A park integrated energy system cold-heat-power optimization scheduling method based on biomass energy and demand response

Abstract

The application discloses a cold-heat-power optimization scheduling method for a biomass energy and demand response based park integrated energy system, and comprises the following steps: constructing a park integrated energy system based on biomass-solar coupling; constructing a user side electricity, heat and cold multi-element load demand response model; constructing a cold-heat-power optimization scheduling model for the park integrated energy system based on biomass energy and demand response, and introducing a ladder type carbon trading mechanism to limit carbon emission. The cold-heat-power optimization scheduling method for the park integrated energy system based on biomass energy and demand response can solve the problems of insufficient energy supply, high carbon emission and high cost of some park integrated energy systems.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization scheduling, and in particular to an optimized scheduling method for cooling, heating and power systems in industrial parks based on biomass energy and demand response. Background Technology

[0002] Socioeconomic development has led to a surge in energy demand and a decline in fossil fuel reserves. The consumption of fossil fuels has caused environmental problems, with massive carbon emissions exacerbating the greenhouse effect and contributing to global warming. Against this backdrop, increasing the use of non-fossil energy and reducing carbon emissions are critical issues that urgently need to be addressed.

[0003] Integrated energy systems (IES) integrate multiple energy sources for joint supply, meeting the multi-energy load demands of end users and serving as a crucial support for promoting the utilization of non-fossil energy and reducing carbon emissions. Currently, most literature focuses on the low-carbon economic operation of IES, such as: Reference 1: "Analysis of Optimal Operation of Regional Integrated Energy Systems Based on Repeated Game Theory [J]. Automation of Electric Power Systems, 2019, 43(14): 81-89.", Reference 2: "Low-Carbon Optimal Scheduling of Integrated Energy Systems Considering Hypercarbon Demand Response [J]. Power System Technology, 2024, 48(05): 1863-1872.", and Reference 3: "Carbon Capture-Electricity-Gas Coordinated Optimal Scheduling Model Considering Spatiotemporal Diffusion and Carbon Sink [J]. Automation of Electric Power Systems, 2023, 47(02): 15-23." These references significantly improve the low-carbon economic operation of integrated energy systems, but they do not consider user-side energy consumption behavior and source-side energy supply structure, thus affecting the low-carbon economic operation of the system.

[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 economical system operation. For example, references 4 ("Optimal scheduling of integrated energy systems considering tiered carbon trading mechanisms" [J]. China Electric Power, 1-12) and 5 ("Optimal scheduling strategy of multi-microgrid integrated energy systems based on integrated demand response and master-slave game" [J]. Proceedings of the CSEE, 2021, 41(4): 1307-1321, 1538)) establish 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 simple, and it fails to effectively improve the economic benefits of the system.

[0005] Biomass is a renewable organic matter produced by green plants through photosynthesis, and can be categorized into multiple sectors including agriculture, forestry, aquaculture, and waste management. Biomass energy has a shorter carbon emission cycle than fossil fuels, allowing emitted CO2 to complete its carbon cycle naturally, resulting in near-zero net CO2 emissions; therefore, biomass is called a carbon-neutral fuel. Biomass resources are characterized by their widespread availability, short regeneration cycle, low pollution, and high energy potential. Utilizing biomass through advanced energy processing methods can help solve the IES (Environmentally, Environmentally, and Biomass) energy supply problems in agricultural parks or remote rural areas, improve agricultural production and lifestyles, and promote the construction of modern agricultural parks.

[0006] Most current literature studies the low-carbon economic characteristics of biomass and solar collector coupling, as well as the integration of biomass and traditional energy sources into integrated energy systems (IES). For example, literature 6: "Research on the Integration of Biomass with Solar and Geothermal Energy in Building-CCHP Systems [D]. Hunan University, 2018," literature 7: "Research on Two-Stage Robust Capacity Configuration of Biomass Gas-Solar-Wind Integrated Energy System [D]. Shandong University, 2022," and literature 8: "Optimization of Biomass Integrated Energy System Considering Tiered Carbon-Green Certificate Joint Trading and Demand Response [J]. Electric Power Science and Engineering, 2024, 40(07):10-25." These literatures can fully utilize biomass resources and significantly reduce carbon emissions and total system cost. However, they do not consider that solar collectors only supply heat to the biomass gasification pond, resulting in energy waste, and they do not consider the impact of P2G and CCS coupling equipment on the participation of the BSC system in IES. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method for optimizing the scheduling of cooling, heating and power in 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] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A method for optimizing the scheduling of cooling, heating, and power in a park's integrated energy system based on biomass energy and demand response includes 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 demand response model for multiple loads (electricity, heat, and cooling) on ​​the user side;

[0012] Step 3: Construct an optimized scheduling model for the integrated energy system of the park based on biomass energy and demand response, and introduce a tiered carbon trading mechanism to limit carbon emissions.

[0013] Step 1 involves constructing a comprehensive energy system for the park based on biomass-solar coupling, specifically including:

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

[0015] (2) Construct a coupled device model of P2G and CCS for electro-gas conversion;

[0016] (3) Construct a model of the energy conversion device;

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

[0018] (1) Constructing a biomass-solar energy coupling device model, specifically including:

[0019] 1) Biomass gasification model

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

[0021]

[0022] In the formula: η b,t and T t These represent the gas production rate and temperature of the gasification pool at time t, respectively; T0 is the optimal temperature of the biomass gasification pool, typically taken as 35℃; P BSC,b,t and m bio,t t represents the biomass input power and biomass mass flow rate at time t, respectively; LHV represents the lower heating value of biomass.

[0023] 2) Biomass gasification pond model

[0024] The gasification efficiency of a biomass gasification tank is closely related to both the tank temperature and the ambient temperature. Considering that solar collectors and heat exchangers jointly provide the energy to maintain a suitable temperature in the tank, the heat balance formula and heat dissipation model within the biomass gasification tank are as follows:

[0025]

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

[0027] 3) Solar collector model

[0028] Considering that the solar collectors prioritize supplying heat to the gasification pool, and that excess heat energy can be directly supplied to the heat load, the solar collector model is as follows:

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

[0030] In the formula: P TC,t η TC,t and G TC,t Let n be the output power, conversion efficiency, and solar radiation intensity of the solar collector at time t; TC Q represents the number of solar collectors. TC This refers to the area of ​​a single solar collector.

[0031] 4) Biomass-Solar Coupled BSC System Model

[0032] Part of the biomass gas is used to generate electricity through combustion in an internal combustion engine, and part is used to generate heat through a heat exchanger. The generated heat energy is used partly to meet the heat load, partly to supply the gasification pond, and partly to supply the absorption chiller for refrigeration. The biomass-solar coupled BSC system model is as follows:

[0033]

[0034] In the formula: P BSC,e,t P BSC,h,t and P BSC,c,t Let P represent the electrical power, thermal power, and cooling power output by the BSC at time t; BSC,b,t P is the total power input to the BSC at time t; HRB,h,t P is the heat energy supplied to the chiller at time t; HRB,tc,t To supply heat energy to the gasification pool; P TC,h,t The heat energy supplied to the solar collector at time t is the heat load; η BSC,e η BSC,h and η BSC,c These represent the electrical, thermal, and cooling efficiencies of the BSC output, respectively; P GT,h,t Let t be the energy required for the biomass gasification cell at time t.

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

[0036]

[0037] In the formula: and These are the upper and lower limits of the BSC input power, respectively; and These are the upper and lower limits of the BSC ramp power. and These are the upper and lower limits of the input power for the absorption chiller; and These are the upper and lower limits of the ramp power input for the absorption chiller; and These are the upper and lower limits of the heat energy supplied to the gasification tank, respectively. and These are the upper and lower limits of the ramping power of the gasification tank, respectively.

