Optimized scheduling method and device for source-load collaborative carbon reduction of integrated energy system

Abstract

The invention discloses an optimal scheduling method and device for source-load collaborative carbon reduction of an integrated energy system, and belongs to the field of energy system scheduling operation. The method comprises the following steps: establishing an integrated energy system carbon flow model taking an energy network node as a center according to the transmission characteristics of the carbon emission of the integrated energy system; wherein the energy network comprises a power network, a thermal network and a gas network; according to the carbon flow model and the carbon transaction cost of the upper layer and the lower layer of the integrated energy system, establishing a double-layer optimization scheduling model of the integrated energy system; and carrying out iterative solution on the double-layer optimization scheduling model, and carrying out convergence constraint on a solution result by using a dichotomy to obtain an optimal scheduling scheme of the integrated energy system. The method can effectively improve the carbon reduction operation scheduling effect of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system dispatching and operation, and particularly relates to an optimal dispatching method and device for collaborative carbon reduction of sources and loads in an integrated energy system. Background Art

[0002] Energy optimization is developing towards the direction of green and low-carbon. As an important carrier of the new energy system, the integrated energy system can effectively improve the consumption capacity of renewable energy through the mutual conversion of its various energies, and at the same time contribute to improving the low-carbon economic dispatching decision-making level of the integrated energy system with large-scale renewable energy access.

[0003] In related technologies, the optimal operation of the integrated energy system mostly adopts a dispatching strategy that is independent between electricity and carbon, ignoring the coupling relationship between carbon emissions and power dispatching, resulting in limited effects in improving the low-carbon and economic performance of the integrated energy system operation.

[0004] Based on this, there is an urgent need for an optimal dispatching method and device for collaborative carbon reduction of sources and loads in an integrated energy system to solve the above technical problems. Summary of the Invention

[0005] The present invention provides an optimal dispatching method and device for collaborative carbon reduction of sources and loads in an integrated energy system, which can solve the problem that the carbon reduction operation dispatching effect of the integrated energy system in related technologies is not obvious. The technical solutions are as follows:

[0006] On the one hand, an optimal dispatching method for collaborative carbon reduction of sources and loads in an integrated energy system is provided. The method includes:

[0007] According to the transmission characteristics of carbon emissions in the integrated energy system, establish a carbon flow model of the integrated energy system centered on energy network nodes; wherein, the energy network includes a power network, a heat network, and a gas network;

[0008] According to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system, establish a two-layer optimal dispatching model of the integrated energy system;

[0009] Iteratively solve the two-layer optimal dispatching model, and use the bisection method to perform convergence constraint on the solution result to obtain the optimal dispatching scheme of the integrated energy system.

[0010] On the other hand, an optimal dispatching device for collaborative carbon reduction of sources and loads in an integrated energy system is provided. The device includes:

[0011] The first modeling module is used to establish a carbon flow model of the integrated energy system centered on energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system; wherein, the energy network includes a power network, a heat network, and a gas network;

[0012] A second modeling module, configured to establish a two - layer optimal scheduling model of the integrated energy system according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system;

[0013] A calculation module, configured to perform iterative solution on the two - layer optimal scheduling model and use the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling plan of the integrated energy system.

[0014] On the other hand, a computer device is provided. The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the above - mentioned optimal scheduling method for collaborative carbon emission reduction of the source, load, network, and storage in the integrated energy system.

[0015] On the other hand, a computer - readable storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above - mentioned optimal scheduling method for collaborative carbon emission reduction of the source, load, network, and storage in the integrated energy system are implemented.

[0016] On the other hand, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the above - mentioned optimal scheduling method for collaborative carbon emission reduction of the source, load, network, and storage in the integrated energy system are implemented.

[0017] The technical solution provided by the present invention can at least bring the following beneficial effects: By accurately characterizing the carbon emission characteristics of each link of the source, network, load, and storage in the integrated energy system, a carbon emission flow model of the integrated energy system is built from the time - space dimension, expanding its application scenarios in the low - carbon economic scheduling of the integrated energy system. Based on the principle of proportional sharing, the carbon emission responsibility is attributed to the load side through the node carbon intensity, and an optimal economic scheduling model of the integrated energy system is constructed, which can reduce the low - carbon operation cost of the integrated energy system. Through the node carbon intensity and electricity price signals, the load side is prompted to guide flexible loads to perform demand response, and a low - carbon scheduling model of the integrated energy system with collaborative optimization of the source and load is established, which can provide theoretical support for the participation of demand - side resources in low - carbon emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the optimal scheduling method for collaborative carbon emission reduction of the source, load, network, and storage in the integrated energy system provided by an embodiment of the present invention;

[0020] Figure 2 It is a schematic diagram of dichotomy iteration provided by an embodiment of the present invention;

[0021] Figure 3 It is a topological structure diagram of an integrated energy test system with electrical / thermal / gas coupling provided by an embodiment of the present invention;

[0022] Figure 4 a - 4b are respectively a simulation diagram of the Monte Carlo method and a wind power output diagram after scenario reduction provided by an embodiment of the present invention;

[0023] Figure 5 a - 5c are respectively diagrams of the flexible loads of nodes 1 - 3 varying with the node carbon emission intensity and time - of - use electricity price provided by an embodiment of the present invention, and 5d - 5f are respectively schematic diagrams of the loads of nodes 1 - 3 before and after demand response;

[0024] Figure 6 a - 6c are respectively curves of the carbon intensity of nodes 1 - 3 varying with the installed capacity of wind power provided by an embodiment of the present invention;

[0025] Figure 7 It is a structural diagram of an optimized dispatching device for collaborative carbon emission reduction of the source and load in an integrated energy system provided by an embodiment of the present invention;

[0026] Figure 8 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0028] As described above, certain research results have been achieved in the optimal operation of the integrated energy system with multi - energy coupling. However, most of the existing research results adopt a scheduling strategy in which electricity and carbon are independent of each other, ignoring the coupling relationship between carbon emissions and power scheduling, resulting in limited effects in improving the low - carbon and economic performance of the integrated energy system operation.

[0029] Based on this, the concept of the present invention lies in, on the basis of carrying out research on the calculation of the whole - process carbon emission flow of the "source - grid - load - storage" in the integrated energy system, using the node carbon intensity to guide the flexible load for demand response to improve the low - carbon and economic performance of the integrated energy system.

[0030] The following describes the specific implementation manners of the above concept.

[0031] Please refer to Figure 1 , an optimal scheduling method for collaborative carbon emission reduction of source and load in an integrated energy system provided by an embodiment of the present invention, the method comprising:

[0032] Step 100, establish a carbon flow model of the integrated energy system centered on energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system; wherein, the energy network includes a power network, a heat network, and a gas network;

[0033] Step 102, establish a two-layer optimal scheduling model of the integrated energy system according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system;

[0034] Step 104, perform iterative solution on the two-layer optimal scheduling model, and use the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling plan of the integrated energy system.

