Pre-scheduling and re-scheduling carbon over-demand response method based on source-load collaborative carbon reduction

Abstract

The present application relates to the technical field of power system, and particularly relates to a pre-scheduling-rescheduling super-carbon demand response method based on source-load collaborative carbon reduction, which comprises the following steps: analyzing a pre-scheduling-rescheduling two-stage low-carbon scheduling mechanism, analyzing a new energy scene operation architecture containing a photo-thermal power station, establishing a pre-scheduling-rescheduling two-stage demand response model, and building a pre-scheduling-rescheduling two-stage low-carbon economic scheduling model according to a super-carbon demand response super-carbon electricity price. In the present application, the photo-thermal power station and the wind power station and the carbon capture power plant are jointly operated on the source side, the output structure of the source side is optimized, the carbon emission of the traditional gas turbine is limited, and the carbon capture level is improved. The present application also establishes a pre-scheduling-rescheduling two-stage super-carbon demand response mechanism to deeply mine the low-carbon space of the system. The super-carbon demand response is an important guarantee for preventing excessive carbon emission after the price-type demand response, a super-carbon electricity price is formed, the low-carbon behavior of the user is driven to change, and low-carbon economic scheduling is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a pre-scheduling-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction. BACKGROUND

[0002] To achieve the "double carbon" goal as soon as possible, it is urgent to analyze the new situation of energy saving and carbon reduction, green development, and put forward new ideas for energy saving and carbon reduction. Northwest China is an important oil and gas supply base and has abundant solar and thermal resources, with unique advantages in forming an integrated energy system (IES) with a high proportion of new energy. In addition, installing carbon capture devices on existing thermal power units for low-carbon modification is also an important technical means to achieve the double carbon goal. However, the strong mismatch between source and load power leads to high carbon emissions, which is a problem to be solved.

[0003] Optimizing load curve fluctuations and source-side output structure is the key to solving the problem of high carbon emissions. From the source side, the emerging solar-thermal power advantage in the northwest region has eased the uncertainty problem brought about by the rapid development of new energy; from the load side, the implementation of carbon reduction incentives such as demand response has opened up new ideas for system energy saving and carbon reduction. Therefore, improving demand response capability and mitigating energy output fluctuations are of great significance for deepening the carbon reduction space of the system.

[0004] The source-side concentrating solar power plant (CSPP) has both the low-carbon nature of clean energy and the dispatchability of conventional energy, and its "friendliness" to the grid is gradually being recognized. Its thermal storage device can ensure that the CSPP is a stable and clean low-carbon power and heating method in the integrated energy system, and at the same time plays the role of "clean energy for clean energy", providing a new research idea for the carbon reduction direction of the system.

[0005] Priced-based demand response (PBDR) is a method that generates a reasonable time-of-use pricing scheme to encourage users to consider their own economic benefits, thereby changing their electricity consumption behavior to reduce electricity costs, achieving system peak shaving and valley filling, and reducing system carbon emissions. However, the process ignores a key incentive signal for guiding user-side carbon reduction - the electricity carbon emission factor, and the energy saving and carbon reduction space needs to be optimized. Dynamic carbon emission factor can enable the load side to effectively perceive the source-side carbon emission level to guide users to adjust the electricity timing to achieve active low carbon demand response (LCDR), thereby improving the low carbon level of the system. SUMMARY

[0006] The purpose of the present application is to overcome the defects of the prior art, propose a pre-scheduling-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction, establish a new low-carbon demand response method combining carbon emission factor and electricity price mechanism, explore the influence of the advantages of emerging concentrated solar power on system energy saving and emission reduction, and study the carbon reduction benefits of low-carbon demand response in a high-proportion new energy scenario.

[0007] Considering the advantages of emerging concentrated solar power and the limitations of price-based demand response in reducing carbon emissions through time-of-use pricing, the main research focuses on pre-scheduling-rescheduling two-stage low-carbon scheduling mechanism analysis, high-proportion new energy scenario operation architecture analysis with concentrated solar power stations, pre-scheduling-rescheduling two-stage demand response modeling, and pre-scheduling-rescheduling two-stage low-carbon economic dispatching model.

[0008] To achieve the above purpose, the present application adopts the following specific technical solutions:

[0009] The pre-scheduling-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction provided by the present application comprises the following steps:

[0010] Step one, analyze the pre-scheduling-rescheduling two-stage low-carbon scheduling mechanism, use price-based demand response (PBDR) in the pre-scheduling stage to guide user-side flexible resources to optimize the load curve by generating a time-of-use pricing scheme; and use ultra-carbon demand response (UCDR) in the rescheduling stage to mobilize users to optimize the load curve through low-carbon behavior transfer by using ultra-carbon pricing;

[0011] Step two, analyze the operation architecture of the new energy scenario with concentrated solar power stations (CSPP), introduce CSPP, wind power stations, and carbon capture power plants into the integrated energy system (IES) for joint operation, and verify the carbon reduction benefits of UCDR in the new energy scenario;

[0012] Step three, establish a pre-scheduling-rescheduling two-stage demand response model, including a pre-scheduling PBDR time-of-use pricing model and a rescheduling UCDR ultra-carbon pricing model, to form an ultra-carbon price that drives user low-carbon transfer;

[0013] Step four, build a pre-scheduling-rescheduling two-stage low-carbon economic dispatching model based on the UCDR ultra-carbon price, and use the pre-scheduling stage unit output objective function, pre-scheduling stage unit output constraint condition, rescheduling stage UCDR objective function, and rescheduling stage UCDR constraint condition to realize source-load collaborative carbon reduction scheduling and improve energy saving and emission reduction level.

