flow; is the natural gas flow converted by the electric-to-gas equipment P2G; is the pipeline natural gas flow, is the natural gas demand; is the natural gas amount required by the gas load; is the head pipeline gas pressure; is the end pipeline gas pressure; The load scheduling constraint includes the time-space transfer and interruption limit of the shiftable load and interruptible load.

[0010] In the preferred scheme, the load scheduling constraint is expressed as: (11); (12); (13); (14); In the formula, , and are the load after the price-type demand response, the shiftable load amount at a certain moment and the interruptible load amount, respectively; is the unit carbon reward and punishment cost of the shiftable load under different carbon emission conditions; , , are the unit carbon reward and punishment coefficients of the shiftable load under different dynamic carbon emission factor intervals; is the dynamic carbon emission factor, , are the dynamic carbon emission factor grading interval points; is the maximum shiftable load at a single moment.

[0011] In the preferred scheme, according to the constraint that the total amount of the shiftable load in the entire scheduling period is unchanged, and the constraint that the shiftable load amount at each moment is within a certain range, the interruptible load is required to satisfy the time period interruption amount and the total interruption amount in the period constraint, and the formula is as follows: (15); (16); (17); wherein: is the unit carbon reward and punishment cost of interruptible load under different carbon emission situations; is the maximum interruptible load at a single time; is the maximum value of interruptible load in the entire dispatching period.

[0012] In the preferred scheme, in S4, a modified IEEE-39 node power system and a Belgium 20 node natural gas system are used for simulation, including gas turbine units, coal-fired units, wind turbine units, and electric-to-gas equipment, and electric-gas coupling is realized through gas turbine and P2G equipment.

[0013] In a second aspect, a system for low-carbon dispatching of an integrated energy system considering integrated demand response is provided, which is applicable to the method for low-carbon dispatching of an integrated energy system considering integrated demand response, and comprises: a carbon emission flow calculation module configured to construct a demand response optimization model based on a carbon emission flow theory, and to calculate carbon emission flows of nodes at each time period, wherein the carbon emission flows comprise carbon flow rates, carbon flow densities, branch carbon flow densities, and node carbon potentials; a dynamic carbon emission factor module configured to obtain carbon emission flows, to calculate dynamic carbon emission factors based on data provided by a typical daily load curve, and to guide a demand response mechanism of a user response price and a carbon signal by using the dynamic carbon emission factors, so that the user shifts loads from a high-carbon emission factor period to a low-carbon emission factor period to realize load regulation and carbon emission reduction, specifically: according to the dynamic carbon emission factors, high-carbon, flat-carbon, and low-carbon electricity use time periods are divided, the electricity price is increased at the high-carbon time period, the electricity price is decreased at the low-carbon time period, and the electricity price is unchanged at the flat-carbon time period, and the load at the high-carbon emission time period is shifted to the low-carbon emission time period by the electricity price to reduce carbon emission of a power distribution network; a demand response model module configured to construct demand response models of industrial users and residential users respectively according to user characteristics, wherein the demand response model of the industrial user takes low dispatching cost and low carbon emission as targets, and the demand response model of the residential user takes electricity use satisfaction and low carbon satisfaction as targets; a simulation verification module configured to perform simulation verification in a modified IEEE-39 node power system and a Belgium 20 node natural gas system, and to obtain a low-carbon dispatching result.