[0035] In the embodiment of the present invention, by accurately characterizing the carbon emission characteristics of each link of the integrated energy system source / network / load / storage, a carbon emission flow model of the integrated energy system is built from the time and space dimensions, expanding its application scenarios in the low-carbon economic scheduling of the integrated energy system. Based on the principle of proportional sharing, the carbon emission responsibility is attributed to the load side through the node carbon intensity, and an optimal economic scheduling model of the integrated energy system is constructed, which can reduce the low-carbon operation cost of the integrated energy system. Through the node carbon intensity and electricity price signal, the load side is prompted to guide the flexible load to perform demand response, and a low-carbon scheduling model of the integrated energy system with collaborative optimization of source and load is established, which can provide theoretical support for the participation of demand-side resources in low-carbon emission reduction.

[0036] The following describes Figure 1 the execution manner of each step shown.

[0037] First, for step 100, according to the transmission characteristics of carbon emissions in the integrated energy system, establish a carbon flow model of the integrated energy system centered on energy network nodes.

[0038] In view of the fact that carbon emissions on the energy side can be transmitted to the load side with the energy flow as the carrier, therefore, based on the carbon flow tracking method of proportional sharing, define the carbon flow rate to characterize the carbon emission intensity f passing through the energy system nodes and lines per unit time CEFR :

[0039] f CEFR = dCD / dt

[0040] In the formula, CD is the carbon emission amount flowing through the energy system nodes and lines during the t time period.

[0041] In an embodiment of the present invention, a carbon flow model is established through the following process: a carbon flow model of the energy network is established according to the absolute value of the multi-energy flow data flowing into the energy network node and the carbon emission intensity of the production energy subsystem; according to the node access relationships between the energy network and the load subsystem, the energy coupling subsystem, and the energy storage subsystem respectively, carbon flow models of the load subsystem, the energy coupling subsystem, and the energy storage subsystem are established in sequence.

[0042] In an embodiment of the present invention, a carbon flow model of the energy network is established through the following process: according to the absolute value of the energy flow data flowing into the node, the active absolute quantity matrix of the node is calculated; the active absolute quantity matrix is solved according to the power of the production energy subsystem connected to the node, and a carbon flow model of the energy network is established according to the solution result.

[0043] Specifically, taking a power network with N nodes ignoring network losses as an example, the branch currents flowing into and out of node i at time t have the same absolute value and opposite algebraic values, which conforms to Kirchhoff's current law. Different from node energy flow, node carbon intensity is not affected by the energy flow out of the node. Therefore, the calculation of carbon flow only considers the absolute value of the energy flow into the node, and based on this, the active absolute quantity matrix P of the node is established N :

[0044]

[0045] where i, j ∈ N; is the absolute value of the power flow into node j; N is the total number of nodes in the power network; ij ∈ J + is the branch ij with node j as the end node; is the active power of branch ij; k ∈ j is the unit k connected to node j; is the power of the kth generator set connected to node j.

[0046] According to the above formula, it can be seen that the jth row diagonal element of matrix P N is equal to the sum of the jth row diagonal elements of the N-order branch power flow distribution matrix P B and the K×N-order unit injection distribution matrix P G matrix. Therefore, when the matrices P B and P G of the power network are known, matrix P N can be calculated through the following formula:

[0047]

[0048] where ζ N+K is an N+K-order row vector, and all elements in the vector are 1.

[0049] Therefore, the carbon flow model of node j in the power network can be obtained as follows:

[0050]

[0051] Where: and are the carbon emission intensities of power network node j, branch ij and generator k at time t respectively.

[0052] According to the nature of carbon flow, It can be replaced by the carbon emission intensity of the branch starting node i, that is, the carbon flow model of the above power network can be rewritten as:

[0053]

[0054] In the formula, is an N-dimensional unit row vector, where the j-th element is 1; is the node carbon intensity vector; is the carbon emission intensity of node i; is the carbon emission intensity vector of the generator set.

[0055] Simultaneous Matrix P N The calculation formula can be obtained:

[0056]

[0057] Combining the above two formulas, we can get:

[0058]

[0059] Since the matrix P N It is a diagonal matrix, so the above formula can be expanded to all nodes of the power network to establish the carbon flow model of all nodes:

[0060]

[0061] This formula clarifies the relationship between carbon emissions and current in the power grid. The carbon flow model of the power grid can be calculated based on the absolute value of the current flowing into the node and the carbon emission intensity of the generator set.

[0062] It is worth noting that, similar to the above derivation process, carbon emissions in the thermal network and gas network are also transmitted to the load side along with the medium flowing in the pipeline. The carbon flow model of the thermal network is:

[0063]

[0064] In the formula, and are the carbon emission intensity of thermal network node j′, branch i′j′ and cogeneration unit k′ at time t respectively; is the energy flow into branch i′j′ at time t; is the thermal power of the combined heat and power unit at time t;

[0065] The carbon flow model of the gas network is:

[0066]

[0067] In the formula, and are the carbon emission intensities of gas network node j″, branch i″j″ and gas source k″ at time t, respectively; is the energy flow into branch i″j″ at time t; is the gas source k″ flow rate at time t.

[0068] The detailed derivation processes of the two are similar to those of the power network and will not be elaborated here.

[0069] Furthermore, the carbon flow models of the load subsystem, energy coupling subsystem and energy storage subsystem are related to the access nodes of the energy grid.

[0070] First, for the load subsystem, according to the nature and use of the load, the load can be divided into residential load, commercial load and industrial load. After calculating the carbon emission intensity of each node through the carbon emission tracking method, each type of load receives the node carbon intensity signal, and the flexible load can be guided to respond to the dispatching instruction by using the incentive contract. That is, the flexible load of the load subsystem is adjusted according to the node carbon intensity of the power network. The incentive contract stipulates the flexible load response amount, compensation cost and response duration. According to the existing research results, the flexible load can be divided into three categories: electric vehicles, curtailable load and shiftable load. On this basis, the carbon flow model of the load subsystem is established in the following way.

[0071] For electric vehicles, when the electric vehicle signs an agreement with the load subsystem, the load subsystem can dispatch the electric vehicle according to the provisions of the incentive contract. The Monte Carlo method is used to analyze the uncertainty of electric vehicles, including the daily driving mileage, access time, departure time, target state of charge and initial access state of charge of electric vehicles.

[0072] The daily driving mileage x of electric vehicles follows a lognormal distribution, and its probability density function f(x) is:

[0073]

[0074] In the formula, σ is the standard deviation of the daily driving mileage, taking 0.9; μ is the expected value of the daily driving mileage, taking 3.68.

[0075] The access time, departure time and target state of charge of electric vehicles also conform to the lognormal distribution, and their probability density function f(tr ) is as follows:

[0076]

[0077] Wherein, t, σ, and μ are time, standard deviation, and expected value respectively; t1 and t2 are the times when the electric vehicle enters and exits the residential area, σ1 = 1.25, μ1 = 18.5, σ2 = 2.14, μ2 = 7.62; the times when the electric vehicle enters and exits the industrial area are opposite to those of the residential area; t3 and t4 are the times when the electric vehicle enters and exits the commercial area, σ3 = 2.14, μ3 = 7.62, σ4 = 1.25, μ4 = 20.5; t5 is the target state of charge of the electric vehicle, σ5 = 0.1, μ5 = 0.85.