[0014] Further, in step one, the dynamic carbon emission factor is calculated in the pre-scheduling stage to dynamically represent the system carbon level, the preliminary estimated system carbon emission is obtained, and PBDR is used to guide user-side flexible resources to optimize the load curve to prevent excessive carbon emissions;

[0015] The rescheduling stage is to mobilize users to transfer low-carbon behaviors and reduce carbon emissions of the system by using UCDR when the super-carbon event occurs in the pre-scheduling stage. The dynamic carbon emission factor obtained in the pre-scheduling stage is used as the penalty coefficient of the pre-scheduling PBDR time-of-use electricity price to form a super-carbon electricity price. According to the load curve optimized by UCDR, the system is driven to achieve a deep low-carbon state.

[0016] Further, in step two, the photothermal power plant (CSPP) mainly includes a collector field, a power generation system and a heat storage system. The collector field converts solar radiation into heat energy that can be stored in the heat storage system or directly heat steam to supply the power generation system for power generation. The heat storage system makes the CSPP have flexible dispatchability to ensure the stability and controllability of power generation and heat supply of the CSPP.

[0017] Further, in step three, the pre-scheduling PBDR time-of-use electricity price model is as follows:

[0018]

[0019] Wherein, f, p and g represent the peak, flat and valley periods; and are the electric loads of each period before and after the implementation of PBDR, respectively; is the electricity price of each period before the implementation of PBDR; ΔD f , ΔD p and ΔD g are the electricity price change amounts after the implementation of PBDR; M is the user satisfaction; and E is the electricity price elasticity matrix.

[0020] The rescheduling UCDR super-carbon electricity price model is as follows:

[0021] E JP,t ≥ E threshold ;

[0022]

[0023]

[0024] Wherein, E JP,t is the system net carbon emission in period t, E threshold is the set carbon emission threshold; A t is the state variable of the system; α CP,t is the dynamic carbon emission factor, e i is the carbon emission intensity of the thermal power unit i, P Ji,t is the net output of the i-th thermal power unit in period t, P W,t and P CSP,t are the wind power and CSPP on-grid power in period t, respectively, and n represents the number of carbon capture power plants. is a dimensionless processed dynamic carbon emission factor, is a minimum value of the dynamic carbon emission factor, is a maximum value of the dynamic carbon emission factor; P UCDR,t is the electricity price of each period after UCDR, P PBDR,t is the electricity price of each period after PBDR.

[0025] Further, in step four, the pre-scheduling stage unit output target function is as follows:

[0026]

[0027] wherein F is the total system operation cost of pre-scheduling, F E,t is the energy cost of the system in period t, F QT,t is the start-stop cost of the system in period t, F YW,t is the operation and maintenance cost of the system in period t, F CS,t is the carbon sequestration cost of the system in period t, F CJ,t is the carbon trading cost of the system in period t, F curt,t is the wind curtailment cost of the system in period t;

[0028] The expression of the energy cost F E,t is as follows:

[0029]

[0030] wherein P Gi,t is the total output of the i-th thermal power unit in period t, a i , b i , c i is the consumption characteristic coefficient of the i-th thermal power unit, C g is the unit price of purchased natural gas, Q g,t is the gas consumption of the system in period t, and n is the number of thermal power units;

[0031] The expression of the start-stop cost F QT,t is as follows:

[0032]

[0033] wherein U i,t is the running state of the thermal power unit i in period t, U i,t = 1 indicates that the thermal power unit i is running in period t, U i,t = 0 indicates that the thermal power unit i is stopped in period t, f qt,i is the start-stop cost of the unit i;

[0034] The expression of the operation and maintenance cost F YW,t is as follows:

[0035]

[0036] where m represents the number of devices in the IES system, P i,t is the total output of the system in the t period, s i is the operation and maintenance coefficient of the i-th device;

[0037] Carbon sequestration cost F CS,t is expressed as follows:

[0038]

[0039] where, is the methane generation amount of the system in the t period, η P2G,t is the operation efficiency of P2G in the t period, P P2G,t is the electric power consumed by P2G in the t period; H g is the heating value of natural gas, taken as 39 MJ / m 3 ; is the density of carbon dioxide, C S is the carbon sequestration price per unit mass of carbon dioxide, E Ci,t is the total carbon capture amount of the i-th carbon capture power plant in the t period, Q C,t is the carbon utilization amount of the system in the t period;

[0040] Carbon trading cost F CJ,t is expressed as follows:

[0041]

[0042] where, C J is the carbon trading price, E Ji,t is the net carbon emission amount of the system in the t period, α and β are the carbon quota coefficients of the coal-fired unit and the gas-fired unit, ω is the conversion coefficient of thermal power to electric power, P CHP,t is the electric power generated by the gas turbine (CHP) in the t period, H CHP,t , H GB,t is the output thermal power of the CHP and the gas boiler in the t period.

[0043] Further, in step four, the pre-scheduling stage unit output constraint conditions include power balance constraints, carbon capture power plant operation constraints, solar thermal power station operation constraints, and PBDR constraints;

[0044] The expression of the power balance constraint is as follows:

[0045]

[0046] Q BUY,t +Q P2G,t -QCHP,t - Q GB,t = Q Load,t ;

[0047] Q min ≤ Q BUY,t ≤ Q max ;

[0048] where P L,t is the electrical load power in the time period t, H Load,t is the required thermal load power in the time period t, is the TES supplied thermal load power in the time period t, Q BUY,t is the purchased natural gas quantity in the time period t, Q P2G,t is the P2G produced gas quantity in the time period t, Q Load,t is the natural gas load quantity, Q CHP,t , Q GB,t is the consumed natural gas quantity of CHP and gas boiler in the time period t;

[0049] The expression of carbon capture power plant operation constraints is as follows:

[0050]

[0051] where P Yi,t is the operation energy consumption of carbon capture power plant in the time period t, P D is the fixed energy consumption of carbon capture power plant, λ is the required electrical energy per unit of CO2 captured, E Gi,t is the carbon emission of the i-th thermal power unit in the time period t, γ is the carbon emission coefficient of thermal power unit, θ is the carbon capture level of carbon capture power plant, θ max is the maximum carbon capture level;