[0014] This embodiment calculates carbon emission flows by constructing a demand response optimization model based on carbon emission flow theory, accurately transferring carbon emissions from the "source" side to the "load" side. It then uses the carbon emission flows to calculate a dynamic carbon emission factor and uses this factor to guide user response to electricity prices and carbon signals, achieving coordinated regulation of load and carbon emissions. After constructing demand response models for industrial and residential users, simulations were conducted on an improved IEEE-39 node power system and a Belgian 20 node natural gas system, realizing low-carbon dispatching functionality. This significantly reduces carbon emissions within a dispatching cycle, achieving carbon reduction from both the source and load sides. It substantially improves the capacity for renewable energy absorption, increases the proportion of clean energy in the system, promotes the transformation of the energy structure towards low-carbon development, balances and improves system economics and user satisfaction, enhances system operational stability and multi-energy complementarity, and ensures the reliability and flexibility of energy supply. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a reference diagram of the workflow of the present invention; Figure 2 This is a 24-hour node carbon potential diagram in an embodiment of the present invention; Figure 3 This is a node carbon potential diagram in an embodiment of the present invention; Figure 4 This is a schematic diagram of the IEEE-39 node power system and the Belgian 20 node natural gas system in an embodiment of the present invention; Figure 5 This is a power grid demand response diagram in an embodiment of the present invention; Figure 6 This is a demand response diagram of the natural gas network in an embodiment of the present invention; Figure 7 This is a diagram of the unit output in an embodiment of the present invention. Detailed Implementation

[0016] Example 1 like Figures 1-7 As shown, a method for low-carbon dispatching of an integrated energy system considering comprehensive demand response includes the following steps: S1: Construct a demand response optimization model based on carbon emission flow theory to calculate the carbon emission flow of each node in each time period. The carbon emission flow includes carbon flow rate, carbon flow density, branch carbon flow density and node carbon potential.

[0017] S2: Obtain carbon emission flow based on data provided by typical daily load curve (statistical analysis of target area user electricity consumption in the past few years), calculate dynamic carbon emission factor, and use dynamic carbon emission factor to guide user response to price and carbon signal demand response mechanism, so that users shift load from high carbon emission factor period to low carbon emission factor period to achieve load regulation and carbon emission reduction, specifically: According to the dynamic carbon emission factor, high carbon, flat carbon and low carbon electricity time is divided, the price is increased at high carbon time, the price is reduced at low carbon time, and the price is unchanged at flat carbon time. Through the price, the load at high carbon emission time is shifted to low carbon emission time, reducing the carbon emission of the power distribution network.

[0018] S3: According to the characteristics of users, demand response models of industrial users and residential users are respectively constructed, wherein the demand response model of industrial users aims to low dispatching cost and low carbon emission, and the demand response model of residential users aims to electricity satisfaction and low carbon satisfaction.

[0019] S4: The demand response model is simulated and verified in the improved IEEE-39 node power system and Belgium 20 node natural gas system to obtain low carbon dispatching results.

[0020] The embodiment calculates carbon emission flow by constructing a demand response optimization model based on carbon emission flow theory, accurately transfers carbon emission from the "source" side to the "load" side; then calculates the dynamic carbon emission factor using the carbon emission flow, and uses the dynamic carbon emission factor to guide the demand response mechanism of user response to price and carbon signal, realizes the coordinated regulation of load and carbon emission, constructs the demand response model of industrial users and residential users, and then simulates and verifies in the improved IEEE-39 node power system and Belgium 20 node natural gas system, realizes the low carbon dispatching function, significantly reduces the carbon emission in a dispatching period, realizes carbon emission reduction from the source side and the load side, greatly improves the renewable energy consumption capacity, improves the proportion of clean energy in the system, promotes the transformation of energy structure to low carbonization, and takes into account and improves the economic efficiency and user satisfaction of the system, enhances the stability and multi-energy complementarity of the system operation, and ensures the reliability and flexibility of energy supply.

[0021] The carbon emission flow theory proposed in step S1 of the embodiment constructs a demand response optimization model, and carbon dioxide in the energy industry is mainly generated from the source side, while the final driving force of carbon emission is at the load side. Therefore, the carbon emission flow of each node at each period can be calculated according to the known power flow distribution, including the calculation of carbon flow rate, carbon flow density, branch carbon flow density and node carbon potential.

[0022] Carbon flow rate (CEFR) represents the carbon emission corresponding to the energy flow through the network node or branch per unit time, as shown in formula (1).

[0023] (1).