[0038] (2) Constructing a coupled device model of P2G and CCS for electro-gas conversion, specifically including:

[0039] The coupled power-to-gas (P2G) and carbon capture system (CCS) equipment can absorb excess wind and solar energy and reduce CO2 emissions. The coupling model of P2G and CCS is as follows:

[0040] 1) Electrolytic cell EL

[0041]

[0042] In the formula: P EL,e,t Let t be the electrical energy input to EL; η is the hydrogen energy input to EL at time t; EL The energy conversion efficiency of EL; These are the upper and lower limits of the input EL power, respectively; These represent the upper and lower limits of EL ramp power, respectively.

[0043] 2) Methane reactor MR

[0044]

[0045] In the formula: P represents the hydrogen energy input to MR at time t. MR,g,t η is the gas power output of MR at time t; MR The energy conversion efficiency of MR; These are the upper and lower limits of the hydrogen power input to MR, respectively; These represent the upper and lower limits of MR ramp power, respectively.

[0046] 3) Hydrogen fuel cells (HFC)

[0047]

[0048] In the formula: P represents the hydrogen power input to the HFC at time t. HFC,e,t η is the output electrical power of the HFC at time t; HFC The efficiency of HFC energy conversion; These are the upper and lower limits of the input HFC hydrogen power, respectively; These represent the upper and lower limits of HFC ramp power, respectively.

[0049] 4) Carbon Capture System (CCS)

[0050] Considering the coupling failure of P2G (Power to Gas) and CCS (Carbon Capture System) due to insufficient wind and solar energy output, a CO2 storage tank is introduced into the CCS to strengthen the coupling between P2G and CCS. The CO2 mainly comes from the gas-fired boiler and the biomass-solar coupled system (BSC). The CCS model is as follows:

[0051]

[0052] In the formula: P CCS,t P CCS,f,t and P CCS,o,t These represent the total energy consumption, fixed energy consumption, and operating energy consumption of the CCS at time t; ε represents the mass of CO2 captured by the CCS at time t; CCS The energy consumption coefficient of CCS; and The CO2 masses produced by GB and BSC at time t are respectively; ω CCS The efficiency of CO2 capture by CCS; and The amounts of CO2 supplied by CCS and the amount of CO2 sealed are respectively the amount required by MR at time t.

[0053] The operating power constraints and ramp-up power constraints for carbon capture CCS are as follows:

[0054]

[0055] In the formula: These are the upper and lower limits of CCS energy consumption, respectively. These represent the upper and lower limits of CCS ramp energy consumption, respectively.

[0056] (3) Constructing an energy conversion device model specifically includes:

[0057] The energy conversion equipment includes an electric boiler (EB), an electric chiller (ERU), and a gas-fired boiler (GB). The electric boiler (EB) converts electrical energy into heat energy, the electric chiller (ERU) converts electrical energy into cold energy, and the gas-fired boiler (GB) burns natural gas to generate heat energy. Its model is as follows:

[0058] 1) Electric Boiler EB

[0059]

[0060] In the formula: P EB,e,tP represents the electrical power consumed by EB at time t. EB,h,t η is the thermal power generated by EB at time t; EB The heating efficiency of EB; These are the upper and lower limits of the power consumption of EB, respectively. These are the upper and lower limits of EB ramp power.

[0061] 2) Electric Refrigeration Unit (ERU)

[0062]

[0063] In the formula: P ERU,e,t P represents the electrical energy consumed by the ERU at time t. ERU,c,t η represents the cold energy generated by ERU at time t; ERU For ERU cooling efficiency; These are the upper and lower limits of the input ERU electrical power; These are the upper and lower limits of ERU ramp power, respectively.

[0064] 3) Gas-fired boilers GB

[0065]

[0066] In the formula: P GB,g,t P GB,h,t η represents the input gas power and output thermal power of GB at time t, respectively; GB GB energy conversion efficiency; These are the upper and lower limits of the GB input power; These are the upper and lower limits of the climbing power.

[0067] (4) The specific steps for constructing an energy storage device model include:

[0068] Multi-element energy storage includes electrical energy storage, thermal energy storage, cold energy storage, and hydrogen energy storage, and its general model is as follows:

[0069] 1) Charge / discharge power and state constraints:

[0070]

[0071] In the formula: x represents the type of energy storage device, represented by e, h, c, and H2 for electrical, thermal, cold, and hydrogen energy storage, respectively; P x,cha,t P x,dis,t Represent the charging and discharging power of energy storage device x at time t, respectively; I x,cha,t I x,dis,t Let represent the charging and discharging states of energy storage device x at time t, respectively; and These represent the upper and lower limits of the charging power of energy storage device x, respectively. and These represent the upper and lower limits of the energy release power of the energy storage device x, respectively.

[0072] 2) Energy storage state continuity constraints:

[0073]

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

[0075] Step 2 involves establishing a multi-load demand response model for electricity, heat, and cooling on the user side, specifically including:

[0076] (1) Price-based demand response

[0077] Because different types of loads have varying sensitivities to the same electricity price signal, price-based demand response loads are categorized into reduceable loads and transferable loads. The electricity price-electricity elasticity matrix method is commonly used to model price-based demand response, representing the relationship between user electricity consumption behavior and electricity price changes. Its expression is as follows:

[0078]

[0079] In the formula: e t,j Let ΔP be the elasticity coefficient of the electrical load at time i with respect to time j; i and P i 0 Let Δρ be the change in electrical load at time i and the initial electrical load, respectively. j and Let $j$ be the rate of change of electricity price at time $j$ and the initial electricity price, respectively.

[0080] For electricity, heat, and cooling loads, the elasticity coefficient between the load at time i and the price at time j, based on their respective time-of-use prices, is defined as follows:

[0081]

[0082] In the formula: and P L,i These represent the initial load at time i and the load after the response price change, respectively. and ρ j These are the initial price and the price after the response, respectively; when i = j, it is E. L(i), called the self-elasticity coefficient, represents the change in load during period i relative to the change in price during the same period; when i ≠ j, it is called the cross-elasticity coefficient, representing the change in load during period i relative to the change in price during period j. Generally, E L (i)≤0, E L (i,j)≥0.

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

[0084]

[0085] In the formula: P L (i) and P L，0 (i) represent the load after the price change at time i and the initial load, respectively; E L (i, j) is the cross-elasticity coefficient, representing the change in load during time period i and the change in price during time period j; ρ0(j) and ρ(j) represent the initial electricity price and the change in electricity price at time j, respectively.

[0086] Let λ L (i) represents the user's load change rate in the i-th time period after time-of-use pricing:

[0087]

[0088] Among them, Called the price fluctuation ratio, it describes the magnitude of price fluctuations caused by time-of-day pricing, and takes into account load shifting and reduction. λ L (i) is:

[0089]

[0090] In the formula: the first term E L (i, j)k(j) represents the load transferred from time i to time j; the second term E L (i)k(i) represents the load that can be reduced at time i. E L (i) is called the self-elasticity coefficient, which represents the change in load during time period i relative to the change in price during the same time period; k(i) is the price fluctuation ratio, which represents the magnitude of price fluctuation caused by time-of-use prices.

[0091] Therefore, the load variation rate between each pair of peak, flat, and valley periods can be obtained.

[0092]

[0093] In the formula: T f T p and T g Peak, flat, and trough periods are defined based on time-of-use prices; λ fp , λ fgand λ pg These represent the shifts from peak hours to normal hours, from peak hours to trough hours, and from normal hours to trough hours, respectively; λ ff , λ pp and λ gg These represent the load reduction during peak, normal, and off-peak hours, respectively; k f k p and k g These represent the price fluctuation ratios for peak, normal, and off-peak hours, respectively; E L (i) represents the self-elasticity coefficient at time i; E L (i,j) represents the cross-elasticity coefficient at time i to time j.

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

[0095]

[0096] In the formula: and P load These represent the load after implementing price-based demand response and the initial load, respectively; P f P p and P g These are the peak and valley loads of price-based demand response, respectively.