[0052] The expression of solar-thermal power plant operation constraints is as follows:

[0053]

[0054] where, is the total heat collected by HF of CSPP in the time period t, η g-r is the light-heat conversion efficiency of HF, S HF is the total area of HF, D t is the solar radiation index in the time period t, η r-d is the heat-electricity conversion efficiency of steam turbine, is the heat power provided by HF to generator in the time period t, is the heat power provided by TES to generator in the time period t, is the heat power input of TES in the time period t, is the heat power that cannot be utilized by CSPP in the time period t, is the heat power output of TES in the time period t;

[0055] The PBDR constraints include price constraints, load transfer amount constraints, and user satisfaction constraints, and the expressions are as follows:

[0056]

[0057] In the formula, D g , D p , and D f are the valley, flat, and peak period prices after PBDR, D gd is a fixed price before PBDR, P Load,t is the electric load power of each period after PBDR, M min is the minimum user satisfaction, χ is the peak-valley price spread ratio, and ε1 is the PBDR single-point load transfer limit value.

[0058] Further, the objective function and constraint conditions of the unit output plan in the rescheduling stage are the same as those in the pre-scheduling stage, and the UCDR objective function in step four in the rescheduling stage is as follows:

[0059] max f = I (E Load,t ) - E Load,t P UCDR,t ;

[0060] In the formula, f is the total consumer surplus of users, E Load,t is the load amount of period t after UCDR, and I (E Load,t ) represents the electricity utility function.

[0061] A commonly used quadratic function is used to represent the electricity utility obtained by users changing the electricity behavior, which will affect a certain user response amount, and the specific expression is as follows:

[0062]

[0063] In the formula, represents the electricity preference coefficient of users;

[0064] In step four, the UCDR constraint conditions in the rescheduling stage include load transfer amount constraints and user response amount constraints, and the expressions are as follows:

[0065]

[0066] In the formula, ε2 is the UCDR single-point load transfer limit value.

[0067] The present application can achieve the following technical effects:

[0068] The application optimizes the output structure of the source side, limits the carbon emission of the traditional gas turbine, improves the carbon capture level, and excavates the energy-saving and carbon-reducing space of the system, proves that the solar thermal power station can become the core energy supply equipment of the future comprehensive energy system, and realizes green and low-carbon transformation.

[0069] Compared with the traditional price type demand response, the application establishes a two-stage ultra carbon demand response (UCDR) mechanism to deeply excavate the low-carbon space of the system. As an important guarantee for preventing excessive carbon emission after the price type demand response, the ultra carbon demand response forms an ultra carbon price, drives the user to transfer low-carbon behavior, and realizes low-carbon economic dispatching.

[0070] The source side introduces the solar thermal power station, the load side introduces the ultra carbon demand response, realizes the source-load collaborative carbon reduction scheduling, maximizes the system regulation ability, and improves the system energy-saving and emission-reducing level, which shows that the method provided by the application has important significance for the low-carbon development of the comprehensive energy system containing the solar thermal power station. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 It is a flowchart of the pre-scheduling-re-scheduling ultra carbon demand response method based on source-load collaborative carbon reduction provided by the embodiment of the application.

[0072] Figure 2 It is a power flow diagram of a solar thermal power station (CSPP) provided by the embodiment of the application.

[0073] Figure 3 It is an actual response diagram of the system ultra carbon demand response (UCDR) in scenario 3 provided by the embodiment of the application.

[0074] Figure 4 It is an actual response diagram of the system UCDR in scenario 5 provided by the embodiment of the application.

[0075] Figure 5 It is an electric power balance diagram of the system in scenario 5 provided by the embodiment of the application.

[0076] Figure 6 It is a thermal power balance diagram of the system in scenario 5 provided by the embodiment of the application.

[0077] Figure 7 It is a charging and discharging heat state diagram of the CSPP in scenario 5 provided by the embodiment of the application.

[0078] Figure 8 It is a system low-carbon characteristic analysis diagram under different carbon emission thresholds provided by the embodiment of the application.

[0079] Figure 9is a use analysis chart of electricity under different carbon emission thresholds provided by an embodiment of the present application.

[0080] Figure 10 is a sensitivity analysis chart of carbon trading price provided by an embodiment of the present application. DETAILED DESCRIPTION

[0081] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the following description, the same modules are denoted by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0082] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and do not constitute a limitation on the present application.

[0083] An embodiment of the present application provides a pre-dispatching-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction, comprising the following steps:

[0084] Step one, pre-dispatching-rescheduling two-stage low-carbon dispatching mechanism analysis.

[0085] 1. Pre-dispatching mechanism analysis.

[0086] The pre-dispatching-rescheduling two-stage dispatching mechanism is as shown in Figure 1 The pre-dispatching stage is to evaluate the carbon emissions of the system in advance to prevent the frequent occurrence of ultra-carbon events in which the carbon emissions exceed a certain threshold, and to avoid excessive emissions. The pre-dispatching takes the minimum system comprehensive operation cost as the target, and system safety and stability as the constraint, to realize unit pre-output, and to obtain a dynamic carbon emission factor to realize dynamic representation of the system carbon level, and to obtain a preliminary estimated system carbon emission. In order to prevent the system carbon emission from being too high, the pre-dispatching stage uses price-based demand response (PBDR) to guide the user side flexibility resource to optimize the load curve. PBDR is to generate a reasonable time-of-use electricity price scheme to encourage users to consider their own economy, so as to change their own electricity consumption behavior to reduce electricity cost, and to realize system peak clipping and valley filling. However, time-of-use electricity price cannot represent the system carbon emission level, and still leads to the occurrence of ultra-carbon events, and the space for energy saving and carbon reduction needs to be optimized, and rescheduling needs to be performed.

[0087] 2. Rescheduling mechanism analysis.