[0078] Combined with the daily driving mileage of the electric vehicle, the initial access power E of the electric vehicle can be obtained es,0 is as follows:

[0079] E es,0 = E es - xE km

[0080] Wherein: E es is the rated capacity; x is the daily driving mileage of the electric vehicle; E km is the power consumption per 100 kilometers of the electric vehicle.

[0081] Thus, the carbon emission of the electric vehicle accessing the power grid node j at time t is as follows:

[0082]

[0083] Wherein: are the charging and discharging powers of the electric vehicle accessing the power grid node j at time t respectively.

[0084] For the load that can be curtailed and transferred, the load that can be curtailed refers to the load that cannot be transferred but can be curtailed by a certain proportion within a certain period. The operating period of the load that can be transferred is relatively flexible. Under the condition of ensuring that its cumulative operating duration remains unchanged, interruption is allowed and the interruption duration is not fixed, but the total amount of load transferred in and out remains unchanged. The carbon emission change of node j after the demand response of the load that can be curtailed and the load that can be transferred is as follows:

[0085]

[0086] Wherein: are the input and output powers of the load that can be transferred at time t respectively; is the amount of load curtailed at time t; is the 0-1 state variable of the load that can be curtailed at time t; is the upper limit value of the load that can be curtailed; are the start and end time periods of the load curtailment response respectively; is the upper limit of the load curtailment response period; are the in-transfer and out-transfer response statuses of the shiftable load at time t, represented by 0-1 variables; are the upper limit values of the in-transfer and out-transfer of the shiftable load respectively; and are the start and end time periods of the in-transfer and out-transfer of the shiftable load respectively.

[0087] Combined with the calculation formulas of electric vehicles and load curtailment and shiftable loads, it can be seen that the actual carbon emissions of the load at node j at time t are closely related to the initial load, the charging and discharging amount of electric vehicles, the load curtailment, and the response amount of the shiftable load. is:

[0088]

[0089] In the formula, is the initial electrical load at node j at time t.

[0090] In the embodiments of the present invention, the carbon flow model of the energy coupling subsystem is established based on the energy interaction processes of single-input and single-output devices represented by gas turbines and single-input and multi-output devices represented by combined heat and power units.

[0091] Specifically, carbon emissions will flow in multiple energy systems during the energy conversion process of energy coupling devices. Therefore, the carbon emission interaction between multiple energy systems can be quantified by calculating the carbon emissions of energy coupling devices, and the idea of its carbon flow model is as follows.

[0092] For a single-input and single-output device represented by a gas turbine, its carbon flow model is as follows:

[0093]

[0094] When the conversion efficiency of the gas turbine is known, the relationship between the carbon emission flow at the gas input port and the carbon emission flow at the power output port of the gas turbine is as follows:

[0095]

[0096] In the above two formulas, and are the carbon emission flows at the gas input port and the power output port of the gas turbine connecting the gas network node i″ and the power network node j at time t respectively; and are the gas consumption and electric power of the gas turbine at time t respectively; is the conversion efficiency of the gas turbine.

[0097] For a single-input multi-output device represented by a combined heat and power unit, its carbon flow model is as follows:

[0098]

[0099] In the formula, and are respectively the gas consumption, electric power, and heat power of the combined heat and power unit connecting the gas network node i″, the power network node j, and the heat network node j′ at time t; and are respectively the electric efficiency and heat efficiency of the combined heat and power unit.

[0100] According to the principle of carbon emission conservation, the total carbon emission at the input port is equal to the total carbon emission at the output port:

[0101]

[0102] In the formula, and are respectively the carbon emissions at the gas input port, the electric power output port, and the heat output port of the gas turbine connecting the gas network node i″, the power network node j, and the heat network node j′ at time t.

[0103] Since the carbon emission intensity at the electric power output port and the heat output port of the combined heat and power unit is inversely proportional to the efficiency respectively:

[0104]

[0105] Therefore, by combining the above two formulas, the carbon intensity relationship between the coupling nodes of the gas network and the power network and the heat network can be obtained:

[0106]

[0107] In the embodiment of the present invention, the carbon flow model of the energy storage subsystem is determined according to its charge and discharge process.

[0108] Specifically, when the electric energy storage stores electric energy, it also stores the carbon emissions flowing with the electric energy, as follows:

[0109]

[0110] In the formula: is the carbon emissions stored by the electric energy storage at node j at time t; is the charging power of the electric energy storage at time t; is the carbon intensity of node j at time t; Δt is the scheduling interval.

[0111] When the electrical energy storage releases electrical energy, it also releases the carbon emissions flowing with the electrical energy, as follows:

[0112]

[0113] Wherein, is the carbon emissions released by the electrical energy storage at node j at time t; is the discharging power of the electrical energy storage at time t; is the carbon emission intensity released by the electrical energy storage at time t; is the discharging efficiency of the electrical energy storage; is the remaining capacity of the electrical energy storage at time t-1.

[0114] Then, for step 102, according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system, a two-layer optimal scheduling model of the integrated energy system is established.

[0115] In the embodiment of the present invention, the integrated energy system is divided into an upper-layer production capacity module and a lower-layer load module. The carbon trading costs are considered in both the upper and lower layers, and the upper and lower layers are coupled through the node carbon intensity, time-of-use electricity price and load energy consumption demand. The upper layer first calculates the energy supply cost and carbon trading cost according to the carbon quota coefficient to adjust the equipment output plan. Then, based on the carbon flow tracing, the node carbon intensity is calculated and transmitted to the lower layer together with the time-of-use electricity price. The lower layer can participate in the carbon market trading and bear the corresponding carbon emission responsibility.

[0116] For the upper-layer production capacity module, due to the uncertainty of the upper-layer wind power output, a large number of wind power output scenarios are simulated by the Monte Carlo method and reduced to typical wind power output scenarios based on K-means scenario clustering. When the upper-layer module trades with the external carbon market, the equipment output plan is adjusted with the goal of minimizing the total cost. Combining the node energy flow balance constraint, it can be known that the equipment energy supply amount is equal to the sum of the load energy consumption amount and the line transmission energy amount, and the carbon emissions of the line loss part are attributed to the energy supply side to bear.

[0117] Thus, the objective function of the upper-layer optimal scheduling model can be established as:

[0118]

[0119] Wherein, F IES,SO is the upper-layer objective function; is the carbon trading cost within the scheduling period; is the operating cost of the cold / heat / electricity equipment within the scheduling period.

[0120] To further control the carbon emissions of the integrated energy system, it is necessary to construct a stepped upper-layer carbon trading model based on the node carbon intensity.

[0121] First, determine the total carbon emissions of the upper-layer production capacity of the integrated energy system according to the carbon flow model, and calculate the actual carbon emissions of the upper-layer production capacity based on the total carbon emissions of the upper-layer production capacity and the initial carbon quota of the upper layer. For example, if the carbon emissions are higher than the initial carbon quota, carbon emission rights quotas need to be purchased. Conversely, the remaining quotas can be sold to obtain benefits. The actual carbon emissions are as follows:

[0122]

[0123] In the formula, is the total carbon emissions of the integrated energy system within the scheduling period; N t is the scheduling period, taking 24, and the scheduling time interval takes 1 hour; are the carbon emission quota coefficients of the gas turbine and the combined heat and power unit respectively; are the electric powers of the gas turbine and the combined heat and power unit at the power grid node j respectively. If the above equipment is not connected to node j, the power generation is equal to 0; is the heat power of the combined heat and power unit at the heat network j'; N h is the number of nodes in the heat network; is the power purchased from the municipal power grid; is the carbon emission quota coefficient for power purchase, taking 0.728 tonCO2 / MW.