[0097] (2) Alternative demand response

[0098] Since the substitution relationship between heat load and cooling load is relatively small, and the substitution between heat load and cooling load is not considered, the alternative demand response model is as follows:

[0099]

[0100] In the formula: P eh,t and P ec,t P represents the electrical power replacing the heat power and the cold power at time t, respectively. he,t P represents the heat power that replaces the electrical power at time t. ce,t ω represents the cooling power that replaces the electrical power at time t; e ω h and ω c These represent the proportions of load transfer for electricity, heat, and cooling loads, respectively; ω re ω rh and ω rc These represent the proportions of electrical, heating, and cooling loads that can be replaced; η eh η is the electrothermal conversion coefficient. ec The coefficient of performance for electricity to cooling; and These represent the electricity, heat, and cooling loads after the price-based demand response at time t.

[0101] (3) Demand response results

[0102] After considering price-based and substitution-based demand responses, the final electricity, heat, and cooling loads are:

[0103]

[0104] In the formula: P load,e,t P load,h,t and P load,c,t These are the electrical load, thermal load, and cooling load that participate in demand response (DR).

[0105] Step 3 establishes an optimized scheduling model for the park's integrated energy system (cooling, heating, and power) that considers biomass energy and demand response, and introduces a tiered carbon trading mechanism to limit carbon emissions, specifically including:

[0106] (1) Objective function

[0107] Taking into account the energy purchase cost C of the park's integrated energy system IES buy Cost of curtailing wind and solar power C cut Carbon trading costs and maintenance costs C om ; Construct a system with a 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, as shown in the model below:

[0111]

[0112] In the formula: α e and α g These are the unit prices for electricity and gas purchases, respectively; P e,buy,t P g,buy,t These represent the power and gas purchased at time t, respectively; T is the time period.

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

[0114]

[0115] Where: δ wt δ pv These are the wind curtailment and solar curtailment penalty factors, respectively; P wt,cut,t P pv,cut,t These represent the power of wind and solar power curtailed at time t, respectively.

[0116] 3) Carbon trading costs

[0117] A tiered carbon trading mechanism model is established, as follows:

[0118]

[0119] In the formula: E IES E e,buy E BSC and E GB These are the total IES quota, external power purchase quota, BSC system quota, and GB quota, respectively; χ e , χ g and χ b These are the carbon emission allowance coefficients for units of electricity, natural gas, and biomass gas consumption, respectively. IES,a E e,buy,a P BSC,b,a and P GB,h,a These are the actual carbon emissions from IES, upstream power purchase, BSC system, and GB, respectively; E MR,a E represents the actual amount of CO2 absorbed by the MR. IES,t H represents the carbon emissions trading amount; H is the coefficient corresponding to different carbon emission ranges; E IES，t P represents the carbon emissions trading amount at time t; e,buy，t P represents the amount of electricity purchased from the upper-level power grid at time t; BSC,b，t P represents the input power of the biomass-solar coupled system at time t. GB,h，t Let t be the output thermal power of the gas-fired boiler.

[0120] 4) Operation and maintenance costs

[0121]

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

[0123] (2) Constraints

[0124] 1) Wind and solar power output constraints

[0125]

[0126] In the formula: and These are the upper limits of output for photovoltaic units and wind turbine units, respectively; P pv,t P represents the photovoltaic power generation at time t. wt,t Let t be the power generation capacity of the wind turbine;

[0127] 2) Constraints on electricity and gas purchases

[0128]

[0129] In the formula: and These are the upper and lower limits for the amount of electricity that can be purchased; and These represent the upper and lower limits of gas purchase capacity, respectively; P e,buy,t P represents the power purchased from the upstream power grid at time t. g,buy,t Let t be the power of the gas purchased from the upstream gas network.

[0130] 3) Power balance constraints

[0131]

[0132] In the formula: P wt,t and P pv,t The output power to the wind turbine and photovoltaic system at time t are respectively; P BSC,e,t P represents the output electrical power of the biomass-solar coupled system at time t. HFC,e,t P represents the output electrical power of the hydrogen fuel cell at time t. EL,e,t P represents the electrical power consumed by the electrolytic cell at time t. CCS,t P represents the electrical power consumed by the carbon capture device at time t. ERU,e,t and P EB,e,t The electrical power consumed by the electric chiller and the electric heating equipment at time t are respectively; P load,e,t P represents the electrical load that participates in demand response at time t; e,cha,t and P e,dis,t These represent the charging power and discharging power of the energy storage battery at time t, respectively.

[0133] 4) Thermal power balance constraint

[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] In the formula: P GB,h,t P represents the input thermal power of the gas-fired boiler at time t. BSC,h,t P represents the output thermal power of the biomass-solar coupled system at time t. TC,h,t P represents the output thermal power of the solar collector at time t. EB,h,t P represents the output heat power of the electric heating device at time t. load,h,t P represents the heat load that participates in the demand response at time t; h,cha,t and P h,dis,t These represent the heat charging power and heat dissipation power of the thermal storage device 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] In the formula: P BSC,c,t P represents the output cooling power of the biomass-solar coupled system at time t. ERU,c,t P is the cooling power output of the electric chiller at time t. load,c,t P represents the cooling load that participates in demand response at time t; c,cha,t and P c,dis,t These represent the charging and discharging 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] In the formula: P g,buy,t P is the power of purchasing gas from the upstream gas network at time t; MR,g,t P is the output power of the methane reactor at time t. GB,g,t Let t be the gas consumption power of the gas boiler.

[0142] 7) Hydrogen power balance constraint

[0143]

[0144] In the formula: P EL,H2,t P represents the hydrogen production power of the electrolyzer at time t. HFC,H2,t P represents the hydrogen power consumed by the hydrogen fuel cell at time t. MR,H2,t P represents the hydrogen consumption power of the methane reactor at time t. H2,cha,t and P H2,dis,t These represent the hydrogen charging power and hydrogen discharging power of the hydrogen storage device at time t, respectively.

[0145] This invention provides a method for optimized scheduling of cooling, heating, and power in a park's integrated energy system based on biomass energy and demand response, which has the following technical advantages:

[0146] 1) Due to insufficient energy supply in some integrated energy systems in agricultural parks, high carbon emissions and high costs are prominent issues, and wind and solar power curtailment is quite serious, significantly impacting the stability and economic efficiency of the integrated energy systems. Therefore, step 1 of this invention constructs a biomass-solar coupled system by combining renewable and clean biomass energy with solar collectors, and introduces an electricity-to-gas (P2G) device and a carbon capture system (CCS). The P2G device produces hydrogen through water electrolysis, effectively absorbing new energy sources such as wind and solar power. The CCS captures carbon dioxide, which is then fed into a methane reactor to produce methane, effectively reducing carbon emissions in agricultural parks. Finally, an integrated energy system considering biomass-solar coupling is constructed. This system not only effectively alleviates the energy supply shortage problem in agricultural park integrated energy systems but also significantly improves the low-carbon economic benefits of agricultural parks.

[0147] 2) Because the energy demand of the industrial park can be guided by energy market prices and incentive response mechanisms to adjust user demand, but this capacity is not being utilized. Therefore, step 2 of this invention utilizes the participation of industrial park load in price-based demand response and substitution-based demand response to achieve load reduction, time shifting, and substitution. This helps alleviate the peak-valley difference in the power grid and improves the economic efficiency of the industrial park's integrated energy system.

[0148] 3) To seek a low-carbon and economical operation scheme for the integrated energy system of the park. To this end, step 3 of the present invention establishes an optimized scheduling model for the integrated energy system of the park that considers biomass energy and demand response, and introduces a tiered carbon trading mechanism to further limit carbon emissions. This model can effectively seek the optimal operation scheme for the integrated energy system of the agricultural park.

[0149] 4) In response to the shortcomings mentioned in the background section, this application makes the following improvements:

[0150] ① To address the shortcomings of existing integrated energy systems (IES) that fail to consider user-side energy consumption behavior and source-side energy supply structure, thus impacting the system's low-carbon economic operation, this invention achieves flexible adjustment through the participation of park load in price-based and substitution-based demand responses. By combining biomass resources with solar collectors, a biomass-solar coupled system is constructed to provide electricity, heat, and cooling energy for the park's integrated energy system, thereby improving the low-carbon economic benefits of the park's integrated energy system.