[0088] The rescheduling stage is to mobilize users to transfer low-carbon behaviors by using the ultra-carbon demand response (UCDR) when the ultra-carbon event occurs in the pre-scheduling stage, so as to further reduce the carbon emission of the system. First, the integrated energy service provider needs to determine whether the ultra-carbon event occurs according to the estimated carbon emission in the pre-scheduling stage: if yes, it indicates that the low-carbon space in the pre-scheduling needs to be optimized, and rescheduling needs to be performed. Then, the dynamic carbon emission factor calculated by the pre-scheduling is used as the penalty coefficient of the pre-scheduling PBDR time-of-use price to form the ultra-carbon price, so that the users can effectively perceive the source-side carbon emission level, and thus the UCDR is performed by using the ultra-carbon price to drive the energy consumer to optimize the power consumption behavior. In the process of transferring the power load from the high-carbon period to the low-carbon period, a certain response cost or benefit will be generated. The rescheduling realizes the unit rescheduling according to the load curve optimized by the UCDR, and the target function and constraint condition of the pre-scheduling, so as to drive the system to finally reach a deep low-carbon state; otherwise, it indicates that the pre-scheduling meets the deep low-carbon level, and rescheduling is not needed.

[0089] The users are the indirect bearers of the responsibility of source-side carbon emission, and passively accept the ultra-carbon price to optimize the power consumption behavior according to their own benefits, so as to deeply optimize the low-carbon state of the system. Since the speed of the users to respond to the price change is slow and the time scale is long, the pre-scheduling and the rescheduling are completed in a day in advance, and the frequent change of the price will reduce the participation of the users in the UCDR, so the rescheduling is only performed once.

[0090] Step two, analysis of high-proportion new energy scene operation architecture of the integrated solar-thermal power plant (CSPP).

[0091] The present application is based on the unique solar-thermal resource advantage in the northwest region, and introduces the CSPP into the integrated energy system (IES) from the perspective of the source side, and discusses the carbon reduction capacity brought by the joint operation of the CSPP, the wind power plant and the carbon capture power plant, and verifies the carbon reduction benefit of the UCDR in the high-proportion new energy scene, and the energy flow diagram of the CSPP coupled with the IES is as shown in Figure 2

[0092] ​CSPP is mainly composed of heat collection mirror field (HF), power cycle (PC) and thermal energy storage (TES). Firstly, HF can convert solar radiation into heat energy that can be stored in TES or directly heat steam to supply PC, which uses heated steam to drive steam turbine generator to generate electricity, which is basically the same as the principle of thermal power unit. Moreover, the thermal storage device makes CSPP have flexible dispatchability compared with photovoltaic power station to ensure the stability and controllability of CSPP power generation and heat supply. Therefore, CSPP can be used as a clean and low-carbon power generation and heat supply method in IES, which plays the role of "clean energy consumption of clean energy" and provides a new research idea for the direction of carbon emission reduction of the system.

[0093] Step three, pre-dispatching-re-dispatching two-stage demand response modeling.

[0094] 1. Pre-dispatching PBDR time-of-use price model.

[0095] In the pre-dispatching stage, PBDR uses time-of-use price. In order to ensure the rationality of user response to electricity and avoid excessive response behavior, user electricity satisfaction is introduced into the price elasticity matrix, and the electric load model of PBDR is constructed as follows:

[0096]

[0097] Where f, p, g represent peak, flat and valley periods; and are the electric load of each period before and after the implementation of PBDR, respectively; is the electricity price of each period before the implementation of PBDR; ΔD f , ΔD p , ΔD g is the price change after the implementation of PBDR; M is the user satisfaction; E is the price elasticity matrix.

[0098] 2. Re-dispatching UCDR super-carbon price model.

[0099] The UCDR in the re-dispatching stage uses the net carbon emission of the system in the pre-dispatching stage as the criterion for driving super-carbon events. When the carbon emission exceeds a certain threshold, the energy business will be added with a super-carbon penalty based on the PBDR time-of-use price to form a super-carbon price, which drives the main body to transfer to a deep low-carbon state. The specific criterion is as follows:

[0100] E JP,t ≥ E threshold ; (2)

[0101] E JP,t is the net carbon emission of the system in the period t, E threshold is the carbon emission threshold set by the implementation.

[0102] To better represent whether the super-carbon event occurs or not, the state variable is used for unified description in the present application:

[0103]

[0104] A t is the state variable of the system.

[0105] To ensure the fairness of user response and facilitate unified management, the non-clean energy supply ratio of a certain regional system is used to calculate the 24-hour dynamic average carbon emission factor with 1 hour as the time scale in the pre-dispatching stage, which is used as the unified additional penalty coefficient of the super-carbon price in the re-dispatching stage. The specific calculation is as follows:

[0106]

[0107] α CP,t is the dynamic carbon emission factor, e i is the carbon emission intensity of the thermal power unit i, P Ji,t is the net output of the i-th thermal power unit in the period t, P W,t , P CSP,t are the wind power and the CSPP on-grid power in the period t respectively, and n represents the number of carbon capture power plants.

[0108] To prevent the price from being too large, the dynamic carbon emission factor is normalized and dimensionless. The specific calculation is as follows:

[0109]

[0110] is the dynamic carbon emission factor after dimensionless processing, is the minimum value of the dynamic carbon emission factor, is the maximum value of the dynamic carbon emission factor.

[0111] When the super-carbon event occurs, the super-carbon price of the UCDR added to the energy trader can be calculated based on the PBDR time-of-use price in the pre-dispatching stage in the re-dispatching stage, which encourages the energy trader to make low-carbon decisions. The calculation principle of the UCDR super-carbon price is as follows:

[0112]

[0113] P UCDR,t is the price of each period after UCDR, P PBDR,t is the price of each period after PBDR.​

[0114] Step 4: Two-stage low-carbon economic dispatch model of pre-schedule-re-schedule.