[0124] Furthermore, determine the upper-layer carbon trading cost of the integrated energy system according to the actual carbon emissions of the upper-layer production capacity. Specifically, segment the system carbon emissions, set the length of the carbon emission interval, and as the purchased carbon emission quota increases, the carbon trading price of the corresponding carbon emission interval becomes higher. The stepped carbon trading cost is as follows:

[0125]

[0126] In the formula, λ c is the carbon trading benchmark price; l is the length of the carbon emission interval; α is the price growth coefficient.

[0127] Even further, establish the upper-layer optimal scheduling model of the integrated energy system according to the upper-layer carbon trading cost and the equipment operation cost.

[0128] Specifically, the equipment operation cost is as follows, including the power generation cost of the gas unit the energy supply cost of the combined heat and power unit the wind power generation cost the charging and discharging cost of the energy storage equipment the power purchase and sale cost to the power grid the energy purchase cost from the gas source

[0129]

[0130] In the formula: c gt , c chp are respectively the operating cost coefficients of the gas turbine and the combined heat and power unit; c WT is the cost coefficient of wind power generation; is the actual output of the wind turbine at time t at the power network node j; ε k is the probability of the kth wind power scenario; c WTq is the penalty coefficient for wind power curtailment; is the predicted output of the wind turbine at time t at the power network node j; c cha , c dis are respectively the charge and discharge cost coefficients of the energy storage; are respectively the purchase and sale electricity prices of the integrated energy system to the municipal power grid; are respectively the purchase and sale electricity quantities of the integrated energy system to the municipal power grid; is the gas price; is the gas purchase quantity of the integrated energy system.

[0131] The constraint conditions of the upper-layer optimal scheduling model include generator set constraints, wind power output constraints, energy storage device constraints, and line power flow constraints. Specifically:

[0132] The generator set constraint conditions include output constraints, ramp rate constraints, and start-stop constraints as follows:

[0133]

[0134] In the formula, are respectively the maximum and minimum power of the generator set at the power network node j; are respectively the upward and downward ramp rates of the generator set; are respectively the operating and shutdown times of the generator set in the (t - 1)th period; are respectively the shortest operating and shutdown times of the generator set; u m,t , u m,t-1 are respectively the start-stop states of the generator set at times t and t - 1.

[0135] Wind power output constraint:

[0136] In the formula: is the maximum power of the wind turbine at the power network node j.

[0137] Energy storage device constraint:

[0138]

[0139] In the formula: The charging efficiency of the electrical energy storage at power grid node j; are the remaining capacities of the electrical energy storage at the beginning and end of the scheduling period, respectively; are the maximum and minimum values of the remaining capacity of the electrical energy storage, respectively.

[0140] Line power flow constraint:

[0141]

[0142] In the formula: θ i,t , θ j,t are the phase angles of power grid nodes i and j at time t, respectively; x ij is the reactance of line ij; are the upper and lower limits of the power of branch ij, respectively; θ i,max , θ i,min are the upper and lower limits of the phase angle of node i, respectively.

[0143] The lower-layer load module responds to the node carbon intensity of the upper layer with the goal of minimizing the total cost, adjusts the flexible load power consumption strategy to reduce the carbon emissions and the total cost. Therefore, the objective function of the lower-layer optimal scheduling model is:

[0144]

[0145] In the formula: F IES,LA is the lower-layer objective function; is the carbon trading cost; is the energy consumption cost; is the demand response subsidy cost for the curtailable load and the shiftable load; is the electric vehicle discharging subsidy cost.

[0146] The carbon emissions of the lower-layer load can be calculated through the node carbon intensity, and then the initial carbon emission quota is allocated to each load. If the carbon emissions are higher than the allocated quota, a fine needs to be paid to the upper layer; otherwise, a subsidy from the upper layer can be obtained. The actual carbon emissions CT of the lower-layer load LOAD,LA are as follows:

[0147]

[0148] Among them, is the total carbon emissions of the lower-layer load during the scheduling period; is the initial carbon emission quota coefficient of the lower-layer load; is the initial load of the lower-layer load;

[0149] and The expressions of are as follows in sequence:

[0150]

[0151] In the formula, is the total amount of load that can be curtailed at time t; q cut is the compensation coefficient per unit of load that can be curtailed; are respectively the total amounts of transferred load transferred in and out at time t; q tra,in , q tra,out are respectively the compensation coefficients per unit of transferred load transferred in and out; is the electricity purchase price of the lower - layer load from the upper - layer; q ev is the compensation coefficient per unit of discharge power; is the total amount of electricity discharged by electric vehicles at time t.

[0152] For step 104, the two - layer optimal scheduling model is iteratively solved, and the bisection method is used to perform convergence constraint on the solution result to obtain the optimal scheduling plan of the integrated energy system.

[0153] In the embodiment of the present invention, during the optimization solution, both the upper layer and the lower layer use the Gurobi optimization solver to solve. The updated node carbon intensity of the upper layer is used as a parameter of the lower - layer problem to update the energy demand, and then the updated energy demand is transmitted to the next iterative solution.

[0154] The specific solution process includes: S1. Solve the upper - layer optimal scheduling model according to the initial operation data of the integrated energy system to obtain the unit output data of the integrated energy system and the node carbon emission intensity of the energy network when the cost is the lowest; S2. Solve the lower - layer optimal scheduling model according to the node carbon emission intensity to enable the lower - layer load to perform demand response, and use the bisection method to constrain the oscillating solution result to obtain the load demand data after the lower - layer load completes the demand response; S3. Determine whether the load demand data meets the preset convergence condition; if so, end the solution and determine the current cost data and system carbon emissions as the optimal scheduling plan, otherwise re - solve the upper - layer optimal scheduling model according to the load demand data to obtain an updated value of the node carbon emission intensity, and repeat steps S2 - S3 until the convergence condition is met.

[0155] It should be noted that when the load demands of each lower - layer load in and meet the convergence judgment condition in the th iteration, ε takes 0.05 MW, and the solution process ends.

[0156] As Figure 2As shown, since the uncertainty of the lower-layer load mainly comes from electric vehicles, during the period when electric vehicles are connected and then leave, it is necessary to simultaneously meet the charging demand of electric vehicles and maximize the benefits. With the increase in the number of electric vehicles, the impact on convergence cannot be ignored. Therefore, to prevent oscillation, during the process of solving step S2, the present invention uses the bisection method to constrain the line. The bisection method solves the oscillation problem through an equal-division heuristic method. Its main idea is to provide a feasible interval for the load, and this interval always contains the optimal operating state of the load, and gradually reduces the interval range by updating the lower bound or the upper bound in each iteration until it is less than or equal to the convergence value.

