Park integrated energy system planning method considering comprehensive demand response and carbon emission

Abstract

This invention discloses a planning method for an integrated energy system in a park that considers both demand response and carbon emissions. The method includes: 1. Establishing a tiered carbon emission mechanism model and considering its impact on system operation; 2. Establishing a comprehensive demand response model based on users' real-time electricity, heat, and cooling demands and demand elasticity demand models; 3. Considering actual system constraints, establishing a day-ahead scheduling optimization model for the park's integrated energy system with the optimization objective of minimizing the weighted harmonic coefficient for low-carbon, low-energy consumption, and high user satisfaction, and coordinating and optimizing the supply and demand sides; 4. Constructing a planning model for the park's integrated energy system that considers both demand response and carbon emissions, and considering integrated energy scheduling strategies, using a particle swarm optimization algorithm to obtain the optimal equipment configuration planning scheme for the park's integrated energy system. This invention optimizes the integrated energy system planning scheme while meeting user needs and minimizing carbon emissions, thereby improving the safety and efficiency of the integrated energy system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system planning, and specifically relates to a park planning method that takes into account integrated demand response and carbon emissions; Background Technology

[0002] With the further development of energy technology, integrated energy systems (IES) that couple multiple energy sources such as cooling, heating, and electricity have been studied in depth and widely applied. The progress and development of human society has brought about increasingly severe fossil fuel shortages and environmental pollution problems. The replacement of fossil fuels with renewable energy has been actively promoted, and carbon emission control commitments have been made. Traditional scheduling methods only optimize the supply side of the system and cannot mobilize the potential of the demand side of the system, nor can they control carbon emissions. Existing planning schemes cannot well balance the satisfaction of system users and the safety of system operation. Summary of the Invention

[0003] This invention aims to address the shortcomings of existing technologies by proposing a comprehensive energy system planning method for industrial parks that considers both demand response and carbon emissions. The goal is to reduce system energy consumption and carbon emissions while meeting user energy needs, improve the absorption capacity of renewable energy, thereby enhancing system operational safety and achieving the optimal planning and construction scheme.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0005] This invention discloses a planning method for an integrated energy system in a park that considers comprehensive demand response and carbon emissions. The integrated energy system includes a supply side and a demand side. The supply side includes a combined heat and power (CHP) unit, a gas turbine (GB) unit, and a P2G unit. The demand side includes an electric heat pump (EHP) unit, a central air conditioning (AC) unit, a heat exchanger (HE) unit, and a chiller (AF) unit. The method is characterized by the following steps:

[0006] Step 1: Calculate the initial free carbon emissions of the system using the baseline method:

[0007] Step 1.1: Calculate the free carbon emissions using equations (1)-(4):

[0008]

[0009]

[0010]

[0011]

[0012] In equations (1)-(4), This represents the initial carbon emissions that are requested free of charge from the higher-level energy network. This indicates the initial, uncompensated carbon emissions from a combined heat and power (CHP) unit. This indicates the initial, uncompensated carbon emissions of the gas turbine unit (GB). This represents the initial carbon emissions per unit of electricity generated free of charge. This indicates that electricity is requested from the higher-level energy network during time period t; This represents the initial, uncompensated carbon emissions per unit of heat. The conversion factor for the power generation e of a combined heat and power (CHP) unit to the heat generation h. This indicates that during time period t, the combined heat and power (CHP) unit consumes natural gas energy for heat generation. This indicates that the combined heat and power (CHP) unit consumes natural gas energy for electricity generation during time period t. This represents the natural gas energy consumed by the gas turbine unit's GB during time period t; T represents the set of scheduling times.

[0013] Step 1.2: Calculate the actual carbon emissions using equations (5)-(6):

[0014] M = M elcbuy +M CHP +M GB -M P2G (5)

[0015]

[0016] In equations (5)-(6), M elcbuy M indicates that the electricity carbon emission is claimed from the higher-level energy network. CHP M represents the carbon emissions of a combined heat and power (CHP) unit. P2G Indicates the carbon consumption of the P2G unit; β g This represents the carbon capture factor of the P2G unit; This represents the power consumption of the P2G device during time period t;

[0017] Step 1.3: Calculate the tiered carbon emissions C using equation (7). carbon :

[0018]

[0019] In equation (7), c represents the carbon emission baseline coefficient; α is the growth rate of the carbon emission coefficient; and d is the length of the step interval.

