Dynamic pricing-based optimized scheduling method for electricity-to-ammonia-carbon capture coupled integrated energy system

Abstract

The invention discloses an electricity-to-ammonia-carbon capture coupled comprehensive energy system optimization scheduling method based on dynamic pricing, and belongs to the technical field of comprehensive energy system optimization scheduling. Constructing a comprehensive energy system double-layer optimization model comprising an electrolytic hydrogen production module, an ammonia synthesis module and a carbon capture module; a real-time power supply price and a real-time heat supply price are generated through a dynamic pricing mechanism, and a double-layer interactive optimization framework of an upper-layer comprehensive energy operator and a lower-layer load aggregator is established in combination with electric load and heat load demand response characteristics; and based on the integrated energy system double-layer optimization model, calling a solver to carry out iterative optimization on the output of the energy supply unit, the operation state of the energy storage equipment and the dynamic energy price in the double-layer interaction optimization framework, and obtaining a joint optimal scheduling scheme. According to the method, the problems of traditional time-of-use electricity price stiffness and low-carbon excitation deficiency are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimized scheduling of integrated energy systems, and in particular relates to an optimized scheduling method for an electricity-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing. Background Art

[0002] In the field of optimized scheduling of integrated energy systems, existing technologies mainly use Power-to-Ammonia (P2A) technology to convert surplus renewable energy into green ammonia, and combine it with Carbon Capture and Storage (CCS) technology to achieve low-carbon operation of coal-fired units. Specifically, Power-to-Ammonia technology converts electrical energy into chemical energy storage through the coupling process of hydrogen production by electrolysis of water and ammonia synthesis by nitrogen, and uses the high energy density characteristics of liquid ammonia to adjust the balance of energy supply and demand across seasons. In addition, the traditional Demand Response (DR) mechanism guides users to adjust their energy consumption behavior through time-of-use electricity prices, alleviating the mismatch between the volatility of renewable energy and load demand. Existing technologies also include methods such as hydrogen blending in gas turbines, cascade utilization of waste heat, and coordinated operation of energy storage equipment (such as batteries and hydrogen storage tanks) to improve the system economy and renewable energy absorption capacity.

[0003] However, existing technologies still have significant shortcomings: first, the traditional time-of-use electricity price mechanism is difficult to coordinate the distribution of interests among multiple entities, and ignores the dynamic adjustment potential of P2A for renewable energy consumption, resulting in prominent problems of wind and solar power curtailment in the system; second, existing electricity-to-ammonia models are mostly static modeling, which does not consider the decoupled operation of electrolysis hydrogen production and ammonia synthesis and the flexible adjustment role of hydrogen energy storage, limiting the system's adaptability to intermittent renewable energy; in addition, the coordinated operation of coal-fired units and carbon capture equipment lacks the guidance of a dynamic pricing mechanism, and cannot achieve real-time linkage between carbon emissions and demand response, resulting in high low-carbon operation costs and imbalance in supply and demand of multiple energy flows. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes an optimization scheduling method for an electric-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above objectives, the present invention provides a method for optimizing the scheduling of a power-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing, comprising:

[0006] Based on renewable energy output data and load demand data, a two-layer optimization model for an integrated energy system was constructed, which included an electrolysis hydrogen production module, an ammonia synthesis module, and a carbon capture module.

[0007] Generate real-time electricity and heat prices through a dynamic pricing mechanism, and combine the demand response characteristics of electricity and heat loads to establish a two-tier interactive optimization framework between upper-tier integrated energy operators and lower-tier load aggregators.

[0008] Based on the two-layer optimization model of the integrated energy system, a solver is called to iteratively optimize the output of the energy supply units, the operating status of the energy storage equipment and the dynamic energy price in the two-layer interactive optimization framework to obtain a joint optimal scheduling solution.

[0009] Preferably, the operating method of the electrolysis hydrogen production module includes:

[0010] Based on the amount of electricity input from renewable energy, hydrogen is generated through an electrolyzer and the waste heat generated during the electrolysis process is recovered;

[0011] The hydrogen is input into the hydrogen energy storage device for storage, and the hydrogen storage power and hydrogen release power of the hydrogen storage tank are adjusted according to the ammonia synthesis demand.

[0012] Preferably, the operation method of the ammonia synthesis module includes:

[0013] synthesizing ammonia through a Haber-Bosch reaction based on the hydrogen desorption power of the hydrogen energy storage device and the nitrogen generated by the air separation device;

[0014] The operating conditions of the ammonia synthesis equipment are adjusted in real time based on ammonia energy demand, and the hourly production increase and decrease rates of the ammonia synthesis equipment are constrained.

[0015] Preferably, the method for operating the carbon capture module comprises:

[0016] Introducing green ammonia blending technology in coal-fired units to replace part of the pulverized coal fuel with green ammonia;

[0017] Renewable energy is directly connected to carbon capture equipment to drive its operation, and is linked to the carbon emissions of coal-fired units to generate a dynamic carbon quota trading price.

[0018] Preferably, the method for generating the dynamic pricing mechanism includes:

[0019] Calculate the real-time energy supply price of electric load and thermal load based on the carbon emissions of the upper-layer integrated energy operator and the load demand response level of the lower-layer load aggregator;

[0020] The real-time energy supply price is transmitted to the lower-level load aggregators to guide users to adjust their electricity and heat consumption behaviors, and the adjusted load data is fed back to the upper-level integrated energy operators to update the output of the energy supply units.

[0021] Preferably, the process of establishing a two-layer interactive optimization framework between the upper-layer integrated energy operator and the lower-layer load aggregator includes:

[0022] In the upper-level integrated energy operator model, optimize the output of energy supply units, the operating status of energy storage equipment, and dynamic energy prices, and calculate carbon emission costs and demand response compensation costs;

[0023] In the lower-level load aggregator model, the electric load and the thermal load are adjusted in an interruptible and time-shifted manner according to the dynamic energy price, and a user energy consumption plan is generated and fed back to the upper-level integrated energy operator.

