A low-carbon economic dispatch method for integrated energy systems with source-load-storage coordination

Abstract

This invention discloses a low-carbon economic dispatch method for an integrated energy system with coordinated source-load-storage. Under the tiered carbon trading mechanism, the dispatch potential of each link of the IES source-load-storage system is fully utilized. First, the method divides the operation mode into two modes: a full absorption mode and a partial absorption mode, based on the renewable energy absorption level. Then, the method classifies the electric and thermal virtual energy storage into different types according to their response time and response characteristics, and proposes an electric and thermal virtual energy storage operation scheme. The electric and thermal virtual energy storage operation scheme proposed in this paper is then called upon in both the full absorption mode and the partial absorption mode operation modes. Furthermore, a low-carbon economic dispatch scheme for an integrated energy system with coordinated source-load-storage is proposed. A low-carbon economic dispatch model for an integrated energy system with coordinated source-load-storage is established based on the proposed scheme. Finally, Matlab is used to conduct case analysis, and the effectiveness of the proposed dispatch scheme in reducing carbon emissions, increasing renewable energy absorption, and achieving economic efficiency is verified through comparative analysis of simulation results.

Description

Technical Field

[0001] The present invention relates to a low-carbon economic dispatching method for a source-load-storage coordinated integrated energy system, belonging to the technical field of optimized dispatching of integrated energy systems. Background Art

[0002] With the rapid development of society, carbon emissions are increasing worldwide, and reducing them has become a common global concern. Consequently, insufficient renewable energy consumption and excessive carbon emissions within integrated energy systems (IES) are pressing challenges. Therefore, leveraging the scheduling potential of each link in the coupled energy systems of electricity, heat, and gas, and developing appropriate scheduling plans to fully utilize environmentally friendly renewable energy resources and promote low-carbon operation of IES have become a current research hotspot.

[0003] While the source side of an integrated energy system is the largest source of carbon emissions, emissions from the load side cannot be ignored. Effective approaches for integrated energy systems include increasing the grid penetration of renewable energy and introducing virtual energy storage and carbon capture technologies to fully leverage all aspects of the IES (source, load, and storage) ecosystem.

[0004] These two approaches have the following shortcomings: First, renewable energy output is subject to uncertainty due to natural factors such as season and climate. As the scale of renewable energy grid connection continues to increase, the stability and security of power system operation will also be affected. Issues with renewable energy access and absorption restrict its widespread use in integrated energy systems. Second, while the coupling of electricity, heat, and gas in integrated energy systems has to some extent improved the problem of renewable energy absorption and high carbon emissions, the current amount of unabsorbed renewable energy and carbon emissions in the IES remain large, and the economic efficiency is poor. Therefore, a virtual energy storage system is introduced to classify load-side flexibility resources as virtual energy storage; at the same time, carbon capture and power-to-gas equipment are introduced to coordinate source, load, and storage.

[0005] However, two shortcomings remain: 1. There is a lack of operational analysis and solution development for virtual energy storage systems; 2. There is a lack of operational analysis and proposed source-load-storage coordinated scheduling solutions for the introduced IES. Therefore, overcoming the shortcomings of existing technologies is an urgent issue in the field of integrated energy system optimization and scheduling technology. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a low-carbon economic scheduling method for an integrated energy system with coordinated source-load-storage, fully coordinate all links of the integrated energy system, and tap the scheduling potential of each link of the integrated energy system "source-load-storage".

[0007] To solve the above technical problems, the technical solution of the present invention is: a low-carbon economic dispatch method for an integrated energy system with source-load-storage coordination, the innovation of which lies in the following steps:

[0008] Step A: First, the source side and the load side jointly determine the system operation mode. According to the different levels of renewable energy utilization, the integrated energy system is divided into two operation modes: full absorption mode and incomplete absorption mode.

[0009] Step B: Based on the response time and response characteristics of electric virtual energy storage and thermal virtual energy storage, the integrated energy system is divided into Class A, Class B, and Class C, and an electric virtual energy storage operation plan and a thermal virtual energy storage operation plan are given. Class A: Electric virtual energy storage or thermal virtual energy storage is transferred within 4-8 hours, and the total energy consumption during this time period remains unchanged;

[0010] Category B: Electric virtual energy storage or thermal virtual energy storage is transferred within 4 hours, and the total energy consumption during this period remains unchanged;

[0011] Category C: load curtailment with electric virtual energy storage or thermal virtual energy storage;

[0012] Step C: Based on step A, the electric virtual energy storage operation plan and the thermal virtual energy storage operation plan described in step B are called to obtain a low-carbon economic dispatch plan for the integrated energy system with source-load-storage coordination;

[0013] Step D: Based on the source-load-storage coordinated integrated energy system low-carbon economic dispatch scheme in step C, a source-load-storage coordinated integrated energy system low-carbon economic dispatch model is established;

[0014] Step E: Based on the source-load-storage coordinated integrated energy system low-carbon economic dispatch model in step D, use MATLAB to simulate the improved IEEE30-node system.

[0015] Preferably, the integrated energy system in step A includes an external power grid, a carbon capture unit, electricity storage, heat storage and hydrogen storage equipment, a gas turbine, a waste heat boiler, a gas boiler, an electrolysis gas equipment, a virtual energy storage system and a load.

[0016] Preferably, the different levels of renewable energy utilization in step A are: defining the system operation mode based on the size of the renewable energy absorption margin and its changing trend, or judging the operation mode of the integrated energy system based on the cross-principle of considering economy, safety, low carbon and priority absorption of renewable energy, and whether the system retains a certain absorption margin for renewable energy.

