Timing planning method for integrated energy system of park considering wind-solar access and electric-thermal hybrid energy storage

Abstract

The present invention discloses a method for timing planning of a park integrated energy system taking into account wind-solar access and electric-thermal hybrid energy storage, including: 1. establishing a wind-solar uncertainty model based on opportunity constraints; 2. establishing a hybrid energy storage model containing electric energy storage and thermal energy storage; 3. establishing a two-stage P2G model and a park integrated energy system scheduling optimization model with the minimum weighted sum of user load fluctuation variance and system carbon emissions as the optimization target; 4. constructing a park integrated energy system timing planning model taking into account wind-solar access and electric-thermal hybrid energy storage, and considering the operation scheduling strategy of the integrated energy system in the timing planning model, using an improved genetic algorithm to solve the proposed planning model, and obtaining the optimal equipment configuration and construction timing planning scheme of the park integrated energy system. The present invention ensures the safe and stable operation of the system while meeting user needs and taking into account the fluctuation of renewable energy, and provides a timing planning scheme for the integrated energy system on this basis.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy system planning, and in particular relates to a time sequence planning method for an integrated energy system in a park taking into account wind and solar access and electric-thermal hybrid energy storage; Background Art

[0002] With the further development of energy technology, the integrated energy system (IES) coupled with multiple energy sources such as cooling, heating, and electricity has been deeply studied and widely used. The volatility, intermittency, and randomness of wind power and photovoltaics have brought many uncertainties to the operation of the integrated energy system containing renewable energy, and have brought huge challenges to the safe operation of the system. Traditional planning schemes complete the investment and construction of the system in the early stage, which cannot optimize resource allocation well, resulting in redundant equipment configuration in the early stage of operation, waste of resources, and equipment aging in the later stage of operation, which may not meet the energy demand of the load. Summary of the invention

[0003] The present invention aims to address the deficiencies of the above-mentioned prior art and proposes a timing planning method for a comprehensive energy system in a park that takes into account wind and solar access and electric-thermal hybrid energy storage, in order to stabilize the load supply and reduce carbon emissions on the basis of adapting to the fluctuations of renewable energy, thereby improving the safety and environmental protection of the system operation. In order to meet the demand for load growth and avoid redundancy of park equipment, the timing of its construction is fully considered during planning and construction.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0005] The characteristic of the timing planning method of the park integrated energy system taking into account wind and solar access and electric and thermal hybrid energy storage of the present invention is that it is carried out according to the following steps:

[0006] Step 1: Establish an uncertain wind and solar output model based on chance constraints:

[0007] Step 1.1: Define the wind and solar output constraint using formula (1):

[0008] Pr{(P t WTact +P t PVact )≥(P t WT +P t PV )}≥c u (1)

[0009] In formula (1), P t Wact and P t PVactare the actual power generated by wind power and photovoltaic power in period t; P t WT and P t PV is the dispatch output of wind power and photovoltaic power in the system during period t; Pr{·} is the probability of an event occurring; c u is the confidence level;

[0010] Step 1.2: Use equations (2) to (4) to fit the wind power and photovoltaic power output errors:

[0011]

[0012] F(x)=[1+e -α(x-γ) ] -β (3)

[0013]

[0014] In formula (2) to formula (4), x is the wind and solar power output error; f(x) is the probability density function of x; F(x) is the cumulative distribution function of x; α, β and γ are the shape parameters of VPD distribution; F -1 (c u ) is the confidence level c u The wind and solar output error of the VPD fitting is shown below;

[0015] Step 1.3: Use formula (5) to establish deterministic constraints:

[0016] (P t WTact +P t PVact )-(P t WT +P t PV )≥F -1 (c u ) (5)

[0017] Step 2: Establish an electric-thermal hybrid energy storage model and a two-stage P2G model:

[0018] Step 2.1: Use equations (6) to (11) to establish an electric energy storage model:

[0019]

[0020] μ ES,min M ES ≤S t ES ≤μ ES,max M ES (7)

