Park integrated energy system cooling, heating and power optimization scheduling method based on biomass energy and demand response

Abstract

The invention discloses a park comprehensive energy system cooling, heating and power optimization scheduling method based on biomass energy and demand response. The method comprises the following steps: constructing a park comprehensive energy system based on biomass-solar energy coupling; constructing a user side power, heat and cold multi-element load demand response model; a park integrated energy system cooling, heating and power optimization scheduling model based on biomass energy and demand response is constructed, and a stepped carbon transaction mechanism is introduced to limit carbon emission. According to the park comprehensive energy system cooling, heating and power optimization scheduling method based on biomass energy and demand response, the problems of insufficient energy supply, high carbon emission and high cost of partial park comprehensive energy systems can be solved.

Description

Technical Field

[0001] The present invention relates to the field of optimization and scheduling of integrated energy systems, and in particular to a cooling, heating and electricity optimization and scheduling method for integrated energy systems in a park based on biomass energy and demand response. Background Art

[0002] Social and economic development has led to an increasing demand for energy and a reduction in fossil energy reserves. The consumption of fossil energy has brought about environmental problems, and large amounts of carbon emissions have exacerbated the greenhouse effect, causing global warming. In this context, increasing the use of non-fossil energy and reducing carbon emissions are key issues that need to be addressed urgently.

[0003] IES internally couples multiple energy sources for joint supply to meet the terminal multi-energy load demand, which is an important support for promoting the utilization of non-fossil energy and reducing carbon emissions. At present, most of the literature focuses on the low-carbon economic operation of IES, such as Literature 1: "Analysis of Optimal Operation of Regional Integrated Energy System Based on Repeated Game [J]. Automation of Electric Power Systems, 2019, 43(14): 81-89.", Literature 2: "Low-carbon Optimal Dispatch of Integrated Energy System Considering Super-carbon Demand Response [J]. Power System Technology, 2024, 48(05): 1863-1872.", Literature 3: "Carbon Capture-Power-to-Gas Coordinated Optimal Dispatch Model Considering Spatiotemporal Diffusion and Carbon Sink [J]. Automation of Electric Power Systems, 2023, 47(02): 15-23." The above literature has greatly improved the low-carbon economic operation of the integrated energy system, but did not consider the energy consumption behavior on the user side and the energy supply structure on the source side, resulting in the low-carbon economic operation of the system being affected.

[0004] Demand response is a way for users to participate in system scheduling. By guiding users to adjust their energy demand through energy market prices and incentive response mechanisms, it can effectively reduce load fluctuations and achieve economic operation of the system. For example, Reference 4: "Optimal Dispatch of Integrated Energy System Considering the Stepped Carbon Trading Mechanism [J]. China Electric Power, 1-12.", Reference 5: "Optimal Dispatch Strategy of Multi-Microgrid Integrated Energy System Based on Comprehensive Demand Response and Master-Slave Game [J]. Proceedings of the CSEE, 2021, 41(4): 1307-1321, 1538.", the above references established demand response models for electricity, heat and cooling loads, which can effectively improve the flexibility of system operation and significantly reduce system operating costs and carbon emissions. However, the above references only consider demand response, the system is relatively single, and fail to effectively improve the economic benefits of the system.

[0005] Biomass is a renewable organic matter produced by green plants through photosynthesis, which can be divided into agriculture, forestry, aquaculture, waste and other fields. Biomass energy has a shorter carbon emission cycle than fossil energy, which makes the emission of CO 2 Completing the carbon cycle in nature, i.e., net emission of CO2 Close to zero, so biomass is called a carbon neutral fuel. Biomass resources are universal, have a short regeneration cycle, little pollution, and have great energy potential. By using advanced energy processing methods to utilize them, it will help solve the IES energy supply problem in agricultural parks or remote rural areas, improve agricultural production and lifestyle, and promote the construction of modern agricultural parks.

[0006] At present, most of the literature studies the low-carbon economic characteristics of IES coupled with biomass and solar thermal collectors, as well as traditional energy. For example, literature 6: "Study on the Integration of CCHP Systems of Biomass Coupled with Solar Energy and Geothermal Energy [D]. Hunan University, 2018.", literature 7: "Research on Two-stage Robust Capacity Configuration of Biomass Gas-Solar Energy-Wind Integrated Energy System [D]. Shandong University, 2022." and literature 8: "Optimization of Biomass Integrated Energy System Considering Stepped Carbon-Green Certificate Joint Trading and Demand Response [J]. Electric Power Science and Engineering, 2024, 40(07): 10-25." The above literature can make full use of biomass resources and significantly reduce carbon emissions and total system costs. However, it does not consider that solar thermal collectors only supply heat to the biomass gasification pool, resulting in energy waste, and does not consider the impact of P2G and CCS coupling equipment on the participation of BSC system in IES. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response, which can solve the problems of insufficient energy supply, high carbon emissions and high costs in some park integrated energy systems.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response comprises the following steps:

[0010] Step 1: Construct a comprehensive energy system for the park based on biomass-solar energy coupling;

[0011] Step 2: Construct a user-side electricity, heat and cold multi-load demand response model;

[0012] Step 3: Construct a cooling, heating and electricity optimization scheduling model for the park's integrated energy system based on biomass energy and demand response, and introduce a step-by-step carbon trading mechanism to limit carbon emissions.

[0013] In step 1, a comprehensive energy system for the park based on biomass-solar coupling is constructed, which specifically includes:

[0014] (1) Construct a biomass-solar coupling device model;

[0015] (2) Construct a power-to-gas (P2G) and carbon capture (CCS) coupling equipment model;

[0016] (3) Construct an energy conversion device model;

[0017] (4) Construct an energy storage device model.

[0018] (1) The biomass-solar coupling device model is constructed, which specifically includes:

[0019] 1) Biomass gasification model

[0020] After being heated by the preheater, the biomass enters the gasification tank. Its gas production rate is related to the temperature. The biomass gas production rate and biomass output model are as follows:

[0021]

[0022] Where: η b,t and T t are the gas production rate and temperature of the gas pool at time t; T 0 The most suitable temperature for biomass gasification pool is generally 35℃; BSC,b,t and m bio,t are the biomass input power and biomass mass flow rate at time t respectively; LHV is the lower heating value of biomass.

[0023] 2) Biomass gasification pool model

[0024] The gas production efficiency of the biomass gasification pool is closely related to the temperature inside the pool and the ambient temperature. Considering that the solar collector and heat exchanger jointly provide energy to maintain the appropriate temperature of the gas pool, the heat balance formula and heat dissipation model in the biomass gasification pool are as follows:

[0025]

[0026] Where: ρ and C p is the density and specific heat capacity of biomass materials; V d is the volume of the gasification pool; HRT is the time the material is in the gas pool; T amb,t is the ambient temperature at time t; U h and S d is the total heat transfer coefficient and total heat dissipation area of ​​the gasification pool; P GT,h,t and P GT,loss,t They are the energy and heat dissipation required by the gasification pool at time t respectively.

[0027] 3) Solar collector model

[0028] Considering that the solar collector gives priority to supplying heat to the vaporization pool, the excess heat energy can be directly shared with the heat load. The solar collector model is as follows:

[0029] PTC,t =n TC η TC,t G TC,t Q TC (3);

[0030] Where: P TC,t , η TC,t and G TC,t is the output power, conversion efficiency and light radiation intensity of the solar collector at time t; n TC is the number of collectors; Q TC is the area of ​​a single collector.

[0031] 4) Biomass-solar coupled BSC system model

[0032] Part of the biomass gas is burned in an internal combustion engine to generate electricity, and part of it is used to generate heat through a heat exchanger. Part of the generated heat energy meets the heat load, part is supplied to the gasification pool, and the other part is supplied to the absorption refrigeration machine for cooling; the biomass-solar coupled BSC system model is as follows:

[0033]

[0034] Where: P BSC,e,t , P BSC,h,t and P BSC,c,t are the electrical power, thermal power and cooling power output by BSC at time t respectively; P BSC,b,t is the total power input to BSC at time t; P HRB,h,t P is the heat energy supplied to the refrigerator at time t; HRB,tc,t P is the heat energy supplied to the gasification pool; TC,h,t The thermal energy that supplies the heat load to the solar collector at time t; η BSC,e , η BSC,h and η BSC,c are the electrical, thermal and cooling efficiencies of BSC output respectively; P GT,h,t is the energy required by the biomass gasification pool at time t.

[0035] The operation constraints and ramp constraints of the biomass-solar coupled BSC system model are as follows:

[0036]

[0037] Where: and They are the upper and lower limits of BSC input power respectively; and The upper and lower limits of BSC climbing power; and The upper and lower limits of the input absorption chiller power; and The upper and lower limits of the input absorption chiller climbing power; and are the upper and lower limits of the heat energy delivered to the gasification pool, respectively; and They are respectively the upper and lower limits of the climbing power of the gasification pool.