[0024] Carbon intensity (CI) represents the carbon emission corresponding to unit power, CI includes three concepts of generator carbon intensity, branch carbon intensity and node carbon intensity, and the unit is t / (MW·h). Generator carbon intensity (GCI) represents the real-time power generation carbon intensity of the power plant, which is obtained according to the power generation characteristics, and is represented by . Branch carbon intensity (BCI) represents the carbon emission of the power generation side caused by the consumption of unit power transmitted by the branch, that is, the ratio of branch carbon flow rate to active power flow, and the calculation formula is: (2).

[0025] Node carbon intensity (NCI) represents the equivalent carbon emission of the power generation side caused by the consumption of unit power at the node. Numerically equal to the weighted average of the carbon intensity of all branches flowing into the node with respect to the active power flow, and is represented by e . For example, the carbon intensity of node n is: (3) ; In the formula: is the carbon intensity of node ; is the carbon emission intensity of generator ; is the power injected into node by the branch; is the output power of generator of node ; is the power injected into the node by the branch; i is the number of branches connected to the node and injecting power, and the node carbon intensity diagrams are shown in and Figure 2 . 3

[0026] As shown in Figure 3 , the present embodiment improves the real-time and accuracy of the carbon emission distribution characteristics of each period and each node through node carbon intensity calculation and dynamic analysis.

[0027] ​In step S2 of this embodiment, a demand response mechanism is proposed to guide users' responses to electricity prices and carbon signals through a dynamic carbon emission factor, thereby achieving load regulation and carbon emission reduction. This mechanism guides users to shift their load from periods with higher carbon emission factors to periods with lower carbon emission factors. The dynamic carbon emission factor reflects the differences in carbon emissions generated by users' electricity consumption behavior at different times, and its mathematical model is as follows: (4); In the formula: In order to be in Carbon emission factor at any given moment; The set of nodes within the coverage area; For nodes The load at any given time; For nodes The magnitude of the carbon potential.

[0028] In step S3 of this embodiment, based on user characteristics, an industrial demand response model aimed at low scheduling costs and low carbon emissions, and a residential demand response model aimed at electricity satisfaction and low carbon emissions satisfaction, are constructed respectively. Load demand response is divided into two categories: one is a price-based demand response based on dynamic carbon emission factors, which guides users to reduce load during high-carbon periods and increase electricity consumption during low-carbon periods according to the temporal changes of carbon emission factors, thereby shifting load to low-carbon emission periods and reducing the overall emissions of the system; the other is an incentive-based demand response based on carbon incentive mechanisms, which guides users to actively adjust their load response behavior by setting reward prices under different carbon emission levels.

[0029] The objective function of the industrial user demand response model is: (5); In the formula: , They are respectively t Industrial load electricity prices before and after demand response; , They are respectively t Industrial load before and after demand response; , These represent industrial load carbon emissions before and after demand response, respectively. Compensation for industrial demand response costs; The objective function of the residential user demand response model is: (6); In the formula: For demand response The absolute value of the change in residential load at any given time; Before demand response Resident load at any given time; , The resident load carbon emission amount before and after the demand response, respectively; The equivalent carbon emission amount of the resident load after the demand response.

[0030] In this embodiment, the calculation of the dynamic carbon emission factor reflects the carbon emission difference of user electricity in different time periods in real time, which can effectively guide users to transfer the load from the high carbon period to the low carbon period, realizes the direct correlation of "load adjustment-carbon emission reduction", and overcomes the defects of the single regulation mode of the traditional demand response which only depends on the electricity price. Relying on the dynamic carbon emission factor, two types of demand response mechanisms of price type and incentive type are constructed: the price type response guides the load transfer through "high carbon period high price, low carbon period low price", and the incentive type response encourages users to actively adjust electricity through differential carbon reward and punishment price. The double mechanism forms a complement, improves the enthusiasm of users to participate in low-carbon dispatching, and strengthens the effect of carbon emission reduction on the load side.

[0031] In this embodiment, the constraint conditions of the low-carbon demand response model include: 1) Power system operation constraints The direct current flow constraint is adopted as the power network constraint, and the constraint conditions include node power balance constraint, phase angle constraint, unit output constraint and transmission line constraint.