[0017] Preferably, the complete absorption mode in step A is a period of time when the renewable energy is fully absorbed and the demand for electricity load is high during the operation of the integrated energy system; the incomplete absorption mode is a period of time when the renewable energy is insufficiently absorbed and the demand for electricity load is low during the operation of the integrated energy system; and according to whether the renewable energy is fully absorbed, it is divided into an incomplete absorption mode of renewable energy and a complete absorption mode of renewable energy.

[0018] Preferably, the electric virtual energy storage operation scheme in step B is as follows: when renewable energy is fully absorbed, electricity demand is high, and Class A transferable electric loads are dispatched through incentive means to perform peak shaving and valley filling to reduce the peak regulation pressure of the system; Class B transferable electric loads and Class C reducible electric loads are used as backup resources to fill the fluctuations caused by the uncertainty of renewable output due to their short scheduling time; when the electricity load demand is low, the electric virtual energy storage exhibits valley filling characteristics, and part of the transferable electric load is moved to this time period to thereby improve the absorption of renewable energy.

[0019] Preferably, the thermal virtual energy storage operation scheme in step B is as follows: when renewable energy is not fully absorbed and heat demand is high, Class A transferable heat load is called upon to reduce the heat load level, reduce the output of the CHP unit, and increase the space for renewable energy to be connected to the grid; Class B and Class C heat loads increase the peak-shaving resources to further provide space for renewable energy to be connected to the grid; when heat demand is low, the thermal virtual energy storage presents a valley-filling effect, so that the CHP unit increases its output to meet the heat demand, and at the same time, the electrical output of the CHP unit replaces part of the purchased energy output.

[0020] Preferably, the source-load-storage coordinated integrated energy system low-carbon economic dispatch method also includes constraints on each link, including power balance constraints, virtual energy storage system constraints, unit operation constraints and constraints on each device in the integrated energy system.

[0021] The advantages of the present invention are: based on the analysis of the source-load-storage coordinated operation mechanism of the integrated energy system, the present invention proposes a low-carbon economic scheduling plan for the integrated energy system, establishes a low-carbon economic scheduling model, analyzes and models the operation of the virtual energy storage system, and analyzes the impact of source-load-storage coordinated scheduling on the low-carbon economic operation of IES based on the research results, and derives a low-carbon economic scheduling method for the integrated energy system considering source-load-storage coordination.

[0022] Power-to-gas equipment connects the grid to the gas grid, while gas turbines and electric boilers connect the grid to the heat grid. Meanwhile, gas turbines and gas boilers connect the heat grid to the gas grid, achieving power-heat-gas energy coupling. To address rising carbon emissions during periods of full renewable energy consumption, an electric virtual energy storage operation solution is employed to reduce the electrical load in this mode, thereby lowering unit output and enabling electric energy storage to further reduce carbon emissions. This also reduces IES operating costs in this mode.

[0023] With this proposed thermal virtual energy storage operation scheme, some transferable heat loads can be utilized in this mode, with the CHP units generating increased power to meet the transferred heat demand. This increased CHP unit output also reduces electricity purchase costs and the resulting carbon trading costs. Because carbon trading prices rise with increasing dispatch time, and the carbon emissions per unit of heat from CHP units are less than the quota, the overall CHP cost decreases, thereby reducing IES economic costs.

[0024] When renewable energy is not fully absorbed, in order to solve the problem of insufficient utilization of renewable energy under this mode, first, the adjustable characteristics of virtual energy storage are used to move the transferable electric load to this mode, so as to achieve the effect of raising the demand for some electric loads so that it can absorb some renewable energy. If some renewable energy is still not absorbed after the above steps, P2G and electric boilers are activated to convert excess electricity into natural gas and heat energy and integrate them into the gas and heat networks. Carbon capture equipment captures the released CO2, reducing carbon emissions while providing raw materials for P2G. In addition, this application introduces a ladder carbon trading mechanism to reduce the system's carbon emissions and improve the system's economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0026] Figure 1 It is a structural schematic diagram of the integrated energy system in a low-carbon economic dispatch method of an integrated energy system with source-load-storage coordination according to the present invention.

[0027] Figure 2 It is a structural diagram of an improved IEEE30 node system in a low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system of the present invention.

[0028] Figure 3 This is a MATLAB optimization solution flow chart of a low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system of the present invention.

[0029] Figure 4 This is a basic data diagram of wind turbine output, electrical load and thermal load requirements in a low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system of the present invention.

[0030] Figure 5 It is a structural schematic diagram of the optimal operation scheme of the power supply system of the integrated energy system in the low-carbon economic dispatch method of the integrated energy system with source-load-storage coordination of the present invention.

[0031] Figure 6 It is a structural schematic diagram of the optimal operation scheme of the heating system of the integrated energy system in the low-carbon economic dispatch method of the integrated energy system with source-load-storage coordination of the present invention.

[0032] Figure 7 It is a structural schematic diagram of the electric load operation plan of the integrated energy system before and after the virtual energy storage response in the low-carbon economic dispatch method of the integrated energy system with source-load-storage coordination of the present invention.

[0033] Figure 8 It is a thermal load operation plan of an integrated energy system before and after virtual energy storage response in a low-carbon economic dispatch method of an integrated energy system with source-load-storage coordination according to the present invention.

[0034] Figure 9 It is a structural schematic diagram of the impact of different scheduling schemes on the carbon emissions of the IES system in a low-carbon economic scheduling method of a source-load-storage coordinated integrated energy system of the present invention and the impact of different scheduling schemes on the carbon emissions of the IES system.