[0024] Preferably, the constraints of the energy supply units in the upper-level integrated energy operator model include:

[0025] The hydrogen blending ratio of the gas turbine does not exceed its maximum allowable value, and its electrical power output meets the preset ramp rate and ramp rate limits;

[0026] The thermal power output of the waste heat boiler does not exceed its maximum recovered waste heat and matches the waste heat release of the gas turbine.

[0027] Preferably, the method for adjusting demand response in the lower load aggregator model includes:

[0028] According to the interruptibility coefficient and shiftability coefficient of the electric load, part of the load during peak hours is transferred to off-peak hours or directly interrupted;

[0029] According to the time-of-use gas price of the heat load, part of the heat load during the peak period is transferred to the period of low gas price, and the waste heat recovery device is used to supplement the heating gap.

[0030] Preferably, the method for determining the joint optimal scheduling solution includes:

[0031] Set the cost weight coefficients of the upper-level integrated energy operator and the lower-level load aggregator, and calculate the joint operating cost through multiple iterations;

[0032] When the combined operating cost reaches a minimum value, the corresponding energy supply unit output, energy storage device status and dynamic energy price are output.

[0033] Preferably, the method for calling the solver includes:

[0034] The CPLEX solver is used to solve the upper-layer integrated energy operator model and the lower-layer load aggregator model respectively, and the coordinated optimization of dynamic energy price and load adjustment is achieved through the interaction of upper and lower-layer data;

[0035] Set the maximum number of iterations and time limit, and output the final scheduling plan when the termination conditions are met.

[0036] Compared with the prior art, the present invention has the following advantages and technical effects:

[0037] The present invention provides an optimization and scheduling method for an electric-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing, comprising: first, constructing a two-layer optimization model of the integrated energy system including an electrolysis hydrogen production module, an ammonia synthesis module, and a carbon capture module based on renewable energy output data and load demand data; second, generating real-time power supply prices and heat supply prices through a dynamic pricing mechanism, and establishing a two-layer interactive optimization framework of upper-level integrated energy operators and lower-level load aggregators in combination with the demand response characteristics of electric load and thermal load; finally, based on the two-layer optimization model of the integrated energy system, calling a solver to iteratively optimize the output of the energy supply units, the operating status of the energy storage equipment, and the dynamic energy price in the two-layer interactive optimization framework to obtain a joint optimal scheduling solution.

[0038] To address the shortcomings of existing static modeling of power-to-ammonia (P2A) conversion, this paper proposes a two-stage dynamic P2A model. This model decouples hydrogen production from ammonia synthesis and introduces hydrogen storage and hydrogen energy utilization units. By coupling waste heat recovery with the multi-energy flow of electricity, hydrogen, ammonia, and heat, this model enhances the system's flexible operation capabilities. To further tap into the potential for deep carbon reduction, this paper proposes a collaborative operating framework for green ammonia blending and renewable energy-driven carbon capture (CCS). Furthermore, this paper establishes a model in which renewable energy directly powers CCS, achieving a zero-carbon connection between energy input and carbon treatment.

[0039] To address the rigidity of traditional time-of-use electricity prices and the lack of low-carbon incentives, this paper proposes a dynamic pricing mechanism that comprehensively considers demand response and carbon emissions, constructs an IEO-LA two-tier operation model based on the dynamic pricing mechanism, and adjusts the energy supply price of the energy supply unit in real time based on carbon emissions and demand response, thereby optimizing load energy consumption behavior and energy supply mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0041] Figure 1 Schematic diagram of the two-stage electro-ammonia conversion mechanism according to an embodiment of the present invention;

[0042] Figure 2 This is a diagram of the operating mechanism of a P2A-CCS coordinated coal-fired unit according to an embodiment of the present invention;

[0043] Figure 3 IES schematic diagram of an embodiment of the present invention;

[0044] Figure 4 A flowchart for solving a double-layer model according to an embodiment of the present invention;

[0045] Figure 51 is a prediction curve of electric load, thermal load and renewable energy output according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] Example 1

[0049] This embodiment provides a method for optimizing the scheduling of a power-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing, including:

[0050] S1. Based on renewable energy output data and load demand data, a two-layer optimization model for an integrated energy system is constructed, which includes an electrolysis hydrogen production module, an ammonia synthesis module, and a carbon capture module.

[0051] Furthermore, the operating method of the electrolysis hydrogen production module includes:

[0052] Based on the amount of electricity input from renewable energy, hydrogen is generated through an electrolyzer and the waste heat generated during the electrolysis process is recovered;

[0053] The hydrogen is input into the hydrogen energy storage device for storage, and the hydrogen storage power and hydrogen release power of the hydrogen storage tank are adjusted according to the ammonia synthesis demand.

[0054] Specifically, P2A unit equipment plays an important role in realizing energy conversion and achieving electricity-ammonia-heat coupling. According to the P2A energy conversion relationship, P2A is divided into two stages. In the first stage, the system uses renewable energy to electrolyze water as raw material through an alkaline electrolyzer to provide hydrogen raw material for electricity to ammonia. In the second stage, the system uses a low-temperature air compressor to separate and produce nitrogen using air as fuel according to the actual ammonia demand in the system, and at the same time uses hydrogen and nitrogen to synthesize ammonia using the Haber-Bosch method. The two-stage electricity to ammonia operation process is as follows Figure 1 shown.

[0055] (1) Electrolyzer operation model

[0056] The electrolyzer utilizes renewable energy and uses water as raw material to electrolyze water into green hydrogen, providing hydrogen raw material for ammonia synthesis. Since water electrolysis to produce hydrogen releases heat into the air, this embodiment recovers the waste heat from the electrolysis process in the electrolyzer to achieve cascade energy utilization. The electrolyzer operation model for cascade energy utilization is shown in formula (1):

[0057]

[0058] Where P PEM,e-in,t is the PEM power input during period t; is the hydrogen energy output during period t; P PEM,h-out,t is the waste heat recovery amount in period t; is the hydrogen production coefficient of PEM; η PEM,h-out is the PEM heat release coefficient; P PEM,e-in,max With P PEM,e-in,min is the maximum and minimum power input of PEM; R PEM,up With R PEM,down is the maximum climbing and descending coefficient when the PEM is running. In this embodiment, R PEM,up With R PEM,down 0.32 and -0.32.