[0032] 2) Natural gas network constraints The natural gas network constraints mainly consider the node supply-demand balance constraint, node pressure constraint, gas network pipeline constraint and gas source constraint, and the formulas are respectively: (7); (8); (9); (10); In the formula, is the outflow of natural gas flow; is the natural gas flow converted by the P2G electric-to-gas device; is the pipeline natural gas flow, is the natural gas demand; is the natural gas required by the gas load; is the head pipeline gas pressure; is the end pipeline gas pressure.

[0033] 3) Load dispatching constraints, including the time and space transfer and interruption limit of the transferable load and interruptible load, and the expression is: (11); (12); (13); (14); wherein: , and are the load after price-type demand response, the amount of translatable load at a certain moment, and the amount of interruptible load, respectively; is the unit carbon reward and punishment cost of translatable load under different carbon emission conditions; , , are the unit carbon reward and punishment coefficients of translatable load under different dynamic carbon emission factor intervals, respectively; is a dynamic carbon emission factor, , are the dynamic carbon emission factor interval points; is the maximum translatable load at a single moment.

[0034] According to the constraint that the total amount of translatable load in the dispatching period is unchanged according to formula (13), and the constraint that the amount of translatable load at each moment is within the range according to formula (14), the interruptible load is required to satisfy the period interruption amount and the total interruption amount in the period constraint, and the formula is as follows: (15); (16); (17); wherein: is the unit carbon reward and punishment cost of interruptible load under different carbon emission conditions; is the maximum interruptible load at a single moment; is the maximum value of interruptible load in the entire dispatching period.

[0035] wherein, formula (16) represents the limitation of the amount of interrupted load at each moment, and formula (17) represents the total amount limitation of interruptible load in the dispatching period.

[0036] In this embodiment, the dispatching feasibility is improved through multiple constraints, and the practicality and stability of the dispatching scheme are improved. After the industrial users participate in the coordinated demand response, the electricity cost is further reduced, a large amount of load adjustment is contributed to the system carbon emission reduction, and the synergy of enterprise benefits and system low-carbon targets is realized.

[0037] In this embodiment, the improved IEEE-39 node and Belgium 20 node system are used in step S4 to perform example simulation, and the results show that the dispatching model can effectively reduce the carbon emission amount of the comprehensive energy system.

[0038] As Figure 4The electric-gas coupling system diagram is shown, and the example is analyzed by using the improved IEEE-39 node system and the Belgium 20 node system: in the improved 39 node power grid, 3 are gas turbine units, 2 are wind turbine units, and the remaining 5 are coal-fired units, wherein the gas turbine units G1, G2 and G3 are connected to the power grid nodes 30, 33 and 37 and the natural gas nodes 3, 6 and 19 respectively, and the wind turbine units W1 and W2 are connected to the power grid nodes 35 and 36 respectively.

[0039] The power grid and the natural gas are coupled through the gas turbine and the electric-to-gas equipment P2G, wherein the electric-to-gas equipment P2G1 and P2G2 are connected to the power grid nodes 10 and 20 and the natural gas nodes 5 and 10 respectively, the maximum electric power is set to 500 MW and 600 MW respectively, and the conversion efficiency is 25%. The Belgium 20 node system includes 20 nodes, 19 pipelines and 6 gas source stations.

[0040] Scheme 1: traditional scheduling (control group).

[0041] Scheme 2: only incentive demand response is considered on the load side.

[0042] Scheme 3: only price demand response is considered on the load side.

[0043] Scheme 4: price demand response and incentive demand response are considered on the load side.

[0044] Table 1 is a comparison table of optimization results of each scenario scheme, and the optimization results include operation cost and carbon emission.