[0151] ② To address the shortcomings of existing integrated energy systems (IES) that only consider demand response, are relatively simple, and fail to effectively improve system economic efficiency, this invention introduces an electricity-to-gas (P2G) device, a carbon capture system (CCS) device, an electric boiler (EB) device, an electric chiller (ERU) device, a gas boiler (GB) device, and an electric thermal energy storage device into the integrated energy system of the agricultural park. The P2G device produces hydrogen through water electrolysis, effectively absorbing new energy sources such as wind and solar power. The CCS device captures carbon dioxide and feeds it into a methane reactor to produce methane, effectively reducing carbon emissions in the agricultural park. The EB device converts electrical energy into heat, the ERU converts electrical energy into cold energy, and the gas boiler converts natural gas into heat, achieving mutual energy conversion. The electric thermal energy storage device effectively realizes energy time-shifting, which is beneficial to improving the low-carbon economic benefits of the agricultural park.

[0152] ③ To address the shortcomings of only considering the supply of heat from solar collectors to the biomass gasification pond, which leads to energy waste, and neglecting the impact of P2G and CCS coupling equipment on the BSC system's participation in the Integrated Energy System (IES), this invention considers that the solar collectors have the highest output at midday, but the outside temperature is high at this time, and the gasification pond requires less energy, resulting in heat energy waste. Therefore, this invention prioritizes the supply of heat from solar collectors to the gasification pond, and the excess heat energy can be directly used to supply the heat load, reducing energy waste. Furthermore, this invention introduces an electricity-to-gas (P2G) device and a carbon capture and control (CCS) device. The P2G device produces hydrogen through water electrolysis, effectively absorbing new energy sources such as wind and solar power. The CCS device captures carbon dioxide and feeds it into the methane reactor to produce methane, effectively reducing carbon emissions in agricultural parks. Attached Figure Description

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

[0154] Figure 1 This is a framework diagram of an Integrated Energy System (IES).

[0155] Figure 2 This is a schematic diagram of the BSC system.

[0156] Figure 3 The diagram shows the power output of wind and solar power, as well as the electricity, heat, and cooling loads.

[0157] Figure 4 A graph showing the time-of-use electricity price, heating price, and cooling price for users.

[0158] Figure 5 Optimization diagram of electrical load before and after demand response DR.

[0159] Figure 6 Optimization diagram of heat load before and after demand response DR.

[0160] Figure 7Optimization diagram of cooling load before and after demand response DR.

[0161] Figure 8(a) shows the power balance diagram for scenario 1.

[0162] Figure 8(b) shows the power balance diagram for scenario 2.

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

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

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

[0166] Figure 10(b) shows the cooling power balance diagram for scenario 2. Detailed Implementation

[0167] A method for optimizing the scheduling of cooling, heating, and power in a park's integrated energy system based on biomass energy and demand response includes the following steps:

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

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

[0170] 1) Biomass gasification model

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

[0172]

[0173] In the formula: η b,t and T t These represent the gas production rate and temperature of the gasification pool at time t, respectively; T0 is the optimal temperature of the biomass gasification pool, typically taken as 35℃; P BSC,b,t and m bio,t t represents the biomass input power and biomass mass flow rate at time t, respectively; LHV represents the lower heating value of biomass.

[0174] 2) Biomass gasification pond model

[0175] The gas production efficiency of a biomass gasification tank is closely related to both the tank's internal temperature and the ambient temperature. This invention considers using a combination of solar collectors and heat exchangers to provide the energy needed to maintain a suitable temperature in the tank. The heat balance formula and heat dissipation model within the biomass gasification tank are as follows:

[0176]

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

[0178] 3) Solar collector model

[0179] The solar collector's output is highest at midday, but the outside temperature is high at this time, and the gasification pool requires less energy, resulting in wasted heat energy. Therefore, this invention considers that the solar collector prioritizes supplying heat to the gasification pool, and the excess heat energy can be directly supplied to 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] In the formula: P TC,t η TC,t and G TC,t Let n be the output power, conversion efficiency, and solar radiation intensity of the solar collector at time t; TC Q represents the number of solar collectors. TC This refers to the area of ​​a single solar collector.

[0182] 4) Biomass-Solar Coupled BSC System Model

[0183] Part of the biomass gas is used to generate electricity through internal combustion engines, and another part is used to generate heat through heat exchangers. The generated heat energy is used partly to meet the heat load, partly to supply the gasification pond, and partly to supply the absorption chiller for refrigeration. The biomass-solar coupled BSC system model is as follows:

[0184]

[0185] In the formula: P BSC,e,t P BSC,h,t and P BSC,c,t Let P represent the electrical power, thermal power, and cooling power output by the BSC at time t; BSC,b,t P is the total power input to the BSC at time t; HRB,h,t P is the heat energy supplied to the chiller at time t; HRB,tc,t To supply heat energy to the gasification pool; P TC,h,tThe heat energy supplied to the solar collector at time t is the heat load; η BSC,e η BSC,h and η BSC,c These represent the electrical, thermal, and cooling efficiencies of the BSC output, respectively; P GT,h,t Let t be the energy required for the biomass gasification cell at time t.

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

[0187]

[0188] In the formula: and These are the upper and lower limits of the input power of the BSC system, respectively. and The upper and lower limits of ramp power for BSC system; and The upper and lower limits of the input absorption chiller power; and The upper and lower limits of the ramp power of the input absorption chiller; and These are the upper and lower limits of the heat energy supplied to the gasification pool, respectively. and These represent the upper and lower limits of the ramp power for the gasification tank.

[0189] (2) Constructing a model of a coupled electro-gas (P2G) and carbon capture system (CCS)

[0190] The coupled power-to-gas (P2G) and carbon capture system (CCS) can absorb excess wind and solar energy and reduce CO2 emissions. The coupling model for P2G and CCS is as follows:

[0191] 1) Electrolytic cell EL

[0192]

[0193] In the formula: P EL,e,t Let t be the electrical energy input to EL; η is the hydrogen energy input to EL at time t; EL The energy conversion efficiency of EL; These are the upper and lower limits of the input EL power, respectively; These represent the upper and lower limits of EL ramp power, respectively.

[0194] 2) Methane reactor MR

[0195]

[0196] In the formula: P represents the hydrogen energy input to MR at time t.MR,g,t η is the gas power output of MR at time t; MR The energy conversion efficiency of MR; These are the upper and lower limits of the hydrogen power input to MR, respectively; These represent the upper and lower limits of MR ramp power, respectively.

[0197] 3) Hydrogen fuel cells (HFC)

[0198]

[0199] In the formula: P represents the hydrogen power input to the HFC at time t. HFC,e,t η is the output electrical power of the HFC at time t; HFC The efficiency of HFC energy conversion; These are the upper and lower limits of the input HFC hydrogen power, respectively; These represent the upper and lower limits of HFC ramp power, respectively.

[0200] 4) Carbon Capture System (CCS)

[0201] Considering the coupling failure issue between power-to-gas (P2G) and carbon capture system (CCS) due to insufficient wind and solar power output, a CO2 storage tank is introduced into the CCS to strengthen the coupling between P2G and CCS. CO2 mainly comes from gas-fired boilers and BSC systems. The CCS model is as follows:

[0202]

[0203] In the formula: P CCS,t P CCS,f,t and P CCS,o,t These represent the total energy consumption, fixed energy consumption, and operating energy consumption of the CCS at time t; ε represents the mass of CO2 captured by the CCS at time t; CCS The energy consumption coefficient of CCS; and The CO2 masses produced by GB and BSC at time t are respectively; ω CCS The efficiency of CO2 capture by CCS; and The amounts of CO2 supplied by CCS and the amount of CO2 sealed are respectively the amount required by MR at time t.

[0204] The operating power constraints and ramp-up power constraints for carbon capture CCS are as follows:

[0205]

[0206] In the formula: These are the upper and lower limits of CCS energy consumption, respectively. These represent the upper and lower limits of CCS ramp energy consumption, respectively.