[0038] (2) The power-to-gas (P2G) and carbon capture (CCS) coupling equipment model is constructed, including:

[0039] Power-to-gas (P2G) and carbon capture (CCS) coupling equipment can absorb excess wind and solar energy and reduce CO 2 The emission, power-to-gas (P2G) and carbon capture (CCS) coupling model is as follows:

[0040] 1) Electrolyzer EL

[0041]

[0042] Where: P EL,e,t is the electrical energy input to EL at time t; is the hydrogen energy of EL input at time t; η EL is the energy conversion efficiency of EL; are the upper and lower limits of the input EL power, respectively; They are the upper and lower limits of EL climbing power respectively.

[0043] 2) Methane Reactor MR

[0044]

[0045] Where: is the hydrogen energy input into MR at time t; P MR,g,t is the gas power output by MR at time t; η MR is the energy conversion efficiency of MR; are the upper and lower limits of hydrogen power input to MR, respectively; They are the upper and lower limits of MR climbing power respectively.

[0046] 3) Hydrogen fuel cell HFC

[0047]

[0048] Where: is the hydrogen power input to HFC at time t; P HFC,e,t is the HFC output power at time t; η HFC The efficiency of HFC energy conversion; are the upper and lower limits of the input HFC hydrogen power respectively; They are the upper and lower limits of HFC climbing power respectively.

[0049] 4) Carbon capture CCS

[0050] Considering the problem of coupling failure between power-to-gas (P2G) and carbon capture CCS when wind and solar energy output is insufficient, CO is introduced into carbon capture CCS. 2 Liquid storage tank, strengthening the coupling of power-to-gas (P2G) and carbon capture (CCS), CO 2 Mainly from gas boilers and biomass-solar coupling system BSC, the carbon capture CCS model is as follows:

[0051]

[0052] Where: P CCS,t , P CCS,f,t and P CCS,o,t are the total energy consumption, fixed energy consumption and operating energy consumption of CCS at time t respectively; is the CO captured by CCS at time t 2 Quality; CCS is the energy consumption coefficient of CCS; and are the CO generated by GB and BSC at time t 2 Quality; CCS Capturing CO for CCS 2 efficiency; and Provide CO for the CCS required by MR at time t 2 Amount and CO 2 Storage volume.

[0053] Among them: The operating power constraint and ramp power constraint of carbon capture CCS are as follows:

[0054]

[0055] Where: They are the upper and lower limits of CCS energy consumption respectively; They are the upper and lower limits of CCS climbing energy consumption respectively.

[0056] (3) Construct an energy conversion device model, including:

[0057] Energy conversion equipment includes electric boiler EB, electric refrigerator ERU and gas boiler GB. Electric boiler EB is used to convert electrical energy into heat energy, electric refrigerator ERU converts electrical energy into cold energy, and gas boiler GB burns natural gas to generate heat energy. Its model is as follows:

[0058] 1) Electric boiler EB

[0059]

[0060] Where: P EB,e,tis the electric power consumed by EB at time t; P EB,h,t is the thermal power generated by EB at time t; η EB is the heating efficiency of EB; They are the upper and lower limits of EB power consumption respectively; It is the upper and lower limits of EB climbing power.

[0061] 2) Electric Refrigerator ERU

[0062]

[0063] Where: P ERU,e,t is the electric energy consumed by ERU at time t; P ERU,c,t is the cooling energy generated by ERU at time t; η ERU is the ERU cooling efficiency; The upper and lower limits of the input ERU electric power; They are the upper and lower limits of ERU climbing power respectively.

[0064] 3) Gas boiler GB

[0065]

[0066] Where: P GB,g,t , P GB,h,t They represent the input gas power and output thermal power of GB at time t respectively; η GB is GB energy conversion efficiency; The upper and lower limits of GB input power; The upper and lower limits of climbing power.

[0067] The energy storage device model constructed in (4) specifically includes:

[0068] Multi-energy storage includes electric energy storage, thermal energy storage, cold energy storage and hydrogen energy storage. Its general model is as follows:

[0069] 1) Charging and discharging power and state constraints:

[0070]

[0071] Where: x is the type of energy storage device, represented by e, h, c and H respectively. 2 Represents electricity, heat, cold and hydrogen energy storage; P x,cha,t , P x,dis,t Respectively represent the charging and discharging power of energy storage device x at time t; I x,cha,t ,I x,dis,t They represent the charging and discharging states of the energy storage device x at time t respectively; and Respectively represent the upper and lower limits of the charging power of the energy storage device x; and They respectively represent the upper and lower limits of the energy storage device x's discharge power.

[0072] 2) Energy storage state continuity constraint:

[0073]

[0074] Where: S x,t is the storage capacity at time t; δ x is the self-loss coefficient of the energy storage device; η x,cha , η x,dis The charging and discharging efficiency of energy storage; is the upper and lower limits of energy storage capacity; S x，1 is the storage capacity of energy storage device x at time 1; S x，24 is the storage capacity of energy storage device x at 24 hours.

[0075] In step 2, a user-side electricity, heat and cold multi-load demand response model is established, which specifically includes:

[0076] (1) Price-based demand response

[0077] Since different types of loads have different sensitivities to the same electricity price signal, the price-based demand response load is divided into curtailable load and transferable load. The price-based demand response is modeled using the electricity price elasticity matrix method to express the relationship between the user's electricity consumption behavior and electricity price changes. The expression is as follows:

[0078]

[0079] Where: e t,j is the elastic coefficient of the electric load at time i to time j; ΔP i and P i 0 are the load change and initial load at time i; Δρ j and are the electricity price change rate and initial electricity price at time j respectively.

[0080] For electricity, heating and cooling loads, according to their respective time-of-use prices, the elasticity coefficient between the load at time i and the price at time j for the same load is defined as follows:

[0081]

[0082] Where: and P L,i are the initial load at time i and the load after the response price changes respectively; and ρ j are the initial price and the price after response respectively; when i=j, it is E L(i) is called the self-elasticity coefficient, which indicates the change of load in period i relative to the change of price in the same period; when i≠j, it is called the cross-elasticity coefficient, which indicates the change of load in period i relative to the change of price in period j. L (i)≤0, E L (i,j)≥0.

[0083] The change of load in period i for time-of-use price is as follows:

[0084]

[0085] Where: P L (i) and P L，0 (i) represents the load after the response price change and the initial load at time i respectively; E L (i, j) is the cross elasticity coefficient, which indicates the change in load in period i for the change in price in period j; ρ 0 (j) and ρ(j) represent the initial electricity price and the change in electricity price at time j, respectively.

[0086] Let λ L (i) After the time-of-use price is expressed, the load change rate of the user in the i-th period is as follows:

[0087]

[0088] Among them, It is called the price fluctuation ratio, which describes the price fluctuation caused by the time-sharing price and takes into account the transfer and reduction of load. L (i) is:

[0089]

[0090] Where: The first term E L (i, j)k(j) is the load transferred from time i to time j; the second term E L (i)k(i) is the load that can be reduced at time i. L (i) is called the self-elasticity coefficient, which indicates the change in load in period i relative to the change in price in the same period; k(i) is the price fluctuation ratio, which indicates the price fluctuation caused by the time-sharing price.

[0091] Therefore, the load change rate between the peak, flat and valley periods can be obtained as

[0092]

[0093] Where: T f 、T p and T g Peak, flat and valley periods divided according to the intraday price; fp, fg and λ pg They are respectively the shift from peak time to normal time, the shift from peak time to valley time, and the shift from normal time to valley time; ff , pp and λ gg Represents the load reduction during peak hours, normal hours and valley hours respectively; k f , k p and k g are the price fluctuation ratios during peak hours, normal hours and valley hours respectively; E L (i) is the elastic coefficient at time i; E L (i,j) is the cross elasticity coefficient at time i to time j.

[0094] In summary, the load after implementing price-based demand response is

[0095]

[0096] Where: and P load are the load after implementing price-based demand response and the initial load; P f , P p and P g They are price-based demand response for pre-peak, flat and valley loads respectively.

[0097] (2) Alternative demand response

[0098] Since the substitution relationship between heating load and cooling load is small, the substitution between heating load and cooling load is not considered. The substitution demand response model is as follows:

[0099]

[0100] Where: P eh,t and P ec,t are the electric power replacing the heat power and the cold power at time t respectively; P he,t P is the thermal power that replaces the electrical power at time t; ce,t is the cooling power that replaces the electric power at time t; e ,ω h and ω c are the load proportions of electricity, heat and cooling load transfer respectively; ω re ,ω rh and ω rc are the proportions of electricity, heat and cooling loads that can be transferred to replacement; η eh is the electrical-to-heat coefficient; η ec is the electricity-to-cooling coefficient; and They are the electricity, heating and cooling loads after price-based demand response at time t.