[0115] 1. Objective function of unit output during the pre-scheduling phase.

[0116] The pre-scheduling phase aims to minimize the overall system operating cost, which mainly includes energy costs, start-up and shutdown costs, operation and maintenance costs, wind curtailment costs, carbon trading costs, and carbon sequestration costs, as detailed below:

[0117]

[0118] Where F is the total operating cost of the pre-scheduled system, F E,t Let F be the energy cost of the system during time period t. QT,t Let F be the system start-up and shutdown cost during time period t. YW,t Let F be the system's operation and maintenance cost during time period t. CS,t Let F be the carbon sequestration cost of the system during time period t. CJ,t Let F be the carbon trading cost of the system during period t. curt,t Let t be the system's wind curtailment cost during time period t.

[0119] 1) Energy costs.

[0120] Energy costs mainly include the system's natural gas costs and coal consumption costs. Coal consumption costs can be represented by a quadratic function of its output power. Natural gas costs are related to the amount of natural gas purchased and the unit price of gas, as detailed below:

[0121]

[0122] In the formula, P Gi,t Let a be the total output of the i-th thermal power unit during time period t. i b i c i Let C be the consumption characteristic coefficient of the i-th thermal power unit. g Q represents the price of natural gas purchased from external suppliers. g,t Let t represent the amount of natural gas purchased by the system during time period t, and n represent the number of thermal power units.

[0123] 2) Start-up and shutdown costs

[0124] Start-up and shutdown costs F QT,t The expression is as follows:

[0125]

[0126] In the formula, U i,t For the operating status of thermal power unit i during time period t, U i,t =1 indicates that thermal power unit i is operating during time period t, U i,t= 0 represents that the thermal power unit t is stopped at the t period, f qt,i is the start-stop cost of the unit t.

[0127] 3) Operation and maintenance cost

[0128] The operation and maintenance cost is mainly calculated by the maintenance coefficient of each device in the IES, and can be specifically expressed as follows:

[0129]

[0130] In the formula, m represents the number of devices in the IES system, P i,t is the total output of the i-th device in the system in the t period, s i is the operation and maintenance coefficient of the i-th device.

[0131] 4) Carbon sequestration cost

[0132] According to the chemical reaction equation, CO2 and CH4 have a volume conservation relationship, so the utilization amount of carbon dioxide can be obtained, and the required sequestration amount of carbon dioxide is calculated, and the carbon sequestration cost F CS,t The expression is as follows:

[0133]

[0134] In the formula, is the methane generation amount of the system in the t period, η P2G,t is the operation efficiency of P2G in the t period, P P2G,t is the electric power consumed by P2G in the t period; H g is the heat value of natural gas, and is 39 MJ / m 3 ; is the density of carbon dioxide, C S is the unit mass carbon dioxide sequestration price, E Ci,t is the total carbon dioxide capture amount of the i-th carbon capture power plant in the t period, Q C,t is the carbon dioxide utilization amount of the system in the t period.

[0135] 5) Carbon trading cost

[0136] The baseline method is adopted to allocate carbon quota for free, the excess carbon quota of the system can be sold, and the system with insufficient compliance needs to additionally purchase carbon quota, and the carbon trading cost F CJ,t can be expressed as follows:

[0137]

[0138] In the formula, C J is the carbon trading price, E Ji,tis the net carbon emission of the system in time period t, a and β are the carbon quota coefficients of coal-fired units and gas-fired units, ω is the conversion coefficient of thermal power to electric power, P CHP,t is the electric power generated by gas turbine (CHP) in time period t, H CHP,t , H GB,t is the output thermal power of CHP and gas boiler in time period t.

[0139] 2. Unit output constraint condition in pre-scheduling stage.

[0140] 1) Power balance constraint.

[0141]

[0142] Q BUY,t + Q P2G,t - Q CHP,t - Q GB,t = Q Load,t ; (16)

[0143] Q min ≤ Q BUY,t ≤ Q max ; (17)

[0144] In the formula, P L,t is the electric load power in time period t, H Load,t is the required thermal load power in time period t, is the thermal load power supplied by TES in time period t, Q BUY,t is the amount of natural gas purchased from outside the system in time period t, Q P2G,t is the amount of P2G gas in time period t, Q Load,t is the natural gas load, Q CHP,t , Q GB,t is the amount of natural gas consumed by CHP and gas boiler in time period t.

[0145] 2) Carbon capture power plant operation constraint.

[0146]

[0147] In the formula, P Yi,t is the operation energy consumption of carbon capture power plant in time period t, P D is the fixed energy consumption of carbon capture power plant, λ is the electric energy required for capturing unit CO2, E Gi,t is the carbon emission of the i-th thermal power unit in time period t, γ is the carbon emission coefficient of thermal power unit, θ is the carbon capture level of carbon capture power plant, θ max is the maximum carbon capture level.

[0148] 3) Operation constraint of solar-thermal power station.

[0149]

[0150] where, Q is the total heat collected by HF for the period t, η g-r η is the light-heat conversion efficiency of HF, S HF S is the total area of HF, D t D is the solar radiation index for the period t, η r-d η is the heat-electricity conversion efficiency of the steam turbine, Q is the heat power provided by HF to the generator for the period t, Q is the heat power provided by TES to the generator for the period t, Q is the heat power input of TES for the period t, Q is the heat power that cannot be utilized by CSPP for the period t, Q is the heat power output of TES for the period t.

[0151] 4) PBDR constraint

[0152] It mainly includes electricity price constraint, load transfer amount constraint, user satisfaction constraint, and the specific expressions are as follows:

[0153]

[0154] where, D g , D p , D f is the electricity price of valley, flat, and peak periods after PBDR, D gd is the fixed electricity price before PBDR, P Load,t is the electricity load power of each period after PBDR, M min is the minimum user satisfaction, χ is the peak-valley electricity price draw ratio, and ε1 is the PBDR single-point load transfer limit value.