[0207] (3) Constructing an energy conversion device model

[0208] Energy conversion equipment mainly includes an electric boiler (EB), an electric chiller (ERU), and a gas-fired boiler (GB). The electric boiler (EB) converts electrical energy into heat energy, the electric chiller (ERU) converts electrical energy into cold energy, and the gas-fired boiler (GB) burns natural gas to generate heat energy. Its model is as follows:

[0209] 1) Electric Boiler EB

[0210]

[0211] In the formula: P EB,e,t P represents the electrical power consumed by EB at time t. EB,h,t η is the thermal power generated by EB at time t; EB The heating efficiency of EB; These are the upper and lower limits of the power consumption of EB, respectively. These are the upper and lower limits of EB ramp power.

[0212] 2) Electric Refrigeration Unit (ERU)

[0213]

[0214] In the formula: P ERU,e,t P represents the electrical energy consumed by the ERU at time t. ERU,c,t η represents the cold energy generated by ERU at time t; ERU For ERU cooling efficiency; The upper and lower limits of the input ERU electrical power; These represent the upper and lower limits of ERU ramp power, respectively.

[0215] 3) Gas-fired boilers GB

[0216]

[0217] In the formula: P GB,g,t P GB,h,t η represents the input gas power and output thermal power of GB at time t, respectively; GB GB energy conversion efficiency; GB input power upper and lower limits; These are the upper and lower limits of climbing power.

[0218] (4) Constructing an energy storage device model

[0219] Multi-element 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) Charge / discharge power and state constraints:

[0221]

[0222] In the formula: x represents the type of energy storage device, represented by e, h, c, and H2 for electrical, thermal, cold, and hydrogen energy storage, respectively; P x,cha,t P x,dis,t Represent the charging and discharging power of energy storage device x at time t, respectively; I x,cha,t I x,dis,t Let represent the charging and discharging states of energy storage device x at time t, respectively; and These represent the upper and lower limits of the charging power of energy storage device x, respectively. and These represent the upper and lower limits of the energy release power of the energy storage device x, respectively.

[0223] 2) Energy storage state continuity constraints:

[0224]

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

[0226] Step 2: Establish a demand response model for multiple loads (electricity, heat, and cooling) on ​​the user side.

[0227] In demand response, electrical load can be adjusted based on time-of-use pricing. Similarly, heating and cooling loads can be adjusted separately based on time-of-use heating and cooling prices, thus smoothing the load curve. Furthermore, considering the substitutability between electrical, heating, and cooling loads, the energy conversion equipment within the integrated energy system allows a load to not only participate in its own load response but also substitute for other loads, thereby achieving energy substitution and further realizing the economical operation of the integrated energy system.

[0228] Therefore, this invention classifies load response into two types: one is price-based demand response, which reduces and shifts load based on the time-of-use pricing characteristics of energy; the other is substitution-based demand response, which substitutes different types of energy based on the substitution relationship between loads.

[0229] (1) Price-based demand response

[0230] Because different types of loads exhibit varying sensitivities to the same electricity price signal, price-based demand response loads are categorized into reduceable and transferable loads. The electricity price-electricity elasticity matrix method is commonly used to model price-based demand response, representing the relationship between user electricity consumption behavior and electricity price changes. Its expression is as follows:

[0231]

[0232] In the formula: e t,j Let ΔP be the elasticity coefficient of the electrical load at time i with respect to time j; i and P i 0 Let Δρ be the change in electrical load at time i and the initial electrical load, respectively. j and Let $j$ be the rate of change of electricity price at time $j$ and the initial electricity price, respectively.

[0233] For electricity, heat, and cooling loads, the elasticity coefficient between the load at time i and the price at time j, based on their respective time-of-use prices, is defined as follows:

[0234]

[0235] In the formula: and P L,i These represent the initial load at time i and the load after the response price change, respectively. and ρ j These are the initial price and the price after the response, respectively; when i = j, it is E. L (i), called the self-elasticity coefficient, represents the change in load during period i relative to the change in price during the same period; when i ≠ j, it is called the cross-elasticity coefficient, representing the change in load during period i relative to the change in price during period j. Generally, E L (i)≤0, E L (i,j)≥0.

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

[0237]

[0238] In the formula: P L (i) and P L，0 (i) represent the load after the price change at time i and the initial load, respectively; E L (i, j) is the cross-elasticity coefficient, representing the change in load during time period i and the change in price during time period j; ρ0(j) and ρ(j) represent the initial electricity price and the change in electricity price at time j, respectively.

[0239] Let λ L (i) represents the user's load change rate in the i-th time period after time-of-use pricing:

[0240]

[0241] Among them, Called the price fluctuation ratio, it describes the magnitude of price fluctuations caused by time-of-day pricing, and takes into account load shifting and reduction. λ L (i) is:

[0242]

[0243] In the formula: the first term E L (i, j)k(j) represents the load transferred from time i to time j; the second term E L (i)k(i) represents the load that can be reduced at time i. E L (i) is called the self-elasticity coefficient, which represents the change in load during time period i relative to the change in price during the same time period; k(i) is the price fluctuation ratio, which represents the magnitude of price fluctuation caused by time-of-day price.

[0244] Therefore, the load variation rate between each pair of peak and valley periods can be obtained.

[0245]

[0246] In the formula: T f T p and T g Peak, flat, and trough periods are defined based on time-of-use prices; λ fp , λ fg and λ pg These represent the shifts from peak hours to normal hours, from peak hours to trough hours, and from normal hours to trough hours, respectively; λ ff , λ pp and λ gg These represent the load reduction during peak, normal, and off-peak hours, respectively; k f k p and k g These represent the price fluctuation ratios for peak, normal, and off-peak hours, respectively; E L (i) represents the self-elasticity coefficient at time i; E L (i,j) represents 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] In the formula: and P load These represent the load after implementing price-based demand response and the initial load, respectively; Pf P p and P g These are the peak and off-peak loads for price-based demand response. Based on these methods, electricity, heat, and cooling loads are reduced and shifted.

[0251] (2) Alternative demand response

[0252] Users have flexibility in meeting various load demands, and can choose multiple energy sources to meet their energy needs simultaneously. For example, for electricity demand, in scenarios involving partial electric heating or cooling, the heat and cooling power provided by the Integrated Energy System (IES) can be directly selected to meet the demand; for heating demand, in scenarios involving partial heating, in addition to directly using the IES, electric heating products can also be used indirectly; the same applies to cooling demand. Substitute demand response can not only meet different load demands but also alleviate situations where the IES's supply of a certain energy source is insufficient. Since the substitution relationship between heat load and cooling load is relatively small, this invention does not consider the mutual substitution between heat load and cooling load. The substitute demand response model is as follows:

[0253]

[0254] In the formula: P eh,t and P ec,t P represents the electrical power replacing the heat power and the cold power at time t, respectively. he,t P represents the heat power that replaces the electrical power at time t. ce,t ω represents the cooling power that replaces the electrical power at time t; e ω h and ω c These represent the proportions of load transfer for electricity, heat, and cooling loads, respectively; ω re ω rh and ω rc These represent the proportions of electrical, heating, and cooling loads that can be replaced; η eh η is the electrothermal conversion coefficient. ec The coefficient of performance for electricity to cooling; and These represent the electricity, heat, and cooling loads after the price-based demand response at time t.

[0255] (3) Demand response results

[0256] After considering price-based and substitution-based demand responses, the final electricity, heat, and cooling loads are:

[0257]

[0258] In the formula: P load,e,t P load,h,t and P load,c,tThese are the electrical load, thermal load, and cooling load that participate in demand response (DR).

[0259] Step 3: Establish an optimized scheduling model for the park's integrated energy system, taking into account biomass energy and demand response, and introduce a tiered 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 Cost of curtailing wind and solar power (C) cut Carbon trading costs and maintenance costs C om , construct with 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, as shown in the model below:

[0265]

[0266] In the formula: α e and α g These are the unit prices for electricity and gas purchases, respectively; P e,buy,t P g,buy,t These represent the power and gas purchased at time t, respectively; T is the time period.