[0157] The specific steps of the bisection method are as follows: If oscillation occurs in the k-th iteration, the energy consumption cost of the lower-layer load is Let the maximum energy consumption cost at time t be The minimum energy consumption cost at time t is And set it as the operating interval of the lower-layer load, and the optimal operating state is within this interval.

[0158] Step 1: Let the average energy consumption cost at time t be

[0159] Step 2: Add a constraint condition to the lower-layer load: Solve the two-layer model. This step divides the current operating interval into two halves. If the convergence condition is met, terminate the iteration. Otherwise, Execute Step 3;

[0160] Step 3: Add a constraint condition to the lower-layer load: Solve the two-layer model. This step obtains a new operating interval containing the optimal state. If the convergence condition is met, terminate the iteration. Otherwise, execute Step 4;

[0161] Step 4: If Then the optimal state is within Update the lower bound, let If Then update the upper bound, let Then return to Step 1 until the convergence condition is met.

[0162] Next, an embodiment is used to verify the effectiveness of the method proposed by the present invention.

[0163] The topological structure of the integrated energy test system coupling electricity / heat / gas is as Figure 3As shown in the figure, the improved IEEE 14-node power grid is coupled with the lower-layer loads for analysis. The residential lower-layer load LA1, commercial lower-layer load LA2, and industrial lower-layer load LA3 are connected to node 14, node 11, and node 2 respectively. Wind power with a capacity of 600 MW is connected to node 14. The relevant parameters of the gas turbine unit are shown in Table 1, and the time-of-use electricity price is shown in Table 2.

[0164] Table 1

[0165]

[0166] Table 2

[0167] Name Time period Time-of-use electricity price / yuan / kWh Peak time period 8～11,18～23 0.9164 Normal time period 12～17 0.6164 Valley time period 0～7,23～24 0.3113

[0168] A wind turbine with a capacity of 600 MW is connected to node 14. After generating 1000 wind power scenarios using the Monte Carlo method, 4 types of wind power output curves are obtained through scenario reduction, as shown in Figure 4 a and Figure 4 b. The probabilities of each scenario are 0.281, 0.239, 0.221, and 0.259 respectively. The cost coefficient of wind power generation is 60 yuan / MW, and the penalty coefficient for curtailed wind power is 250 yuan / MW. The carbon trading benchmark price is 252 yuan / ton, the price growth coefficient is 0.25, and the carbon emission interval length is 25 tons. The contract parameters of the shiftable load and the curtailable load are shown in Table 3 and Table 4. The numbers of electric vehicles in LA1, LA2, and LA3 are 800, 300, and 500 respectively, and the relevant parameters are shown in Table 5.

[0169] Table 3

[0170] Parameter name Transfer-in compensation coefficient / yuan / MWh Transfer-out compensation coefficient / yuan / MWh Value 38 / 40 / 45 28 / 30 / 35 Parameter name Transfer-in upper limit value / MWh Transfer-out upper limit value / MWh Value 2.5 / 10 / 5.5 3 / 15 / 7.5 Parameter name Transfer-in start time / h Transfer-in end time / h Value 1 / 1 / 1 16 / 16 / 17 Parameter name Transfer-out start time / h Transfer-out end time / h Value 17 / 17 / 18 22 / 21 / 22

[0171] Table 4

[0172] Parameter name Reduction compensation coefficient / yuan / MWh Reduction power upper limit value / MWh Value 233 / 260 / 250 1 / 3 / 2 Parameter name Reduction start time / h Reduction end time / h Value 12 / 12 / 12 20 / 20 / 21 Parameter name Response time period upper limit / h —— Value 5 / 5 / 5 ——

[0173] Table 5

[0174] Parameter name Rated capacity / MWh Discharge subsidy coefficient / yuan / MWh Value 0.057 100 Parameter name Charge and discharge power upper limit / MW SOC upper and lower limits / % Value 0.015 / 0.007 10 / 90

[0175] By setting up 5 schemes as shown in Table 6, the effect of the coordinated guidance of the node carbon intensity and the time-of-use electricity price on the flexible load demand response is analyzed to verify the effectiveness of the proposed scheme of the present invention. The total operating cost of the integrated energy system and its composition under each scheme are shown in Table 7.

[0176] Table 6

[0177] Name Flat electricity price Time-of-use electricity price Demand response Tiered carbon trading Plan 1 √ × × × Plan 2 × √ √ × Plan 3 × √ × √ Plan 4 √ × √ √ Plan 5 × √ √ √

[0178] Table 7

[0179]

[0180] As can be seen from Table 7, based on Scheme 1, in Scheme 2, the upper layer of the system guides the lower-layer load to perform demand response through time-of-use electricity price, and adjusting the energy consumption curve of the lower-layer load can effectively reduce the carbon trading cost of the upper layer of the system, which has decreased by 73.46%; the natural gas cost has decreased by 0.98%, and the final total cost has decreased by 300,500 yuan. Based on Scheme 2, in Scheme 5, the LA can participate in carbon trading, which is conducive to mobilizing the enthusiasm of the lower-layer load for low-carbon energy conservation. Therefore, the carbon trading cost of the upper layer of the system is further reduced, and it can even make a profit of 59,700 yuan. The natural gas cost has decreased by 1.42%, and the final total cost has decreased by 253,000 yuan. Therefore, the method proposed in the present invention can achieve low-carbon economic operation of the upper layer of the system, and verifies the effectiveness of the economic dispatching strategy for source-load collaborative carbon reduction from the perspective of the upper layer of the system.

[0181] Next, analyze from the perspective of the lower-layer load. The comparison of carbon emissions and costs of the lower-layer load under 5 schemes is shown in Table 8.

[0182] Table 8

[0183]

[0184]

[0185] As can be seen from Table 6 and Table 8, in Schemes 1 and 3, the load demand response is not guided by time-of-use electricity price or node carbon intensity, and the carbon emissions are the highest, and the total cost of Scheme 3 is the highest. In Scheme 2, the load demand response is guided by time-of-use electricity price, and in Scheme 4, the load demand response is guided by node carbon intensity. Therefore, the carbon emissions of both schemes have decreased.

[0186] In Scheme 5, the load demand response is guided by time-of-use electricity price and node carbon intensity. Compared with Scheme 1, although Scheme 5 increases the carbon trading cost, it reduces the electricity purchase cost of each lower-layer load and effectively reduces the total cost. And the carbon emissions of each lower-layer load in Scheme 5 have decreased by 20 tons, 12.4 tons and 31.5 tons respectively. Therefore, the method proposed in the present invention can enable the lower-layer load to take into account both low-carbon and economic characteristics.

[0187] Compared with Scheme 2, Scheme 5 takes into account carbon emission trading. Under the influence of time-of-use electricity price and node carbon intensity, the electricity purchase cost of the lower-layer load is reduced, and the carbon emissions are significantly reduced, by 2.97 tons, 0.26 tons and 12.45 tons respectively. This shows that using time-of-use electricity price and node carbon intensity to guide load demand response can effectively improve the low-carbon and economic characteristics of the lower-layer load.

[0188] Compared with Scheme 4, Scheme 5 takes into account the time-of-use electricity price. The enthusiasm of the load to participate in demand response has been improved, and the carbon emissions of each lower-level load have been reduced by 3.17 tons, 1.46 tons, and 6.9 tons respectively, and the carbon trading cost has also been reduced accordingly.