[0035] Figure 10 It is a structural schematic diagram of the carbon emissions at each level, P2G carbon dioxide consumption and optimal carbon quota allocation scheme within a scheduling cycle of the integrated energy system in a low-carbon economic scheduling method of the source-load-storage coordinated integrated energy system of the present invention.

[0036] Figure 11 This is a structural diagram of IES operating costs and carbon emissions under different scenarios in a low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system of the present invention. DETAILED DESCRIPTION

[0037] The low-carbon economic dispatching method of the source-load-storage coordinated integrated energy system of the present invention fully coordinates all links of the integrated energy system and taps the dispatching potential of each link of the system's "source-load-storage".

[0038] 1. Building a coordinated energy system of source, load and storage

[0039] The integrated energy system includes an external power grid, carbon capture units, electricity storage, heat storage and hydrogen storage equipment, gas turbines, waste heat boilers, gas boilers, electrolysis equipment, virtual energy storage systems and various loads.

[0040] 2. Operational analysis and model division of the integrated energy system with source-load-storage coordination

[0041] Based on the level of renewable energy utilization, the IES is divided into two operating modes: "full absorption mode" and "incomplete absorption mode." Next, considering the response time and response characteristics of the two virtual energy storages, electricity and heat, they are divided into three categories: A, B, and C. Operational schemes for electricity and heat virtual energy storage are then proposed, and these schemes are invoked under the two operating modes proposed in this paper. In the "full absorption mode," virtual energy storage coordinates with the source side to reduce system carbon emissions and the operating costs of the IES. In the "incomplete absorption mode," virtual energy storage collaborates with P2G and carbon capture to absorb more renewable energy, and the carbon capture device can further reduce carbon emissions.

[0042] 3. Propose a low-carbon economic dispatch scheme for an integrated energy system that considers source-load-storage coordination

[0043] Principles for classifying IES operating modes: The source side and the load side jointly determine the system operating mode; the system operating mode is defined based on the size of the renewable energy absorption margin and its changing trend; considering the intersection of economy, safety, low carbon and priority absorption of renewable energy, the IES operating mode is determined based on whether the system retains a certain absorption margin for renewable energy.

[0044] During IES operation, the main periods of insufficient renewable energy absorption are periods of low electricity demand. This paper focuses on developing renewable energy absorption plans during these periods. During the phase of full renewable energy absorption, the primary goal should be to reduce IES carbon emissions. Therefore, this paper categorizes the scenarios into "incomplete renewable energy absorption mode" and "complete renewable energy absorption mode" based on whether renewable energy is fully absorbed.

[0045] Virtual energy storage operation scheme: This paper further considers the response characteristics and response time differences of comprehensive virtual energy storage based on the consideration of only electrical virtual energy storage. Because the peak and valley characteristics of electrical and thermal loads are complementary in time distribution, and the thermal load can still meet user comfort requirements after the thermal energy supply is stopped for 1 hour, this paper divides virtual energy storage into the following three categories based on the classification of user electricity usage time and different response times:

[0046] Category A: Electricity and heat virtual energy storage is transferred within 8 hours, and the total energy consumption during this period remains unchanged.

[0047] Category B: Electricity and heat virtual energy storage is transferred within 4 hours, and the total energy consumption during this period remains unchanged.

[0048] Category C: Electricity and heat can reduce loads.

[0049] Electric Virtual Energy Storage Operational Scheme: When renewable energy is fully absorbed, electricity demand is high. Class A shiftable loads are dispatched through incentives to reduce peak loads and fill valleys, alleviating system peak-shaving pressure. Class B shiftable loads and Class C curtailable loads, due to their short dispatch times, serve as backup resources to offset fluctuations caused by uncertain renewable output. When electricity demand is low, electric virtual energy storage exhibits valley-filling properties, shifting some shiftable loads to these times and thereby improving renewable energy absorption.

[0050] Thermal Virtual Energy Storage Operation: When renewable energy is not fully absorbed and heat demand is high, Class A transferable heat loads are deployed to reduce the heat load, reduce CHP unit output, and increase renewable energy accessibility. Class B and C heat loads are increased to reduce peak loads, further providing accessibility for renewable energy. When heat demand is low, thermal virtual energy storage provides a valley-filling effect, increasing CHP unit output to meet heat demand. Simultaneously, the CHP unit's electrical output partially replaces the output of purchased energy.

[0051] The renewable energy consumption surplus P1(t) is:

[0052] P1(t)=P el,t -P ebuy,t,min -P e,it -P re,it (1)

[0053] Where: P el,t is the electric load value at time t; P e,it P is the electric power output by gas turbine i connected to the grid at time t; ebuy,t,min is the amount of electricity purchased from the power grid at time t; P re,it is the amount of renewable energy consumed in period t.

[0054] 1) Incomplete consumption mode of renewable energy:

[0055] In this mode, the following conditions are met:

[0056] P1(t)≤0 (2)

[0057] When P1(t)=0, the absorption margin change trend ΔP1(t)<0 adopts the incomplete absorption mode.

[0058] To solve the problem of insufficient utilization of renewable energy under this model, this paper adopts the following solution:

[0059] (1) First, by utilizing the adjustable characteristics of virtual energy storage, the transferable electric load is moved to this mode to achieve the effect of raising the demand for some electric loads so that it can absorb some renewable energy;

[0060] (2) If some renewable energy is still not absorbed after the above steps, P2G and electric boilers will be used to convert excess electricity into natural gas and heat energy and integrate them into the gas and heat networks.