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

[0268]

[0269] Where: δ wt δ pv These are the wind curtailment and solar curtailment penalty factors, respectively; P wt,cut,t P pv,cut,t These represent the power of wind and solar power curtailed at time t, respectively.

[0270] 3) Carbon trading costs

[0271] This invention establishes a tiered carbon trading mechanism model, as follows:

[0272]

[0273] In the formula: E IES E e,buy E BSCand E GB These are the total IES quota, external power purchase quota, BSC system quota, and GB quota, respectively; χ e , χ g and χ b These are the carbon emission allowance coefficients for units of electricity, natural gas, and biomass gas consumption, respectively. IES,a E e,buy,a P BSC,b,a and P GB,h,a These are the actual carbon emissions from IES, upstream power purchase, BSC system, and GB, respectively; E MR,a E represents the actual amount of CO2 absorbed by the MR. IES,t E represents the carbon emissions trading amount; H is the coefficient corresponding to different carbon emission ranges. IES，t P represents the carbon emissions trading amount at time t; e,buy，t P represents the amount of electricity purchased from the upper-level power grid at time t; BSC,b，t P represents the input power of the biomass-solar coupled system at time t. GB,h，t Let t be the output thermal power of the gas-fired boiler.

[0274] 4) Operation and maintenance costs

[0275]

[0276] Where: β z The unit operation and maintenance cost of equipment type z; P z,t Let z be the output power of the z-th 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. Details are as follows:

[0279] 1) Wind and solar power output constraints

[0280]

[0281] In the formula: and These are the upper limits of output for photovoltaic units and wind turbine units, respectively; P pv,t P represents the photovoltaic power generation at time t. wt,t Let t be the power generation capacity of the wind turbine.

[0282] 2) Constraints on electricity and gas purchases

[0283]

[0284] In the formula: and These are the upper and lower limits for the amount of electricity that can be purchased; and These represent the upper and lower limits of gas purchase capacity, respectively; P e,buy,t P represents the power purchased from the upstream power grid at time t. g,buy,t Let t be the power of the gas purchased from the upstream gas network.

[0285] 3) Power balance constraints

[0286]

[0287] In the formula: P wt,t and P pv,t The output power to the wind turbine and photovoltaic system at time t are respectively; P BSC,e,t P represents the output electrical power of the biomass-solar coupled system at time t. HFC,e,t P represents the output electrical power of the hydrogen fuel cell at time t. EL,e,t P represents the electrical power consumed by the electrolytic cell at time t. CCS,t P represents the electrical power consumed by the carbon capture device at time t. ERU,e,t and P EB,e,t The electrical power consumed by the electric chiller and the electric heating equipment at time t are respectively; P load,e,t P represents the electrical load that participates in demand response at time t; e,cha,t and P e,dis,t These represent the charging power and discharging power of the energy storage battery at time t, respectively.

[0288] 4) Thermal power balance constraint

[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] In the formula: P GB,h,t P represents the input thermal power of the gas-fired boiler at time t. BSC,h,t P represents the output thermal power of the biomass-solar coupled system at time t. TC,h,t P represents the output thermal power of the solar collector at time t. EB,h,t P represents the output heat power of the electric heating device at time t. load,h,t P represents the heat load that participates in the demand response at time t; h,cha,t and P h,dis,t These represent the heat charging power and heat dissipation power of the thermal storage device at time t, respectively.

[0291] 5) Cold power balance constraint

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

[0293] In the formula: P BSC,c,t P represents the output cooling power of the biomass-solar coupled system at time t. ERU,c,t P is the cooling power output of the electric chiller at time t. load,c,t P represents the cooling load that participates in demand response at time t; c,cha,t and P c,dis,t These represent the charging and discharging 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] In the formula: P g,buy,t P is the power of purchasing gas from the upstream gas network at time t; MR,g,t P is the output power of the methane reactor at time t. GB,g,t Let t be the gas consumption power of the gas boiler.

[0297] 7) Hydrogen power balance constraint

[0298]

[0299] In the formula: Let t be the hydrogen production power of the electrolyzer at time t; Let t be the hydrogen power consumed by the hydrogen fuel cell; The hydrogen consumption power of the methane reactor at time t; and These represent the hydrogen charging power and hydrogen discharging power of the hydrogen storage device at time t, respectively.

[0300] This invention selects an agricultural park in Gansu, China as an example for case analysis, performing optimized scheduling on a 24-hour cycle with a 1-hour time interval. Parameters of the BSC system coupled equipment are shown in Table 1; gasification pool parameters are shown in Table 2; wind power and photovoltaic output, as well as electricity, heat, and cooling load data are shown below. Figure 3 This example uses the CPLEX solver for optimized solution.

[0301] Table 1 BSC Coupling Device Parameters

[0302]

[0303] Table 2 Parameters of Biomass Gasification Tank

[0304]

[0305] To verify the rationality of the user-side participation in demand response (DR), carbon capture system (CCS), and power-to-gas (P2G) coupling model in this invention, and to demonstrate the superiority of the BSC coupling system over a combined cooling, heating, and power (CCHP) system using natural gas as feedstock, the scheduling results of the following four scenarios are compared and analyzed:

[0306] Scenario 1: Consider the BSC system participating in the Integrated Energy System (IES), load participating in Demand Response (DR), and carbon capture system (CCS) coupled with power-to-gas (P2G).

[0307] Scenario 2: Considering the participation of the BSC system in the Integrated Energy System (IES), with carbon capture system (CCS) coupled with power-to-gas (P2G), and without considering load participation in demand response (DR);

[0308] Scenario 3: Considering BSC system participation in IES and load participation in DR, without considering P2G and CCS;

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

[0310] (1) Economic and carbon emission analysis

[0311] The optimized scheduling results for each scenario are shown in Table 3. As shown in Table 3, compared to Scenario 2, Scenario 1 saw a reduction of RMB 382.2 in system energy purchase costs, a decrease of 5.6%. Carbon trading costs decreased by RMB 576.6, a decrease of 8.3%. The total cost decreased by RMB 1122.2. This is because user-side participation in demand-side response, including load reduction, transfer, and substitution, smooths the load curve and makes the Integrated Energy System (IES) more economical to operate. Compared to Scenario 3, Scenario 1 saw a reduction of RMB 397.1 in wind and solar curtailment costs, a decrease of 21.5%. Carbon trading costs decreased by RMB 676.7, a decrease of 9.7%, and carbon emissions decreased by 516.2 kg. This is because the Integrated Energy System (IES) incorporates power-to-gas (P2G) and carbon capture system (CCS) equipment. When wind and solar output is high, excess wind and solar power can be absorbed through the P2G, while the CCS captures CO2 emissions from the BSC system and the gas-fired boiler's GB (Gas-fired boiler) emissions, reducing system carbon emissions. Compared to Scenario 4, Scenario 1 reduces energy purchase costs by 8483.6 yuan (57.1%), total costs by 9610.9 yuan (36.9%), and carbon emissions by 2182.6 kg. This is because, compared to the traditional combined cooling, heating, and power (CCHP) system in Scenario 4 which uses natural gas as feedstock, the BSC system in Scenario 1 uses biomass as its primary energy source, enabling full utilization of waste, reducing energy consumption, and lowering system costs.

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

[0313] Comparing Scenario 1 and Scenario 2, incorporating electricity, heat, and cooling loads into Demand Response (DR) effectively reduces fluctuations, making the Integrated Energy System (IES) more economical to operate. Figure 8-10 shows the impact of DR on the output of various devices. After electricity loads participate in DR, electricity demand increases during the periods (00:00-08:00 and 22:00-24:00), leading to increased electricity purchases by the IES. During the period (14:00-17:00), cooling load demand decreases, reducing the output of electric refrigeration equipment, which the BSC system can largely meet. After heat loads participate in DR, heat load demand decreases during the period (00:00-08:00), reducing the output of gas boilers. During the period (10:00-16:00), heat load demand increases, leading to increased output of electric boilers. After cooling loads participate in DR, the BSC system output increases during the period (14:00-18:00), reducing the cooling costs of the IES.