[0189] Therefore, although the results of the present invention increase the carbon trading cost, the dependence on the energy side can be reduced through load demand response, the carbon emissions can be effectively reduced, and both low carbon and economy can be taken into account.

[0190] To further study the role of time-of-use electricity price and nodal carbon intensity in guiding load demand response, taking Scheme 5 as an example, analyze the flexible load of each lower-level load and the change curve of nodal carbon intensity, as Figure 5 shown.

[0191] From Figure 3 and Figure 5 (a) to Figure 5 (c), it can be seen that LA1 and the wind turbine are connected to Figure 5 Node 14, and the wind turbine has near-zero carbon emissions, so the change in the nodal carbon intensity of LA1 is relatively obvious. While LA3 is connected to Figure 5 Node 2, which is close to the gas turbine unit, so the nodal carbon intensity of this node is affected by the gas turbine unit. Since the carbon emission coefficient of the gas turbine unit is relatively large, the change in the nodal carbon intensity of LA3 is relatively small.

[0192] During the period when the nodal carbon intensity is relatively low, LA1 and LA3 guide the charging of electric vehicles. As the nodal carbon intensity increases, the charging amount gradually decreases and the discharging amount gradually increases. LA1 and LA2 transfer to the load when the nodal carbon intensity is relatively low, and transfer out of the load or cut the load when the nodal carbon intensity is relatively high, so as to reduce the electricity purchase amount from the integrated energy service provider.

[0193] Since the time-of-use electricity price is higher than the carbon price, Figure 4 the load demand response in changes significantly following the time-of-use electricity price. When the change trends of the time-of-use electricity price and the nodal carbon intensity are different, the load demand response will also be adjusted to a certain extent following the nodal carbon intensity. During the periods of 04:00 - 06:00 and 24:00, the electricity price is relatively low while the nodal carbon intensity is relatively high, and the charging amount of electric vehicles of LA1 significantly decreases and the transferred load amount decreases at 04:00 - 06:00. For LA1, it shows that the proportion of carbon trading cost in these periods is relatively large, and the load demand response changes more significantly following the nodal carbon intensity.

[0194] Similarly, the electricity price is low at 04:00 while the nodal carbon intensity is high. The charging volume of electric vehicles in LA2 and LA3 slightly decreases, and the shiftable load does not transfer in. The electricity price is low and the nodal carbon intensity is low at 07:00, and the charging volume of electric vehicles significantly increases. During the normal period from 12:00 to 16:00, the electricity price is at the normal level while the nodal carbon intensity is high, and the charging volume of electric vehicles decreases.

[0195] In addition, since the responsive amount of the curtailable load and shiftable load in LA1 is small but the number of electric vehicles is large, LA1 mobilizes the charging and discharging of electric vehicles more to meet the low-carbon and economic requirements. While the responsive amount of the curtailable load and shiftable load in LA2 and LA3 is large but the number of electric vehicles is small, LA2 and LA3 call on the curtailable load and shiftable load more to reduce carbon emissions. Therefore, different types of lower-layer loads can maximize demand response according to their load characteristics and incentive mechanisms to achieve the reduction of electricity purchase cost and low-carbon emission reduction.

[0196] The load changes of LA1, LA2, and LA3 before and after demand response are as Figure 5 (d) to Figure 5 (f) shown. Taking LA1 as an example, before the response, the electric vehicles do not need to charge, forming an obvious load peak, and the load peak-valley difference is large. After the demand response, LA1 transfers the peak load to the low load period through the orderly charging and discharging of electric vehicles, load reduction, and transfer, reducing the power consumption pressure during the peak load period, and the load peak-valley difference is significantly reduced.

[0197] To further analyze the influence of the installed capacity of wind power on the carbon emissions and costs of the lower-layer loads, the installed capacity of wind power is set to 500, 600, and 700 MW respectively. First, the nodal carbon intensity is analyzed. The change curves of LA1, LA2, and LA3 in Scheme 5 are as Figure 6 shown. As Figure 6 shown, with the increase of the installed capacity of wind power, the output of gas turbines decreases. Wind power is a clean energy source with near-zero carbon emissions. Therefore, with the improvement of the wind power utilization rate, the overall nodal carbon intensity shows a downward trend. Therefore, the reasonable configuration of the installed capacity of wind power helps to reduce the nodal carbon intensity of the system.

[0198] Combined with the change of the nodal carbon intensity, the carbon emissions and total costs of each lower-layer load are further analyzed. The changes in carbon emissions and total costs are shown in Table 9.

[0199] Table 9

[0200]

[0201] Combined with Figure 6As can be seen from Table 9, as the installed wind power capacity increases, the carbon intensity of the node decreases, and the carbon emissions of the lower-layer load decrease accordingly. The carbon emission cost borne by the lower-layer load is reduced, which can effectively reduce the total cost. Among the three lower-layer loads, LA3 has a large original load and is more sensitive to the change of node carbon intensity, so the change trend of LA3 is more obvious, while the changes of LA1 and LA2 are relatively gentle.

[0202] Please refer to Figure 7 , an optimization scheduling device for collaborative carbon reduction of sources and loads in an integrated energy system is provided in an embodiment of the present invention. The device includes:

[0203] The first modeling module 700 is used to establish an integrated energy system carbon flow model centered on the energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system; wherein, the energy network includes a power network, a heat network, and a gas network;

[0204] The second modeling module 702 is used to establish a two-layer optimization scheduling model of the integrated energy system according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system;

[0205] The calculation module 704 is used to perform iterative solution on the two-layer optimization scheduling model and use the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling scheme of the integrated energy system.

[0206] In the embodiment of the present invention, when the first modeling module 700 executes the operation of establishing an integrated energy system carbon flow model centered on the energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system, it is specifically used to perform the following operations:

[0207] Establish the carbon flow model of the energy network according to the absolute value of the multi-energy flow data flowing into the energy network nodes and the carbon emission intensity of the production energy subsystem;

[0208] According to the node access relationship between the energy network and the load subsystem, the energy coupling subsystem, and the energy storage subsystem respectively, establish the carbon flow models of the load subsystem, the energy coupling subsystem, and the energy storage subsystem in sequence.

[0209] In the embodiment of the present invention, establishing the carbon flow model of the energy network according to the absolute value of the multi-energy flow data flowing into the energy network nodes and the carbon emission intensity of the production energy subsystem includes:

[0210] Calculate the active absolute quantity matrix of the node according to the absolute value of the energy flow data flowing into the node;

[0211] Solve the active absolute quantity matrix according to the power of the production capacity subsystem connected to the node, and establish the carbon flow model of the energy network according to the solution result; wherein, the carbon flow model of the power network is established by the following formula:

[0212]

[0213] In the formula, and are the carbon emission intensities of power network node j, branch ij, and generator set k at time t, respectively; is the active power of branch ij; is the power of the kth generator set connected to node j;

[0214] The carbon flow model of the thermal network is established by the following formula:

[0215]

[0216] In the formula, and are the carbon emission intensities of thermal network node j′, branch i′j′, and combined heat and power unit k′ at time t, respectively; is the energy flow flowing into branch i′j′ at time t; is the thermal power of the combined heat and power unit at time t;

[0217] The carbon flow model of the gas network is established by the following formula:

[0218]

[0219] In the formula, and are the carbon emission intensities of gas network node j″, branch i″j″, and gas source k″ at time t, respectively; is the energy flow flowing into branch i″j″ at time t; is the gas source k″ flow rate at time t.