[0020] Step 2: Based on the real-time electricity, heating, and cooling demand of users and the theory of demand elasticity, establish a comprehensive demand response model:

[0021] Step 2.1: Calculate the cooling-heating-electricity substitution coefficient using equations (8)-(10):

[0022]

[0023]

[0024]

[0025] In equations (8)-(10), and The direct energy demand for electricity, heat and cooling for user i in time period t; and This represents the energy consumption preferences of user type i for cooling and heating loads during time period t. Let represent the thermoelectric substitution coefficient and the cooling electrical substitution coefficient, respectively, and be derived from... and After normalization, we get δ. i This represents the proportion of non-rigid electrical loads for user type i;

[0026] Step 2.2: Calculate the overall demand response using equations (11) and (12):

[0027]

[0028]

[0029] In equations (11)-(12), ε represents the original electrical load of user type i during time period t; tt′ ε represents the demand elasticity coefficient between time periods t and t′; when t = t′, ε tt′ This represents the demand elasticity coefficient; This represents the base electricity demand penalty for time period t′; This represents the change in electricity demand penalty during time period t′; and This represents the electrical load, thermal load, and cooling load response of user type i during time period t;

[0030] Step 2.3: Calculate the comfort compensation amount using equation (13):

[0031]

[0032] In equation (13), C comf λ represents the amount of comfort response compensation. comf This indicates the amount of comfort compensation per unit of energy.

[0033] Step 3: Establish a day-ahead scheduling optimization model for the park's integrated energy system. Considering actual constraints and using a weighted harmonic coefficient of low carbon, low energy consumption, and high user satisfaction as the optimization objective function, coordinate and optimize the supply and demand sides:

[0034] Step 3.1: Calculate the weighted harmonic coefficient of the system's low carbon, low energy consumption, and high user satisfaction using equations (14)-(16):

[0035] min C = C buy +C carbon +C pena +C comf (14)

[0036]

[0037]

[0038] In equations (14)-(16), C buy It is to request total energy from the higher-level energy network; C pena It is a punishment for abandoning the beautiful scenery; and These represent the amount of electricity, natural gas, and heat requested from the higher-level energy network during time period t, respectively; c pena It is the penalty coefficient for abandoning scenic views; and This refers to the predicted power of wind and solar power; P pv,t and P wt,t It represents the actual photovoltaic power and wind power absorbed by the system during time period t;

[0039] Step 3.2: Define energy supply-side constraints using equations (17)-(19):

[0040]

[0041]

[0042]

[0043] In equations (17)-(19), and η represents the output of the P2G unit, the electrical output of the CHP cogeneration equipment, the thermal output of the CHP cogeneration equipment, and the output of the gas boiler GB during system time period t; η represents the equipment efficiency.

[0044] Step 3.2: Define energy demand-side constraints using equations (20)-(24):

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] In equations (20)-(24), and This represents the output of the electric heat pump (EHP), central air conditioning (AC), heat exchanger (HE), and chiller (AF) during system time period t. and This represents the electrical load demand, heat load demand, and cooling load demand of the system during time period t before the response.

[0051] Step 3.3: Define equipment operating constraints using equations (25)-(31):

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] Step 3.4: Define the wind and solar power output constraints using equations (32) and (33):

[0060]

[0061]

[0062] Step 3.5: Define the constraint on the change in electricity demand penalty using equation (34):

[0063]

[0064] Step 4: Construct a comprehensive energy system planning model for the park that takes into account both demand response and carbon emissions.

[0065] Step 4.1: Construct the objective function of the park's integrated energy system planning model using equations (35)-(37):

[0066]

[0067]

[0068]

[0069] In equations (35)-(37), Ω Y For the planning year collection; Ω D For the planned equipment set; Ω represents the set of types for the d-th device. S A collection of quarters; C invest For investment in consumables; For the operating consumables in year y; For the c-th type of investment consumables for the d-th device; x c,d This is a Boolean variable representing whether to invest in type c of equipment in category d. n is the weighted harmonic sum of low-carbon, low-energy consumption, and high user satisfaction on a typical day in the s-th quarter of year y; s ρ represents the number of days in the s-th quarter; ρ represents the loss rate.

[0070] Step 4.2: Define the investment type constraint for the equipment using equation (38):

[0071]

[0072] Equation (38) indicates that for any d-type equipment, the type of investment does not exceed one.