[0059] In order to achieve flexible ammonia synthesis, this embodiment introduces hydrogen energy storage in the first stage of P2A. By introducing hydrogen energy storage equipment, smooth regulation of hydrogen energy is achieved, thereby improving the flexibility of ammonia synthesis. The operating model of the hydrogen energy storage equipment is shown in formula (2):

[0060]

[0061] In the formula is the hydrogen storage capacity of the hydrogen storage tank during period t; and are the hydrogen storage power and hydrogen release power of the hydrogen storage tank during period t respectively; and Respectively represent the maximum and minimum values ​​of the hydrogen storage power of the hydrogen storage tank; and are the maximum and minimum values ​​of hydrogen discharge power of the hydrogen storage tank respectively; and is the maximum and minimum value of the hydrogen storage capacity of the hydrogen storage tank; and The binary coefficients represent the hydrogen storage tank's hydrogen discharge and storage operating states, 1 for operation and 0 for stop; is the hydrogen release coefficient of the hydrogen storage tank itself; and It is the hydrogen storage efficiency and hydrogen release efficiency of the hydrogen storage tank.

[0062] Furthermore, the operation method of the ammonia synthesis module includes:

[0063] synthesizing ammonia through a Haber-Bosch reaction based on the hydrogen desorption power of the hydrogen energy storage device and the nitrogen generated by the air separation device;

[0064] The operating conditions of the ammonia synthesis equipment are adjusted in real time based on ammonia energy demand, and the hourly production increase and decrease rates of the ammonia synthesis equipment are constrained.

[0065] Specifically, (2) ammonia synthesis unit operation model

[0066] Because the ASU's nitrogen separation technology offers flexible operating conditions, allowing for real-time adjustment of nitrogen production based on ammonia demand, this embodiment combines a nitrogen synthesis unit with a Haber ammonia synthesis plant to construct a flexible ammonia synthesis unit. Furthermore, because the chemical reaction of hydrogen and nitrogen to ammonia is exothermic, the power-to-ammonia operation model, taking this exothermic nature into account, is as follows:

[0067]

[0068] Where P P2A,e-in,t is the power consumption in period t in P2A; P P2A,e-in,r The electrical energy consumed by P2A to synthesize 1 mol of ammonia; is the P2A ammonia synthesis rate; is the calorific value of hydrogen; is the ammonia output power of P2A during period t; is the calorific value of ammonia; ΔH is the heat released by synthesizing 1 mol of ammonia from nitrogen and hydrogen; P PSA,e-in,t is the power consumption of P2A during period t; P PSA,e-in,r The electrical energy consumed to separate 1 mol of nitrogen; P PSA,e-in,max 、P PSA,e-in,min They are the maximum and minimum power consumption of ASU respectively.

[0069] Since the power-to-ammonia synthesis equipment can only adjust its operating conditions once per hour, and the rate of increase and decrease of ammonia production for each adjustment is limited to 10% and 20% of its production capacity respectively, the operating constraints of the ammonia synthesis equipment are as follows:

[0070] R P2A,down P PSA,e-in,max ≤P PSA,e-in,t -P PSA,e-in,t-1 ≤R P2A,up P PSA,e-in,max (4)

[0071] Where P PSA,e-in,max is the maximum power consumption of P2A; R P2A,up With R P2A,down They are the climbing and descending coefficients of P2A respectively.

[0072] Furthermore, the operation method of the carbon capture module includes:

[0073] Introducing green ammonia blending technology in coal-fired units to replace part of the pulverized coal fuel with green ammonia;

[0074] Renewable energy is directly connected to carbon capture equipment to drive its operation, and is linked to the carbon emissions of coal-fired units to generate a dynamic carbon quota trading price.

[0075] Specifically, (3) Coal-fired unit operation model with P2A-CCS collaboration

[0076] Traditional coal-fired units use pulverized coal as a power generation fuel, resulting in high carbon emissions and difficulty ensuring low-carbon operation of integrated energy systems. Ammonia, with its high heat density and zero carbon footprint, can effectively reduce coal-fired units' reliance on carbon-based fuels by blending it with pulverized coal, promoting low-carbon operation. Green electricity-to-ammonia conversion utilizes surplus renewable energy to synthesize ammonia using water and air as feedstock, replacing pulverized coal with surplus wind and solar power.

[0077] At the same time, carbon capture and storage technology, as one of the effective methods for coal-fired units to reduce carbon emissions, can be combined with coal-fired unit ammonia-blended power generation technology to synergistically reduce carbon emissions. Therefore, this embodiment installs carbon capture equipment on coal-fired units that burn ammonia. Its operating principle is as follows: Figure 2 shown.

[0078] S2. Generate real-time electricity and heat prices through a dynamic pricing mechanism. Combined with the demand response characteristics of electricity and heat loads, establish a two-tier interactive optimization framework between upper-tier integrated energy operators and lower-tier load aggregators.

[0079] Furthermore, the method for generating the dynamic pricing mechanism includes:

[0080] Calculate the real-time energy supply price of electric load and thermal load based on the carbon emissions of the upper-layer integrated energy operator and the load demand response level of the lower-layer load aggregator;

[0081] The real-time energy supply price is transmitted to the lower-level load aggregators to guide users to adjust their electricity and heat consumption behaviors, and the adjusted load data is fed back to the upper-level integrated energy operators to update the output of the energy supply units.

[0082] Furthermore, the process of establishing a two-layer interactive optimization framework between the upper-layer integrated energy operator and the lower-layer load aggregator includes:

[0083] In the upper-level integrated energy operator model, optimize the output of energy supply units, the operating status of energy storage equipment, and dynamic energy prices, and calculate carbon emission costs and demand response compensation costs;

[0084] In the lower-level load aggregator model, the electric load and the thermal load are adjusted in an interruptible and time-shifted manner according to the dynamic energy price, and a user energy consumption plan is generated and fed back to the upper-level integrated energy operator.