[0021]

[0022]

[0023]

[0024]

[0025] In formula (6) to formula (11), is the remaining amount of energy stored in the period t; μ ES,loss is the self-loss coefficient of electric energy storage; μ ES,sto and μ ES,rel are the charging efficiency and discharging efficiency of energy storage respectively; P t ES,sto and P t ES,rel are the charging power and discharging power of the energy storage during period t respectively; M ES is the electrical energy storage capacity; μ ES,min and μ ES,max are the lower limit and upper limit of the proportion of the remaining storage capacity to the total capacity respectively; is the initial remaining capacity of the energy storage; T is the dispatching period; and are Boolean variables, indicating that the energy storage is in a charging state and in a discharging state respectively;

[0026] Step 2.2: Use equation (12)-(13) to establish a thermal energy storage model:

[0027]

[0028]

[0029] In formula (12)-formula (13), is the heat released or absorbed by the thermal energy storage during period t; and are the upper limit of heat released and the upper limit of heat absorbed within the unit scheduling time respectively;

[0030] Step 2.3: Use formula (14) to establish a two-stage P2G model:

[0031]

[0032] In formula (14), is the amount of electricity consumed by the electrolytic cell during period t; η EL is the electrolyzer efficiency; Providing hydrogen energy to the electrolyzer for hydrogen fuel cells to generate electricity; Providing hydrogen energy to the electrolyzer for heat generation by hydrogen fuel cells; The energy provided by the electrolyzer to the methane reactor during period t;

[0033] Step 3: Establish a day-ahead dispatch optimization model for the integrated energy system of the park, taking into account the operating constraints of system equipment, and taking the minimum weighted sum of user load fluctuation variance and system carbon emissions as the objective function:

[0034] Step 3.1: Use equations (15) to (17) to calculate the objective function of the running model:

[0035] min C op =ω 1 C 1 +ω 2 C 2 (15)

[0036]

[0037]

[0038] In formula (15) to formula (17), C op is the weighted sum; C 1 is the user load fluctuation variance; C 2 Carbon emissions from electricity and natural gas claimed from the upper energy grid; 1 and ω 2 C 1 and C 2 The weight of and is the user's electricity load demand, heating load demand and cooling load demand during period t; β elc is the carbon emissions per unit of electricity; gas is the carbon emissions produced per unit of natural gas;

[0039] Step 3.2: Define the energy balance constraint using equations (18) to (21):

[0040]

[0041]

[0042]

[0043]

[0044] In formula (18) to formula (21), The amount of electricity claimed from the superior energy network during period t; η T is the transformer efficiency; is the natural gas energy used for power generation by the combined heat and power equipment during period t; CCHPelc The power generation efficiency of the combined cooling, heating and power equipment; is the amount of electricity consumed by the electric heat pump during period t; is the power consumed by the air conditioner during period t; η HFCelc The power generation efficiency of hydrogen fuel cells; is the amount of natural gas applied for from the superior energy network during period t; η MR is the efficiency of the methane reactor; is the natural gas energy used by the combined heat and power equipment to produce heat during period t; is the natural gas energy used by the combined heat and power equipment to produce cooling during period t; CCHPheat is the heat generation efficiency of the combined cooling, heating and power equipment; η EHP is the efficiency of the electric heat pump; is the heat generation efficiency of the hydrogen fuel cell; η CCHPcool is the cooling efficiency of the combined heat and power equipment; η AC The cooling efficiency of the air conditioner;

[0045] Step 3.3: Define the equipment operation constraints using equations (22) to (27):

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In formula (23)-formula (24), and They are the lower and upper limits of the heat-to-electricity ratio of the combined cooling, heating and power equipment, respectively; and are the lower and upper limits of the cooling-to-electricity ratio of the combined cooling, heating and power equipment respectively;

[0053] Step 4: Construct a time-series planning model for the park's integrated energy system that takes into account wind and solar access and electric-thermal hybrid energy storage:

[0054] Step 4.1: Use equations (28) to (31) to construct the objective function of the park integrated energy system time series planning model:

[0055]

[0056]

[0057]

[0058]

[0059] In formula (28)-(31), Ω Y is the planned annual set; Ω D A collection of equipment for planning; is the collection of class d equipment; C invest For construction consumables; C y is the operating consumables in year y; r CR is the recovery factor; rate is the discount rate; Construction consumables for equipment of type c of category d in year y; x y,c,d is a Boolean variable, indicating whether the cth model of the dth category equipment is selected in the yth year; C op is the weighted sum of the daily user load fluctuation variance and system carbon emissions in year y; n is the number of days in a year;

[0060] Step 4.2: Use formula (32) to define the equipment selection constraints:

[0061]

[0062] Formula (32) indicates that for any type d equipment in year y, no more than one model is selected;

[0063] Step 4.3: Define the load constraint using equation (33):

[0064]

[0065] In formula (33), is the annual growth rate of load; P 0 is the load value of the current year; P y is the load value in year y;

[0066] Step 4.4: Define the system operation related constraints as: in the yth year, satisfy the system operation constraints of equations (1) to (14), (18) to (27);

[0067] Step 5: Use the improved genetic algorithm to solve the time sequence planning model of the park integrated energy system:

[0068] Step 5.1: Input initial parameters, including: population number μ, crossover probability P c , mutation probability P m , the maximum number of iterations λ;

[0069] Step 5.2: Generate μ initial samples and construct a population set P = {n 1 ,n 2 ,n 3 ,…nk ,…n μ}, where n k is the kth individual in Ω Y The timing from Ω D Select the equipment timing planning scheme composed of different equipment; and have:

[0070]

[0071] Among them, n yp,dq It represents the capacity selection plan for the equipment of type d in year y;

[0072] According to the equipment capacity timing planning scheme, the load demand in year y is updated, and the objective function shown in formula (28) is used as the individual fitness h(n k ), initialize the empty set P′;

[0073] Step 5.3, generate a random number r between 0 and 1, if r>P c , then go to step 5.4, otherwise, extract individual n from the population set P according to the fitness size as the selection probability x and n y After the crossover operation, the new individual n is obtained z , and calculate n z The fitness h(n z ), then go to step 5.5;

[0074] Step 5.4: Randomly select an individual n x , and according to the mutation probability P m For individual n x After the mutation operation, the new individual n is obtained z , calculate n z The fitness h(n z );

[0075] Step 5.5: The new individual n z Add to the set P′, update the original population set P with P∪P′, calculate the individual with the highest fitness value in the population set P and use it as the current optimal individual;

[0076] Step 5.6: Return to step 5.3-step 5.5 to iterate until the current number of iterations exceeds λ; thereby obtaining the final optimal individual and serving as the optimal timing planning solution.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present invention takes into account the uncertainty of wind and solar power and electric-thermal hybrid energy storage to stabilize the supply system load and reduce system carbon emissions under the condition of renewable energy fluctuations, and provides a comprehensive energy system timing planning scheme based on the construction timing, so as to realize the efficient operation of the comprehensive energy system, enhance the absorption capacity of renewable energy, and improve the safety and reliability of the comprehensive energy system. Specifically:

[0079] 1) The uncertainty of wind and solar power output is taken into account to ensure the safe and reliable operation of the system;

[0080] 2) An electric-thermal hybrid energy storage model and a two-stage P2G model were established respectively. The energy storage device was used to smooth the fluctuation of renewable energy, and the two-stage P2G model was used to improve the flexibility of the power-to-gas process;

[0081] 3) A day-ahead dispatch optimization model for the integrated energy system of the park was established, with the objective function of minimizing the weighted sum of user load fluctuation variance and system carbon emissions, which improved user system user satisfaction and reduced system carbon emissions;