[0073] Step 4.3: Calculate the load forecast value P for the s-th quarter of year y using equation (39). j,y,s :

[0074] P j,y,s =(1+γ) t P j,0,s ,j∈Ω B ,y∈Ω Y ,s∈Ω S (39)

[0075] In equation (39), γ is the annual load growth rate; P i,0,s This represents the load value of node j in the s-th quarter of the current year.

[0076] Step 5: Solve the integrated energy system planning model of the park using the particle swarm optimization algorithm:

[0077] Step 5.1: Input initial parameters, including: particle swarm population size M, learning factors c1 and c2, inertia weight w, and particle swarm generation number M. c Monte Carlo simulation number M s Confidence interval β;

[0078] Step 5.2: Randomly generate M initial particles and form a particle set M = {m1, m2, ..., m}. k ,…,m M}, where m k The k-th particle, representing the particle from Ω D Choose the k-th planning scheme composed of different devices, and m k ={m k1 ,m k2 ,…,m kd ,…m kD}, where m kd This represents the capacity of the d-type device selected by the k-th particle;

[0079] Step 5.3: Calculate the investment and consumables based on the equipment capacity planning scheme corresponding to each particle;

[0080] Step 5.4: According to Equation (39), update the load demand for year y and calculate the operating materials for a typical day in the s quarter of year y; calculate the total operating materials for the planned year, and use the sum of the calculated investment materials and the weighted harmonic number of low carbon, low energy consumption and high user satisfaction as the fitness of each particle.

[0081] Step 5.5: Update the particle position and velocity to obtain new particles;

[0082] Step 5.6: Repeat steps 5.3-5.5 until the given number of generations M of particle swarm reproduction is reached. c until;

[0083] Step 5.7: Use the equipment capacity corresponding to the best particle as the optimal planning scheme for the park's integrated energy system.

[0084] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0085] This invention considers carbon emissions and integrated demand response to reduce the operating materials of system operators while meeting user needs and being as low-carbon and environmentally friendly as possible. It provides an integrated energy system planning scheme, thereby controlling system carbon emissions, enhancing the system's renewable energy absorption capacity, and thus improving the reliability and efficiency of the integrated energy system. Attached Figure Description

[0086] Figure 1 This is a diagram of the park's integrated energy system architecture.

[0087] Figure 2 A schematic diagram of a tiered carbon emission mechanism;

[0088] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0089] In this embodiment, a comprehensive energy system planning method for industrial parks that considers integrated demand response and carbon emissions is applied to, for example, Figure 1 The integrated energy system of the park shown includes a supply side and a demand side. The supply side includes a combined heat and power (CHP) unit, a gas turbine (GB) unit, and a P2G unit. The demand side includes an electric heat pump (EHP) unit, a central air conditioning (AC) unit, a heat exchanger (HE) unit, and a chiller (AF) unit. The main steps of the planning method for this integrated energy system of the park include:

[0090] 1) Fully leverage the optimization potential of the user side through comprehensive demand response, and incorporate it into the objective function of the running model according to... Figure 2 Considering carbon emissions, the carbon emissions of the wind and solar power curtailment control system, and the enhancement of renewable energy absorption capacity, a planning model is established based on the operation model. The proposed planning scheme can fully adapt to the low-carbon, low-energy consumption, and high-user-satisfaction operation scheme.

[0091] 2) Based on the energy consumption history data of system users, the cooling, heating and electricity substitution coefficients of different types of users at different time periods are obtained, thereby calculating the comprehensive demand response, which can take into account the satisfaction of system users.

[0092] 3) A day-ahead scheduling optimization model for the park's integrated energy system was established. The objective function is to minimize the weighted harmonic number of the system with low carbon, low energy consumption and high user satisfaction. This includes the amount requested from the upper-level energy network, carbon emissions, wind and solar curtailment penalties, and comprehensive demand response comfort compensation. The constraints include integrated energy balance constraints, equipment operation constraints, and wind and solar power output constraints.