[0101] (3) Demand response results

[0102] After price-based demand response and substitution-based demand response, the final electricity, heating and cooling loads are:

[0103]

[0104] Where: P load,e,t , P load,h,t and P load,c,t They are the electric load, heating load and cooling load after participating in demand response DR.

[0105] In step 3, a cooling, heating and electricity optimization dispatching model of the park comprehensive energy system considering biomass energy and demand response is established, and a step-by-step carbon trading mechanism is introduced to limit carbon emissions, including:

[0106] (1) Objective function

[0107] Comprehensively consider the energy purchase cost C of the park's integrated energy system IES buy , wind and solar curtailment costs C cut , carbon trading costs and operation and maintenance costs C om ; Construct with total cost C total The minimum low-carbon economy targets are as follows:

[0108]

[0109] 1) Energy purchase cost C buy

[0110] Energy purchase cost C buy The main costs are electricity and gas purchase costs, and the model is as follows:

[0111]

[0112] Where: α e and α g are the unit prices of electricity and gas respectively; P e,buy,t , P g,buy,t They are the power of electricity and gas purchased at time t respectively; T is the time period.

[0113] 2) Cost of curtailing wind and solar power C cut

[0114]

[0115] Where: wt ,δ pv are the penalty factors for wind and solar abandonment, respectively; P wt,cut,t , P pv,cut,t are the wind power and solar power abandoned at time t respectively.

[0116] 3) Carbon trading costs

[0117] Establish a ladder-type carbon trading mechanism model, the model is as follows:

[0118]

[0119] Where: E IES 、E e,buy 、E BSC and E GB They are the total quota of IES, external power purchase quota, BSC system quota and GB quota; e , χ g and χ b E are the carbon emission quota coefficients per unit of electricity, natural gas and biomass gas consumption respectively. IES,a 、E e,buy,a , P BSC,b,a and P GB,h,a are the actual carbon emissions of IES, power purchase from higher authorities, BSC system and GB; E MR,a is the CO actually absorbed by MR 2 Quantity; E IES,t is the carbon emission trading amount; H is the coefficient corresponding to different carbon emission ranges; E IES，t is the carbon emission trading amount at time t; P e,buy，t is the amount of electricity purchased from the upper power grid at time t; P BSC,b，t is the input power of the biomass-solar coupling system at time t; P GB,h，t is the thermal power output of the gas boiler at time t.

[0120] 4) Operation and maintenance costs

[0121]

[0122] Where: β z is the unit operation and maintenance cost of the zth type of equipment; P z,t is the output power of the zth device at time t; z is the type of device.

[0123] (2) Constraints

[0124] 1) Wind and solar power output constraints

[0125]

[0126] Where: and are the output limits of photovoltaic units and wind turbine units respectively; P pv,t is the photovoltaic power generation power at time t; P wt,t is the wind turbine power generation at time t;

[0127] 2) Restrictions on purchasing electricity and gas

[0128]

[0129] Where: and They are the upper and lower limits of the purchased power respectively; and are the upper and lower limits of gas purchase power respectively; P e,buy,t P is the power purchased from the upper grid at time t; g,buy,t The gas power purchased from the superior gas network at time t.

[0130] 3) Electric power balance constraints

[0131]

[0132] Where: P wt,t and P pv,t are the power output to the wind turbine and photovoltaic at time t respectively; P BSC,e,t P is the output power of the biomass-solar coupling system at time t; HFC,e,t P is the output power of the hydrogen fuel cell at time t; EL,e,t P is the power consumed by the electrolytic cell at time t; CCS,t P is the power consumed by the carbon capture equipment at time t; ERU,e,t and P EB,e,t P is the power consumed by the electric refrigerator and electric heating equipment at time t respectively; load,e,t is the electric load after participating in demand response at time t; P e,cha,t and P e,dis,t are the charging power and discharging power of the energy storage battery at time t respectively.

[0133] 4) Thermal power balance constraints

[0134] P GB,h,t +P BSC,h,t +P TC,h,t +P EB,h,t +P h,dis,t =P load,h,t +P h,cha,t (34);

[0135] Where: P GB,h,t P is the thermal power input to the gas boiler at time t; BSC,h,t P is the thermal power output of the biomass-solar coupling system at time t; TC,h,t P is the thermal power output of the solar collector at time t; EB,h,t P is the thermal power output of the electric heating equipment at time t; load,h,t is the heat load after participating in demand response at time t; P h,cha,t and P h,dis,tare the charging power and releasing power of the heat storage equipment at time t respectively.

[0136] 5) Cold power balance constraint

[0137] P BSC,c,t +P ERU,c,t +P c,dis,t =P load,c,t +P c,cha,t (35);

[0138] Where: P BSC,c,t P is the cooling power output of the biomass-solar coupling system at time t; ERU,c,t P is the cooling power output of the electric refrigerator at time t; load,c,t is the cooling load after participating in demand response at time t; P c,cha,t and P c,dis,t are the cooling power and cooling discharge power of the cold storage equipment at time t respectively.

[0139] 6) Natural gas power balance constraints

[0140] P g,buy,t +P MR,g,t =P GB,g,t (36);

[0141] Where: P g,buy,t P is the gas power purchased from the upper gas network at time t; MR,g,t is the output power of the methane reactor at time t; P GB,g,t is the gas consumption of the gas boiler at time t.

[0142] 7) Hydrogen power balance constraints

[0143]

[0144] Where: P EL,H2,t P is the hydrogen production power of the electrolyzer at time t; HFC,H2,t P is the hydrogen power consumed by the hydrogen fuel cell at time t; MR,H2,t is the hydrogen consumption power of the methane reactor at time t; P H2,cha,t and P H2,dis,t are the hydrogen charging power and hydrogen discharging power of the hydrogen storage equipment at time t respectively.

[0145] The present invention provides a cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response, which has the following technical effects:

[0146] 1) Due to insufficient energy supply in some integrated energy systems of parks, high carbon emissions and high costs are prominent, and the phenomenon of wind and light abandonment is relatively serious, which has a great impact on the stability and economy of the integrated energy system. To this end, step 1 of the present invention constructs a biomass-solar coupling system by combining renewable and clean biomass energy with solar collectors, and introduces power-to-gas P2G equipment and carbon capture CCS equipment. The power-to-gas P2G equipment produces hydrogen by electrolyzing water, which can effectively absorb new energy sources such as wind and light. The carbon capture CCS equipment can capture carbon dioxide and produce methane for the methane reactor, which can effectively reduce the carbon emissions of the agricultural park. Finally, a park integrated energy system considering biomass-solar coupling is constructed, which can not only effectively alleviate the problem of insufficient energy supply in the integrated energy system of the agricultural park, but also greatly improve the low-carbon economic benefits of the agricultural park.

[0147] 2) Since the park load can guide users to adjust their energy demand through energy market prices and incentive response mechanisms, it has not been utilized. Therefore, step 2 of the present invention enables the park load to participate in price-based demand response and alternative demand response to achieve reduction, time shift and substitution of the park load. This is conducive to alleviating the peak-to-valley difference of the power grid and improving the economic benefits of the park's comprehensive energy system.

[0148] 3) To seek a low-carbon economic operation plan for the integrated energy system of the park. To this end, step 3 of the present invention establishes a cooling, heating and electricity optimization scheduling model for the integrated energy system of the park that takes into account biomass energy and demand response, and introduces a step-by-step carbon trading mechanism to further limit carbon emissions. The model can effectively seek the optimal operation plan for the integrated energy system of the agricultural park.

[0149] 4) In view of the defects mentioned in the background section, this application makes the following improvements:

[0150] ①. Aiming at the defect that the existing integrated energy system IES does not consider the energy consumption behavior on the user side and the energy supply structure on the source side, which affects the low-carbon economic operation of the system, the present invention realizes flexible adjustment through the park load participation price-based demand response and alternative demand response. By combining biomass resources with solar collectors, a biomass-solar coupling system is constructed to provide electricity, heat and cold energy for the park integrated energy system, thereby improving the low-carbon economic benefits of the park integrated energy system.

[0151] ②. Aiming at the defects that the existing integrated energy system IES only considers demand response, the system is relatively simple and fails to effectively improve the economic benefits of the system, the present invention introduces power-to-gas P2G equipment, carbon capture CCS equipment, electric boiler EB equipment, electric refrigerator ERU equipment, gas boiler GB equipment and electric heat and cold energy storage equipment into the park integrated energy system. The power-to-gas P2G equipment produces hydrogen by electrolyzing water, which can effectively absorb new energy such as wind and light. The carbon capture CCS equipment can capture carbon dioxide and produce methane for the methane reactor, which can effectively reduce the carbon emissions of the agricultural park. The electric boiler EB equipment can convert electrical energy into thermal energy, the electric refrigerator ERU can realize the conversion of electrical energy into cold energy, and the gas boiler can realize the conversion of natural gas into thermal energy, realizing the mutual conversion between energy sources. The electric heat and cold energy storage equipment can effectively realize the time shift of energy, which is conducive to improving the low-carbon economic benefits of the agricultural park.