[0155] 3. UCDR objective function in the rescheduling stage.

[0156] The UCDR in the rescheduling stage takes the maximum consumer surplus, i.e., the minimum electricity purchase cost and the maximum electricity utility, as the objective function. The electricity purchase price in the rescheduling stage is the super-carbon electricity price calculated by the UCDR super-carbon electricity price model, and the objective function is specifically expressed as follows:

[0157] max f = I (E Load,t ) - E Load,t P UCDR,t ; (21)

[0158] where, f is the total consumer surplus of the user, E Load,t is the load amount for the period t after UCDR, I (E Load,t ) represents the electricity utility function.

[0159] The present application adopts a common quadratic function to represent the power consumption utility obtained by the user changing the power consumption behavior, which will affect a certain user response amount, and the specific expression is as follows:

[0160]

[0161] In the formula, The power consumption preference coefficient of the user is represented.

[0162] 4. UCDR constraint condition in the rescheduling stage

[0163] The UCDR constraint condition mainly includes a load transfer amount constraint and a user response amount constraint, and the expression is as follows:

[0164]

[0165] In the formula, ε2 is a UCDR single-point load transfer limit value.

[0166] The target function and the constraint condition of the unit output plan in the rescheduling stage are the same as those in the pre-scheduling stage.

[0167] The pre-scheduling-rescheduling two-stage carbon demand response method based on source-load collaborative carbon reduction provided by the embodiments of the present application will be further described below in combination with specific scenarios.

[0168] In order to verify the effectiveness of the low-carbon scheduling method provided by the present application, the following scenarios are set for comparative analysis:

[0169] Scenario 1, basic scenario, without considering CSPP, PBDR and UCDR;

[0170] Scenario 2, considering PBDR;

[0171] Scenario 3, introducing UCDR on the basis of scenario 2;

[0172] Scenario 4, introducing CSPP on the basis of scenario 2;

[0173] Scenario 5, introducing UCDR on the basis of scenario 4.

[0174] An example simulation is performed for the five scenarios, and the cost comparison is shown in Table 1.

[0175] Table 1: System operation cost and carbon emission amount under each scenario

[0176]

[0177] As shown in Table 1, the total carbon emissions of Scenario 2 and Scenario 3 decreased by 205.2 tons and 297.6 tons respectively compared to Scenario 1, representing reductions of 12.2% and 17.7%. This demonstrates that UCDR can further optimize the system's low-carbon space, reduce system carbon emissions, and verify its low-carbon characteristics. To address the load level-limited carbon capture energy consumption, Scenario 4 introduces the CSPP unique to Northwest China into the IES, constructing an IES with a high proportion of new energy sources. Scenario 4 shows a 48.7% reduction in carbon emissions compared to Scenario 2. CSPP is a clean and low-carbon power source with energy time-shifting characteristics, which can improve carbon capture levels and significantly reduce carbon emissions. The total system costs of Scenario 2 and Scenario 3 decreased by RMB 21,800 and RMB 38,800 respectively compared to Scenario 1, representing reductions of 1.5% and 2.7%. The total cost of Scenario 5 also decreased compared to Scenario 4.

[0178] The actual UCDR response diagram of the system in scenario three is as follows: Figure 3 As shown. By Figure 3 It can be seen that the original load peak-to-valley difference was 84MW, and after PBDR response, the load peak-to-valley difference was 66.9MW, a decrease of 17.1MW, or 20.4%, compared to the original load; after UCDR, the load peak-to-valley difference was 50.7MW, a decrease of 33.3MW, or 39.6%, compared to the original load. This is because under the action of UCDR, super-carbon events are triggered during the periods of 13:00 and 17:00-19:00, causing the load during high-carbon periods with high carbon emission factor levels to shift to low-carbon periods with low carbon emission factor levels, further smoothing the peaks and filling the valleys, and promoting the reduction of system carbon emissions.

[0179] The actual response diagram of the UCDR system in Scenario 5 is as follows: Figure 4 As shown. To verify the carbon reduction benefits of UCDR in a high-proportion renewable energy scenario, Scenario 5 introduces UCDR based on Scenario 4, with a carbon emission threshold set at 50 tons. Figure 4 It can be seen that the super-carbon event was triggered at 19:00, driving the system to transition to a low-carbon state. Compared with Scenario 4, the total carbon emissions of the system in Scenario 5 decreased by 39.2 tons, or 5.2%. It is evident that the established UCDR can effectively explore the low-carbon space containing a high proportion of new energy IES.

[0180] The power balance diagram of the system in Scenario 5 is as follows: Figure 5 As shown, the load fluctuations in Scenario 5 are more stable after UCDR. Furthermore, the introduction of IES into CSPP limits the output of thermal power units and CHP, reducing the consumption of high-carbon energy and thus reducing the overall net carbon emissions of the system. It also significantly increases the operating energy consumption of P2G equipment, promotes natural gas production within the system, reduces the amount of purchased natural gas, and improves the system's economic efficiency.

[0181] The thermal power balance diagram of the system in Scenario 5 is as follows: Figure 6The CSPP is introduced into the IES, which relieves the thermal output of the CHP, makes the gas boiler and the CSPP become the main heat supply power, reduces the consumption of primary energy on the source side, reduces carbon emissions from the source, and verifies the feasibility of the CSPP in auxiliary heating.

[0182] Figure 7 In order to store the charging and discharging state of the heat storage device of the CSPP in the IES, the CSPP stores heat energy when the solar radiation index is high at noon, and releases part of the heat at 18:00 to generate electricity and heat to meet the electricity and heat load demand. In the evening period, the heat storage device releases most of the heat to supply heat, thereby reducing the consumption of natural gas and reducing the carbon emissions of the system. Therefore, the CSPP has good energy time shift characteristics.