[0085] In this embodiment, the constraints of the energy supply units in the upper-level integrated energy operator model include:

[0086] The hydrogen blending ratio of the gas turbine does not exceed its maximum allowable value, and its electrical power output meets the preset ramp rate and ramp rate limits;

[0087] The thermal power output of the waste heat boiler does not exceed its maximum recovered waste heat and matches the waste heat release of the gas turbine.

[0088] Furthermore, the method for adjusting demand response in the lower load aggregator model includes:

[0089] According to the interruptibility coefficient and shiftability coefficient of the electric load, part of the load during peak hours is transferred to off-peak hours or directly interrupted;

[0090] According to the time-of-use gas price of the heat load, part of the heat load during the peak period is transferred to the period of low gas price, and the waste heat recovery device is used to supplement the heating gap.

[0091] Specifically, the integrated energy system operation framework is constructed as follows: Figure 3 As shown. Figure 3 As shown in the figure, the operation of an integrated energy system is divided into a two-tiered framework: the upper tier is the Integrated Energy Operator (IEO) and the lower tier is the Load Aggregator (LA). The upper tier of the IEO in this two-tiered framework includes energy supply units and energy storage units. Energy supply units primarily include wind power, photovoltaic power, gas turbines, gas boilers, coal-fired units, and waste heat boilers. Energy storage units primarily include hydrogen storage, batteries, and heat storage tanks. The lower tier, the Load Aggregator, primarily includes electrical and thermal loads.

[0092] The upper-level integrated energy operator uses optimization calculations to determine the output of each device. Based on carbon emissions and load demand response levels, it further calculates a dynamic energy price. This information, along with the device output and price information, is then transmitted to the lower-level load aggregators. Based on this information, the lower-level load aggregators formulate energy usage plans for electric and thermal loads and pass them on to the upper-level integrated energy operator to influence the upper-level energy price.

[0093] (1) IEO other energy supply unit models

[0094] This embodiment models the operation models of gas turbines, gas boilers, waste heat boilers, coal-fired units, power storage and heat storage tanks.

[0095] 1) Hydrogen-blended gas turbine

[0096] The gas turbine in this embodiment uses hydrogen energy synthesized in the first stage of electricity-to-ammonia conversion as fuel, reducing the amount of natural gas used by the gas turbine. The gas turbine hydrogen blending operation model is as follows:

[0097]

[0098] Where a gas 、b gas with c gas is the natural gas combustion efficiency; P GT,e-out,t is the electrical power output of the gas turbine during period t; is the output power of hydrogen from the gas turbine during period t; LHV gas is the calorific value of natural gas; F GT,gas-in,t is the natural gas consumption of the gas turbine during period t; is the hydrogen blending ratio of the gas turbine during period t; P GT,h-loss,t is the waste heat output power of the gas turbine during period t; η GT,e-out and η GT,h-loss They are the gas turbine power generation efficiency and waste heat release efficiency respectively.

[0099] 2) Waste heat boiler

[0100] The waste heat boiler can recover the waste heat from the gas turbine, converting low-grade heat energy into high-grade heat energy, and improving the energy efficiency of the system. The operation model of the waste heat boiler is as follows:

[0101]

[0102] Where P WHB,h-out,t With P WHB,h-in,t is the heat power output and heat power recovery of the waste heat boiler; η WHB,h-out and η WHB,h-in are the heat conversion coefficient and heat recovery coefficient of the waste heat boiler respectively.

[0103] 3) Gas boiler

[0104] Gas boilers use natural gas as fuel and burn it to supply energy to the system's heat load. The operating model of a gas boiler is as follows:

[0105] P GB,h-out,t =η GB,h-out F GB,gas-in,t (7)

[0106] Where P GB,h-out,t is the thermal power output of the gas boiler during period t; η GB,h-out is the operating efficiency of the gas turbine; F GB,gas-in,tis the natural gas consumption of the gas turbine during period t.

[0107] 4) Battery

[0108] The battery is charged during the nighttime low load period and discharged during the peak load period, thus reducing the peak-valley difference of the system equivalent load. The battery operation model is as follows:

[0109] E BS,e,t+1 =E BS,e,t +(α BS,e,ch,t P BS,e,ch,t η BS,e,ch -(α BS,e,dis,t P BS,e,dis,t / η BS,e,dis )) (8)

[0110] Where E BS,e,t is the storage capacity of BS during period t; BS,e,ch,t With α BS,e,dis,t are binary parameters of the BS storage and discharge operation states, 1 for operation and 0 for shutdown; P BS,e,ch,t With P BS,e,dis,t are the storage power and discharge power of BS in period t respectively; η BS,e,ch and η BS,e,dis are the storage efficiency and discharge efficiency of BS respectively.

[0111] 5) Heat storage tank

[0112] Thermal storage tanks can effectively store heat energy generated by heating units such as gas boilers and release the stored heat energy during peak heat load periods, enabling the system to flexibly supply heat loads. Thermal storage tanks themselves exhibit self-heating properties. The operating model for thermal storage tanks, taking into account their heat dissipation characteristics, is as follows:

[0113]

[0114] Where E HST,h,t is the heat storage capacity of HST during period t; α HST,h,ch,t With α HST,h,dis,t are binary parameters of the HST heat storage and heat release operation states, 1 for operation and 0 for shutdown; P HST,h,ch,t With P HST,h,dis,t are the heat storage power and heat release power of HST in period t respectively; η HST,h,ch and η HST,h,dis are the heat storage efficiency and heat release efficiency of HST, respectively.

[0115] (2) LA Electricity and Heating Integrated Demand Response Model

[0116] Electric and heating loads can be divided into interruptible loads and time-shiftable loads based on demand characteristics. Time-shiftable loads can shift loads from certain periods to other periods, reducing the difference between peak and valley load periods. Interruptible loads directly interrupt part of the load through compensation.