[0152] ③. In view of the defects of only considering solar collectors to supply heat to the biomass gasification pool, resulting in energy waste, and not considering the impact of P2G and CCS coupling equipment on the BSC system's participation in the integrated energy system IES, the present invention takes into account that the solar collector has the highest output at noon during the day, but the outside temperature is high at this time, and the energy required by the gasification pool is less, resulting in heat energy waste. Therefore, the present invention selects solar collectors to preferentially supply heat to the gasification pool, and the excess heat energy can be directly shared with the heat load to reduce energy waste. In addition, the present invention introduces power-to-gas P2G equipment and carbon capture CCS equipment. The power-to-gas P2G equipment produces hydrogen by electrolyzing water, which can effectively absorb new energy sources such as wind and light. The carbon capture CCS equipment can capture carbon dioxide and generate methane to the methane reactor, which can effectively reduce carbon emissions from agricultural parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0153] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0154] Figure 1 This is the framework diagram of the integrated energy system IES.

[0155] Figure 2 Schematic diagram of the BSC system.

[0156] Figure 3 It provides wind and solar output as well as electricity, heating and cooling load diagrams.

[0157] Figure 4 It is the user-side time-of-use electricity price, heating price and cooling price chart.

[0158] Figure 5 This is the electricity load optimization diagram before and after demand response DR.

[0159] Figure 6 This is the heat load optimization diagram before and after demand response DR.

[0160] Figure 7This is the cooling load optimization diagram before and after demand response DR.

[0161] FIG8( a ) is the electric power balance diagram of scenario 1.

[0162] FIG8( b ) is the electric power balance diagram of scenario 2.

[0163] Figure 9(a) is the thermal power balance diagram of scenario 1.

[0164] Figure 9(b) is the thermal power balance diagram of scenario 2.

[0165] Figure 10(a) is the cooling power balance diagram for scenario 1.

[0166] Figure 10(b) is the cooling power balance diagram for scenario 2. DETAILED DESCRIPTION

[0167] A cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response comprises the following steps:

[0168] Step 1: Construct a comprehensive energy system for the park based on biomass-solar energy coupling, introduce power-to-gas and carbon capture coupling equipment, multi-energy conversion equipment and energy storage equipment, and establish models for each device.

[0169] (1) Constructing a biomass-solar coupling device model

[0170] 1) Biomass gasification model

[0171] After being heated by the preheater, the biomass enters the gasification tank. Its gas production rate is related to the temperature. The biomass gas production rate and biomass output model are as follows:

[0172]

[0173] Where: η b,t and T t are the gas production rate and temperature of the gas pool at time t; T 0 The most suitable temperature for biomass gasification pool is generally 35℃; BSC,b,t and m bio,t are the biomass input power and biomass mass flow rate at time t respectively; LHV is the lower heating value of biomass.

[0174] 2) Biomass gasification pool model

[0175] The gas production efficiency of the biomass gasification pool is closely related to the temperature in the pool and the ambient temperature. The present invention considers that the solar collector and heat exchanger are combined to provide energy to maintain the appropriate temperature of the gas pool. The heat balance formula and heat dissipation model in the biomass gasification pool are as follows:

[0176]

[0177] Where: ρ and C p is the density and specific heat capacity of biomass materials; V d is the volume of the gasification pool; HRT is the time the material is in the gas pool; T amb,t is the ambient temperature at time t; U h and S d is the total heat transfer coefficient and total heat dissipation area of ​​the gasification pool; P GT,h,t and P GT,loss,t They are the energy and heat dissipation required by the gasification pool at time t respectively.

[0178] 3) Solar collector model

[0179] The solar collector has the highest output at noon, but the outside temperature is high at this time, and the energy required by the vaporization pool is less, resulting in heat energy waste. Therefore, the present invention considers that the solar collector gives priority to supplying heat to the vaporization pool, and the excess heat energy can be directly shared with the heat load. The solar collector model is as follows:

[0180] P TC,t =n TC η TC,t G TC,t Q TC (3);

[0181] Where: P TC,t , η TC,t and G TC,t is the output power, conversion efficiency and light radiation intensity of the solar collector at time t; n TC is the number of collectors; Q TC is the area of ​​a single collector.

[0182] 4) Biomass-solar coupled BSC system model

[0183] Part of the biomass gas is burned in an internal combustion engine to generate electricity, and part of it is used to generate heat through a heat exchanger. Part of the generated heat energy meets the heat load, part is supplied to the gasification tank, and the other part is supplied to the absorption refrigeration machine for cooling. The biomass-solar coupled BSC system model is as follows:

[0184]

[0185] Where: P BSC,e,t , P BSC,h,t and P BSC,c,t are the electrical power, thermal power and cooling power output by BSC at time t respectively; P BSC,b,t is the total power input to BSC at time t; P HRB,h,t P is the heat energy supplied to the refrigerator at time t; HRB,tc,t P is the heat energy supplied to the gasification pool; TC,h,tThe thermal energy that supplies the heat load to the solar collector at time t; η BSC,e , η BSC,h and η BSC,c are the electrical, thermal and cooling efficiencies of BSC output respectively; P GT,h,t is the energy required by the biomass gasification pool at time t.

[0186] The operation constraints and ramp constraints of the BSC system (biomass-solar coupled BSC system) are as follows:

[0187]

[0188] Where: and They are the upper and lower limits of the BSC system input power respectively; and The upper and lower limits of the BSC system climbing power; and The upper and lower limits of the input absorption chiller power; and Input the upper and lower limits of the absorption chiller's ramp power; and They are the upper and lower limits of the heat energy delivered to the gasification pool; and They are respectively the upper and lower limits of the climbing power of the transport gasification pool.

[0189] (2) Construct a power-to-gas (P2G) and carbon capture (CCS) coupling equipment model

[0190] Power-to-gas (P2G) and carbon capture (CCS) coupling equipment can absorb excess wind and solar energy and reduce CO 2 Emissions. The coupling model of power-to-gas (P2G) and carbon capture (CCS) is as follows:

[0191] 1) Electrolyzer EL

[0192]

[0193] Where: P EL,e,t is the electrical energy input to EL at time t; is the hydrogen energy of EL input at time t; η EL is the energy conversion efficiency of EL; are the upper and lower limits of the input EL power, respectively; They are the upper and lower limits of EL climbing power respectively.

[0194] 2) Methane Reactor MR

[0195]

[0196] Where: is the hydrogen energy input into MR at time t; P MR,g,t is the gas power output by MR at time t; η MR is the energy conversion efficiency of MR; are the upper and lower limits of hydrogen power input to MR, respectively; They are the upper and lower limits of MR climbing power respectively.

[0197] 3) Hydrogen fuel cell HFC

[0198]

[0199] Where: is the hydrogen power input to HFC at time t; P HFC,e,t is the HFC output power at time t; η HFC The efficiency of HFC energy conversion; are the upper and lower limits of the input HFC hydrogen power respectively; They are the upper and lower limits of HFC climbing power respectively.

[0200] 4) Carbon capture CCS

[0201] Considering the problem of coupling failure between power-to-gas (P2G) and carbon capture CCS when wind and solar energy output is insufficient, CO is introduced into carbon capture CCS. 2 Liquid storage tanks to enhance the coupling between power-to-gas (P2G) and carbon capture (CCS). 2 Mainly from gas boilers and BSC systems. The carbon capture CCS model is as follows:

[0202]

[0203] Where: P CCS,t , P CCS,f,t and P CCS,o,t are the total energy consumption, fixed energy consumption and operating energy consumption of CCS at time t respectively; is the CO captured by CCS at time t 2 Quality; CCS is the energy consumption coefficient of CCS; and are the CO generated by GB and BSC at time t 2 Quality; CCS Capturing CO for CCS 2 efficiency; and Provide CO for the CCS required by MR at time t 2 Amount and CO 2 Storage volume.

[0204] Among them: The operating power constraint and ramp power constraint of carbon capture CCS are as follows:

[0205]

[0206] Where: They are the upper and lower limits of CCS energy consumption respectively; They are the upper and lower limits of CCS climbing energy consumption respectively.

[0207] (3) Constructing energy conversion equipment model

[0208] Energy conversion equipment mainly includes electric boiler EB, electric refrigerator ERU and gas boiler GB. Electric boiler EB is used to convert electrical energy into heat energy, electric refrigerator ERU converts electrical energy into cold energy, and gas boiler GB burns natural gas to generate heat energy. Its model is as follows:

[0209] 1) Electric boiler EB

[0210]

[0211] Where: P EB,e,t is the electric power consumed by EB at time t; P EB,h,t is the thermal power generated by EB at time t; η EB is the heating efficiency of EB; They are the upper and lower limits of EB power consumption respectively; It is the upper and lower limits of EB climbing power.