[0183] The sensitivity analysis is as follows:

[0184] The selection of the carbon emission threshold in the rescheduling stage is the key to define the super carbon event and whether the UCDR low carbon response is needed. When different carbon emission thresholds are selected, the low carbon state of the system will also change. From Figure 8 , Figure 9 It can be seen that for the first three scenarios, when the carbon emission threshold is selected as 50 tons, the carbon emission of scenario three after the UCDR is the lowest, but the electricity utility decreases, which reduces the user's electricity satisfaction and the user's willingness to participate in the response, which is not conducive to the implementation of the UCDR. When the carbon emission threshold is selected as 60 tons in the first three scenarios, the carbon emission decreases and the electricity utility is basically unchanged.

[0185] And for scenario four and scenario five, when the carbon emission threshold is selected as 50 tons, the carbon emission of the system is the lowest and the electricity utility is the largest. Therefore, the selection of the carbon emission threshold for defining the super carbon event should consider the balance between the carbon emission of the system and the electricity utility of the user.

[0186] The sensitivity analysis of the carbon trading price is carried out for scenario five. From (a) in Figure 10 It can be seen that as the carbon trading price increases, the carbon trading income decreases first and then increases, and the total cost increases first and then decreases. When the carbon trading price is less than 70 yuan, the system needs to purchase additional carbon emission rights for carbon trading, and the income is negative. When the carbon trading price is greater than 70 yuan, the carbon emission of the system is less than the obtained carbon emission rights, and the system can make a profit through carbon trading.

[0187] From (b) in Figure 10As can be seen from (b) in FIG. 6, as the carbon trading price increases, the output structure of the unit is continuously optimized, and the IES is driven to generate low-carbon power, and the carbon emission of the system is in a clear downward trend. When the carbon trading price is 80 yuan, the carbon emission of the system tends to be flat under the influence of the carbon trading price. At this time, the system basically reaches the optimal low-carbon output plan to meet the system load level. It can be seen that reasonable selection of the carbon trading price is conducive to energy saving and carbon reduction of the system.

[0188] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the present specification and the features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0189] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

[0190] The specific embodiments of the present application described above do not constitute a limitation on the scope of protection of the present application. Any various other corresponding changes and modifications made according to the technical concept of the present application shall be included in the scope of protection of the claims of the present application.

Claims

1. A pre-scheduling-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction, characterized in that, Comprising the following steps: Step one, analyze the pre-scheduling-rescheduling two-stage low-carbon scheduling mechanism, the pre-scheduling stage adopts price-based demand response PBDR, and generates time-of-use price scheme to guide the user side flexibility resource to optimize the load curve; the rescheduling stage adopts ultra-carbon demand response UCDR, and uses ultra-carbon price to mobilize users to optimize the load curve by low-carbon behavior transfer; Step two, analyze the new energy scene operation architecture of the CSPP containing the photothermal power station, introduce the CSPP, wind farm and carbon capture power plant into the integrated energy system IES for joint operation, and verify the carbon reduction benefit of UCDR under the new energy scene; Step three, establish a pre-scheduling-rescheduling two-stage demand response model, including a pre-scheduling PBDR time-of-use price model and a rescheduling UCDR ultra-carbon price model, to form an ultra-carbon price driving user low-carbon transfer; The pre-scheduling PBDR time-of-use price model is as follows: Wherein, f, p, g represent peak, flat, valley three periods; and Respectively, the electrical load of each period before and after the implementation of PBDR; The electricity price of each period before the implementation of PBDR; ΔD f , ΔD p , ΔD g The electricity price change after the implementation of PBDR; M is the user satisfaction; E is the electricity price elasticity matrix; The rescheduling UCDR ultra-carbon price model is as follows: E JP,t ≥E threshold ; wherein, E JP,t is the system net carbon emission in period t, E threshold is the carbon emission threshold value of the implementation setting; A t is the state variable of the system; a CP,t is the dynamic carbon emission factor, e i is the carbon emission intensity of the thermal power unit i, P Ji,t is the net output of the i-th thermal power unit in period t, P W,t , P CSP,t are the wind power and the CSPP on-grid power in period t, respectively, and n represents the number of carbon capture power plants; is the dimensionless processed dynamic carbon emission factor, is the minimum value of the dynamic carbon emission factor, is the maximum value of the dynamic carbon emission factor; P UCDR,t is the electricity price of each period after UCDR, P PBDR,t is the electricity price of each period after PBDR; Step four, according to the UCDR ultra-carbon price, build a pre-scheduling-rescheduling two-stage low-carbon economic dispatching model, through the pre-scheduling stage unit output target function, pre-scheduling stage unit output constraint condition, rescheduling stage UCDR target function, rescheduling stage UCDR constraint condition, for realizing source-load collaborative carbon reduction dispatching and improving energy saving and emission reduction level.

2. The pre-dispatching-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction according to claim 1, characterized in that, In step one, the dynamic carbon emission factor is obtained in the pre-scheduling stage to realize dynamic representation of system carbon level, the preliminary estimated system carbon emission is obtained, the PBDR is used to guide the user side flexibility resource to optimize the load curve, and the carbon emission is prevented from being too high; In the rescheduling stage, when the ultra-carbon event occurs in the pre-scheduling stage, the UCDR is used to mobilize users to transfer low-carbon behavior and reduce the carbon emission of the system, the dynamic carbon emission factor obtained in the pre-scheduling stage is used as the penalty coefficient of the pre-scheduling PBDR time-of-use price to form the ultra-carbon price, according to the load curve optimized by the UCDR, the system is driven to reach a deep low-carbon state.

3. The pre-dispatching-rescheduling hyper-carbon demand response method based on source-load collaborative carbon reduction according to claim 1, characterized in that, In step two, the photothermal power station CSPP mainly includes a collector field, an electricity generation system and a heat storage system, the collector field converts solar radiation into heat energy which can be stored in the heat storage system or directly heats steam to supply the electricity generation system for power generation; the heat storage system enables the CSPP to have flexible dispatchability to ensure the stability and controllability of power generation and heat supply of the CSPP.

