A double-layer capacity configuration method and system for a multi-energy complementary park

Abstract

The present invention discloses a dual-layer capacity configuration method and system for a multi-energy complementary park, including calculating the park's carbon emissions and carbon reduction based on the multi-energy complementary park's carbon accounting boundary, and then calculating the park's low-carbon evaluation coefficient; calculating the green certificate transaction cost based on the number of green certificates held by the park and the green certificate quota index; constructing an upper-layer configuration planning model for the park with the lowest annual comprehensive cost of the multi-energy complementary park as the optimization goal and considering the maximum capacity limit as a constraint; constructing a lower-layer operation optimization model for the park with the lowest daily operating cost of the multi-energy complementary park considering the green certificate transaction cost as the optimization goal and considering the low-carbon evaluation coefficient as a constraint; and jointly solving the park's upper-layer configuration planning model and the park's lower-layer operation optimization model to obtain the dual-layer capacity configuration parameters for the multi-energy complementary park. The present invention achieves in-depth exploration of the park's carbon reduction potential while comprehensively considering the park's economy and supply-demand balance.

Description

Technical Field

[0001] The present invention belongs to the technical field of park capacity configuration, and in particular relates to a double-layer capacity configuration method and system for a multi-energy complementary park. Background Art

[0002] As a complex of commercial, industrial, and residential areas, the park has varying energy demands across various facilities and businesses. It also features concentrated energy use, high unit energy consumption, and complex carbon emission sources. These high energy consumption levels have a significant impact on the environment. Building a multi-energy, complementary park architecture that integrates the production, conversion, storage, and consumption of multiple energy sources can effectively integrate multiple energy types to ensure an efficient and stable energy supply and maximize energy efficiency, while also reducing carbon emissions and promoting sustainable development.

[0003] Park capacity configuration is the most critical task in building a multi-energy complementary park and is the basis for optimizing system operation. Insufficient system configuration will not be able to meet various energy supply demands, and the renewable energy absorption capacity will be limited; the transition of system configuration will directly lead to increased costs. The best capacity configuration optimization result is to ensure that the system meets various constraints and restrictions, and achieve the coordinated optimization of energy, economic, environmental and other goals, to meet the various energy supply needs of the system and the investment goals of decision-makers. In the existing technology, the capacity configuration method of multi-energy complementary parks that takes into account both economy and environmental protection usually considers economy and environmental protection as optimization goals, ignoring the mandatory constraints of carbon reduction targets on the park. Summary of the Invention

[0004] In response to the above problems, the present invention proposes a two-layer capacity configuration method and system for a multi-energy complementary park, introduces a low-carbon evaluation coefficient as a constraint of the lower-layer operation optimization model, and realizes in-depth exploration of the park's carbon reduction potential under the premise of comprehensively considering the park's economy and supply and demand balance.

[0005] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for configuring double-layer capacity in a multi-energy complementary park, comprising:

[0007] Based on the carbon accounting boundary of the multi-energy complementary park, calculate the park's carbon emissions and carbon emission reductions;

[0008] Calculate the park's low-carbon evaluation coefficient based on the park's carbon emissions and carbon emission reductions;

[0009] Calculate the transaction cost of green certificates based on the number of green certificates held by the park and the green certificate quota indicators;

[0010] Taking the lowest annual comprehensive cost of the multi-energy complementary park as the optimization goal and considering the maximum capacity limit as the constraint condition, the upper-level configuration planning model of the park is constructed;

[0011] Taking the lowest daily operating cost of the multi-energy complementary park as the optimization goal, the daily operating cost of the multi-energy complementary park includes the green certificate transaction cost, and considering the low-carbon evaluation coefficient as a constraint condition, constructing the park's lower-level operation optimization model;

[0012] Based on the upper-level configuration planning model of the park and the lower-level operation optimization model of the park, a double-layer capacity configuration model of a multi-energy complementary park is constructed, the double-layer capacity configuration model of the multi-energy complementary park is solved, and the double-layer capacity configuration parameters of the multi-energy complementary park are obtained.

[0013] Optionally, the calculation formula for the park's carbon emissions is:

[0014] E=E buy +E CHP +E GB

[0015]

[0016]

[0017]

[0018] Where, E is the carbon emissions of the park, E buy Carbon emissions from electricity purchases for the park; η buy P is the carbon emission factor of electricity purchased from the grid; buy,t E is the amount of electricity purchased at time t; CHP is the carbon emissions of the CHP unit; η CHP,P is the carbon emission factor of the CHP unit power generation side; P CHP,t is the power generation of the CHP unit gas turbine at time t; W is the unit heat supply correction coefficient; η CHP,H is the carbon emission factor of the heating side of the CHP unit; is the heat-to-electricity ratio of the CHP unit; E GB is the carbon emission of the gas boiler unit; η GB is the carbon emission factor of the gas boiler; H GB,t The gas boiler supplies heat at time t;

[0019] The calculation formula for the park's carbon emission reduction is:

[0020]

[0021]

[0022]

[0023]

[0024] R P2G =G P2G,t

[0025] Where R is the carbon emission reduction of the park, R p Carbon emission reduction for renewable energy power generation; η P is the carbon emission per unit of electricity in the park; P PV,t 、P WT,t are the power generation of photovoltaic and wind turbines at time t; R h Carbon emission reduction for air source heat pump heating; η h is the carbon emission per unit of heat energy in the park, that is, the ratio of carbon emission generated by heat use to heat use; H air,t is the heating power of the air source heat pump at time t; R fuel is the fuel carbon emission reduction; η gas is the equivalent carbon emission per unit of natural gas; F P2G,t is the output of synthesis gas from the power-to-gas unit at time t; R P2G is the carbon emission reduction of the power-to-gas unit; G P2G,t is the amount of CO2 captured by the power-to-gas unit at time t.