[0061] 2) Renewable energy complete consumption model:

[0062] This mode satisfies:

[0063] P1(t)>0 (3)

[0064] If P1(t)≥c(c>0) or 0<P1(t)<c, the complete absorption mode is adopted. When P1(t)=0, ΔP1(t)<0, the incomplete absorption mode is adopted.

[0065] This model mainly solves the problems of IES carbon emissions and system operation economy. This article adopts the following solutions:

[0066] (1) To solve the problem of rising carbon emissions, an electric virtual energy storage operation scheme is used to reduce the electric load level in this mode, thereby reducing the unit output, and at the same time, electric energy storage is enabled to further reduce carbon emissions;

[0067] (2) To reduce the IES operating cost in this mode. By adopting the thermal virtual energy storage operation scheme proposed in this paper, part of the transferable heat load can be used in this mode, and the CHP unit increases its output to meet the transferred heat demand. At the same time, the increase in the CHP unit's power output can reduce part of the electricity purchase cost and the resulting carbon trading cost. Because the carbon trading price increases with the increase in scheduling time, and the carbon emissions per unit of heat of the CHP unit are less than the quota, the comprehensive cost of CHP is reduced, which can reduce the economic cost of IES.

[0068] For the two modes mentioned above, the situation in each mode is different, and the objectives in different modes adapt to the needs of the current situation, so the objective functions in different modes should also be different.

[0069] 4. Based on the proposed scheme, a low-carbon economic dispatch model of the integrated energy system with source-load-storage coordination was established

[0070] 1) Incomplete absorption of renewable energy: Virtual energy storage, power-to-gas equipment, and electric boilers are added to improve the absorption capacity of renewable energy. The objective function is established with the goal of maximizing the absorption of renewable energy:

[0071]

[0072] Where: J represents the amount of renewable energy consumed; T represents the dispatch period; P w,it 、P pv,it is the absorption capacity of wind power and photovoltaic power; m, m w 、m pv are the number of renewable energy, wind power and photovoltaic units participating in the dispatch respectively.

[0073] 2) Complete absorption of renewable energy: With the goal of minimizing the comprehensive operating cost of IES, the IES low-carbon economic dispatch model is constructed by comprehensively considering the system operating cost, virtual energy storage compensation cost, P2G operation and maintenance cost, and carbon trading cost. The objective function is:

[0074] minf=f1+f2+f3+f4 (6)

[0075] Where: f is the comprehensive operating cost of the system; f1 is the system operating cost; f2 is the virtual energy storage compensation cost; f3 is the P2G operation and maintenance cost; f4 is the carbon trading cost.

[0076] f1 includes electricity purchase cost F1, gas turbine operating cost F2, gas boiler operating cost F3 and wind power maintenance cost F4. The formula is as follows:

[0077] f1=F1+F2+F3+F4 (7)

[0078] F1 is as follows:

[0079]

[0080] Where: a t is the electricity price at time t.

[0081] Gas turbines are responsible for supplying heat loads and are not allowed to shut down during operation. Therefore, their operating costs are primarily natural gas fuel costs F2, which can be expressed as follows:

[0082]

[0083] Where: ρ gas is the natural gas price / yuan; η rq is the electrical efficiency of the gas turbine; N is the number of gas turbines.

[0084] For gas boilers, its operating cost is mainly the fuel cost F3, which is expressed as follows:

[0085]

[0086] Where: P rg,it is the thermal power output of gas boiler i during period t; η rg is the efficiency of the gas boiler; N rg is the number of gas turbines.

[0087] Since the output of renewable energy depends entirely on its own size, and renewable energy has strong randomness and uncertainty, it will have a great impact on the equipment during operation, so it will generate certain operation and maintenance costs. The formula for calculating the maintenance cost of renewable energy is:

[0088]

[0089] Where: K r is the renewable energy operation and maintenance cost corresponding to unit power generation; m is the number of renewable energy units.

[0090] Virtual energy storage is conducive to the smooth operation of the system, can improve the consumption of renewable energy, and thus reduce carbon emissions. In this paper, virtual energy storage is mainly divided into three types of loads: A, B, and C. The compensation cost of the two types of loads A and B and the incentive cost of the type C load are as follows:

[0091]

[0092] Where: N sa 、N sb and N c N is the number of virtual energy storage of Class A, Class B and Class C; ha 、N hb With N k is the amount of thermal virtual energy storage of Class A, Class B and Class C; v sa 、v sb and v c is the compensation coefficient of class A, class B and class C virtual energy storage; v ha 、v hb and v k is the thermal virtual energy storage compensation coefficient for Class A, Class B and Class C; They are the equivalent charging and discharging power of Class A electric virtual energy storage respectively; They are the equivalent charging and discharging power of Class B electric virtual energy storage respectively; They are the equivalent charging and discharging power of Class A thermal virtual energy storage; are the equivalent charging and discharging power of Class B thermal virtual energy storage; S ic,t 、h ic,t They are the call amounts of Class C electric and thermal virtual energy storage respectively.

[0093] Power-to-gas technology includes two processes: water electrolysis and methanogenesis. The relationship between the volume of methane produced and the power consumed by the power-to-gas device during time period t is:

[0094]

[0095] Where: η P2G Producing conversion efficiency for P2G devices; P P2G,t is the operating power consumption of P2G during period t; is the calorific value of natural gas.

[0096] Volume of methane produced is equal to the volume of CO2 consumed by the reaction, then:

[0097]

[0098] Where: Q t,P2G is the CO2 mass required for the operation of the power-to-gas device during period t; is the gaseous density of CO2.