[0082] 4) An improved genetic algorithm is used to solve the equipment capacity timing planning model. The day-ahead scheduling optimization model of the park's integrated energy system is called for each sampling result. The system's operating constraints are considered in the planning scheme, which improves the accuracy of the planning scheme and achieves efficient solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is a schematic diagram of the park's integrated energy system;

[0084] Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0085] In this embodiment, Figure 1 The system shows a comprehensive energy system for a park. The system applies for electricity and natural gas from the superior energy network, and is equipped with wind farms and photovoltaic stations to absorb renewable energy. Natural gas supplies cooling, heating and electricity loads to system users through CCHP equipment. Electricity can be converted into natural gas through P2G equipment, and can also supply cooling loads through central air conditioning. The system can be controlled in real time through electric and thermal hybrid energy storage. P2G equipment can be further divided into electrolyzers, methane reactors and hydrogen fuel cells; the electrolyzer consumes electricity to electrolyze water to produce hydrogen, part of the hydrogen enters the methane reaction gas to prepare methane as fuel, and the remaining hydrogen is supplied to the hydrogen fuel cell.

[0086] In this embodiment, the main steps of a method for timing planning of a park integrated energy system taking into account wind-solar access and electric-thermal hybrid energy storage include: fitting wind-solar prediction errors according to historical data of wind power and photovoltaic power generation and converting opportunity constraints into deterministic constraints; establishing an electric-thermal hybrid energy storage and two-stage P2G operation model; establishing a system operation model based on wind-solar access and electric-thermal hybrid energy storage taking into account user load fluctuation variance and system carbon emissions as objective functions; establishing a system timing planning model based on the operation model with investment and operation consumables and minimum and considering the construction sequence; iterating and solving through an improved genetic algorithm to obtain the optimal equipment capacity timing planning scheme for the park integrated energy system; specifically, the following steps are followed:

[0087] Step 1: Establish an uncertain wind and solar output model based on chance constraints:

[0088] Step 1.1: Define the wind and solar output constraint using formula (1):

[0089] Pr{(P t WTact +P t PVact )≥(P t WT +P t PV )}≥c u (1)

[0090] Formula (1) indicates that the probability that the actual output of wind power and photovoltaic power exceeds the dispatched output of system wind power and photovoltaic power is not less than c u ;

[0091] In formula (1), P t Wact and P t PVact P is the actual power generated by wind power and photovoltaic power during period t; t WT and P t PV is the dispatching output of wind power and photovoltaic power in the system during period t; Pr{·} is the probability of an event occurring; c u is the confidence level;

[0092] Step 1.2: Use equations (2) to (4) to fit the wind power and photovoltaic power output errors:

[0093]

[0094] F(x)=[1+e -α(x-γ) ] -β (3)

[0095]

[0096] Formula (2) is the probability density function of VPD distribution; Formula (3) is the cumulative distribution function of VPD distribution; Formula (4) is the inverse function of the cumulative distribution function of VPD distribution;

[0097] In formula (2)-(4), x is the wind and solar power output error; f(x) is the probability density function of x; F(x) is the cumulative distribution function of x; α, β and γ are the shape parameters of VPD distribution; F -1 (c u ) is the confidence level c u The wind and solar output error of the VPD fitting is shown below;

[0098] Step 1.3: Use formula (5) to transform the chance constraint into a deterministic constraint:

[0099] (P t WTact +P t PVact )-(P t WT +P t PV )≥F -1 (c u ) (5)

[0100] Formula (5) indicates that the probability that the actual output of wind power and photovoltaic power exceeds the dispatched output of system wind power and photovoltaic power is not less than c u ;

[0101] Step 2: Establish an electric-thermal hybrid energy storage model and a two-stage P2G model:

[0102] Step 2.1: Use equations (6) to (11) to establish an electric energy storage model:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Formula (6) indicates that the remaining capacity of the energy storage in period t is equal to the remaining capacity of the energy storage in period t-1 plus the charging capacity in period t minus the discharging capacity and self-loss capacity in period t; Formula (7) is the remaining capacity constraint of the energy storage; Formula (8) indicates that the remaining capacity of the energy storage remains unchanged after a scheduling cycle T; Formula (9) is the upper limit constraint of the charging power of the energy storage; Formula (10) is the upper limit constraint of the discharging power of the energy storage; Formula (11) indicates that the energy storage cannot be charged and discharged at the same time;

[0110] In formula (6) to formula (11), is the remaining amount of energy stored in the period t; μ ES,loss is the self-loss coefficient of electric energy storage; μ ES,sto and μ ES,rel P is the charging efficiency and discharging efficiency of electric energy storage; t ES,sto and P t ES,rel M is the charging power and discharging power of the energy storage during period t; ES is the electrical energy storage capacity; μ ES,min and μ ES,max The lower and upper limits of the ratio of the remaining storage capacity to the total capacity; is the initial remaining capacity of the energy storage; T is the dispatching period; and is a Boolean variable, which means it is in charging state or in discharging state;

[0111] Step 2.2: Use equation (12)-(13) to establish a thermal energy storage model:

[0112]

[0113]

[0114] Formula (12) is the remaining heat constraint of thermal energy storage; Formula (13) indicates that the remaining power of thermal energy storage remains unchanged after a scheduling cycle T;

[0115] In formula (12)-formula (13), is the heat released or absorbed by the thermal energy storage during period t; and The upper limit of heat released and absorbed in unit dispatching time;

[0116] Step 2.3: Use formula (14) to establish a two-stage P2G model:

[0117]

[0118] Formula (14) indicates that the hydrogen produced by the electrolyzer can be used to generate electricity and heat for the hydrogen fuel cell and to synthesize methane in the methane reactor;

[0119] In formula (14), is the amount of electricity consumed by the electrolytic cell during period t; η EL is the electrolyzer efficiency; Providing hydrogen energy to the electrolyzer for hydrogen fuel cells to generate electricity; The electrolyzer provides hydrogen energy that the hydrogen fuel cell uses to produce heat; The energy provided by the electrolyzer to the methane reactor during period t;

[0120] Step 3: Establish a day-ahead dispatch optimization model for the integrated energy system of the park, taking into account the operating constraints of the system equipment, and taking the minimum weighted sum of the user load fluctuation variance and the system carbon emissions as the objective function:

[0121] Step 3.1: Use equations (15) to (17) to calculate the objective function of the running model:

[0122] min C=ω 1 C 1 +ω 2 C 2 (15)

[0123]

[0124]

[0125] Formula (15) is the objective function of the system operation model; Formula (16) is the fluctuation variance of the system user's heating and cooling load; Formula (17) is the carbon emissions of the system;

[0126] In formula (15) to formula (17), C op is the weighted sum; C 1 is the user load fluctuation variance; C 2 Carbon emissions from electricity and natural gas claimed from the upper energy grid; 1 and ω 2 C 1 and C 2 The weight of and is the user's electricity load demand, heating load demand and cooling load demand during period t; β elc is the carbon emissions per unit of electricity; gas is the carbon emissions produced per unit of natural gas;

[0127] Step 3.2: Define the energy balance constraint using equations (18) to (21):

[0128]

[0129]

[0130]

[0131]

[0132] Formula (18) is the system's electric energy balance constraint; Formula (19) is the system's natural gas balance constraint; Formula (20) is the system's thermal energy balance constraint; Formula (21) is the system's cold energy balance constraint;