[0220] In the embodiments of the present invention, according to the node access relationships between the energy network and the load subsystem, the energy coupling subsystem, and the energy storage subsystem, the carbon flow models of the load subsystem, the energy coupling subsystem, and the energy storage subsystem are established in sequence, including:

[0221] Perform demand response adjustment on the flexible load of the load subsystem according to the node carbon intensity of the power network to obtain the carbon flow model of the load subsystem:

[0222]

[0223] Wherein, is the actual carbon emission of the load subsystem of node j at time t; is the initial electrical load of node j at time t; is the carbon emission of the electric vehicle connected to node j of the power grid at time t; is the carbon emission change of node j after demand response for the load that can be curtailed and the load that can be shifted;

[0224] According to the energy interaction process between the gas turbine and the combined heat and power unit in the energy coupling subsystem, a carbon flow model of the energy coupling subsystem is established:

[0225]

[0226] In the formula, and are respectively the carbon emission flow at the gas input port and the carbon emission flow at the power output port of the gas turbine connecting the gas network node i″ and the power network node j in time period t; and are respectively the carbon emission at the gas input port, the carbon emission at the power output port and the carbon emission at the heat output port of the combined heat and power unit connecting the gas network node i″, the power network node j and the heat network node j′ in time period t; and are respectively the electrical efficiency and the thermal efficiency of the combined heat and power unit;

[0227] According to the charge and discharge process of the energy storage subsystem, a carbon flow model of the energy storage subsystem is established:

[0228]

[0229] In the formula: is the carbon emission stored in the electrical energy storage located at node j at time t; is the charging power of the electrical energy storage at time t; is the carbon intensity of node j at time t; Δt is the scheduling interval; is the carbon emission released by the electrical energy storage located at node j at time t; is the discharging power of the electrical energy storage at time t; is the carbon emission intensity released by the electrical energy storage at time t; is the discharging efficiency of the electrical energy storage.

[0230] In the embodiment of the present invention, when the second modeling module 702 executes to establish a two-layer optimal scheduling model of the integrated energy system according to the carbon flow model and the carbon trading cost of the upper and lower layers of the integrated energy system, it is specifically used to perform the following operations:

[0231] Determine the total carbon emissions of the upper-layer production capacity of the integrated energy system according to the carbon flow model, and calculate the actual carbon emissions CT of the upper-layer production capacity based on the total carbon emissions of the upper-layer production capacity and the initial carbon quota of the upper layer. IESSO :

[0232]

[0233] Wherein, is the total carbon emissions of the upper layer of the integrated energy system during the scheduling period; N t is the scheduling period; are the carbon emission quota coefficients of the gas turbine and the combined heat and power unit respectively; are the electric powers of the gas turbine and the combined heat and power unit at the power network node j respectively; is the heat power of the combined heat and power unit at the heat network node j'; N h is the number of nodes in the heat network; is the purchased electric power from the municipal power grid; is the purchased electricity carbon emission quota coefficient;

[0234] Determine the upper-layer carbon trading cost of the integrated energy system according to the actual carbon emissions of the upper-layer production capacity, and establish an upper-layer optimal scheduling model of the integrated energy system based on the upper-layer carbon trading cost and the equipment operation cost;

[0235] Determine the total carbon emissions of the lower-layer load of the integrated energy system according to the carbon flow model, and calculate the actual carbon emissions CT of the lower-layer load based on the total carbon emissions of the lower-layer load and the initial carbon quota of the lower layer. LOAD,LA :

[0236]

[0237] Wherein, is the total carbon emissions of the lower-layer load during the scheduling period; is the initial carbon emission quota coefficient of the lower-layer load; is the initial load of the lower-layer load;

[0238] Determine the lower-layer carbon trading cost of the integrated energy system according to the actual carbon emissions of the lower-layer load, and establish a lower-layer optimal scheduling model of the integrated energy system based on the lower-layer carbon trading cost and the load transfer cost.

[0239] In the embodiment of the present invention, when the calculation module 704 performs iterative solution of the double-layer optimal scheduling model and uses the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling scheme of the integrated energy system, it is specifically used to perform the following operations:

[0240] S1. Solve the upper-layer optimal scheduling model according to the initial operation data of the integrated energy system to obtain the unit output data of the integrated energy system and the node carbon emission intensity of the energy network when the cost is the lowest;

[0241] S2. Solve the lower-layer optimal scheduling model according to the node carbon emission intensity to enable the lower-layer load to perform demand response, and use the bisection method to constrain the oscillating solution results to obtain the load demand data after the lower-layer load completes the demand response;

[0242] S3. Determine whether the load demand data meets the preset convergence condition; if so, end the solution and determine the current cost data and system carbon emissions as the optimal scheduling plan, otherwise re-solve the upper-layer optimal scheduling model according to the load demand data to obtain an updated value of the node carbon emission intensity, and repeat steps S2 - S3 until the convergence condition is met.

[0243] It should be noted that: for the optimal scheduling device for collaborative carbon reduction of the source and load of the integrated energy system provided in the above embodiments, only the above-mentioned division of each functional subsystem is used as an example. In practical applications, the above functions can be allocated to different functional subsystems according to needs, that is, the internal structure of the device is divided into different functional subsystems to complete all or part of the functions described above. In addition, the optimal scheduling device for collaborative carbon reduction of the source and load of the integrated energy system provided in the above embodiments and the embodiments of the optimal scheduling method for collaborative carbon reduction of the source and load of the integrated energy system belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0244] An embodiment of the present application also provides a computer device. Please refer to Figure 8 This computer device includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. The at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the optimal scheduling method for collaborative carbon reduction of the source and load of the integrated energy system provided in each of the above method embodiments.

[0245] An embodiment of the present application also provides a computer-readable storage medium. At least one instruction, at least one program, a code set, or an instruction set is stored on the computer-readable storage medium. The at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the optimal scheduling method for collaborative carbon reduction of the source and load of the integrated energy system provided in each of the above method embodiments.

[0246] An embodiment of the present application further provides a computer program product, which includes a computer program. The processor of the computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the optimal scheduling method for source-load collaborative carbon reduction of the integrated energy system described in any one of the above embodiments.

[0247] For the convenience of description, when describing the above system or device, it is divided into various subsystems or units according to functions for separate description. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0248] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0249] Finally, it should also be noted that in this article, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0250] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. An optimal scheduling method for collaborative carbon reduction of sources and loads in an integrated energy system, characterized in that, The method includes: According to the transmission characteristics of carbon emissions in the integrated energy system, establish a carbon flow model of the integrated energy system centered on the energy network nodes; wherein, the energy network includes a power network, a heat network, and a gas network; According to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system, establish a two-layer optimal scheduling model of the integrated energy system; Perform iterative solution on the two-layer optimal scheduling model, and use the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling plan of the integrated energy system.