[0026] Optionally, the calculation formula for the park low-carbon evaluation coefficient is:

[0027]

[0028] Where β is the low-carbon evaluation coefficient; R is the carbon emission reduction of the park; and E is the carbon emission of the park.

[0029] Optionally, the calculation formula for the green certificate transaction cost is:

[0030] C t =c t (U b -U r )

[0031]

[0032]

[0033] c t =α t -β t (U r -U b )

[0034] α t =c t,0

[0035]

[0036] Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b is the green certificate quota indicator; U r is the number of green certificates held by the park, η b P is the quota coefficient of the allocated green certificate quantity; load,t is the electrical load at time t; η t The number of green certificates that can be obtained for each kWh of renewable energy generation; P rew,t is the renewable energy power generation at time t, α t , β t are the two positive parameters of the inverse price function of the Cournot model; c t,0 is the basic price of green certificate transaction; δ t It is the historical data green certificate transaction price ratio.

[0037] Optionally, the method for constructing the park upper-layer configuration planning model includes:

[0038] The objective function of constructing the park upper-level configuration planning model is to minimize the annual comprehensive cost, which includes the equipment investment cost and the system operation cost, wherein the system operation cost is returned by the park upper-level configuration planning model;

[0039] Constructing a corresponding constraint condition for the upper-level configuration planning model of the park, wherein the constraint condition is that the configuration capacity of the park units does not exceed the maximum installed capacity that the park can bear;

[0040] An upper-level configuration planning model is established based on the objective function and constraints to decide the configuration capacity of each equipment to be built in the multi-energy complementary park, and the model is sent down to the lower-level operation optimization model of the park as the equipment output constraint.

[0041] Optionally, the park upper-level configuration planning model includes an objective function and constraints;

[0042] The objective function is:

[0043]

[0044] Where, C, C iv 、C oe,q are annual comprehensive cost, equipment investment cost, and typical day q operating cost respectively; Q is the number of typical days; T q is the number of days corresponding to the qth typical day; System operating costs;

[0045] The equipment investment cost calculation formula is:

[0046]

[0047] Where C iv is the equipment investment cost; c i N is the investment cost of the equipment i per unit capacity to be built; i is the installed capacity of the equipment i to be built; γ is the capital discount rate; r i is the life cycle of the equipment i to be built; N iv The number of types of equipment to be invested and constructed in the park;

[0048] The constraint condition includes: the installed capacity of each equipment to be built does not exceed the maximum installed capacity limit that the park can bear, which is expressed as:

[0049] 0≤N i ≤N i,max

[0050] Where N i is the installed capacity of the equipment i to be built; N i,max It is the maximum installed capacity that the park can bear, that is, the upper limit of the installed capacity of the equipment to be built.

[0051] Optionally, the method for constructing the park lower-level operation optimization model includes:

[0052] Constructing an objective function for the park's lower-level operation optimization model, wherein the objective function is to minimize daily operating costs, which include electricity purchase costs, fuel costs, equipment maintenance costs, carbon trading costs, and wind and solar curtailment costs;

[0053] Constructing constraints for the park's lower-level operation optimization model, including equipment output constraints, cooling, heating, and electricity power balance constraints, energy storage constraints, distribution network power purchase constraints, and low-carbon evaluation coefficient constraints;

[0054] A lower-level operation optimization model is established based on the objective function and constraints to determine the operating output of each device in the multi-energy complementary park, and the equipment operation strategy and system operation cost are returned to the upper-level configuration planning model.

[0055] Optionally, the objective function of the park lower layer operation optimization model is:

[0056] minC oe =C b +C f +C t +C c +C m +C a

[0057] Where C oe 、C b 、C f 、C c 、C t、C m 、C a They are typical daily operating costs, electricity purchase costs, fuel costs, carbon trading costs, green certificate trading costs, maintenance costs and wind and solar power curtailment costs;

[0058] The formula for calculating the electricity purchase cost is:

[0059]

[0060] Where C b is the electricity purchase cost, c b,t is the electricity price at time t; P buy,t The power purchased by the distribution network at time t;

[0061] The fuel cost calculation formula is:

[0062]

[0063] Where C f is the fuel cost, c gas is the purchase price of natural gas; G CHP,t , G GB,t are the air intake of the CHP unit and the air intake of the gas boiler at time t, respectively. P2G,t is the amount of synthesis gas produced by the power-to-gas unit at time t;