[0314] (3) Comprehensive evaluation considering the BSC system's integration with the park's integrated energy system IES

[0315] To analyze in detail the differences between the BSC system in Scenario 1 and the traditional combined cooling, heating, and power (CCHP) system in Scenario 4, this invention introduces primary energy saving rate (PESR), CO2 emission saving rate (CEER), and total cost saving rate (TCSR) as system evaluation indicators from economic, energy, and environmental perspectives. Detailed model reference: "Research on Two-Stage Robust Capacity Configuration of Biomass Gas-Solar-Wind Integrated Energy System [D]. Shandong University, 2022." Where P... PEC1 P PEC4 E CDE1 E CDE4 C COST1 C COST4 These represent the daily total primary energy consumption (PEC), CO2 emissions (CDE), and total cost for scenarios 1 and 4, respectively.

[0316] Table 4 Evaluation Indicators

[0317]

[0318] As shown in Table 4, although the primary energy consumption of Scenario 1 is 14.2% greater than that of Scenario 4, its CO2 emissions are reduced by 13.3% and the total cost is reduced by 36.8%. It can be seen that by utilizing biomass waste, the system operating cost and CO2 emissions can be effectively reduced. Therefore, the BSC system has better characteristics than the traditional cogeneration system participating in the integrated energy system (IES).

Claims

1. A method for optimal scheduling of cooling, heating and power of a campus integrated energy system based on biomass energy and demand response, characterized in that, The method comprises the following steps: Step 1: constructing a biomass-solar coupling park comprehensive energy system; Step 2: constructing a user-side electricity, heat and cold multi-element load demand response model; Step 3: constructing a park comprehensive energy system cold-heat-power optimization scheduling model based on biomass energy and demand response, and introducing a step-type carbon trading mechanism to limit carbon emissions; In step 1, the biomass-solar coupling park comprehensive energy system is constructed, specifically including: (1) constructing a biomass-solar coupling equipment model; (2) constructing an electric-gas P2G and carbon capture CCS coupling equipment model; (3) constructing an energy conversion equipment model; (4) constructing an energy storage equipment model; In (1), the biomass-solar coupling equipment model is constructed, specifically including: 1) biomass gasification model After the biomass is heated by the preheater and enters the gasification pool, the gas production rate is related to the temperature, so the biomass gas production rate and biomass output model are as follows: （1）； wherein: and are the gas production rate and the temperature of the gasifier at time t, respectively is the optimum temperature of the biomass gasifier, taken as ; and are the biomass input power and the biomass mass flow rate at time t, respectively is the lower heating value of the biomass​​ 2) biomass gasification pool model The solar heat collector and the heat exchanger jointly provide energy to maintain the appropriate temperature of the gas pool, and the heat balance equation and heat loss model in the biomass gasification pool are as follows: （2）； wherein: and is the density and specific heat capacity of the biomass material; is the volume of the gasification tank; is the time of the material in the gas tank; is the ambient temperature at time t; and is the total heat transfer coefficient and total heat dissipation area of the gasification tank; and is the energy required by the gasification tank and the heat dissipation amount at time t, respectively; 3) solar heat collector model The excess heat energy can be directly shared with the heat load; the solar heat collector model is as follows: （3）； In the formula: , and For solar collectors in Output power, conversion efficiency, and luminous radiation intensity at any given time; This refers to the number of solar collectors; The area of ​​a single solar collector; 4) biomass-solar coupling BSC system model Part of the biomass gas is burned in the internal combustion engine to generate electricity, and part of it is used to produce heat through the heat exchanger, and the generated heat energy partly meets the heat load, partly supplies the gasification pool, and the other part supplies the absorption refrigeration machine for refrigeration; the biomass-solar coupling BSC system model is as follows: （4）； wherein: , and are the electrical, thermal and cold power output by the BSC at time ; is the total power input to the BSC at time ; is the thermal energy supplied to the chiller at time ; is the thermal energy supplied to the gasifier at time ; is the thermal energy supplied to the thermal load at time ; , and are the electrical, thermal and cold efficiency of the BSC output; is the energy required by the biomass gasifier at time ; The operation constraints and climbing constraints of the biomass-solar coupling BSC system model are as follows: wherein: and are the upper and lower limits of the BSC input power; and are the upper and lower limits of the BSC ramp power; and are the upper and lower limits of the input to the absorption chiller power; and are the upper and lower limits of the input to the absorption chiller ramp power; and are the upper and lower limits of the thermal energy delivered to the gasifier; and are the upper and lower limits of the gasifier ramp power; In step 2, the user-side electricity, heat and cold multi-element load demand response model is established, specifically including: (1) price-type demand response; (2) alternative-type demand response; (3) demand response results.

2. The method of claim 1, wherein, In (2), the electric-gas P2G and carbon capture CCS coupling equipment model is constructed, specifically including: The electric-gas P2G and carbon capture CCS coupling equipment can consume excess wind and solar energy and reduce CO2 emissions, and the electric-gas P2G and carbon capture CCS coupling model is as follows: 1) electrolytic cell EL wherein: is the electrical energy input to the EL at time t; is the hydrogen energy input to the EL at time t; is the energy conversion efficiency of the EL; , are the upper and lower limits of the power input to the EL, respectively; , are the upper and lower limits of the ramping power of the EL, respectively; 2) methane reactor MR wherein: P in is the hydrogen power input to the MR at time t; P out is the gas power output from the MR at time t; η is the energy conversion efficiency of the MR; P in, min and P in, max are the lower and upper limits, respectively, of the hydrogen power input to the MR; P in, min and P in, max are the lower and upper limits, respectively, of the hydrogen power input to the MR; P climb, min and P climb, max are the lower and upper limits, respectively, of the MR climb power; P climb, min and P climb, max are the lower and upper limits, respectively, of the MR climb power; 3) hydrogen fuel cell HFC wherein: is the hydrogen power input to the HFC at time t; is the electrical power output from the HFC at time t; is the efficiency of the HFC energy conversion; , are the upper and lower limits, respectively, of the hydrogen power input to the HFC; , are the upper and lower limits, respectively, of the HFC ramp power. 4) carbon capture CCS A CO2 storage tank is introduced in the carbon capture CCS to enhance the coupling of the electric-gas P2G and carbon capture CCS, and the carbon capture CCS model is as follows: （9）； In the formula: , and These represent the total energy consumption, fixed energy consumption, and operating energy consumption of the CCS at time t; Captured by CCS at time t quality; The energy consumption coefficient of CCS; and The data generated by GB and BSC at time t are respectively. quality; Captured by CCS Efficiency; and Provide the CCS required for MR at time t respectively Quantity and Storage quantity; The operation power constraints and climbing power constraints of the carbon capture CCS are as follows: ； In the formula: , are the upper and lower limits of CCS energy consumption, respectively; , are the upper and lower limits of CCS ramp energy consumption, respectively.

3. The method of claim 1, wherein, In (3), the energy conversion equipment model is constructed, specifically including: The energy conversion equipment includes an electric boiler EB, an electric refrigerator ERU and a gas boiler GB, the electric boiler EB is used to convert electric energy into heat energy, the electric refrigerator ERU converts electric energy into cold energy, and the gas boiler GB burns natural gas to generate heat energy; the model is as follows: 1) electric boiler EB wherein: Peb(t) is the electrical power consumed by EB at time t; Qeb(t) is the thermal power generated by EB at time t; ηeb is the heating efficiency of EB; , Pmin, Pmax are the lower and upper limits of the electrical power consumed by EB, respectively; , Pmin, Pmax are the lower and upper limits of the electrical power consumed by EB, respectively; 2) electric refrigerator ERU In the formula: is the electrical energy consumed by the ERU at time t; is the cold energy generated by the ERU at time t; is the refrigeration efficiency of the ERU; , are the upper and lower limits of the input electrical power of the ERU; , are the upper and lower limits of the climbing power of the ERU, respectively. 3) gas boiler GB wherein: , respectively represent the input power and the output thermal power of the GB at time t; is the energy conversion efficiency of the GB; , are the upper and lower limits of the input power of the GB; , are the upper and lower limits of the ramping power.