[0117] 1) Electric load demand response

[0118] The actual electric load of the integrated energy system after electric load demand response is as follows:

[0119]

[0120] Where P a,load,e,t is the actual electrical load of the system during period t; P load,e,t P is the system's electric load before the comprehensive demand response in period t; TSL,e,t With P IL,e,t are the time-shiftable load and the interruptable load in period t respectively; α TSL,e,t With α IL,e,t is the state coefficient of the time-shiftable and interruptible power generation of the electric load, 1 means it occurs, and 0 means it does not occur; η TSL,e,t and η IL,e,t are the interruptibility coefficient and shiftability coefficient of the electric load.

[0121] 2) Heat load demand response

[0122] The actual heat load of the integrated energy system after heat load demand response is as follows:

[0123]

[0124] Where P a,load,h,t is the actual electrical load of the system during period t; P load,h,t P is the system's electric load before the comprehensive demand response in period t; TSL,h,t With P IL,h,t are the time-shiftable load and the interruptable load in period t respectively; α TSL,h,t With α IL,h,t is the state coefficient of the time-shiftable and interruptible power generation of the electric load, 1 means it occurs, and 0 means it does not occur; η TSL,h,t and η IL,h,t are the interruptibility coefficient and shiftability coefficient of the electric load.

[0125] 3) Overall user satisfaction

[0126] To evaluate the impact of the integrated demand response for electric and thermal loads on user energy consumption experience, this embodiment quantifies user energy consumption experience through comprehensive user satisfaction. The comprehensive user satisfaction under the integrated demand response for electric and thermal loads is as follows:

[0127]

[0128] Where US e,t With US h,t They are user satisfaction with electricity and heat; UCS e-h,t Indicates the overall satisfaction of users with energy consumption.

[0129] IEO-LA Two-Level Optimization Model,The proposed scheduling model consists of two parts: the objective,function and various operational constraints, which are described in detail as follows.

[0130] 1 Objective Function

[0131]

[0132] Where C IEO is the total operating cost of the upper-layer IEO; C IEO,buy is the energy purchase cost of IEO; C IEO,ab C is the IEO abandoned energy cost; IEO,run The operating cost of the unit supplying energy to IEO; C IEO,off-on The start-up and shutdown costs of the IEO power supply unit; C IEO,pollute is the pollution cost of IEO emission gas; C IEO,IDR Compensation costs for implementing integrated demand response for IEOs.

[0133] (1) IEO energy purchase cost:

[0134] The IEO energy purchase cost mainly includes the cost of purchasing electricity, gas, and coal. The IEO energy purchase cost is shown in formula (14).

[0135]

[0136] (2) IEO abandoned energy cost:

[0137] The IEO energy curtailment cost consists of the wind curtailment cost and the solar curtailment cost. The IEO energy curtailment cost is shown in formula (15).

[0138]

[0139] Where η ab,WT and η ab,PV are the penalty costs for unit wind curtailment and unit solar curtailment respectively; P WT,e-ab,t With P PV,e-ab,t is the amount of wind and solar power curtailed by IEO at time t.

[0140] (3) IEO power supply unit operating costs:

[0141] The operating costs of IEO energy supply units are mainly related to the output of wind power, photovoltaic power, gas turbines, gas boilers, and coal-fired units. The operating costs of IEO energy supply units are shown in formula (16).

[0142]

[0143] Where α WT,t , α PV,t , α GT,t , α GB,t With α coal,t They represent the binary state variables of wind power, photovoltaic power, gas turbine, gas boiler and coal-fired units in period t, 1 means operation and 0 means shutdown; β WT,e , β PV,e , β GT,e and β coal,e Represent the unit power supply costs of wind power, photovoltaic power, gas turbine, gas boiler and coal-fired units respectively; β GB,h represents the unit heating cost of the gas boiler; P WT,e-out,t 、P PV,e-out,t 、P GT,e-out,t With P coal,e-out,t They represent the power output of wind power, photovoltaic power, gas turbine and coal-fired units during period t respectively; P GB,h-out,t is the thermal power output of the gas boiler during period t.

[0144] (4) Startup and shutdown costs of IEO energy supply units:

[0145]

[0146] Where C off-on,GT 、C off-on,GB with C off-on,coal Represent the single start-up and shutdown costs of gas turbines, gas boilers and coal-fired units respectively.

[0147] (5) IEO pollutant emission costs:

[0148] The IEO pollutant emission cost mainly consists of system carbon emission cost and nitrogen oxide emission cost. The IEO pollutant emission cost is shown in formula (18).

[0149]

[0150] Where C off-on,GT 、C off-on,GB with C off-on,coal Represent the single start-up and shutdown costs of gas turbines, gas boilers and coal-fired units respectively.

[0151] The tiered carbon emissions trading mechanism divides carbon emissions into multiple segments and gradually increases the system carbon emission unit price as the tiers increase, thereby suppressing system carbon emissions. The system carbon emission operating cost under the tiered carbon emissions trading mechanism is shown in Equation (19).

[0152]

[0153] In the formula is the carbon emission base price for tiered carbon emission trading; ε is the carbon price growth coefficient for tiered carbon emission trading.

[0154] Nitrogen oxides (NOx), one of the main gases contributing to the greenhouse effect, are primarily emitted from the combustion of fossil fuels such as natural gas and pulverized coal. For example, the combustion of natural gas in gas turbines and gas-fired boilers, and the combustion of pulverized coal in coal-fired units, are the primary sources of IEO NOx emissions. IEO NOx emissions are shown in Equation (20).

[0155]

[0156] In the formula is the pollutant emission equivalent of IEO; is the pollution coefficient of nitrogen oxides; and are the nitrogen oxide emission coefficients for the combustion of pulverized coal, ammonia and natural gas, respectively.

[0157] Similar to the tiered carbon emissions trading, this embodiment uses a piecewise function to calculate the IEO nitrogen oxide emission cost. The IEO nitrogen oxide emission cost is shown in formula (21).