[0212] 2) Electric Refrigerator ERU

[0213]

[0214] Where: P ERU,e,t is the electric energy consumed by ERU at time t; P ERU,c,t is the cooling energy generated by ERU at time t; η ERU is the ERU cooling efficiency; The upper and lower limits of the input ERU electric power; They are the upper and lower limits of ERU climbing power respectively.

[0215] 3) Gas boiler GB

[0216]

[0217] Where: P GB,g,t , P GB,h,t They represent the input gas power and output thermal power of GB at time t respectively; η GB is GB energy conversion efficiency; The upper and lower limits of GB input power; It is the upper and lower limits of climbing power.

[0218] (4) Constructing energy storage equipment model

[0219] Multi-energy storage mainly consists of electrical energy storage, thermal energy storage, cold energy storage and hydrogen energy storage. Its general model is as follows:

[0220] 1) Charging and discharging power and state constraints:

[0221]

[0222] Where: x is the type of energy storage device, represented by e, h, c and H respectively. 2 Represents electricity, heat, cold and hydrogen energy storage; P x,cha,t , P x,dis,t Respectively represent the charging and discharging power of energy storage device x at time t; I x,cha,t ,I x,dis,t They represent the charging and discharging states of the energy storage device x at time t respectively; and Respectively represent the upper and lower limits of the charging power of the energy storage device x; and They respectively represent the upper and lower limits of the energy storage device x's discharge power.

[0223] 2) Energy storage state continuity constraint:

[0224]

[0225] Where: S x,t is the storage capacity at time t; δ x is the self-loss coefficient of the energy storage device; η x,cha , η x,dis The charging and discharging efficiency of energy storage; is the upper and lower limits of energy storage capacity; S x，1 is the storage capacity of energy storage device x at time 1; S x，24 is the storage capacity of energy storage device x at 24 hours.

[0226] Step 2: Establish a user-side electricity, heat and cooling multi-load demand response model.

[0227] When the electric load participates in the demand response, the electric load can be adjusted according to the time-of-use electricity price; and for the heat load and cooling load, they can also be adjusted according to the time-of-use heat price and cooling price respectively, so as to smooth the load curve. In addition, considering the mutual substitution relationship between electric, heat and cooling loads, by using the energy conversion equipment within the integrated energy system, a load can not only participate in its own load response, but also participate in the response by replacing other loads, so as to realize the mutual substitution of energy and further realize the economic operation of the integrated energy system.

[0228] Therefore, the present invention divides load response into two types: one is price-type demand response, which reduces and shifts the load through the time-of-use price characteristics of energy; the other is substitution-type demand response, which realizes the substitution of different types of energy according to the mutual substitution relationship between loads.

[0229] (1) Price-based demand response

[0230] Since different types of loads have different sensitivities to the same electricity price signal, the price-based demand response load is divided into curtailable load and transferable load. The price-based demand response is modeled using the electricity price elasticity matrix method, which represents the relationship between the user's electricity consumption behavior and the change in electricity prices. The expression is as follows:

[0231]

[0232] Where: e t,j is the elastic coefficient of the electric load at time i to time j; ΔP i and P i 0 are the load change and initial load at time i; Δρ j and are the electricity price change rate and initial electricity price at time j respectively.

[0233] For electricity, heating and cooling loads, according to their respective time-of-use prices, the elasticity coefficient between the load at time i and the price at time j for the same load is defined as follows:

[0234]

[0235] Where: and P L,i are the initial load at time i and the load after the response price changes respectively; and ρ j are the initial price and the price after response respectively; when i=j, it is E L (i) is called the self-elasticity coefficient, which indicates the change of load in period i relative to the change of price in the same period; when i≠j, it is called the cross-elasticity coefficient, which indicates the change of load in period i relative to the change of price in period j. L (i)≤0, E L (i,j)≥0.

[0236] The change of load in period i for time-of-use price is as follows:

[0237]

[0238] Where: P L (i) and P L，0(i) represents the load after the response price change and the initial load at time i respectively; E L (i, j) is the cross elasticity coefficient, which indicates the change in load in period i for the change in price in period j; ρ 0 (j) and ρ(j) represent the initial electricity price and the change in electricity price at time j, respectively.

[0239] Let λ L (i) After the time-of-use price is expressed, the load change rate of the user in the i-th period is as follows:

[0240]

[0241] Among them, It is called the price fluctuation ratio, which describes the price fluctuation caused by the time-sharing price and takes into account the transfer and reduction of load. L (i) is:

[0242]

[0243] Where: The first term E L (i, j)k(j) is the load transferred from time i to time j; the second term E L (i)k(i) is the load that can be reduced at time i. L (i) is called the self-elasticity coefficient, which indicates the change in load in period i relative to the change in price in the same period; k(i) is the price fluctuation ratio, which indicates the size of price fluctuation caused by intraday prices.

[0244] Therefore, the load change rate between the peak, flat and valley periods can be obtained as

[0245]

[0246] Where: T f 、T p and T g Peak, flat and valley periods divided according to the intraday price; fp , fg and λ pg They are respectively the shift from peak time to normal time, the shift from peak time to valley time, and the shift from normal time to valley time; ff , pp and λ gg Represents the load reduction during peak hours, normal hours and valley hours respectively; k f , k p and k g are the price fluctuation ratios during peak hours, normal hours and valley hours respectively; E L (i) is the elastic coefficient at time i; E L (i,j) is the cross elasticity coefficient at time i to time j.

[0247] In summary, the load after implementing price-based demand response is

[0248]

[0249]

[0250] Where: and P load are the load after implementing price-based demand response and the initial load; P f , P p and P g They are price-based demand response pre-peak, flat and valley loads. According to the above methods, the electricity, heating and cooling loads are reduced and transferred.

[0251] (2) Alternative demand response

[0252] Users have flexibility in their demands for various types of loads, and can choose multiple energy sources to meet their own energy needs in the same period. For example, for electricity demand, in scenarios where part of the electricity is used for heating or cooling, the heating power and cooling power provided by the integrated energy system IES can be directly selected to meet the demand; for heat demand, in scenarios where part of the heating is provided, in addition to directly using the integrated energy system IES for heating, electric heating products can also be used indirectly; the same is true for cooling demand. Alternative demand response can not only meet the demands of different loads, but also alleviate the situation where a certain energy supply of the integrated energy system IES is insufficient. Since the substitution relationship between heating load and cooling load is small, the present invention does not consider the mutual substitution between heating load and cooling load. The alternative demand response model is as follows:

[0253]

[0254] Where: P eh,t and P ec,t are the electric power replacing the heat power and the cold power at time t respectively; P he,t P is the thermal power that replaces the electrical power at time t; ce,t is the cooling power that replaces the electric power at time t; e ,ω h and ω c are the load proportions of electricity, heat and cooling load transfer respectively; ω re ,ω rh and ω rc are the proportions of electricity, heat and cooling loads that can be transferred to replacement; η eh is the electrical-to-heat coefficient; η ec is the electricity-to-cooling coefficient; and They are the electricity, heating and cooling loads after price-based demand response at time t.

[0255] (3) Demand response results

[0256] After price-based demand response and substitution-based demand response, the final electricity, heating and cooling loads are:

[0257]

[0258] Where: P load,e,t , P load,h,t and P load,c,t They are the electric load, heating load and cooling load after participating in demand response DR.

[0259] Step 3: Establish an optimal scheduling model for cooling, heating and electricity of the park’s integrated energy system that takes into account biomass energy and demand response, and introduce a stepped carbon trading mechanism to limit carbon emissions.

[0260] (1) Objective function

[0261] The park comprehensively considers the energy purchase cost C of the integrated energy system IES buy , wind and solar cost C cut , Carbon trading costs and operation and maintenance costs C om , construct with a total cost C total The minimum low-carbon economy targets are as follows:

[0262]

[0263] 1) Energy purchase cost C buy

[0264] Energy purchase cost C buy The main costs are electricity and gas purchase costs, and the model is as follows:

[0265]

[0266] Where: α e and α g are the unit prices of electricity and gas respectively; P e,buy,t , P g,buy,t They are the power of electricity and gas purchased at time t respectively; T is the time period.

[0267] 2) Cost of curtailing wind and solar power C cut

[0268]

[0269] Where: wt ,δ pv are the penalty factors for wind and solar abandonment, respectively; P wt,cut,t , P pv,cut,t are the wind power and solar power abandoned at time t respectively.