[0093] 4) According to Figure 3 The particle swarm optimization algorithm is used to solve the equipment capacity planning model. For each sampling result, the day-ahead scheduling optimization model of the park's integrated energy system is called to calculate the planning and operating consumables. The best particle is used as the optimal planning scheme for the optimization problem. Specifically, the process is as follows:

[0094] Step 1: The initial free carbon emissions of the system are calculated using the baseline method:

[0095] Step 1.1: Calculate the free carbon emissions using equations (1)-(4):

[0096]

[0097]

[0098]

[0099]

[0100] Equation (1) represents the total initial free carbon emissions of the system; Equations (2), (3) and (4) represent the free carbon emissions from applying for electricity from the upper-level energy network, the free carbon emissions from the combined heat and power (CHP) equipment, and the free carbon emissions from the gas turbine (GB), respectively.

[0101] In equations (1)-(4), This represents the initial carbon emissions that are requested free of charge from the higher-level energy network. This indicates the initial, uncompensated carbon emissions from a combined heat and power (CHP) unit. This indicates the initial, uncompensated carbon emissions of the gas turbine unit (GB). This represents the initial carbon emissions per unit of electricity generated free of charge. This indicates that electricity is requested from the higher-level energy network during time period t; This represents the initial, uncompensated carbon emissions per unit of heat. This represents the conversion factor for the power generation of a combined heat and power (CHP) unit to the heat generation. This indicates that during time period t, the combined heat and power (CHP) unit consumes natural gas energy for heat generation. This indicates that the combined heat and power (CHP) unit consumes natural gas energy for electricity generation during time period t. This indicates the natural gas energy consumed by the gas turbine unit GB during time period t;

[0102] Step 1.2: Calculate the actual carbon emissions using equations (5)-(6):

[0103] M = M elcbuy +M CHP +M GB -M P2G (5)

[0104]

[0105] Equation (5) represents the actual carbon emissions of the system; Equation (6) represents the amount of carbon absorbed by the P2G unit;

[0106] Equations (5) and (6) are used to calculate the actual carbon emissions of the calculation system, M. elcbuy M indicates that the electricity carbon emission is claimed from the higher-level energy network. CHP This indicates the CHP carbon emissions of combined heat and power (CHP) equipment, in M. P2G Indicates the carbon consumption of the P2G unit; β g This represents the carbon capture factor of the P2G unit; This represents the electricity consumed by the P2G unit during time period t;

[0107] Step 1.3: Calculate the tiered carbon emissions using equation (7):

[0108]

[0109] In equation (7), c represents the carbon emission baseline coefficient; α is the growth rate of the carbon emission coefficient; and d is the length of the step interval.

[0110] Step 2: Based on the real-time electricity, heating, and cooling demand of users and the theory of demand elasticity, establish a comprehensive demand response model:

[0111] Step 2.1: Calculate the cooling-heating-electricity substitution coefficient using equations (8)-(10):

[0112]

[0113]

[0114]

[0115] Equations (8) and (9) are the calculation methods for the electrical-cooling-heat substitution coefficient; Equation (10) is the constraint on the electrical-cooling-heat substitution coefficient.

[0116] In equations (8)-(10), and The direct energy demand for electricity, heat and cooling for user i in time period t; and This represents the energy consumption preferences of user type i for cooling and heating loads during time period t. Let represent the thermoelectric substitution coefficient and the cooling electrical substitution coefficient, respectively, and be derived from... and After normalization, we get δ. i This represents the proportion of non-rigid electrical loads for user type i;

[0117] Step 2.2: Calculate the overall demand response using equations (11) and (12):

[0118]

[0119]

[0120] Equation (11) is the electrical load response calculated based on the demand elasticity theory; Equation (12) is the electrical cooling and heating load response.

[0121] In equations (11)-(12), ε represents the original electrical load of user type i during time period t; tt′ ε represents the demand elasticity coefficient between time periods t and t′; when t = t′, ε tt′ This represents the demand elasticity coefficient. This represents the base electricity demand penalty for time period t′; This represents the change in electricity demand penalty during time period t′; and This represents the electrical, heating, and cooling load response of the i-th type of user during time period t;

[0122] Step 2.3: Calculate the comfort compensation amount using equation (13):

[0123]

[0124] Equation (13) is used to calculate the comfort compensation amount generated by the system due to the comprehensive demand response;

[0125] In equation (13), C comf λ represents the amount of comfort response compensation. comf This indicates the amount of comfort compensation per unit of energy.