[0158]

[0159] In the formula is the IEO nitrogen oxide emission cost during period t; is the pollutant emission cost per unit of nitrogen oxides; This is the IEO nitrogen oxide emission standard.

[0160] (6) IEO demand response compensation costs:

[0161]

[0162] Where η IL,e and η IL,h They are the unit compensation coefficients for interrupted electrical load and interrupted thermal load respectively.

[0163] 2 Constraints

[0164] The constraints of the upper-level IEO optimization model mainly include power balance constraints, other energy supply unit operation constraints and demand response constraints.

[0165] (1) Power balance constraints:

[0166] The power balance constraints are mainly electric power balance constraints and thermal power balance constraints. The electric power balance and thermal power balance constraints of IEO are shown in formula (23):

[0167]

[0168] (2) Operation constraints of energy supply units:

[0169] 1) Gas turbine:

[0170] As a controllable unit, the gas turbine is subject to ramping constraints. In addition, to ensure stable operation of the gas turbine, the hydrogen blending ratio of the gas turbine needs to be constrained. The operating constraints of the gas turbine in the hydrogen blending power generation mode are shown in Equation (24).

[0171]

[0172] Where P GT,e-out,max With P GT,e-out,min Respectively represent the maximum and minimum electrical power output of the gas turbine; R GT,up With R GT,down They represent the climbing efficiency and downhill efficiency of the gas turbine respectively; is the maximum hydrogen blending ratio of the gas turbine.

[0173] 2) Waste heat boiler:

[0174] The heat supply of the waste heat boiler is subject to the upper and lower limits of the heat output. The operation constraints of the waste heat boiler are shown in formula (25).

[0175] P WHB,h-out,min ≤P WHB,h-out,t ≤P WHB,h-out,max (25)

[0176] Where P WHB,h-out,max With P WHB,h-out,min are the maximum and minimum values ​​of the heat output of the waste heat boiler respectively.

[0177] 3) Gas boiler:

[0178] The operating constraints of the gas boiler include ramp constraints and upper and lower energy supply constraints. The operating constraints of the gas boiler are shown in Equation (26).

[0179]

[0180] Where P GB,h-out,max With P GB,h-out,min are the maximum and minimum values ​​of thermal power output of gas boiler respectively; R GB,down With R GB,up They are the ramp-up rate and ramp-down rate of the gas boiler respectively.

[0181] 4) Battery energy storage:

[0182] The BS is subject to state constraints, charge and discharge constraints, and capacity constraints. The BS operation constraints are shown in formula (27):

[0183]

[0184] Where E BS,e,max With E BS,e,min are the maximum and minimum values ​​of the battery storage capacity respectively; P BS,e,ch,max With P BS,e,ch,min are the maximum and minimum values ​​of battery charging power respectively; P BS,e,dis,max With P BS,e,dis,min are the maximum and minimum values ​​of battery discharge power respectively.

[0185] 5) Heat storage tank:

[0186] HST is subject to HST capacity constraints and charging and discharging thermal power constraints. HST operation constraints are as follows:

[0187]

[0188] Where E HST,h,max With E HST,h,min are the maximum and minimum values ​​of the heat storage capacity of the heat storage tank respectively; P HST,h,ch,max With P HST,h,ch,min are the maximum and minimum heat storage power of the heat storage tank respectively; P HST,h,dis,max With P HST,h,dis,min are the maximum and minimum values ​​of the heat release power of the heat storage tank respectively.

[0189] S3. Based on the two-layer optimization model of the integrated energy system, call the solver to iteratively optimize the output of the energy supply units, the operating status of the energy storage equipment and the dynamic energy price in the two-layer interactive optimization framework to obtain a joint optimal scheduling solution.

[0190] Furthermore, the method for determining the joint optimal scheduling solution includes:

[0191] Set the cost weight coefficients of the upper-level integrated energy operator and the lower-level load aggregator, and calculate the joint operating cost through multiple iterations;

[0192] When the combined operating cost reaches a minimum value, the corresponding energy supply unit output, energy storage device status and dynamic energy price are output.

[0193] Furthermore, the calling method of the solver includes:

[0194] The CPLEX solver is used to solve the upper-layer integrated energy operator model and the lower-layer load aggregator model respectively, and the coordinated optimization of dynamic energy price and load adjustment is achieved through the interaction of upper and lower-layer data;

[0195] Set the maximum number of iterations and time limit, and output the final scheduling plan when the termination conditions are met.

[0196] The IEO-LA joint optimal solution and solution process specifically includes:

[0197] (1) Joint optimal solution determination method

[0198] The two-layer optimization model involves the upper-layer IEO and the lower-layer LA, presenting interactive dynamic optimization of power and energy prices between the upper and lower layers. In order to measure the coordinated optimization effect of the upper and lower layers and realize the selection of the optimal coordinated solution, a joint optimal discrimination mechanism considering the upper and lower layer weight coefficients is proposed to coordinate the interests of the upper-layer IEO and the lower-layer LA and prevent the two-layer operation results from being biased towards one side. The joint operation cost of the two-layer operation model is shown in formula (29). The two-layer operation model calculates the joint operation cost of the system through formula (29) during the iteration process, and outputs the minimum value of the joint operation cost after the iteration, so as to achieve the benefits of multiple parties in the upper and lower layers.

[0199]

[0200] Where C IEO-LA is the joint operating cost of the IEO-LA two-tier operating model; C IEO-LA,j is the joint operation cost of the j-th iteration IEO-LA two-layer operation model; μ IEO Represents the cost weight coefficient of the upper-layer IEO; μ LA It represents the cost weight coefficient of the lower layer LA in the jth iteration; j represents the number of iterations of the IEO-LA two-layer operation model; J is the set number of iterations.