[0099] f3 includes the cost of P2G equipment maintenance and the cost of purchasing CO2 from the carbon trading market, so:

[0100]

[0101] Where: P2G is the maintenance cost; The price of purchasing unit mass of CO2.

[0102] This paper adopts a tiered carbon trading mechanism, dividing carbon emissions into four intervals. The higher the carbon emissions, the higher the price per unit of carbon emission rights trading (carbon trading), and the more the system costs. The carbon trading cost expression is as follows:

[0103]

[0104] Where: Q IES is the IES carbon emissions; p is the length of the carbon emission interval for carbon trading; δ is the growth rate of carbon trading prices; β is the carbon trading base price.

[0105] The IES carbon emission expression is as follows:

[0106]

[0107] Where: Q c , Q e They represent the actual carbon emissions of the system and its quota respectively; Q et , Q ret , Q rt , Q get and Q P2Gt are the actual carbon emissions of external electricity purchases, gas turbines, renewable energy, gas boilers, and P2G equipment in the system; Q qt , Q rqt , Q ert , Q gqt and Q eP2Gt Carbon emission quotas for external electricity purchases, gas turbines, renewable energy, gas boilers and P2G equipment in the system.

[0108] In this paper, the power purchased by the upper level is all from coal-fired units, so the carbon emissions of external power purchases are mainly related to their output. Based on the actual situation, this paper incorporates the increase in carbon emissions of external power purchases caused by the uncertainty of renewable energy into the carbon trading cost of renewable energy to compensate for the carbon trading cost of external power purchases. Therefore, the carbon emissions of external power purchases Q et As follows:

[0109]

[0110] Where: λ is the wind power reserve capacity coefficient; l i is the carbon emission intensity per unit electricity of coal-fired unit i.

[0111] Carbon emission quota is determined by the dispatch output of the generator set, Q qt The expression is as follows:

[0112]

[0113] Where: α is the quota coefficient of the gas unit.

[0114] Gas turbines also emit large amounts of carbon during operation, generating carbon trading costs. ret , Q rqt The calculation formula is:

[0115]

[0116] Where: l ir is the unit power carbon emission intensity of gas turbine unit i; c is the conversion coefficient of gas turbine electricity into thermal energy, which is taken as 0.006KJ / (KWh) in this paper, α r is the quota coefficient of the gas unit.

[0117] Renewable energy is a clean new energy source that does not produce carbon emissions during operation. However, the uncertainty of renewable energy will increase the system's additional output when it is connected to the grid. The increase in carbon emissions from coal-fired units caused by the connection of renewable energy to the grid will be included in the carbon trading cost of wind power. rt , Q ert The calculation formula is:

[0118]

[0119] Where: m is the number of renewable energy power plants.

[0120] For gas boilers, since they only provide heat, carbon emissions and carbon emission quotas are calculated based on heat only. get , Q gqt The expression is as follows:

[0121]

[0122] The process of producing natural gas in a power-to-gas device requires the participation of carbon dioxide. Based on this, the carbon trading cost of the power-to-gas device can be expressed as:

[0123]

[0124] Where: l P2G is the amount of CO2 absorbed by the power-to-gas device when it consumes unit electricity, which can be obtained by equations (13) and (14); P2G It is the carbon emission quota of power-to-gas equipment, and its value is 0.

[0125] The constraints include power balance constraints, virtual energy storage system constraints, unit operation constraints, and constraints on each device in IES; specifically:

[0126] System balance constraints

[0127] 1) Electric power balance: In this paper, ignoring network losses, the sum of the grid-connected power of coal-fired units, gas turbines, and renewable energy sources is equal to the sum of the power of the electric load.

[0128]

[0129] Where: n edis,t 、n echar,t P is the binary state variable of the energy storage discharge and charge power at time t, 1 is on and 0 is off; dis,t 、P char,t is the discharge and charge power of the energy storage at time t; u iec,t Indicates whether the Class C electric virtual energy storage is called at time t, with a value of 1 indicating calling and 0 indicating not calling; P d,t is the surplus power at time t.

[0130] Surplus electricity is used for power-to-gas equipment and electric boilers:

[0131] P d,t =n P2G,t P P2G,t +n EB,t P EB,t (25)

[0132] Where: n P2G,t 、n EB,t Represents the state variables of the power-to-gas equipment and the electric boiler during period t, 1 for on and 0 for off; P P2G,t 、P EB,t are the electricity consumption of power-to-gas conversion and the electricity consumption of electric boiler during period t respectively.

[0133] Electric boiler output model:

[0134] HEB,t =n EB,t ×η EB P EB,t (26)

[0135] Where: H EB,t is the heat energy converted by the electric boiler during period t; η EB The conversion efficiency of electric boiler.

[0136] Carbon capture plant model:

[0137]

[0138] Where: P i,t,ccs is the input power of carbon capture during period t; P i,t,ccsmin 、P i,ccse is the minimum input power and rated power for carbon capture; Energy consumption for carbon capture; To handle power consumption; For fixed consumption; is the carbon capture amount; η ccs for carbon capture efficiency; The unit capture power consumption.

[0139] Unit operating constraints:

[0140]

[0141] Where: P ebuy,t,min 、P w,itmin 、P pv,itmin 、P e,itmin is the lower limit of power purchase, wind farm, photovoltaic farm and gas turbine output during period t; P ebuy,t,max 、P w,itmax 、P pv,itmax 、P e,itmax It is the upper limit of electricity output of purchased electricity, wind farm, photovoltaic farm and gas turbine in period t.