2. The method according to claim 1, wherein The step of establishing a carbon flow model of the integrated energy system centered on the energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system includes: Establish a carbon flow model of the energy network according to the absolute value of the multi-energy flow data flowing into the energy network nodes and the carbon emission intensity of the production capacity subsystem; According to the node access relationships between the energy network and the load subsystem, the energy coupling subsystem, and the energy storage subsystem respectively, establish the carbon flow models of the load subsystem, the energy coupling subsystem, and the energy storage subsystem in sequence.

3. The method according to claim 2, characterized in that The step of establishing a carbon flow model of the energy network according to the absolute value of the multi-energy flow data flowing into the energy network nodes and the carbon emission intensity of the production capacity subsystem includes: Calculate the active absolute quantity matrix of the node according to the absolute value of the energy flow data flowing into the node; Solve the active absolute quantity matrix according to the power of the production capacity subsystem connected to the node, and establish a carbon flow model of the energy network according to the solution result; wherein, the carbon flow model of the power network is established by the following formula: wherein, and are the carbon emission intensities of power network node j, branch ij, and generating unit k at time t, respectively; is the active power of branch ij; is the power of the k-th generating unit connected to node j; The carbon flow model of the heat network is established by the following formula: In the formula, and are the carbon emission intensities of the thermal network node j′, the branch i′j′, and the combined heat and power unit k′ at time t, respectively; is the energy flow into the branch i′j′ at time t; is the heat power of the combined heat and power unit at time t; The carbon flow model of the gas network is established by the following formula: In the formula, and are the carbon emission intensities of the gas network node j″, the branch i″j″, and the gas source k″ at time t, respectively; is the energy flow flowing into the branch i″j″ at time t; is the gas source k″ flow rate at time t.

4. The method according to claim 3, wherein The step of establishing the carbon flow models of the load subsystem, the energy coupling subsystem, and the energy storage subsystem in sequence according to the node access relationships between the energy network and the load subsystem, the energy coupling subsystem, and the energy storage subsystem respectively includes: Perform demand response adjustment on the flexible load of the load subsystem according to the node carbon intensity of the power network to obtain the carbon flow model of the load subsystem: Among them, is the actual carbon emission of the load subsystem of node j at time t; is the initial electrical load of node j at time t; is the carbon emission of the electric vehicle connected to node j of the power network at time t; is the change in carbon emissions of node j after demand response for the load that can be curtailed and the load that can be shifted; Establish a carbon flow model of the energy coupling subsystem according to the energy interaction process between the gas turbine and the combined heat and power unit in the energy coupling subsystem: Wherein, and are respectively the carbon emission flows at the gas input port and the carbon emission flows at the power output port of the gas turbine connecting the gas network node i″ and the power network node j at time period t; and are respectively the carbon emissions at the gas input port, the carbon emissions at the power output port and the carbon emissions at the heat output port of the combined heat and power unit connecting the gas network node i″, the power network node j and the heat network node j′ at time period t; and are respectively the electrical efficiency and the thermal efficiency of the combined heat and power unit; Establish a carbon flow model of the energy storage subsystem according to the charge and discharge process of the energy storage subsystem: where: is the carbon emissions stored in the electrical energy storage at node j at time t; is the charging power of the electrical energy storage at time t; is the carbon intensity of node j at time t; Δt is the scheduling interval; is the carbon emissions released by the electrical energy storage at node j at time t; is the discharging power of the electrical energy storage at time t; is the carbon emission intensity released by the electrical energy storage at time t; is the discharging efficiency of the electrical energy storage.

5. The method according to claim 1, characterized in that, The step of establishing a two-layer optimal scheduling model of the integrated energy system according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system includes: Determine the total carbon emissions of the upper-layer production capacity of the integrated energy system according to the carbon flow model, and calculate the actual carbon emissions CT of the upper-layer production capacity based on the total carbon emissions of the upper-layer production capacity and the initial carbon quota of the upper layer IES,SO : Among them, is the total upper-layer carbon emission of the integrated energy system within the scheduling period; N t is the scheduling period; are the carbon emission quota coefficients of the gas turbine and the combined heat and power unit respectively; are the electric powers of the gas turbine and the combined heat and power unit at the power network node j respectively; is the heat power of the combined heat and power unit at the heat network node j'; N h is the number of nodes in the heat network; is the purchased electric power from the municipal power grid; is the purchased electricity carbon emission quota coefficient; Determine the upper-layer carbon trading cost of the integrated energy system according to the actual carbon emissions of the upper-layer production capacity, and establish an upper-layer optimal scheduling model of the integrated energy system according to the upper-layer carbon trading cost and the equipment operation cost; Determine the total carbon emissions of the lower-layer load of the integrated energy system according to the carbon flow model, and calculate the actual carbon emissions CT of the lower-layer load based on the total carbon emissions of the lower-layer load and the initial carbon quota of the lower layer LOAD,LA : Among them, is the total carbon emissions of the lower-layer load within the scheduling period; is the initial carbon emission quota coefficient of the lower-layer load; is the initial load of the lower-layer load; Determine the lower-layer carbon trading cost of the integrated energy system according to the actual carbon emissions of the lower-layer load, and establish a lower-layer optimal scheduling model of the integrated energy system according to the lower-layer carbon trading cost and the load transfer cost.

6. The method according to claim 5, characterized in that The step of performing iterative solution on the two-layer optimal scheduling model and using the bisection method to perform convergence constraint on the solution result to obtain the optimal scheduling plan of the integrated energy system includes: S1. Solve the upper-layer optimal scheduling model according to the initial operation data of the integrated energy system to obtain the unit output data of the integrated energy system and the node carbon emission intensity of the energy network when the cost is the lowest; S2. Solve the lower-layer optimal scheduling model according to the node carbon emission intensity to enable the lower-layer load to perform demand response, and use the bisection method to constrain the oscillating solution results to obtain the load demand data after the lower-layer load completes the demand response; S3. Determine whether the load demand data meets the preset convergence condition; if so, end the solution and determine the current cost data and system carbon emissions as the optimal scheduling plan, otherwise, re-solve the upper-layer optimal scheduling model according to the load demand data to obtain an updated value of the node carbon emission intensity, and repeat steps S2 - S3 until the convergence condition is met.

7. An optimized scheduling device for collaborative carbon emission reduction of sources and loads in an integrated energy system, characterized in that, The device includes: The first modeling module is used to establish a carbon flow model of the integrated energy system centered on the energy network nodes according to the transmission characteristics of carbon emissions in the integrated energy system; wherein, the energy network includes a power network, a heat network, and a gas network; The second modeling module is used to establish a two-layer optimal scheduling model of the integrated energy system according to the carbon flow model and the carbon trading costs of the upper and lower layers of the integrated energy system; The calculation module is used to perform iterative solution on the two-layer optimal scheduling model and use the bisection method to perform convergence constraint on the solution results to obtain the optimal scheduling plan of the integrated energy system.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the method according to any one of claims 1 - 6 above.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 - 6.

10. A computer program product, characterized in that, Including a computer program, and when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 - 6.

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