[0133] In formula (18) to formula (21), The amount of electricity claimed from the superior energy network during period t; η T is the transformer efficiency; is the natural gas energy used for power generation by the combined heat and power equipment during period t; CCHPelc The power generation efficiency of the combined cooling, heating and power equipment; is the amount of electricity consumed by the electric heat pump during period t; is the power consumed by the air conditioner during period t; η HFCelc The power generation efficiency of hydrogen fuel cells; is the amount of natural gas applied for from the superior energy network during period t; η MR is the efficiency of the methane reactor; is the natural gas energy used by the combined heat and power equipment to produce heat during period t; is the natural gas energy used by the combined heat and power equipment to produce cooling during period t; CCHPheat is the heat generation efficiency of the combined cooling, heating and power equipment; η EHP is the efficiency of the electric heat pump; is the heat generation efficiency of the hydrogen fuel cell; η CCHPcool is the cooling efficiency of the combined heat and power equipment; η AC The cooling efficiency of the air conditioner;

[0134] Step 3.3: Define the equipment operation constraints using equations (22) to (27):

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Formula (22) is the power constraint of the combined cooling, heating and power equipment; Formula (23) is the power generation and heat generation operation constraint of the combined cooling, heating and power equipment; Formula (24) is the power generation and cooling generation operation constraint of the combined cooling, heating and power equipment;

[0142] In formula (23)-formula (24), and The lower and upper limits of the heat-to-electricity ratio of the combined cooling, heating and power equipment; and The lower and upper limits of the cooling-to-electricity ratio of the combined cooling, heating and power equipment;

[0143] Step 4: Construct a time-series planning model for the park's integrated energy system that takes into account wind and solar access and electric-thermal hybrid energy storage:

[0144] Step 4.1: Use equations (28) to (31) to construct the objective function of the park integrated energy system time series planning model:

[0145]

[0146]

[0147]

[0148]

[0149] Formula (28) is the objective function of the system planning model; Formula (29) is to calculate the capital recovery coefficient; Formula (30) is to calculate the investment cost; Formula (31) is to calculate the objective function of the operation model;

[0150] In formula (28)-(31), Ω Y is the planned annual set; Ω D A collection of equipment for planning; is the collection of class d equipment; C invest For construction consumables; C y is the operating consumables in year y; r CR is the recovery factor; rate is the discount rate; Construction consumables for equipment of type c of category d in year y; x y,c,d is a Boolean variable, indicating whether the cth model of the dth category equipment is selected in the yth year; C op is the weighted sum of the daily user load fluctuation variance and system carbon emissions in year y; n is the number of days in a year;

[0151] Step 4.2: Use formula (32) to define the equipment selection type constraint:

[0152]

[0153] Formula (32) indicates that for any type d equipment in year y, no more than one model is selected;

[0154] Step 4.3: Define the load constraint using equation (33):

[0155]

[0156] Formula (33) is the calculation formula for the load forecast value in year y, where is the annual growth rate of load; P 0 is the load value of the current year; P y is the load value in year y;

[0157] Step 4.4, define system operation related constraints: the system operation constraints that should be met in year y are the same as equations (1) to (14), (18) to (27) in step 3;

[0158] Step 5: Use the improved genetic algorithm to solve the time sequence planning model of the park integrated energy system:

[0159] Step 5.1: Figure 2 As shown, input the initial parameters, including: population number μ, crossover probability P c , mutation probability P m , the maximum number of iterations λ;

[0160] Step 5.2: Generate μ initial samples and construct a population set P = {n 1 ,n 2 ,n 3 ,…n k ,…n μ}, where n k is the kth individual in Ω Y The timing from Ω D Select the timing plan composed of different devices, and have:

[0161]

[0162] Among them, n yp,dq It represents the capacity selection plan for the equipment of type d in year y;

[0163] According to the equipment capacity timing planning scheme, the load demand in year y is updated, and the objective function shown in formula (28) is used as the individual fitness h(n k ), initialize the empty set P′;

[0164] Step 5.3, generate a random number r between 0 and 1, if r>P c , then go to step 5.4, otherwise, extract individual n from the population set P according to the fitness size as the selection probabilityx and n y After the crossover operation, the new individual n is obtained z , and calculate n z The fitness h(n z ), then go to step 5.5;

[0165] Step 5.4: Randomly select an individual n x , and according to the mutation probability P m For individual n x After the mutation operation, the new individual n is obtained z , calculate n z The fitness h(n z );

[0166] Step 5.5: The new individual n z Add to the set P′, update the original population set P with P∪P′, calculate the individual with the highest fitness value in the population set P and use it as the current optimal individual;

[0167] Step 5.6: Return to step 5.3-step 5.5 to iterate until the current number of iterations exceeds λ; thereby obtaining the final optimal individual and serving as the optimal timing planning solution.