[0126] Step 3: Establish a day-ahead scheduling optimization model for the park's integrated energy system. Considering actual constraints and using the minimum weighted harmonic number of low-carbon, low-energy consumption, and high user satisfaction as the optimization objective function, coordinate and optimize the supply and demand sides:

[0127] Step 3.1: Calculate the weighted harmonic coefficient for low-carbon, low-energy consumption, and high user satisfaction using equations (14)-(16):

[0128] min C = C buy +C carbon +C pena +C comf (14)

[0129]

[0130]

[0131] Equation (14) is the objective function of the system operation model; Equations (15) and (16) are the amount of energy requested from the superior energy network and the penalty for curtailment of wind and solar power;

[0132] In equations (14)-(16), C buy It is to request total energy from the higher-level energy network; C pena It is a punishment for abandoning the beautiful scenery; and These represent the electricity, natural gas, and heat requested from the superior energy network during time period t, respectively; c pena It is the penalty coefficient for abandoning scenic views; and It is the predicted power of wind and solar power; P pv,t and P wt,t It represents the actual photovoltaic power and wind power absorbed by the system during time period t;

[0133] Step 3.2: Define energy supply-side constraints using equations (17)-(19):

[0134]

[0135]

[0136]

[0137] Equation (17) is the energy supply side electrical energy balance constraint; Equation (18) is the energy supply side thermal energy balance constraint; Equation (19) is the energy supply side natural gas balance constraint.

[0138] In equations (17)-(19), and This represents the output of the P2G unit, the electrical output of the cogeneration equipment (CHP), the thermal output of the cogeneration equipment (CHP), and the GB output of the gas-fired boiler; η represents the equipment efficiency.

[0139] Step 3.2: Define energy demand-side constraints using equations (20)-(24):

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Equations (20) and (21) represent the balance of electrical and thermal energy between the energy supply side and the user side; Equation (22) represents the balance of electrical energy on the user side; Equations (23) and (24) represent the balance of thermal and cold energy on the user side.

[0146] In equations (20)-(24), and This represents the output of the electric heat pump (EHP), central air conditioning (AC), heat exchanger (HE), and chiller (AF) during system time period t. and This represents the electrical load demand, heat load demand, and cooling load demand of the system during time period t before the response.

[0147] Step 3.3: Define equipment operating constraints using equations (25)-(31):

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] Equations (25), (26), and (27) are the upper and lower limits of the system's request for electricity, heat, and gas from the upper-level network; Equations (28) and (29) are the power generation capacity constraints and heat generation capacity constraints of the CHP cogeneration equipment; Equations (30) and (31) are the capacity constraints of the GB and P2G gas turbine equipment.

[0156] Step 3.4: Define the wind and solar power output constraints using equations (32) and (33):

[0157]

[0158]

[0159] Equations (32) and (33) are the limits on photovoltaic power output and wind power output, respectively;

[0160] Step 3.5: Define the constraint on the change in electricity demand penalty using equation (34):

[0161]

[0162] Equation (34) represents the upper and lower limits of the change in electricity demand penalty;

[0163] Step 4: Construct a comprehensive energy system planning model for the park that takes into account both demand response and carbon emissions.

[0164] Step 4.1: Construct the objective function of the park's integrated energy system planning model using equations (35)-(37):

[0165]

[0166]

[0167]

[0168] Equation (35) is the objective function of the system planning model; Equations (36) and (37) are the weighted harmonic coefficients for calculating investment, material consumption, low carbon, low energy consumption, and high user satisfaction.

[0169] In equations (35)-(37), Ω Y For the planning year collection; Ω D For the planned equipment set; Ω represents the set of types for the d-th device. S A collection of quarters; C invest For investment in consumables; For the operating consumables in year y; For the c-th type of investment consumables for the d-th device; x c,d This is a Boolean variable; it indicates whether to invest in type c of equipment in category d. The weighted harmonic sum of low-carbon, low-energy consumption, and high user satisfaction for a typical day in the s-th quarter of year y is calculated according to formula (14); n s ρ represents the number of days in the s-th quarter; ρ represents the loss rate.

[0170] Step 4.2: Define the investment type constraint for the equipment using equation (38):

[0171]

[0172] Equation (38) indicates that for any d-type equipment, the type of investment does not exceed one.

[0173] Step 4.3: Calculate the load forecast value P for the s-th quarter of year y using equation (39). j,y,s :

[0174] P j,y,s =(1+γ) t P j,0,s ,j∈Ω B ,y∈Ω Y ,s∈Ω S (39)

[0175] In equation (39), γ is the annual load growth rate; P i,0,s This represents the load value of node j in the s-th quarter of the current year.