4. The method of claim 1, wherein, In (4), the energy storage equipment model is constructed, specifically including: The multi-element energy storage includes electric energy storage, heat energy storage, cold energy storage and hydrogen energy storage, and the general model is as follows: 1) charging and discharging power and state constraints: In the formula: The energy storage device types are represented by e, h, c, and H2, respectively, for electrical, thermal, cold, and hydrogen energy storage. , They represent the energy storage devices at time t. Charging and discharging power; , They represent the energy storage devices at time t. The charging and discharging states; and These represent energy storage devices. The upper and lower limits of charging power; and These represent energy storage devices. Upper and lower limits of energy release power; 2) energy storage state continuity constraints: In the formula: Let be the storage capacity at time t; This is the self-loss coefficient of the energy storage device; , For energy storage charging and discharging efficiency; , These are the upper and lower limits for energy storage capacity; Energy storage device for 1 hour Storage capacity; 24-hour energy storage device Storage capacity.

5. The method of claim 1, wherein, The user-side electric, thermal, and cold multi-element load demand response model in Step 2 is established, specifically including: Due to the differences in sensitivity of different types of loads to the same price signal, the price-type demand response electric load is divided into reducible load and transferable load; （16）； In the formula: is the electric load at the moment t; the elasticity coefficient at the moment t; and respectively are the electric load change amount at the moment t and the initial electric load; and respectively are the electric price change rate at the moment t and the initial electric price; For electricity, heat and cold loads, according to the respective time-of-use prices, the same load at different times of day and different times-of-use prices have different values. The elasticity coefficient between the load at a moment of time and the price at that moment is defined as follows: （17）； wherein: and are respectively the initial load and the load after the price change response; and are respectively the initial price and the price after the response; when , that is , called the own elasticity coefficient, indicates the change in the load in the period relative to the change in the price in the same period; when , called the cross elasticity coefficient, indicates the change in the load in the period relative to the change in the price in the period ; , ; No. The changes in time-of-use pricing due to load during certain periods are as follows: （18）； In the formula: represents the load at the time of the price change and the initial load; is the cross elasticity coefficient, representing the change in the load in the period for the change in the price in the period; and respectively represent the initial electricity price at the time and the change in the electricity price; Let represent the time-sharing price, the user at the first period load change rate as follows: （19）； wherein the is called the price swing ratio, describes the size of the price swing caused by the time-of-use price, and takes into account the shift and curtailment portions, is: （20）； wherein the first term is the load transferred at the moment the second term is the load that can be reduced at the moment; is called the self-elasticity coefficient and represents the change in the load in the period relative to the change in the price in the same period; is the price float ratio and represents the size of the price fluctuation caused by the time-of-use price Therefore, the load change rate between peak, flat, and valley periods can be obtained as （21）； In the formula: , and are respectively peak, flat and valley time periods divided according to time-sharing price; , and are respectively peak time to flat time transfer, peak time to valley time transfer, flat time to valley time transfer; , and respectively represent peak time, flat time and valley time load reduction; , and are respectively peak time, flat time and valley time price floating ratio; is moment elasticity coefficient; is moment cross elasticity coefficient to moment; In summary, the load after implementing price-type demand response is （22）； （23）； wherein: and are the post-implementation price-based demand response load and the initial load, respectively; , and are the pre-price-based demand response peak, flat, and valley loads, respectively. Since the substitution relationship between thermal and cold loads is small, the mutual substitution between thermal and cold loads is not considered, and the substitution-type demand response model is as follows: wherein: and are the electric, thermal and cooling loads, respectively; is the electric power replacing the thermal and cooling power at the moment; is is the thermal power replacing the electric power at the moment; is is the cooling power replacing the electric power at the moment; , and are the load proportions of electric, thermal and cooling load shifting, respectively; , and are the proportions of electric, thermal and cooling load that can be shifted, respectively; is the electric-to-thermal coefficient; is the electric-to-cooling coefficient; , and are the electric, thermal and cooling loads, respectively; are the electric, thermal and cooling loads after the price-based demand response. After price-type demand response and substitution-type demand response, the final electric, thermal, and cold loads are obtained as: （25）； In the formulae: , and are the electrical, thermal and cooling loads after participation in demand response (DR).

6. The method of claim 1, wherein, In Step 3, the cold-heat-power optimization scheduling model of the park integrated energy system considering biomass energy and demand response is established, and a step-type carbon trading mechanism is introduced to limit carbon emissions, specifically including: (1) Objective function Comprehensively consider the purchase cost of the park integrated energy system IES , abandoned wind, light cost , carbon trading cost And operation and maintenance cost ; The low-carbon economic goal of minimizing the total cost is as follows: （26）； 1) cost of purchasing energy Cost of purchase There are costs of electricity and gas purchase, modeled as follows: （27）； In the formula: and These are the unit prices for electricity and gas purchases, respectively. , They are respectively Real-time electricity and gas purchase capacity; For time period; 2) cost of curtailment of wind and solar power （28）； In the formula: , are respectively wind curtailment and light curtailment penalty factors; , are respectively instantaneous wind curtailment and light curtailment power; 3) Carbon trading costs A step-type carbon trading mechanism model is established, and the model is as follows: （29）； In the formula: , , and are the total quota of IES, the quota of external electricity purchase, the quota of BSC system and the quota of GB, respectively; , and are the carbon emission quota coefficients of unit electricity, natural gas and biomass gas consumption, respectively; , , and are the actual carbon emissions of IES, upper electricity purchase, BSC system and GB, respectively; is the amount of CO2 actually absorbed by MR; is the carbon emission trading amount; is the coefficient corresponding to different carbon emission intervals; is the carbon emission trading amount at time t; is the electricity purchase amount from the upper grid at time t; is the input power of the biomass-solar coupling system at time t; is the output heat power of the gas boiler at time t; 4) Operation and maintenance cost （30）； In the formula: For the first Unit operation and maintenance cost of this type of equipment; for Time of the first The output power of this type of equipment; For equipment types; (2) Constraint conditions 1) Wind and solar power output constraint wherein: and are the upper limits of the photovoltaic and wind power outputs, respectively; is is the photovoltaic power at time t; is is the wind power at time t; 2) Electricity and gas purchase constraint In the formula: and are the upper and lower limits of the power purchase, respectively; and are the upper and lower limits of the gas purchase, respectively; is the power purchase from the upper-level power grid at the moment; is the gas purchase from the upper-level gas grid at the moment; 3) Electric power balance constraint （33）； In the formula: and They are respectively It continuously outputs power to wind turbines and solar panels; for The biomass-solar coupled system outputs electrical power at all times; for The constant output power of the hydrogen fuel cell; for The electrolytic cell consumes electrical power at all times; for The carbon capture equipment consumes electrical power at all times; and They are respectively Electric chillers and electric heating equipment consume electrical power at all times; for Always participate in the electrical load response after demand response; and Energy storage batteries Real-time charging and discharging power; 4) Thermal power balance constraint （34）； In the formula: for The constant input heat power of the gas-fired boiler; for The biomass-solar coupled system outputs thermal power at all times; for The solar collector outputs thermal power at all times; for The electric heating equipment outputs heat power at all times; for Constantly participate in the heat load after demand response; and Thermal storage equipment The charging and discharging power at any given moment; 5) Cold power balance constraint （35）； In the formula: for The biomass-solar coupled system outputs cooling power at all times; for The refrigeration unit outputs cooling power at all times; for Always participate in the cooling load after demand response; and They are respectively cold storage equipment The charging and discharging power at any given time; 6) Natural gas power balance constraint （36）； In the formula: is the gas purchase power of the gas network at the moment; is the output power of the methane reactor at the moment; is the gas consumption power of the gas boiler at the moment; 7) Hydrogen power balance constraint （37）； In the formula: for Hydrogen production power of the electrolyzer at any given time; for Hydrogen fuel cell hydrogen consumption power at all times; for Hydrogen consumption power of the methane reactor at any given time; and Hydrogen storage equipment The hydrogen charging power and hydrogen discharging power at any given time.

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