[0142] Restrictions on purchased power units:

[0143] -r di ≤P ebuy,t -P ebuy,(t-1) ≤r ui (29)

[0144] Where: r di 、r ui It is the ramp-down rate and ramp-up rate of the coal-fired unit.

[0145] Gas turbine electrical output related constraints:

[0146] -r e,di ≤P e,it-P e,i(t-1) ≤r e,ui (30)

[0147] Where: r e,di 、r e,ui It is the ramp-down rate and ramp-up rate of the gas turbine electrical output.

[0148] Wind power and photovoltaic related constraints:

[0149]

[0150] Where: P w',it 、P pv',it Predict output for wind power and photovoltaic units.

[0151] Restrictions on power-to-gas equipment and electric boilers:

[0152]

[0153] Where: P P2G,tmax 、P EB,tmax It is the upper limit of operating power of power-to-gas equipment and electric boilers.

[0154] 2) Thermal balance constraints:

[0155]

[0156] Where: n hdis,t 、n hchar,t is the state variable of the thermal energy storage discharge and charge power during period t, 1 is on and 0 is off; P r,it 、P rg,it P is the thermal power output of the gas turbine and gas boiler; hdis,t 、P hchar,t P is the discharge and charge power of thermal energy storage during period t; hl,t is the heat load value during period t; u ihc,t A binary value indicating whether the Class C thermal virtual performance is invoked during period t. A value of 1 indicates invocation, and 0 indicates invocation.

[0157] Gas turbine thermal output related constraints:

[0158]

[0159] Where: P r,itmin 、P r,itmax is the lower and upper limits of gas turbine thermal output; r r,di 、r r,ui It is the ramp-down rate and ramp-up rate of the thermal output of the gas turbine.

[0160] Gas boiler related constraints:

[0161]

[0162] Where: P rg,itmin 、P rg,itmax The lower and upper limits of gas boiler output; r rg,di 、r rg,ui It is the downward climbing rate and upward climbing rate of the gas boiler output.

[0163] 3) Gas balance constraints:

[0164]

[0165] Where: V t is the power output of the gas source during period t; V is the natural gas generated by the power-to-gas equipment during period t; gdis,t 、V gchar,t is the discharge and charge power of the gas energy storage during period t; V gl,t is the gas load value during period t; V rg,it 、V rq,it It represents the natural gas power input to the gas boiler and gas turbine during period t.

[0166] 4) Energy storage model and constraints:

[0167]

[0168] Where: E t is the capacity of energy storage at time t; E tmin 、E tmax is the upper and lower limits of energy storage capacity; E char,t 、E dis,t is the charging and discharging power of energy storage at time t; E char,tmax 、E dis,tmax is the maximum charging and discharging power of energy storage; E E,t is the output power of energy storage at time t; n char,t 、n dis,t are the charging and discharging state parameters of the energy storage during period t.

[0169] Virtual energy storage model and constraints:

[0170] 1) Class A electrical and thermal virtual energy storage:

[0171]

[0172] Where: and It is the equivalent charging and discharging operating state of Class A electrical and thermal virtual energy storage; and is the maximum charging and discharging power of the equivalent Class A electrical and thermal virtual energy storage at time t-1; a,min 、z a,max 、zha,min and z ha,max is the single call duration, minimum call duration, and maximum call duration of Class A electric and thermal virtual energy storage; t ia,start , t ia,end , t iha,start and t iha,end The start and end time of Class A electric and thermal virtual energy storage transfer.

[0173] 2) Class B electrical and thermal virtual energy storage:

[0174] The constraints of the Class B electric and thermal virtual energy storage model are the same as those of the Class A electric and thermal virtual energy storage model, except for the specific response time, which is not described here.

[0175] Class C electric and thermal virtual energy storage:

[0176]

[0177] Where: l c,t 、l c,tmax are the call times and maximum call times of Class C electric virtual energy storage respectively; l k,t 、l k,tmax are the call times and maximum call times of Class C thermal virtual energy storage respectively; t iec,start , t iec,end is the start time and end time of Class C virtual energy storage; t ihc,start , t ihc,end is the start time and end time of Class C thermal virtual energy storage; c,min 、z c,max They represent the single call duration, minimum call duration, and maximum call duration of Class C electric virtual energy storage respectively; k,min 、z k,max They represent the single call duration, minimum call duration and maximum call duration of Class C thermal virtual energy storage respectively.

[0178] This description is based on Figure 2 An example analysis is carried out on the improved IEEE 30-bus system to verify the feasibility and effectiveness of the proposed method.

[0179] The cost of gas turbines is related to the selling price of natural gas. In this paper, we take 2.5 yuan / m 3 The following table lists the equipment carbon emission intensity, quota coefficient, and traditional and tiered carbon trading parameters in IES. If not specified, it is indicated on the equipment nameplate.

[0180] Table 1. Operating parameters of integrated energy system

[0181]

[0182]

[0183] This description uses Matlab to call the CPLEX solver for optimization and solution. See the solution process. Figure 3 First, the data for each device is input and the renewable energy absorption margin P1 is calculated. If P1(t) ≥ c or 0 < P1(t) < c, the full absorption mode is used. Under these conditions, when P1(t) = 0 and ΔP1(t) ≥ 0, the target solution is solved under the full absorption mode; if P1(t) ≤ 0, the partial absorption mode is used. When P1(t) = 0 and ΔP1(t) < 0, the partial absorption mode is still used. The system is simulated with a 24-hour cycle to obtain the output dispatch value of each unit under the low-carbon economic dispatch model of the integrated energy system with source-load-storage coordination.

[0184] In a dispatching cycle, basic data such as fan output, electrical load and heat load demand are as follows: Figure 4 shown.