4. The pre-dispatching-rescheduling super-carbon demand response method based on source-load collaborative carbon reduction according to claim 1, characterized in that, In step four, the pre-scheduling stage unit output target function is as follows: F = F + F + F + F + F + F + F E,t F is the total cost of system operation, F QT,t F is the energy cost of the system in the period t, F YW,t F is the start-stop cost of the system in the period t, F CS,t F is the operation and maintenance cost of the system in the period t, F CJ,t F is the carbon sequestration cost of the system in the period t, F curt,t F is the wind curtailment cost of the system in the period t. The energy cost F E,t The expression of the energy cost F is as follows: In the formula, P Gi,t is the total output of the i-th thermal power unit at time period t, a i , b i , c i is the consumption characteristic coefficient of the i-th thermal power unit, C g is the unit price of natural gas purchased from outside, Q g,t is the gas consumption of the system purchased from outside at time period t, and n is the number of thermal power units. The start-stop cost F QT,t The expression of the start-stop cost F is as follows: In the formula, U i,t is the running state of the thermal power unit i at the time period t, Ui ,t = 1 indicates that the thermal power unit i is running at the time period t, U i,t = 0 indicates that the thermal power unit i is stopped at the time period t, f qt,i is the start-stop cost of the unit t; The operation and maintenance cost F YW,t The expression is as follows: In the formula, m represents the number of each device in the IES system, P i,t is the total output of the system within the t period of time for the i-th device, s i is the operation and maintenance coefficient of the i-th device; The carbon sequestration cost F CS,t The expression of F is as follows: wherein, is the amount of methane generated by the system in the time period t, η P2G,t is the operating efficiency of P2G in the time period t, P P2G,t is the amount of electric power consumed by P2G in the time period t; H g is the heat value of natural gas, taken as 39 MJ / m 3 ; is the density of carbon dioxide, C S is the carbon dioxide storage price per unit mass, E Ci,t is the total amount of carbon dioxide captured by the i-th carbon capture power plant in the time period t, Q C,t is the amount of carbon dioxide used by the system in the time period t; The carbon trading cost F CJ,t The expression is as follows: In the formula, C J is the carbon trading price, E Ji,t is the net carbon emission of the system in the t period, α and β are the carbon quota coefficients of the coal-fired unit and the gas-fired unit, ω is the conversion coefficient of thermal power to electric power, P CHP,t is the electric power generated by the gas turbine CHP in the t period, H CHP,t , H GB,t is the output thermal power of the CHP and the gas boiler in the t period.

5. The pre-dispatching-rescheduling ultra-carbon demand response method based on source-load collaborative carbon reduction according to claim 4, characterized in that, In step four, the pre-scheduling stage unit output constraint condition includes power balance constraint, carbon capture power plant operation constraint, photothermal power station operation constraint and PBDR constraint; The expression of the power balance constraint is as follows: Q BUY,t +Q P2G,t -Q CHP,t -Q GB,t =Q Load,t ; Q min ≤Q BUY,t ≤Q max ; where P L,t is the electrical load power in the time period t, H Load,t is the required thermal load power in the time period t, is the TES supplied thermal load power in the time period t, Q BUY,t is the amount of natural gas purchased from outside the system in the time period t, Q P2G,t is the amount of P2G gas in the time period t, Q Load,t is the natural gas load, Q CHP,t , Q GB,t is the amount of natural gas consumed by the CHP and gas boiler in the time period t; The expression of the carbon capture power plant operation constraint is as follows: wherein P Yi,t is the operation energy consumption of the carbon capture power plant in the time period t, P D is the fixed energy consumption of the carbon capture power plant, λ is the electricity consumption per unit of CO2 captured, E Gi,t is the carbon emission of the i-th thermal power unit in the time period t, γ is the carbon emission coefficient of the thermal power unit, θ is the carbon capture level of the carbon capture power plant, θ max is the maximum carbon capture level; The expression of the photothermal power station operation constraint is as follows: wherein Qtotal is the total heat collected by the HF for the period t, η g-r η is the light-to-heat conversion efficiency of the HF, S HF S is the total area of the HF, D t D is the solar radiation index for the period t, η r-d η is the heat-to-electricity conversion efficiency of the turbine, QH is the thermal power provided by the HF to the generator for the period t, QTES is the thermal power provided by the TES to the generator for the period t, QTESin is the thermal power input to the TES for the period t, Qunavailable is the thermal power unavailable to the CSPP for the period t, QTESout is the thermal power output of the TES for the period t; The PBDR constraint includes price constraint, load transfer amount constraint and user satisfaction constraint, and the expression is as follows: In the formula, D g , D p , D f is the valley, flat, peak period electricity price after PBDR, D gd is the fixed electricity price before PBDR, P Load,t is the electricity load power of each period after PBDR, M min is the minimum user satisfaction, χ is the peak-valley electricity price opening ratio, and ε1 is the PBDR single-point load transfer limit value.

6. The pre-dispatching-rescheduling ultra-carbon demand response method based on source-load synergic carbon reduction according to claim 5, characterized in that, The target function and constraint condition of the rescheduling stage unit output plan are the same as those of the pre-scheduling stage unit output, in step four, the rescheduling stage UCDR target function is as follows: max f = I(E Load,t )- E Load,t P UCDR,t ; In the formula, f is the total consumer surplus of the user, E Load,t is the load amount of the t period after the UCDR, I(E Load,t ) represents the electricity utility function; The quadratic function is used to express the power utility obtained by the user changing the power consumption behavior, which will affect a certain user response amount, and the specific expression is as follows: In the formula, represents the electricity use preference coefficient of the user; In step four, the UCDR constraint condition in the rescheduling stage includes the load transfer amount constraint and the user response amount constraint, and the expression is as follows: In the formula, ε2 is the single-point load transfer limit value of UCDR.

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