[0201] (2) Solution process

[0202] This embodiment uses an upper and lower layer iteration method to solve the IEO-LA two-layer operation model. The two-layer iterative solution steps are as follows:

[0203] Step 1: Construct an IEO optimization scheduling model based on the operating characteristics of energy conversion equipment, energy storage equipment, and energy supply equipment;

[0204] Step 2: Set the operating parameters of each IEO unit and output wind power, photovoltaic, electric load and thermal load data;

[0205] Step 3: Call the CPLEX solver to solve the upper-level optimization scheduling model and obtain the energy scheduling decision plan of the upper-level IEO;

[0206] Step 4: Set the dynamic electricity price and dynamic heat price of different energy supply units according to the dynamic electricity price mechanism, and pass the electricity price and heat price of the energy supply units as input to the lower-level LA optimization model;

[0207] Step 5: Based on the interruptibility and time-shiftability characteristics of the electric and heating loads, a lower-level LA optimization scheduling model is constructed;

[0208] Step 6: Call the CPLEX solver to solve the underlying optimization scheduling model;

[0209] Step 7: Based on the operating costs of the upper IEO and the lower LA, calculate the joint operating cost using (29);

[0210] Step 8: Calculate the joint optimal solution of IEO-LA;

[0211] Step 9: Determine whether the termination condition is met. If the pre-set maximum number of iterations has been reached or the iteration time limit has been exceeded, the iteration process is terminated; otherwise, the response electric and heating load operation plan needs to be passed to the upper IEO, and the iteration is continued in step 3.

[0212] Step 10: Output the joint optimization results obtained through multiple iterations, select the solution with the minimum joint operation cost as the optimal solution, and output the corresponding optimization scheduling decision.

[0213] The solution process is as follows Figure 4 shown.

[0214] Example:

[0215] This simulation analysis is based on an actual integrated energy system in northern China. All simulations were performed in Matlab R2020, using an IBM plug-in and the CPLEX solver to solve the IEO-LA two-level optimization model.

[0216] (1) Parameter settings

[0217] Figure 1 The structure of the integrated energy system (IES) is presented to verify the effectiveness of the proposed model in scheduling optimization and energy saving and consumption reduction. The main equipment of the system is mainly composed of energy conversion equipment, energy supply units and energy storage equipment. The energy conversion equipment in this system is mainly P2A. The energy supply units mainly include coal-fired units, gas turbines, gas boilers and waste heat boilers. The energy storage equipment mainly consists of batteries, heat storage tanks, hydrogen storage tanks and ammonia storage tanks. The parameters of the main equipment in the system are shown in Table 1. In addition, the electricity prices and natural gas prices in different time periods of this embodiment are listed in Table 2 and Table 3 respectively. In addition, Figure 5 The charge demand and renewable energy output forecast curves are shown.

[0218] Table 1

[0219]

[0220]

[0221] Table 2

[0222]

[0223]

[0224] Table 3

[0225]

[0226] Design scenario

[0227] To verify the effectiveness of the P2A-CCS equipment coordinated operation and refined two-stage P2A and IES dual-layer optimization scheduling model mentioned in this chapter, five different scheduling scenarios were set up for analysis. The scenario settings in this chapter are as follows:

[0228] Scenario 1: Considering P2A and waste heat recovery in the system's two-tier optimization scheduling process, ignoring CCS equipment, and performing refined two-stage P2A and IDR;

[0229] Scenario 2: P2A, waste heat recovery, and CCS equipment are considered in the system's two-tier optimization scheduling process, without considering the refined two-stage P2A and IDR;

[0230] Scenario 3: Considering the refined two-stage P2A, waste heat recovery and CCS equipment in the system's two-tier optimization scheduling process, without considering IDR;

[0231] Scenario 4: P2A, waste heat recovery, CCS equipment, and IDR are considered in the system's two-tier optimization scheduling process, without considering the refined two-stage P2A;

[0232] Scenario 5: Whether to consider the refined two-stage P2A, waste heat recovery and CCS equipment and IDR in the system two-tier optimization scheduling process.

[0233] Table 4 shows the operating costs of integrated energy operators and load aggregators under different IES scenarios. As shown in Table 4, compared to Scenario 1, Scenario 2, after factoring in CCS equipment, reduces upper-tier IEO revenue by ¥308.7 and lower-tier LA operating costs by ¥374.23. This is because CCS equipment recovers carbon emissions from coal-fired units. Under the influence of the dynamic pricing mechanism, the energy price of the upper-tier IEO power units decreases, leading to lower upper-tier IEO revenue and lower lower-tier operating costs. Scenario 3 reduces LA operating costs by ¥1682.77 compared to Scenario 2, while IEO revenue also decreases. The system converts renewable energy during off-peak periods into ammonia and hydrogen fuels, reducing fuel costs for coal-fired units and GTs. This reduces the unit energy price for integrated energy operators and increases operating costs for users.

[0234] Table 4

[0235]

[0236] In Scenario 4, after factoring in IDR, LA operating costs decreased by ¥11,081.03 compared to Scenario 2, and IEO profitability increased. Because the demand response model in Scenario 4 incentivizes user participation in load regulation, more users actively participate in load regulation, shifting some of their load to periods of low electricity prices. This results in a simultaneous decrease in IEO revenue and LA operating costs. Furthermore, under the incentives for interruptible and shiftable load compensation, users participating in load regulation optimization receive compensation, which increases LA operating costs. In Scenario 5, the refined two-stage P2A model and demand response reduce the lower-level LA and system operating costs by ¥912.12 and ¥631.61, respectively, compared to Scenario 4, while also slightly reducing the upper-level IEO revenue. This is because the refined two-stage P2A model further enhances the flexibility of IEO energy conversion and multi-energy regulation, increasing load shifting and interruption, and boosting LA operating profits. At the same time, the system purchases renewable energy during the off-peak period of electricity prices and converts electricity into thermal energy through two-stage P2A, reducing the energy purchase cost of the upper-level IEO, offsetting part of the demand response cost, and achieving balanced optimization for both parties.