[0176] Step 5, as follows Figure 3 As shown, the particle swarm optimization algorithm is used to solve the integrated energy system planning model of the park:

[0177] Step 5.1: Input initial parameters, including particle swarm population size M, learning factors c1 and c2, inertia weight w, and particle swarm generation number M. c Monte Carlo simulation number M s Confidence interval β;

[0178] Step 5.2: Randomly generate M initial particles, with particle set M = {m1, m2, ..., m}. k ,…,m M}, where m k This represents the k-th particle, i.e., from Ω. D Choose the k-th planning scheme composed of different devices, and m k ={mk1 ,m k2 ,…,m kd ,…m kD}, where m kd This represents the capacity of the d-type device selected by the k-th particle;

[0179] Step 5.3: For each particle, determine its planned equipment capacity m. k Calculate the investment and consumables;

[0180] Step 5.4: According to Equation (39), update the load demand for year y and calculate the operating materials for a typical day in the s quarter of year y; calculate the total operating materials for the planned year, and use the sum of the calculated investment materials and the weighted harmonic number of low carbon, low energy consumption and high user satisfaction as the fitness of each particle.

[0181] Step 5.5: Update the particle position and velocity to obtain new particles;

[0182] Step 5.6: Repeat steps 5.3-5.5 until the given number of generations M of particle swarm reproduction is reached. c ;

[0183] Step 5.7: Use the equipment capacity corresponding to the best particle as the optimal planning scheme for the park's integrated energy system.