[0185] In order to compare the proposed scheduling schemes, this paper sets Scheme 1 as the traditional tiered carbon trading coordination scheme, which does not consider source-load-storage coordination such as integrated virtual energy storage and carbon capture, nor does it have the model proposed in this paper; Scheme 2 is the scheme proposed in this paper.

[0186] Table 2 Comparison of the proposed scheduling scheme and the traditional scheduling scheme

[0187]

[0188] As shown in Table 2, carbon emissions under Scheme 2 were reduced by 898.84 kg, or 23.7%, compared to Scheme 1. Scheme 2, which incorporates source-load-storage coordination, reduced carbon trading costs by 469.9 yuan, or 29.4%. In terms of overall costs, the higher costs of equipment such as carbon capture contributed to a 6.43% reduction. In summary, this demonstrates the effectiveness of the proposed scheme in low-carbon economic dispatch.

[0189] Figure 5 The optimal operation scenario for the IES power supply system under this scheme is demonstrated. It shows that during periods of high grid electricity prices (8:00-11:00 and 18:00-21:00), the IES reduces power purchases from the grid by increasing gas turbine output and reducing carbon capture power, thereby improving IES operational efficiency. Furthermore, the curtailed renewable energy power remains at zero throughout the dispatch cycle, indicating that the IES system absorbs 100% of renewable energy, with no wind or solar curtailment.

[0190] Figure 6The optimal operation plan for the IES heating system under this scheme is presented. It shows that during peak load demand periods, the IES heat load is shared by electric boilers and recovery heating. During low electricity price periods (1:00-5:00), gas boilers are activated to partially cover the heat load. Analysis shows that the gas boilers, gas turbines, thermal storage devices, and electric boilers complement each other, meeting IES heat demand while further reducing IES system operating costs by increasing electricity purchases during low-load periods.

[0191] In order to verify the effectiveness of the virtual energy storage scheme proposed in this paper, the impact of virtual energy storage on the electricity and heat load power consumption plan and IES operating cost is analyzed respectively. Figure 7 The IES system load operation plan before and after virtual energy storage response is shown. The load curve after virtual energy storage has a significant peak-shaving and valley-filling effect. By shifting Class A loads during peak load periods to off-peak periods and reducing some Class C loads, the load curve is smoothed, system carbon emissions are reduced, and the economic efficiency of the IES system is improved. At the same time, the impact of renewable energy uncertainty on the system is reduced by scheduling Class B loads. Figure 8 The IES system heat load operation plan before and after the virtual energy storage response is shown, showing that heat load demand shifts to other times after the virtual energy storage response. Analysis shows that during peak heat load periods, the system primarily meets IES system heat demand through gas turbines, thermal storage tanks, and dispatching Class A heat loads, ensuring supply and demand balance while reducing the system's electricity and heating costs. Class B and C heat loads are also utilized to increase the system's peak load-shaving resources and maximize the integration of renewable energy.

[0192] In order to compare and analyze the impact of the virtual electric / thermal energy storage proposed in this paper on the operating costs of the IES system, this section sets up four scheduling schemes for comparative analysis, as follows:

[0193] Option 1: No consideration of electrical and thermal virtual energy storage;

[0194] Option 2: Consider thermal virtual energy storage but not electrical virtual energy storage;

[0195] Option 3: Considering electrical virtual energy storage but not thermal virtual energy storage;

[0196] Solution 4: Considering both electrical and thermal virtual energy storage, the comparative analysis of the four solutions is as follows:

[0197] Figure 9(a) shows the impact of different scheduling schemes on IES system carbon emissions. Compared to Scheme 1's carbon emissions of 3344.6 kg, the inclusion of integrated virtual energy storage reduces IES carbon emissions to 2900.3 kg, a 13.28% reduction. Furthermore, compared to Scheme 2's carbon emissions of 3315.3 kg, which only includes thermal virtual energy storage, Scheme 3's carbon emissions are reduced by 20.89% when only electrical virtual energy storage is considered, and Scheme 4's carbon emissions are reduced by 12.5%. It is worth noting that Scheme 3's carbon emissions are lowest when only electrical virtual energy storage is considered, but its omission of thermal virtual energy storage may result in insufficient renewable energy consumption and higher IES operating costs. Scheme 4's inclusion of integrated virtual energy storage increases carbon emissions. This is due to its enhanced time-shifting nature, which reduces grid purchases while increasing the output of controllable units.

[0198] Figure 9 (b) shows the impact of different scheduling schemes on the IES system operating costs. Compared to the operating cost of 8126.1 yuan for Scheme 1, the cost of integrating virtual energy storage is effectively reduced by 1.96%. Furthermore, compared to Scheme 2, which only considers thermal virtual energy storage, Scheme 3, which only considers electrical virtual energy storage, reduces costs by 1.74%.

[0199] Analysis shows that incorporating integrated virtual energy storage effectively reduces IES operating costs and total carbon emissions, and can also mitigate peak-to-valley variations in electric and thermal loads to a certain extent. Furthermore, the impact of electric virtual energy storage on IES after response is greater than that of thermal virtual energy storage.

[0200] The impact of tiered carbon trading on IES operation: This paper analyzes the impact of tiered carbon trading mechanism and carbon capture equipment on IES operation. Figure 10 The data shows carbon emissions for each tier, P2G CO2 consumption, and the optimal carbon quota allocation scheme for a single IES system scheduling cycle. Under the tiered carbon trading mechanism, carbon emissions from the IES system drop from 1,600 kg in Tier 1 to 1,300.3 kg in Tier 2, and finally to zero in Tier 3, only during periods of high gas turbine output (8:00-11:00 and 18:00-21:00). Furthermore, IES rationally allocates carbon quotas during periods of high system carbon emissions. This analysis demonstrates that the tiered carbon trading mechanism effectively limits IES carbon emissions.