[0045] Table 1: Operation cost and carbon emission of each scenario

[0046] As shown in Figure 5 and 6 , the demand response mechanism suppresses or shifts the electricity consumption behavior in the high carbon emission period, and encourages electricity consumption in the low carbon emission period, thereby effectively utilizing the fluctuation characteristics of clean energy (especially wind power). On the one hand, the price demand response relies on the "dynamic carbon emission factor price" to increase the price in the high carbon emission period, and shifts a part of the transferable load or interruptible load to the low carbon emission period, thereby achieving peak load shifting and reducing the overall emission. On the other hand, the incentive demand response sets carbon reward and punishment prices in different periods to guide the load to actively reduce the demand in the high carbon emission period, or to increase the electricity consumption in the period with high wind power output. These two mechanisms have obvious economic incentives for transferable loads and interruptible loads, making them have higher adaptability to the fluctuation of wind power output in the daily stage, and further improving the consumption level of wind power in the system, thereby reducing the output of coal-fired units or other high emission units, achieving "low carbon - economic" double optimization.

[0047] To further illustrate the specific performance of the synergy strategy on the power generation side and the gas network side, the unit output is as shown in FIG. 1. Figure 7 The output of each unit (G1-G10) in different hours is obviously layered: during the day or high load period, part of the coal-fired or gas-fired units bear the base load and peak shaving function, while during the night or low carbon emission factor period, more renewable or clean units have a competitive advantage to achieve higher operation priority. Especially under the joint action of demand response and green certificate-carbon trading, high-emission units voluntarily reduce output or shorten operation time under the pressure of carbon cost, thereby releasing load space to clean energy such as wind power. The load side also further flattens the morning and evening peak valley difference to some extent by shifting the translatable load and interrupting the high-emission period, making the dispatch more stable. At the same time, the pressure level of each node in the natural gas network during the dispatching period also shows a relatively stable distribution, and the synergy strategy not only maintains the balance between supply and demand of the gas network, but also avoids unnecessary peak gas supply. When the power-to-gas equipment or gas-fired generator output changes, the price signal guides the system to flexibly adjust the gas well production and supply of each node under the premise of ensuring the safety of the gas network, realizing the multi-dimensional synergy optimization of "electricity-gas-carbon".

[0048] The related calculations of the embodiment example are carried out on the Matlab R2022b platform, YALMIP modeling is adopted, and the CPLEX solver is called for solution.

[0049] Embodiment 2 Further illustrated in combination with Embodiment 1, a system for low-carbon dispatching of a comprehensive energy system considering comprehensive demand response, applicable to the method for low-carbon dispatching of a comprehensive energy system considering comprehensive demand response in Embodiment 1, comprising: A carbon emission flow calculation module configured to construct a demand response optimization model based on a carbon emission flow theory, and calculate carbon emission flows of each node in each period, wherein the carbon emission flows comprise carbon flow rates, carbon flow densities, branch carbon flow densities, and node carbon potentials.

[0050] A dynamic carbon emission factor module configured to obtain carbon emission flows, and calculate dynamic carbon emission factors based on data provided by a typical daily load curve, and then utilize the dynamic carbon emission factors to guide a demand response mechanism of a user response price and a carbon signal, so that the user shifts load from a high-carbon emission factor period to a low-carbon emission factor period to achieve load regulation and carbon emission reduction. Specifically, according to the dynamic carbon emission factors, high-carbon, flat-carbon, and low-carbon electricity use time points are divided, the electricity price is increased at the high-carbon time point, the electricity price is decreased at the low-carbon time point, and the electricity price is unchanged at the flat-carbon time point, and the load at the high-carbon emission time point is shifted to the low-carbon emission time point by the electricity price to reduce the carbon emission of the power distribution network.

[0051] The demand response model module is configured to construct demand response models of industrial users and residential users respectively according to user characteristics; wherein the demand response model of the industrial user aims at low scheduling cost and low carbon emission, and the demand response model of the residential user aims at power consumption satisfaction and low carbon satisfaction.

[0052] The simulation verification module is configured to perform simulation verification in the improved IEEE-39 node power system and the Belgium 20 node natural gas system to obtain a low-carbon scheduling result.

[0053] The embodiment provides a working process, working details and technical effects of a method for low-carbon scheduling of a comprehensive energy system considering comprehensive demand response, which can be seen in Embodiment 1 and will not be repeated here.