Claims

1. A park integrated energy system planning method considering integrated demand response and carbon emission, the park integrated energy system comprising a supply side and a demand side, the supply side comprising a combined heat and power (CHP) unit, a gas turbine (GB) and a P2G unit, the demand side comprising an electric heat pump (EHP), a central air conditioner (AC), a heat exchanger (HE) and an air chiller (AF), characterized in that, The park comprehensive energy system planning method is carried out according to the following steps: Step one, calculate the initial free carbon emission of the system by the benchmark value method: Step 1.1, calculate the free carbon emission by using formula (1)-(4): in formula (1) - formula (4), represents the free initial carbon emission of the electricity claimed from the upper energy network; represents the free initial carbon emission of the combined heat and power device CHP; represents the free initial carbon emission of the gas turbine unit GB; represents the free initial carbon emission per unit of electricity; represents the electricity claimed from the upper energy network in the t period; represents the free initial carbon emission per unit of heat; represents the conversion coefficient of the power generation amount e of the combined heat and power device to the heat generation amount h; represents the natural gas energy consumed by the combined heat and power device CHP for heat production in the t period; represents the natural gas energy consumed by the combined heat and power device CHP for power generation in the t period; represents the natural gas energy consumed by the gas turbine unit device GB in the t period; T represents a set of scheduling times; Step 1.2, calculate the actual carbon emission by using formula (5)-(6): M = M elcbuy + M CHP + M GB - M P2G (5) In formula (5) - formula (6), M elcbuy represents the carbon emission of the electricity applied for in the upper energy network, M CHP represents the carbon emission of the combined heat and power (CHP) equipment, M P2G represents the carbon consumption of the P2G unit; β g represents the carbon capture coefficient of the P2G unit; represents the electricity consumed by the P2G equipment in the t period. Step 1.3, calculation of the step carbon emissions C with formula (7) carbon : In formula (7), c represents the carbon emission benchmark coefficient; alpha is the growth rate of the carbon emission coefficient; d is the length of the step interval; Step two, based on the real-time electricity, heat and cold demand of the user and the demand elasticity theory, establish a comprehensive demand response model: Step 2.1, calculate the cold-heat-electricity substitution coefficient by using formula (8)-(10): in formula (8) - formula (10), and is the electricity, heat and cold direct energy demand of the i-th type of user at time period t; and represents the energy use preference of the i-th type of user for electric cold load and electric heat load at time period t; respectively represent the heat-electricity substitution coefficient and the cold-electricity substitution coefficient, and are obtained after normalization by and ​ delta i represents the proportion of non-rigid electric loads of the i-th type of user; Step 2.2, calculate the comprehensive demand response amount by using formula (11)-(12): in formulas (11) - (12), denotes the original electricity load of the i-th type of user at time period t; ε tt′ denotes the demand cross-elasticity coefficient between time periods t and t'; when t = t', ε tt′ denotes the demand self-elasticity coefficient; denotes the base electricity demand penalty at time period t'; denotes the electricity demand penalty change amount at time period t'; and denotes the electricity load, heat load and cold load response amount of the i-th type of user at time period t; Step 2.3, calculate the comfort compensation amount by using formula (13): In formula (13), C comf represents a comfort response compensation amount, λ comf represents a comfort compensation amount per unit energy; Step three, establish a day-ahead scheduling optimization model of the park comprehensive energy system, consider the actual constraints and take the low-carbon low-energy consumption high user satisfaction weighted harmonic number as the optimization objective function, and coordinate and optimize the supply and demand sides: Step 3.1, calculate the low-carbon low-energy consumption high user satisfaction weighted harmonic number of the system by using formula (14)-(16): min C=C buy +C carbon +C pena +C comf (14) In formula (14) to formula (16), C buy is the total energy applied for from the upper energy network; C pena is the wind and light penalty; and respectively represent the electricity, the natural gas and the heat applied for from the upper energy network at the t period; c pena is the wind and light penalty coefficient; and are the predicted wind and light power; P pv,t and P wt,t are the actual wind and light power consumed by the system at the t period; Step 3.2, define the energy supply side constraints by using formula (17)-(19): in formulas (17) - (19), and P2G unit output, heat and power output of the combined heat and power plant CHP, and gas boiler GB output at system time period P2G; η denotes the plant efficiency. Step 3.2, define the energy demand side constraints by using formula (20)-(24): in formulae (20) to (24), and denote the system t-period electric heat pump EHP output, central air conditioner AC output, heat exchanger HE output, and refrigerator AF output; and denote the response previous system t-period electric load demand, heat load demand, and cold load demand; Step 3.3, define the equipment operation constraints by using formula (25)-(31): Step 3.4, define the wind and light output constraints by using formula (32)-(33): Step 3.5, define the electric demand penalty change constraint by using formula (34): Step four, build a park comprehensive energy system planning model considering comprehensive demand response and carbon emission: Step 4.1, build the objective function of the park comprehensive energy system planning model by using formula (35)-(37): Ω Y is the set of planning years; Ω D is the set of planned devices; is the set of types of the dth device; Ω S is the set of quarters; C invest is the investment consumable; is the operating consumable of the yth year; is the investment consumable of the dth device of the cth type; x c,d is a Boolean variable indicating whether to invest in the dth device of the cth type; is the low-carbon low-energy high-user-satisfaction weighted harmonic number of the typical day of the s th quarter of the y th year; n s is the number of days of the s th quarter; p is the loss rate; Step 4.2, define the investment type constraints of the equipment by using formula (38): Formula (38) indicates that for any d type equipment, the type of investment is not more than one; Step 4.3, Calculate the load forecast value P for the s-th quarter of the y-th year using formula (39) j,y,s : P j,y,s = (1 + γ) t P j,0,s j e Ω B y e Ω Y s e Ω S (39) In formula (39), γ is a load annual growth rate; P i,0,s is a load value of the current year's s-th quarter node j; Step five, solve the park comprehensive energy system planning model by using particle swarm algorithm: Step 5.1, input initial parameters, including: particle swarm population size M, learning factor c1 and c2, inertia weight w, particle swarm breeding generation number M c , Monte Carlo simulation number M s , confidence interval β; Step 5.2, randomly generate M initial particles and constitute a particle set M = {m1, m2, …, m k M M}, where m k M D is the kth particle, represents the kth planning scheme composed of different devices selected from Ω k M k1 = {m k2 1, m kd 2, …, m kD M kd}, where m k M M represents the capacity of the d-type device selected by the kth particle; Step 5.3, according to the equipment capacity planning scheme corresponding to each particle, calculate the investment consumables; Step 5.4, according to formula (39), update the load demand in the yth year, and calculate the running consumables of the typical day in the s th quarter in the y th year; the sum of the calculated investment consumables and the low-carbon low-energy consumption high user satisfaction weighted harmonic number is taken as the fitness of each particle; Step 5.5, update the particle position and velocity, so as to obtain new particles; Step 5.

6. Repeat steps 5.3 - step 5.5 until a given number of generations of the population M is reached c until a given number of generations of the population M is reached Step 5.7, take the equipment capacity corresponding to the best particle as the optimal planning scheme of the park comprehensive energy system.

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