[0201] To verify the effectiveness of the tiered carbon trading mechanism and further analyze the impact of the carbon trading mechanism, carbon capture devices, and carbon quotas on IES operations, eight scenarios are set up. The specific analysis is as follows:

[0202] Scenario 1: Considering the traditional carbon trading mechanism, without considering carbon capture and carbon quotas;

[0203] Scenario 2: Considering the traditional carbon trading mechanism, carbon capture is considered, but carbon quota is not considered;

[0204] Scenario 3: Considering the traditional carbon trading mechanism, without carbon capture, but considering carbon quotas;

[0205] Scenario 4: Considering traditional carbon trading mechanisms, carbon capture, and carbon quotas;

[0206] Scenario 5: Considering a tiered carbon trading mechanism, without considering carbon capture and carbon quotas;

[0207] Scenario 6: Considering a tiered carbon trading mechanism, carbon capture, and no carbon quotas;

[0208] Scenario 7: Considering a tiered carbon trading mechanism, ignoring carbon capture, and considering carbon quotas;

[0209] Scenario 8: Considering a tiered carbon trading mechanism, carbon capture, and carbon quotas;

[0210] Figure 11 The IES operating costs and carbon emissions under different scenarios are shown. Scenario 8 shows the lowest carbon emissions, while Scenario 5 has the highest operating costs. Notably, compared to Scenario 1, Scenario 5 has higher costs but lower carbon emissions. This analysis shows that, compared to traditional carbon trading mechanisms, the inclusion of a tiered carbon trading mechanism reduces system carbon emissions, but at the expense of costs.

[0211] Compared to Scenario 7, which only considers carbon quotas, Scenario 6, which only considers carbon capture devices, reduces IES carbon emissions to a greater extent. The same conclusion is reached when comparing Scenario 2 and Scenario 3. This analysis shows that carbon capture devices play a greater role in reducing IES carbon emissions.

[0212] The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to the listed embodiments. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solutions and concepts of the present invention within the scope disclosed by the present invention, which fall within the protection scope of the present invention.

Claims

1. A low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system, characterized by The following steps are involved: Step A: First, the source side and the load side jointly determine the system operation mode. According to the different levels of renewable energy utilization, the integrated energy system is divided into two operation modes: full absorption mode and incomplete absorption mode. Step B: According to the response time and response characteristics of electric virtual energy storage and thermal virtual energy storage, the integrated energy system is divided into Class A, Class B and Class C, and the electric virtual energy storage operation plan and the thermal virtual energy storage operation plan are given. Category A: Electric or thermal virtual energy storage is transferred within 4-8 hours, and the total energy consumption during this period remains unchanged; Category B: Electric virtual energy storage or thermal virtual energy storage is transferred within 4 hours, and the total energy consumption during this period remains unchanged; Category C: load curtailment with electric virtual energy storage or thermal virtual energy storage; The electric virtual energy storage operation scheme is as follows: when renewable energy is fully absorbed and electricity demand is high, Class A transferable loads are dispatched through incentives to reduce peak loads and fill valleys, thereby alleviating the peak-shaving pressure on the system. Class B transferable loads and Class C curtailable loads, due to their short dispatch time, serve as backup resources to fill fluctuations caused by uncertain renewable output. When the electricity load demand is low, the electric virtual energy storage exhibits valley-filling characteristics, shifting part of the transferable electricity load to this period, thereby improving the consumption of renewable energy; The thermal virtual energy storage operation scheme is as follows: when renewable energy is not fully absorbed and heat demand is high, Class A transferable heat load is called upon to reduce the heat load level, reduce the output of CHP units, and increase the space for renewable energy to be connected to the grid; Class B and Class C heat loads increase the peak-shaving resources to further provide space for renewable energy to be connected to the grid; when heat demand is low, the thermal virtual energy storage exhibits a valley-filling effect, allowing the CHP units to increase their output to meet heat demand, while the CHP units' electrical output replaces part of the purchased energy output; Step C: Based on step A, call the running plan in step B to obtain the scheduling plan; Step D: Establish a scheduling model based on the scheduling plan in step C; Step E: Based on the scheduling model in step D, use MATLAB to simulate the improved IEEE 30-bus system.

2. The low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system according to claim 1, characterized in that: The integrated energy system in step A includes an external power grid, a carbon capture unit, electricity storage, heat storage and hydrogen storage equipment, a gas turbine, a waste heat boiler, a gas boiler, an electrolysis gas equipment, a virtual energy storage system and a load.

3. The low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system according to claim 1, characterized in that: The different levels of renewable energy utilization in step A are: defining the system operation mode based on the size of the renewable energy absorption margin and its changing trend, or judging the operation mode of the integrated energy system based on the cross-principle of considering economy, safety, low carbon and priority absorption of renewable energy, and whether the system retains a certain absorption margin for renewable energy.

4. The low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system according to claim 1, characterized in that: In the step A, the mode is divided into an incomplete renewable energy absorption mode and a complete renewable energy absorption mode according to whether the renewable energy is fully absorbed.

5. The low-carbon economic dispatch method for a source-load-storage coordinated integrated energy system according to claim 1, characterized in that: It also includes constraints on each link, including power balance constraints, virtual energy storage system constraints, unit operation constraints and constraints on each device in the integrated energy system.

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