[0054] The above-described embodiments are only preferred technical solutions of the present application, and should not be regarded as limitations of the present application. The protection scope of the present application should be the technical solutions recited in the claims, including equivalent replacement solutions of the technical features recited in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present application.

Claims

1. A method for low-carbon scheduling of an integrated energy system considering integrated demand response, characterized in that, The method comprises the following steps: S1: constructing a demand response optimization model based on carbon emission flow theory, and calculating carbon emission flow of each node in each period, wherein the carbon emission flow comprises carbon flow rate, carbon flow density, branch carbon flow density and node carbon potential; S2: obtaining the carbon emission flow, and calculating a dynamic carbon emission factor based on data provided by a typical daily load curve, and then using the dynamic carbon emission factor to guide a demand response mechanism of a user response price and a carbon signal, so that the user shifts load from a high carbon emission factor period to a low carbon emission factor period to achieve load regulation and carbon emission reduction, specifically: according to the dynamic carbon emission factor, high carbon, flat carbon and low carbon electricity time are divided, the price is increased at the high carbon time, the price is reduced at the low carbon time, and the price is unchanged at the flat carbon time, the load at the high carbon emission time is shifted to the low carbon emission time through the price to reduce the carbon emission of the power distribution network; S3: constructing a demand response model of industrial users and residential users according to user characteristics, wherein the demand response model of industrial users takes low dispatching cost and low carbon emission as the target, and the demand response model of residential users takes electricity satisfaction and low carbon satisfaction as the target; S4: the demand response model is simulated and verified in an improved IEEE-39 node power system and a Belgium 20 node natural gas system to obtain a low carbon dispatching result.

2. The method for low-carbon dispatching of the integrated energy system considering integrated demand response according to claim 1, wherein, In the S1, the carbon flow rate, the carbon flow density, the branch carbon flow density and the node carbon potential are calculated as follows: The carbon flow rate is the carbon emission corresponding to the energy flow through the network node or branch per unit time, and the formula is: (1)； The carbon flow density represents the carbon emission corresponding to unit electricity, and the formula is: (2)； In the formula, P is the electricity, and R is the carbon emission; The node carbon potential represents the equivalent carbon emission of the power generation side caused by the consumption of unit electricity at the node, and the formula is: (3)； where: is the carbon potential of the node ; is the carbon emission intensity of the generator ; is the power injected by the branch to the node ; is the output power of the generator at the node ; is the power injected by the branch i to the node; is the number of branches injecting power to the node.

3. The method of claim 1, wherein, In the S2, the dynamic carbon emission factor reflects the difference in carbon emission generated by user electricity consumption behavior at different time periods, and the mathematical model is: (4)； In the formula: is the carbon emission factor at the time point t; is the carbon emission factor at the time point t; is the set of nodes within the covered range; is the node is the load amount at the time point t; is the node is the carbon potential size of the node 4. The method for low-carbon dispatching of integrated energy systems considering integrated demand response according to claim 1, characterized in that, In the S3, the load demand response includes price type demand response based on the dynamic carbon emission factor and incentive type demand response based on the carbon incentive mechanism, the price type demand response guides the user to reduce load at the high carbon period and increase electricity consumption at the low carbon period according to the time sequence change of the carbon emission factor, and the incentive type demand response sets a reward price at different carbon emission levels to guide the user to actively adjust the load response behavior.