[0237] Table 5 shows the operating results for different scenarios. As shown in Table 5, the combined system operating costs and carbon emissions for Scenario 2 were reduced by 238.37 RMB and 1583.94 kg, respectively, compared to Scenario 1. This is because the introduction of CCS equipment in Scenario 2 pre-stored CCS for the carbon-emitting units within the system, reducing system carbon emissions and, consequently, carbon emission costs. The introduction of a refined two-stage P2A model in Scenario 3 reduced the combined system operating costs and carbon emissions by 581.58 kg and 1152.59 RMB, respectively. This is because, after considering this model, the hydrogen generated in the first stage of the P2A can be used as a zero-carbon fuel to fuel the GT's power generation operations, improving the GT's economic efficiency and increasing its output from 2394.16 kW to 6207.41 kW. Although the increase in GT output increased the system's gas purchase costs, it also reduced the generated power and electricity purchase costs of the coal-fired units.

[0238] Table 5

[0239]

[0240] After introducing demand response, Mode 4 significantly reduces the system's combined operating costs compared to Mode 2. This is due to the use of time-of-use electricity and gas prices to adjust user energy consumption, shifting electricity consumption from high-cost to low-cost. This reduces renewable energy curtailment by 221.78 kW, reducing the system's electricity and gas purchase costs. Furthermore, the IEO, through its interruptible load compensation mechanism, encourages user participation in load regulation, improves the operational efficiency of the underlying LA, and reduces the system's combined operating costs. Scenario 5, building on Scenario 4, incorporates a refined two-stage P2A, resulting in a 101.93 kg reduction in system carbon emissions. This is because the P2A, through the flexible regulation of ammonia and hydrogen, reduces the natural gas demand of gas-fired units, lowering both system carbon emissions and gas purchase costs, promoting low-carbon economic operation of the system.

[0241] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the scheduling of a power-to-ammonia-carbon capture coupled integrated energy system based on dynamic pricing, characterized in that: The following steps are involved: Based on renewable energy output data and load demand data, a two-layer optimization model for an integrated energy system was constructed, which included an electrolysis hydrogen production module, an ammonia synthesis module, and a carbon capture module. Generate real-time electricity and heat prices through a dynamic pricing mechanism, and combine the demand response characteristics of electricity and heat loads to establish a two-tier interactive optimization framework between upper-tier integrated energy operators and lower-tier load aggregators. Based on the two-layer optimization model of the integrated energy system, a solver is called to iteratively optimize the output of the energy supply units, the operating status of the energy storage equipment and the dynamic energy price in the two-layer interactive optimization framework to obtain a joint optimal scheduling solution.

2. The method according to claim 1, characterized in that The operating method of the electrolysis hydrogen production module includes: Based on the amount of electricity input from renewable energy, hydrogen is generated through an electrolyzer and the waste heat generated during the electrolysis process is recovered; The hydrogen is input into the hydrogen energy storage device for storage, and the hydrogen storage power and hydrogen release power of the hydrogen storage tank are adjusted according to the ammonia synthesis demand.

3. The method according to claim 2, characterized in that The operating method of the ammonia synthesis module includes: synthesizing ammonia through a Haber-Bosch reaction based on the hydrogen desorption power of the hydrogen energy storage device and the nitrogen generated by the air separation device; The operating conditions of the ammonia synthesis equipment are adjusted in real time based on ammonia energy demand, and the hourly production increase and decrease rates of the ammonia synthesis equipment are constrained.

4. The method according to claim 1, wherein The method for operating the carbon capture module includes: Introducing green ammonia blending technology in coal-fired units to replace part of the pulverized coal fuel with green ammonia; Renewable energy is directly connected to carbon capture equipment to drive its operation, and is linked to the carbon emissions of coal-fired units to generate a dynamic carbon quota trading price.

5. The method according to claim 1, wherein The method for generating the dynamic pricing mechanism includes: Calculate the real-time energy supply price of electric load and thermal load based on the carbon emissions of the upper-layer integrated energy operator and the load demand response level of the lower-layer load aggregator; The real-time energy supply price is transmitted to the lower-level load aggregators to guide users to adjust their electricity and heat consumption behaviors, and the adjusted load data is fed back to the upper-level integrated energy operators to update the output of the energy supply units.

6. The method according to claim 1, characterized in that The process of establishing a two-tier interactive optimization framework between upper-tier integrated energy operators and lower-tier load aggregators includes: In the upper-level integrated energy operator model, optimize the output of energy supply units, the operating status of energy storage equipment, and dynamic energy prices, and calculate carbon emission costs and demand response compensation costs; In the lower-level load aggregator model, the electric load and the thermal load are adjusted in an interruptible and time-shifted manner according to the dynamic energy price, and a user energy consumption plan is generated and fed back to the upper-level integrated energy operator.

7. The method according to claim 6, characterized in that The constraints of the energy supply units in the upper-level integrated energy operator model include: The hydrogen blending ratio of the gas turbine does not exceed its maximum allowable value, and its electrical power output meets the preset ramp rate and ramp rate limits; The thermal power output of the waste heat boiler does not exceed its maximum recovered waste heat and matches the waste heat release of the gas turbine.

8. The method according to claim 6, characterized in that The method for adjusting demand response in the lower load aggregator model includes: According to the interruptibility coefficient and shiftability coefficient of the electric load, part of the load during peak hours is transferred to off-peak hours or directly interrupted; According to the time-of-use gas price of the heat load, part of the heat load during the peak period is transferred to the period of low gas price, and the waste heat recovery device is used to supplement the heating gap.

9. The method according to claim 1, characterized in that The method for determining the joint optimal scheduling solution includes: Set the cost weight coefficients of the upper-level integrated energy operator and the lower-level load aggregator, and calculate the joint operating cost through multiple iterations; When the combined operating cost reaches a minimum value, the corresponding energy supply unit output, energy storage device status and dynamic energy price are output.

10. The method according to claim 1, characterized in that The calling method of the solver includes: The CPLEX solver is used to solve the upper-layer integrated energy operator model and the lower-layer load aggregator model respectively, and the coordinated optimization of dynamic energy price and load adjustment is achieved through the interaction of upper and lower-layer data; Set the maximum number of iterations and time limit, and output the final scheduling plan when the termination conditions are met.

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