[0270] 3) Carbon trading costs

[0271] The present invention establishes a ladder-type carbon trading mechanism model, which is as follows:

[0272]

[0273] Where: E IES 、E e,buy 、E BSC and E GB They are the total quota of IES, external power purchase quota, BSC system quota and GB quota; e , χ g and χ b E are the carbon emission quota coefficients per unit of electricity, natural gas and biomass gas consumption respectively. IES,a 、E e,buy,a , P BSC,b,a and P GB,h,a are the actual carbon emissions of IES, power purchase from higher authorities, BSC system and GB; E MR,a is the CO actually absorbed by MR 2 Quantity; E IES,t is the carbon emission trading amount; H is the coefficient corresponding to different carbon emission ranges. IES，t is the carbon emission trading amount at time t; P e,buy，t is the amount of electricity purchased from the upper power grid at time t; P BSC,b，t is the input power of the biomass-solar coupling system at time t; P GB,h，t is the thermal power output of the gas boiler at time t.

[0274] 4) Operation and maintenance costs

[0275]

[0276] Where: β z is the unit operation and maintenance cost of the zth type of equipment; P z,t is the output power of the zth device at time t; z is the type of device.

[0277] (2) Constraints

[0278] The constraints mainly include wind and solar power output constraints, energy purchase constraints, power balance constraints, equipment energy constraints and energy storage constraints. The details are as follows:

[0279] 1) Wind and solar power output constraints

[0280]

[0281] Where: and are the output limits of photovoltaic units and wind turbine units respectively; Ppv,t is the photovoltaic power generation power at time t; P wt,t is the wind turbine power generation at time t

[0282] 2) Restrictions on purchasing electricity and gas

[0283]

[0284] Where: and They are the upper and lower limits of the purchased power respectively; and are the upper and lower limits of gas purchase power respectively; P e,buy,t P is the power purchased from the upper grid at time t; g,buy,t The gas power purchased from the superior gas network at time t.

[0285] 3) Electric power balance constraints

[0286]

[0287] Where: P wt,t and P pv,t are the power output to the wind turbine and photovoltaic at time t respectively; P BSC,e,t P is the output power of the biomass-solar coupling system at time t; HFC,e,t P is the output power of the hydrogen fuel cell at time t; EL,e,t P is the power consumed by the electrolytic cell at time t; CCS,t P is the power consumed by the carbon capture equipment at time t; ERU,e,t and P EB,e,t P is the power consumed by the electric refrigerator and electric heating equipment at time t respectively; load,e,t is the electric load after participating in demand response at time t; P e,cha,t and P e,dis,t are the charging power and discharging power of the energy storage battery at time t respectively.

[0288] 4) Thermal power balance constraints

[0289] P GB,h,t +P BSC,h,t +P TC,h,t +P EB,h,t +P h,dis,t =P load,h,t +P h,cha,t (34);

[0290] Where: P GB,h,t P is the thermal power input to the gas boiler at time t; BSC,h,t P is the thermal power output of the biomass-solar coupling system at time t; TC,h,t P is the thermal power output of the solar collector at time t; EB,h,tP is the thermal power output of the electric heating equipment at time t; load,h,t is the heat load after participating in demand response at time t; P h,cha,t and P h,dis,t are the charging power and releasing power of the heat storage equipment at time t respectively.

[0291] 5) Cold power balance constraint

[0292] P BSC,c,t +P ERU,c,t +P c,dis,t =P load,c,t +P c,cha,t (35);

[0293] Where: P BSC,c,t P is the cooling power output of the biomass-solar coupling system at time t; ERU,c,t P is the cooling power output of the electric refrigerator at time t; load,c,t is the cooling load after participating in demand response at time t; P c,cha,t and P c,dis,t are the cooling power and cooling discharge power of the cold storage equipment at time t respectively.

[0294] 6) Natural gas power balance constraints

[0295] P g,buy,t +P MR,g,t =P GB,g,t (36);

[0296] Where: P g,buy,t P is the gas power purchased from the upper gas network at time t; MR,g,t is the output power of the methane reactor at time t; P GB,g,t is the gas consumption of the gas boiler at time t.

[0297] 7) Hydrogen power balance constraints

[0298]

[0299] Where: is the hydrogen production power of the electrolyzer at time t; is the hydrogen power consumption of the hydrogen fuel cell at time t; is the hydrogen power consumption of the methane reactor at time t; and are the hydrogen charging power and hydrogen discharging power of the hydrogen storage equipment at time t respectively.

[0300] The present invention selects an agricultural park in Gansu, China as an example for calculation analysis, and optimizes the scheduling with a cycle of 24 hours a day, and the time interval is 1 hour. The BSC system coupling equipment parameters are shown in Table 1; the gasification pool parameters are shown in Table 2; the wind power, photovoltaic output, and electricity, heat and cooling load data are shown in Table 2. Figure 3This example uses the CPLEX solver for optimization.

[0301] Table 1 BSC coupling equipment parameters

[0302]

[0303] Table 2 Biomass gasification pool parameters

[0304]

[0305] In order to verify the rationality of the user-side participation in demand response DR, carbon capture CCS and power-to-gas P2G coupling model in the model of the present invention, as well as the superiority of the BSC coupling system compared with the combined heating and cooling system using natural gas as raw material, the scheduling results of the following four scenarios are selected for comparative analysis:

[0306] Scenario 1: Consider the BSC system participating in the integrated energy system IES, the load participating in demand response DR, and carbon capture CCS coupled with power-to-gas P2G;

[0307] Scenario 2: Considering the BSC system participating in the integrated energy system IES, carbon capture CCS is coupled with power-to-gas P2G, and the load is not considered to participate in demand response DR;

[0308] Scenario 3: Considering BSC system participating in IES, load participating in DR, and ignoring P2G and CCS;

[0309] Scenario 4: Consider the traditional combined cooling, heating and power system participating in the integrated energy system IES, the load participating in demand response DR, and carbon capture CCS coupled with power-to-gas P2G.

[0310] (1) Economic and carbon emission analysis

[0311] The optimization scheduling results of each scenario are shown in Table 3. It can be seen from Table 3 that compared with scenario 2, the system energy purchase cost of scenario 1 was reduced by 382.2 yuan, a decrease of 5.6%. The carbon trading cost was reduced by 576.6 yuan, a decrease of 8.3%. The total cost was reduced by 1122.2 yuan. This is because the user side participated in the demand side response, reduced, transferred and replaced the load, made the load curve relatively smooth, and thus made the operation of the integrated energy system IES more economical; compared with scenario 3, the wind and light abandonment costs of scenario 1 were reduced by 397.1 yuan, a decrease of 21.5%. The carbon trading cost was reduced by 676.7 yuan, a decrease of 9.7%, and carbon emissions were reduced by 516.2 kg. This is because the power-to-gas P2G and carbon capture CCS equipment were introduced into the integrated energy system IES. When the wind and light output are high, the excess wind and light can be absorbed by the power-to-gas P2G, and the carbon capture CCS can capture the CO emitted by the BSC system and the gas boiler GB. 2, reducing system carbon emissions; compared with scenario 4, the energy purchase cost of scenario 1 was reduced by 8483.6 yuan, a decrease of 57.1%, the total cost was reduced by 9610.9 yuan, a decrease of 36.9%, and carbon emissions were reduced by 2182.6kg. This is because compared with the traditional combined cooling, heating and power system in scenario 4, which uses natural gas as raw material, the BSC system in scenario 1 uses biomass as the main energy source, which can fully utilize waste, reduce energy consumption, and reduce system costs.

[0312] (2) User-side demand response analysis

[0313] Comparing scenario 1 and scenario 2, the participation of electricity, heat and cooling loads in demand response DR can effectively reduce fluctuations and make the operation of the integrated energy system IES more economical. The impact of demand response DR on the output of each device before and after is shown in Figure 8-10. After the electricity load participates in demand response, the electricity load demand increases in the time periods (00:00-08:00, 22:00-24:00), and the electricity purchase of the integrated energy system IES increases. In the time period (14:00-17:00), the cooling load demand decreases, the output of electric refrigeration equipment decreases, and the BSC system cooling can meet most of its needs; after the heat load participates in demand response, the heat load demand decreases in the time period (00:00-08:00), and the gas boiler output decreases. In the time period (10:00-16:00), the heat load demand increases, and the output of the electric boiler increases. After the cooling load participates in demand response, the BSC system output increases in the time period (14:00-18:00), reducing the cooling cost of the integrated energy system IES.

[0314] (3) Comprehensive evaluation of BSC system access to the park integrated energy system IES

[0315] In order to analyze in detail the difference between the BSC system in scenario 1 and the traditional cogeneration system in scenario 4, the present invention will introduce the primary energy saving rate (PESR), CO 2 The emission saving rate (CEER) and total cost saving rate (TCSR) are used as the evaluation indicators of the system. The detailed model reference is "Research on Two-stage Robust Capacity Configuration of Biomass Gas-Solar-Wind Integrated Energy System [D]. Shandong University, 2022." Among them, P PEC1 , P PEC4 、E CDE1 、E CDE4 , C COST1 , C COST4 They represent the total primary energy consumption (PEC) and CO per day in scenarios 1 and 4 respectively. 2 Emissions (CDE) and total cost (total cost).