Claims

1. A method for timing planning of a park integrated energy system taking into account wind and solar access and electric-thermal hybrid energy storage, characterized in that: The steps are as follows: Step 1: Establish an uncertain wind and solar output model based on chance constraints: Step 1.1: Define the wind and solar output constraint using formula (1): Pr{(P t WTact +P t PVact )≥(P t WT +P t PV )}≥c u (1) In formula (1), P t Wact and P t PVact are the actual power generated by wind power and photovoltaic power in period t; P t WT and P t PV is the dispatch output of wind power and photovoltaic power in the system during period t; Pr{·} is the probability of an event occurring; c u is the confidence level; Step 1.2: Use equations (2) to (4) to fit the wind power and photovoltaic power output errors: F(x)=[1+e -α(x-γ) ] -β (3) In formula (2) to formula (4), x is the wind and solar power output error; f(x) is the probability density function of x; F(x) is the cumulative distribution function of x; α, β and γ are the shape parameters of VPD distribution; F -1 (c u ) is the confidence level c u The wind and solar output error of the VPD fitting is shown below; Step 1.3: Use formula (5) to establish deterministic constraints: (P t WTact +P t PVact )-(P t WT +P t PV )≥F -1 (c u ) (5) Step 2: Establish an electric-thermal hybrid energy storage model and a two-stage P2G model: Step 2.1: Use equations (6) to (11) to establish an electric energy storage model: In formula (6) to formula (11), is the remaining energy storage capacity during period t; μ ES,loss is the self-loss coefficient of electric energy storage; μ ES,sto and μ ES,rel are the charging efficiency and discharging efficiency of energy storage respectively; P t ES,sto and P t ES,rel are the charging power and discharging power of the energy storage during period t respectively; M ES is the electrical energy storage capacity; μ ES,min and μ ES,max are the lower limit and upper limit of the proportion of the remaining storage capacity to the total capacity respectively; is the initial remaining capacity of the energy storage; T is the dispatching period; and are Boolean variables, indicating that the energy storage is in a charging state and in a discharging state respectively; Step 2.2: Use equation (12)-(13) to establish a thermal energy storage model: In formula (12)-formula (13), is the heat released or absorbed by the thermal energy storage during period t; and are the upper limit of heat released and the upper limit of heat absorbed within the unit scheduling time respectively; Step 2.3: Use formula (14) to establish a two-stage P2G model: In formula (14), is the amount of electricity consumed by the electrolytic cell during period t; η EL is the electrolyzer efficiency; Providing hydrogen energy to the electrolyzer for hydrogen fuel cells to generate electricity; Providing hydrogen energy to the electrolyzer for heat generation by hydrogen fuel cells; The energy provided by the electrolyzer to the methane reactor during period t; Step 3: Establish a day-ahead dispatch optimization model for the integrated energy system of the park, taking into account the operating constraints of system equipment, and taking the minimum weighted sum of user load fluctuation variance and system carbon emissions as the objective function: Step 3.1: Use equations (15) to (17) to calculate the objective function of the running model: min C op =ω1C1+ω2C2 (15) In formula (15) to formula (17), C op is the weighted sum; C1 is the user load fluctuation variance; C2 is the carbon emissions generated by applying for electricity and natural gas from the superior energy network; ω1 and ω2 are the weights of C1 and C2; and is the user's electricity load demand, heating load demand and cooling load demand during period t; β elc is the carbon emissions per unit of electricity; gas is the carbon emissions produced per unit of natural gas; Step 3.2: Define the energy balance constraint using equations (18) to (21): In formula (18) to formula (21), The amount of electricity claimed from the superior energy network during period t; η T is the transformer efficiency; is the natural gas energy used for power generation by the combined heat and power equipment during period t; CCHPelc The power generation efficiency of the combined cooling, heating and power equipment; is the amount of electricity consumed by the electric heat pump during period t; is the power consumed by the air