5. The method for low-carbon dispatching of integrated energy systems considering integrated demand response according to claim 4, characterized in that, In the S3, the objective function of the demand response model of industrial users is: （5）； In the formula: , are respectively t the industrial load price before and after the demand response at the moment; , are respectively t the industrial load before and after the demand response at the moment; , are respectively the carbon emission of the industrial load before and after the demand response; is the industrial demand response compensation cost; The objective function of the demand response model of residential users is: （6）； In the formula: is the absolute value of the change amount of the resident load at the time t after the demand response; is the resident load at the time t before the demand response; , are the carbon emission amounts of the resident load before and after the demand response, respectively; is the equivalent carbon emission amount of the photovoltaic power consumed by the resident load after the demand response.​​ 6. The method for low-carbon dispatching of the integrated energy system considering integrated demand response according to claim 5, characterized in that, The demand response model further comprises the following constraint conditions: The power system operation constraint includes node power balance constraint, phase angle constraint, unit output constraint and transmission line constraint; The natural gas network constraint mainly considers node supply and demand balance constraint, node pressure constraint, gas network pipeline constraint and gas source constraint, and the formulas are respectively: (7)； (8)； (9)； (10)； wherein: is the flow of natural gas out; is the flow of natural gas converted by the electric-to-gas plant P2G; is the flow of pipeline natural gas, is the demand for natural gas; is the amount of natural gas required for gas load; is the head-end pipeline gas pressure; is the end-point pipeline gas pressure; The load dispatching constraint includes time and space transfer and interruption limit of the shiftable load and the interruptible load.

7. The method for low-carbon dispatching of integrated energy systems considering integrated demand response according to claim 6, wherein, The load dispatching constraint is expressed as: （11）； （12）； （13）； （14）； In the formula: , , are the load after price-type demand response, the amount of translatable load at a certain time and the amount of interruptible load respectively; is the unit carbon reward and punishment cost of translatable load under different carbon emission conditions; , , are the unit carbon reward and punishment coefficients of translatable load under different dynamic carbon emission factor intervals respectively; is a dynamic carbon emission factor, , are the grading interval points of the dynamic carbon emission factor; is the maximum translatable load at a single time.

8. The method for low-carbon dispatching of the integrated energy system considering integrated demand response according to claim 7, characterized in that, According to the constraint that the total amount of the shiftable load is unchanged within the entire dispatching period, and the constraint that the amount of the shiftable load at each time is within a range, the interruptible load is set to satisfy the time interruption amount and the total interruption amount within the period constraint, and the formula is as follows: （15）； （16）； （17）； In the formula: is the unit carbon reward and punishment cost of interruptible load under different carbon emission situations; is the maximum interruptible load at a single time; is the maximum value of interruptible load in the entire dispatching period.

9. The method for low-carbon dispatching of integrated energy systems considering integrated demand response according to claim 1, wherein, In the S4, a modified IEEE-39 node power system and a Belgium 20 node natural gas system are used for simulation, including gas turbine units, coal-fired units, wind turbine units and electric-to-gas equipment, and the electric-gas coupling is realized through the gas turbine and the P2G equipment.

10. A system for low-carbon scheduling of an integrated energy system considering integrated demand response, characterized in that, The method for low-carbon scheduling of an integrated energy system considering integrated demand response is suitable for the method of claim 1, and comprises the following steps: a carbon emission flow calculation module configured to construct a demand response optimization model based on a carbon emission flow theory, and to calculate carbon emission flows of each node in each period, wherein the carbon emission flows include carbon flow rates, carbon flow densities, branch carbon flow densities, and node carbon potentials; a dynamic carbon emission factor module configured to obtain the carbon emission flows, to calculate dynamic carbon emission factors based on data provided by a typical daily load curve, and to guide a demand response mechanism of a user response price and a carbon signal by using the dynamic carbon emission factors, so that the user shifts loads from a high-carbon emission factor period to a low-carbon emission factor period to achieve load regulation and carbon emission reduction, and specifically, according to the dynamic carbon emission factors, high-carbon, flat-carbon, and low-carbon electricity use time points are divided, the electricity price is increased at the high-carbon time point, the electricity price is decreased at the low-carbon time point, the electricity price is unchanged at the flat-carbon time point, and the loads at the high-carbon emission time point are shifted to the low-carbon emission time point by using the electricity price to reduce carbon emissions of a power distribution network; a demand response model module configured to construct demand response models of industrial users and residential users according to user characteristics, wherein the demand response model of the industrial user takes low scheduling cost and low carbon emission as targets, and the demand response model of the residential user takes electricity satisfaction and low carbon satisfaction as targets; a simulation verification module configured to perform simulation verification in a modified IEEE-39 node power system and a Belgium 20 node natural gas system, and to obtain low-carbon scheduling results.

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