[0316] Table 4 Evaluation indicators

[0317]

[0318] As shown in Table 4, although the primary energy consumption of scenario 1 is 14.2% higher than that of scenario 4, its CO 2 The emissions were reduced by 13.3% and the total cost was reduced by 36.8%. It can be seen that the utilization of biomass waste can effectively reduce the system operating costs and CO 2 Emissions, therefore, the BSC system has better characteristics than the traditional combined cooling, heating and power system in participating in the integrated energy system IES.

Claims

1. A cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response, characterized in that: The steps include: Step 1: Construct a comprehensive energy system for the park based on biomass-solar energy coupling; Step 2: Construct a user-side electricity, heat and cold multi-load demand response model; Step 3: Construct a cooling, heating and electricity optimization scheduling model for the park's integrated energy system based on biomass energy and demand response, and introduce a step-by-step carbon trading mechanism to limit carbon emissions.

2. According to claim 1, a cooling, heating and electricity optimization scheduling method for a park integrated energy system based on biomass energy and demand response is characterized in that: In step 1, a comprehensive energy system for the park based on biomass-solar coupling is constructed, which specifically includes: (1) Construct a biomass-solar coupling device model; (2) Construct a power-to-gas (P2G) and carbon capture (CCS) coupling equipment model; (3) Construct an energy conversion device model; (4) Construct an energy storage device model.

3. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 2 is characterized in that: (1) The biomass-solar coupling device model is constructed, which specifically includes: 1) Biomass gasification model After being heated by the preheater, the biomass enters the gasification tank. Its gas production rate is related to the temperature. The biomass gas production rate and biomass output model are as follows: Where: η b,t and T t are the gas production rate and temperature of the gas pool at time t; T0 is the most suitable temperature of the biomass gasification pool, which is generally 35°C; P BSC,b,t and m bio,t are the biomass input power and biomass mass flow rate at time t respectively; LHV is the lower heating value of biomass; 2) Biomass gasification pool model The gas production efficiency of the biomass gasification pool is closely related to the temperature inside the pool and the ambient temperature. Considering that the solar collector and heat exchanger jointly provide energy to maintain the appropriate temperature of the gas pool, the heat balance formula and heat dissipation model in the biomass gasification pool are as follows: Where: ρ and C p is the density and specific heat capacity of biomass materials; V d is the volume of the gasification pool; HRT is the time the material is in the gas pool; T amb,t is the ambient temperature at time t; U h and S d is the total heat transfer coefficient and total heat dissipation area of ​​the gasification pool; P GT,h,t and P GT,loss,t are the energy and heat dissipation required by the gasification pool at time t respectively; 3) Solar collector model Considering that the solar collector provides heat to the vaporization pool first, the excess heat energy can be directly shared with the heat load; the solar collector model is as follows: P TC,t =n TC or TC,t G TC,t Q TC (3); Where: P TC,t , η TC,t and G TC,t is the output power, conversion efficiency and light radiation intensity of the solar collector at time t; n TC is the number of collectors; Q TC is the area of ​​a single collector; 4) Biomass-solar coupled BSC system model Part of the biomass gas is burned in an internal combustion engine to generate electricity, and part of it is used to generate heat through a heat exchanger. Part of the generated heat energy meets the heat load, part is supplied to the gasification pool, and the other part is supplied to the absorption refrigeration machine for cooling; the biomass-solar coupled BSC system model is as follows: Where: P BSC,e,t , P BSC,h,t and P BSC,c,t are the electrical power, thermal power and cooling power output by BSC at time t respectively; P BSC,b,t is the total power input to BSC at time t; P HRB,h,t P is the heat energy supplied to the refrigerator at time t; HRB,tc,t P is the heat energy supplied to the gasification pool; TC,h,t The thermal energy that supplies the heat load to the solar collector at time t; η BSC,e , η BSC,h and η BSC,c are the electrical, thermal and cooling efficiencies of BSC output respectively; P GT,h,t is the energy required by the biomass gasification pool at time t; The operation constraints and ramp constraints of the biomass-solar coupled BSC system model are as follows: Where: and They are the upper and lower limits of BSC input power respectively; and The upper and lower limits of BSC climbing power; and The upper and lower limits of the input absorption chiller power; and The upper and lower limits of the input absorption chiller climbing power; and are the upper and lower limits of the heat energy delivered to the gasification pool, respectively; and They are respectively the upper and lower limits of the climbing power of the gasification pool.

4. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 2 is characterized in that: (2) The power-to-gas (P2G) and carbon capture (CCS) coupling equipment model is constructed, including: The power-to-gas (P2G) and carbon capture (CCS) coupling equipment can absorb excess wind and solar energy and reduce CO2 emissions. The power-to-gas (P2G) and carbon capture (CCS) coupling model is as follows: 1) Electrolyzer EL Where: P EL,e,t is the electrical energy input to EL at time t; is the hydrogen energy of EL input at time t; η EL is the energy conversion efficiency of EL; are the upper and lower limits of the input EL power, respectively; They are the upper and lower limits of EL climbing power respectively; 2) Methane Reactor MR Where: is the hydrogen energy input into MR at time t; P MR,g,t is the gas power output by MR at time t; η MR is the energy conversion efficiency of MR; are the upper and lower limits of hydrogen power input to MR, respectively; They are the upper and lower limits of MR climbing power respectively; 3) Hydrogen fuel cell HFC Where: is the hydrogen power input to HFC at time t; P HFC,e,t is the HFC output power at time t; η HFC The efficiency of HFC energy conversion; are the upper and lower limits of the input HFC hydrogen power, respectively; They are the upper and lower limits of HFC climbing power respectively; 4) Carbon capture CCS Considering the problem of coupling failure between power-to-gas (P2G) and carbon capture CCS due to insufficient wind and solar energy output, a CO2 storage tank is introduced into carbon capture CCS to strengthen the coupling between power-to-gas (P2G) and carbon capture CCS. CO2 mainly comes from gas boilers and biomass-solar coupling system BSC. The carbon capture CCS model is as follows: Where: P CCS,t , P CCS,f,t and P CCS,o,t are the total energy consumption, fixed energy consumption and operating energy consumption of CCS at time t respectively; is the mass of CO2 captured by CCS at time t; ε CCS is the energy consumption coefficient of CCS; and are the CO2 masses produced by GB and BSC at time t; ω CCS Efficiency of capturing CO2 for CCS; and The amount of CO2 provided and the amount of CO2 stored by CCS required by MR at time t, respectively; Among them: The operating power constraint and ramp power constraint of carbon capture CCS are as follows: Where: They are the upper and lower limits of CCS energy consumption respectively; They are the upper and lower limits of CCS climbing energy consumption respectively.

5. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 2 is characterized in that: (3) Construct an energy conversion device model, including: The energy conversion equipment includes electric boiler EB, electric refrigerator ERU and gas boiler GB. Electric boiler EB is used to convert electrical energy into heat energy, electric refrigerator ERU converts electrical energy into cold energy, and gas boiler GB burns natural gas to generate heat energy. Its model is as follows: 1) Electric boiler EB Where: P EB,e,t is the electric power consumed by EB at time t; P EB,h,t is the thermal power generated by EB at time t; η EB is the heating efficiency of EB; They are the upper and lower limits of EB power consumption respectively; The upper and lower limits of EB climbing power; 2) Electric Refrigerator ERU Where: P ERU,e,t is the electric energy consumed by ERU at time t; P ERU,c,t is the cooling energy generated by ERU at time t; η ERU is the ERU cooling efficiency; The upper and lower limits of the input ERU electric power; They are the upper and lower limits of ERU climbing power respectively; 3) Gas boiler GB Where: P GB,g,t , P GB,h,t They represent the input gas power and output thermal power of GB at time t respectively; η GB is GB energy conversion efficiency; The upper and lower limits of GB input power; The upper and lower limits of climbing power.

6. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 2 is characterized in that: The energy storage device model constructed in (4) specifically includes: Multi-energy storage includes electric energy storage, thermal energy storage, cold energy storage and hydrogen energy storage. Its general model is as follows: 1) Charging and discharging power and state constraints: Where: x is the type of energy storage device, e, h, c and H2 represent electric, heat, cold and hydrogen energy storage respectively; P x,cha,t , P x,dis,t Respectively represent the charging and discharging power of energy storage device x at time t; I x,cha,t ,I x,dis,t They represent the charging and discharging states of the energy storage device x at time t respectively; and Respectively represent the upper and lower limits of the charging power of the energy storage device x; and They represent the upper and lower limits of the energy storage device x’s discharge power respectively; 2) Energy storage state continuity constraint: Where: S x,t is the storage capacity at time t; δ x is the self-loss coefficient of the energy storage device; η x,cha , η x,dis The charging and discharging efficiency of energy storage; is the upper and lower limits of energy storage capacity; S x，1 is the storage capacity of energy storage device x at time 1; S x，24 is the storage capacity of energy storage device x at 24 hours.

7. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 1 is characterized in that: In step 2, a user-side electricity, heat and cold multi-load demand response model is established, which specifically includes: (1) Price-based demand response Since different types of loads have different sensitivities to the same electricity price signal, the price-based demand response load is divided into curtailable load and transferable load. The price-based demand response is modeled using the electricity price and electricity elasticity matrix method to express the relationship between the user's electricity consumption behavior and electricity price changes. The expression is as follows: Where: e t,j is the elastic coefficient of the electric load at time i to time j; ΔP i and P i 0 are the load change and initial load at time i; Δρ j and are the electricity price change rate and initial electricity price at time j respectively; For electricity, heating and cooling loads, according to their respective time-of-use prices, the elasticity coefficient between the load at time i and the price at time j for the same load is defined as follows: Where: and P L,i are the initial load at time i and the load after the response price changes respectively; and ρ j are the initial price and the price after response respectively; when i=j, it is E L (i) is called the self-elasticity coefficient, which indicates the change of load in period i relative to the change of price in the same period; when i≠j, it is called the cross-elasticity coefficient, which indicates the change of load in period i relative to the change of price in period j. In general, E L (i)≤0, E L (i,j)≥0; The change of load in period i for time-of-use price is as follows: Where: P L (i) and P L，0 (i) represents the load after the response price change and the initial load at time i respectively; E L (i,j) is the cross elasticity coefficient, which indicates the change in load in period i for the change in price in period j; ρ0(j) and ρ(j) represent the initial electricity price and the change in electricity price at time j, respectively; Let λ L (i) After the time-of-use price is expressed, the load change rate of the user in the i-th period is as follows: Among them, It is called the price fluctuation ratio, which describes the price fluctuation caused by the time-sharing price and takes into account the transfer and reduction of load. L (i) is: Where: The first term E L (i,j)k(j) is the load transferred from time i to time j; the second term E L (i)k(i) is the load that can be reduced at time i; E L (i) is called the self-elasticity coefficient, which indicates the change in load in period i relative to the change in price in the same period; k(i) is the price fluctuation ratio, which indicates the price fluctuation caused by the time-sharing price. Therefore, the load change rate between the peak, flat and valley periods can be obtained as Where: T f , T p and T g Peak, flat and valley periods divided according to the intraday price; fp , fg and λ pg They are respectively the shift from peak time to normal time, the shift from peak time to valley time, and the shift from normal time to valley time; ff , pp and λ gg Represents the load reduction during peak hours, normal hours and valley hours respectively; k f , k p and k g are the price fluctuation ratios during peak hours, normal hours and valley hours respectively; E L (i) is the elastic coefficient at time i; E L (i,j) is the cross elastic coefficient at time i to time j; In summary, the load after implementing price-based demand response is Where: and P load are the load after implementing price-based demand response and the initial load; P f , P p and P g They are price-based demand response pre-peak, flat and valley loads; (2) Alternative demand response Since the substitution relationship between heating load and cooling load is small, the substitution between heating load and cooling load is not considered. The substitution demand response model is as follows: Where: P eh,t and P ec,t are the electric power replacing the heat power and the cold power at time t respectively; P he,t P is the thermal power that replaces the electrical power at time t; ce,t is the cooling power that replaces the electric power at time t; e ,ω h and ω c are the load proportions of electricity, heat and cooling load transfer respectively; ω re ,ω rh and ω rc are the proportions of electricity, heat and cooling loads that can be transferred to replacement; η eh is the electrical-to-heat coefficient; η ec is the electricity-to-cooling coefficient; and They are the electricity, heating and cooling loads after price-based demand response at time t; (3) Demand response results After price-based demand response and substitution-based demand response, the final electricity, heating and cooling loads are: Where: P load,e,t , P load,h,t and P load,c,t They are the electric load, heating load and cooling load after participating in demand response DR.

8. The cooling, heating and electricity optimization scheduling method of a park integrated energy system based on biomass energy and demand response according to claim 1 is characterized in that: In step 3, a cooling, heating and electricity optimization dispatching model of the park comprehensive energy system considering biomass energy and demand response is established, and a step-by-step carbon trading mechanism is introduced to limit carbon emissions, including: (1) Objective function Comprehensively consider the energy purchase cost C of the park's integrated energy system IES buy , wind and solar curtailment costs C cut , carbon trading cost C CO2 and operation and maintenance costs C om ; Construct with total cost C total The minimum low-carbon economy targets are as follows: 1) Energy purchase cost C buy Energy purchase cost C buy The main costs are electricity and gas purchase costs, and the model is as follows: Where: α e and α g are the unit prices of electricity and gas respectively; P e,buy,t , P g,buy,t They are the power of electricity and gas purchased at time t respectively; T is the time period; 2) Cost of curtailing wind and solar power C cut Where: wt , δ pv are the penalty factors for wind and solar abandonment, respectively; P wt,cut,t , P pv,cut,t They are wind power abandonment and solar power abandonment at time t respectively; 3) Carbon trading costs Establish a ladder-type carbon trading mechanism model, the model is as follows: Where: E IES 、E e,buy 、E BSC and E GB They are the total quota of IES, external power purchase quota, BSC system quota and GB quota; e , χ g and χ b are the carbon emission quota coefficients per unit of electricity, natural gas and biomass gas consumption; E IES,a 、E e,buy,a , P BSC,b,a and P GB,h,a are the actual carbon emissions of IES, power purchase from higher authorities, BSC system and GB; E MR,a is the amount of CO2 actually absorbed by MR; E IES,t is the carbon emission trading amount; H is the coefficient corresponding to different carbon emission ranges; E IES,t is the carbon emission trading amount at time t; P e,buy，t is the amount of electricity purchased from the upper power grid at time t; P BSC,b，t is the input power of the biomass-solar coupling system at time t; P GB,h，t is the thermal power output of the gas boiler at time t; 4) Operation and maintenance costs Where: β z is the unit operation and maintenance cost of the zth type of equipment; P z,t is the output power of the zth device at time t; z is the type of device; (2) Constraints 1) Wind and solar power output constraints Where: and are the output limits of photovoltaic units and wind turbine units respectively; P pv,t is the photovoltaic power generation power at time t; P wt,t is the wind turbine power generation at time t; 2) Restrictions on purchasing electricity and gas Where: and They are the upper and lower limits of the purchased power respectively; and are the upper and lower limits of gas purchase power respectively; P e,buy,t P is the power purchased from the upper grid at time t; g,buy,t The gas power purchased from the superior gas network at time t; 3) Electric power balance constraints Where: P wt,t and P pv,t are the power output to the wind turbine and photovoltaic at time t respectively; P BSC,e,t P is the output power of the biomass-solar coupling system at time t; HFC,e,t P is the output power of the hydrogen fuel cell at time t; EL,e,t P is the power consumed by the electrolytic cell at time t; CCS,t P is the power consumed by the carbon capture equipment at time t; ERU,e,t and P EB,e,t P is the power consumed by the electric refrigerator and electric heating equipment at time t respectively; load,e,t is the electric load after participating in demand response at time t; P e,cha,t and P e,dis,t are the charging power and discharging power of the energy storage battery at time t respectively; 4) Thermal power balance constraints P GB,h,t +P BSC,h,t +P TC,h,t +P EB,h,t +P h,dis,t =P load,h,t +P h,cha,t (34); Where: P GB,h,t P is the thermal power input to the gas boiler at time t; BSC,h,t P is the thermal power output of the biomass-solar coupling system at time t; TC,h,t P is the thermal power output of the solar collector at time t; EB,h,t P is the thermal power output of the electric heating equipment at time t; load,h,t is the heat load after participating in demand response at time t; P h,cha,t and P h,dis,t are the charging power and releasing power of the heat storage device at time t respectively; 5) Cold power balance constraint P BSC,c,t +P ERU,c,t +P c,dis,t =P load,c,t +P c,cha,t (35); Where: P BSC,c,t P is the cooling power output of the biomass-solar coupling system at time t; ERU,c,t P is the cooling power output of the electric refrigerator at time t; load,c,t is the cooling load after participating in demand response at time t; P c,cha,t and P c,dis,t are the cooling power and cooling discharge power of the cold storage equipment at time t respectively; 6) Natural gas power balance constraints P g,buy,t +P MR,g,t =P GB,g,t (36); Where: P g,buy,t P is the gas power purchased from the upper gas network at time t; MR,g,t is the output power of the methane reactor at time t; P GB,g,t is the gas consumption power of the gas boiler at time t; 7) Hydrogen power balance constraints Where: is the hydrogen production power of the electrolyzer at time t; is the hydrogen power consumption of the hydrogen fuel cell at time t; is the hydrogen power consumption of the methane reactor at time t; and are the hydrogen charging power and hydrogen discharging power of the hydrogen storage equipment at time t respectively.

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