conditioner during period t; η HFCelc The power generation efficiency of hydrogen fuel cells; is the amount of natural gas applied for from the superior energy network during period t; η MR is the efficiency of the methane reactor; is the natural gas energy used by the combined heat and power equipment to produce heat during period t; is the natural gas energy used by the combined heat and power equipment to produce cooling during period t; CCHPheat is the heat generation efficiency of the combined cooling, heating and power equipment; η EHP is the efficiency of the electric heat pump; is the heat generation efficiency of the hydrogen fuel cell; η CCHPcool is the cooling efficiency of the combined cooling, heating and power equipment; η AC The cooling efficiency of the air conditioner; Step 3.3: Define the equipment operation constraints using equations (22) to (27): In formula (23)-formula (24), and They are the lower and upper limits of the heat-to-electricity ratio of the combined cooling, heating and power equipment, respectively; and are the lower and upper limits of the cooling-to-electricity ratio of the combined cooling, heating and power equipment respectively; Step 4: Construct a time-series planning model for the park's integrated energy system that takes into account wind and solar access and electric-thermal hybrid energy storage: Step 4.1: Use equations (28) to (31) to construct the objective function of the park integrated energy system time series planning model: In formula (28)-(31), Ω Y is the planned annual set; Ω D A collection of equipment for planning; is the collection of class d equipment; C invest For construction consumables; C y is the operating consumables in year y; r CR is the recovery factor; rate is the discount rate; Construction consumables for equipment of type c of category d in year y; x y,c,d is a Boolean variable, indicating whether the cth model of the dth category equipment is selected in the yth year; C op is the weighted sum of the daily user load fluctuation variance and system carbon emissions in year y; n is the number of days in a year; Step 4.2: Use formula (32) to define the equipment selection constraints: Formula (32) indicates that for any type d equipment in year y, no more than one model is selected; Step 4.3: Define the load constraint using equation (33): In formula (33), is the annual growth rate of load; P0 is the load value of the current year; P y is the load value in year y; Step 4.4: Define the system operation related constraints as: in the yth year, satisfy the system operation constraints of equations (1) to (14), (18) to (27); Step 5: Use the improved genetic algorithm to solve the park integrated energy system timing planning model: Step 5.1: Input initial parameters, including: population number μ, crossover probability P c , mutation probability P m , the maximum number of iterations λ; Step 5.2: Generate μ initial samples and construct a population set P = {n1, n2, n3, ... n k ,…n μ }, where n k is the kth individual in Ω Y The timing of D Select the equipment timing planning scheme composed of different equipment; and have: Among them, n yp,dq It represents the capacity selection plan for the equipment of type d in year y; According to the equipment capacity timing planning scheme, the load demand in year y is updated, and the objective function shown in formula (28) is used as the individual fitness h(n k ), initialize the empty set P′; Step 5.3, generate a random number r between 0 and 1, if r>P c , then go to step 5.4, otherwise, extract individual n from the population set P according to the fitness size as the selection probability x and n y After the crossover operation, the new individual n is obtained z , and calculate n z The fitness h(n z ), then go to step 5.5; Step 5.4: Randomly select an individual n x , and according to the mutation probability P m For individual n x After the mutation operation, the new individual n is obtained z , calculate n z The fitness h(n z ); Step 5.5: The new individual n z Add to the set P′, update the original population set P with P∪P′, calculate the individual with the highest fitness value in the population set P and use it as the current optimal individual; Step 5.6: Return to step 5.3-step 5.5 to iterate until the current number of iterations exceeds λ; thereby obtaining the final optimal individual and serving as the optimal timing planning solution.

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