[0064] The carbon trading cost calculation formula is:

[0065] C c =c cb (ER P2G )

[0066] Where C c is the carbon trading cost, c cb is the carbon trading price; R P2G Carbon emission reduction of power-to-gas units;

[0067] The calculation formula for the green certificate transaction cost is:

[0068] C t =c t (U b -U r )

[0069] Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b To stipulate the green certificate quota indicators; U r The number of green certificates held for the park;

[0070] The maintenance cost calculation formula is:

[0071]

[0072] Where C m is the maintenance cost, c m,i P is the maintenance coefficient of the i-th equipment equivalent to unit power generation; i,t is the equivalent power generation of equipment i at time t; N m Types of equipment that require operation and maintenance;

[0073] The formula for calculating the cost of curtailed wind and solar power is:

[0074]

[0075] Where C a is the cost of curtailing wind and solar power, c a P is the penalty coefficient for curtailing wind and solar power; PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; PV,max,t 、P WT,max,t are the upper limits of photovoltaic and wind turbine output at time t respectively;

[0076] The constraints of the park's lower-level operation optimization model include cooling, heating, and electricity power balance constraints, equipment output constraints, energy storage constraints, power purchase constraints, and low-carbon evaluation coefficient constraints;

[0077] The cooling, heating and electric power balance constraints are:

[0078]

[0079] Where, P discharge,t P is the energy storage discharge power at time t; charge,t H is the energy storage charging power at time t; discharge,t H is the heat storage tank heat release power at time t; charge,t P is the heat storage tank charging power at time t; CHP,t is the power generation capacity of the CHP unit at time t; P PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; buy,t P is the power purchased at time t; load,t P is the electric load power at time t; ER,t P is the power consumption of the refrigerator at time t; AIR,t is the power consumption of the air source heat pump unit at time t; H GB,t is the heating power of the gas boiler at time t; H AIR,t is the heating power of the air source heat pump at time t; H load,t is the heat load power at time t; H AR,t is the heat consumption power of the absorption refrigerant at time t; C AR,tC is the cooling power of the absorption chiller at time t; ER,t C is the cooling power of the electric refrigerator at time t; load,t is the cooling load power at time t;

[0080] The equipment output constraints are:

[0081]

[0082]

[0083] In the formula, j is the equipment to be built, k is other equipment, P j,t is the equipment output of the jth equipment to be put into construction at time t, P k,t The output of the kth other device at time t, N j is the installed capacity of the jth equipment to be built, P k,max is the maximum output of the kth other device; ΔP CHP , ΔH GB They are the ramp power limits for CHP units and gas boilers;

[0084] The energy storage constraint is:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Where, P charge,t 、P discharge,t are the charging and discharging amounts of the electric energy storage respectively; are the maximum charge and discharge rate of electric energy storage; N PS Configure capacity for electric energy storage; H charge,t 、H discharge,t They are charging and discharging of thermal energy storage respectively; are the maximum charge and discharge rates of thermal energy storage; N HS Allocate capacity for thermal energy storage; S HS,t is the charge state of the thermal energy storage at time t; S PS,t S is the state of charge of the energy storage at time t; HS,min 、S HS,max are the minimum and maximum values ​​of the thermal energy storage charge state respectively; S PS,min 、S PS,max are the minimum and maximum values ​​of the state of charge of the energy storage, respectively; W HS,t 、WPS,t are the thermal energy storage and electrical energy storage at time t respectively; σ HS , σ PS are the self-loss rates of thermal energy storage and electrical energy storage respectively; ρ HS,charge , ρ PS,charge are the thermal energy storage charging and discharging efficiency; ρ PS,charge , ρ PS,discharge are the charging and discharging efficiency of electric energy storage;

[0091] The power purchase constraints of the distribution network are:

[0092] 0≤P buy,t ≤P buy,max

[0093] Where, P buy,t P is the purchased electric power at time t; buy,max The upper limit of power purchase for the distribution network;

[0094] The low carbon evaluation coefficient constraint is:

[0095] 0≤β≤β aim

[0096] Where, β is the low-carbon evaluation coefficient of the park on that day; β aim It is the low-carbon evaluation target of the park, that is, the maximum low-carbon evaluation coefficient.

[0097] Optionally, constructing a multi-energy complementary park double-layer capacity configuration model based on the park upper-layer configuration planning model and the park lower-layer operation optimization model, solving the multi-energy complementary park double-layer capacity configuration model, and obtaining the multi-energy complementary park double-layer capacity configuration parameters include:

[0098] The NSGA-II algorithm is used to solve the upper-level configuration planning model of the campus, determine the optimal configuration of the system, and find the best configuration solution to meet system requirements and maximize the configuration performance of the micro-energy network;

[0099] The CPLEX solver is used to solve the park's lower-level operation optimization model. The lower-level operation optimization model calculates the optimal operation plan of the system based on the optimal configuration given by the upper-level configuration planning model. The lower-level operation optimization model considers the system's real-time load demand, energy supply, and system configuration to determine the best operation strategy.

[0100] In a second aspect, the present invention provides a dual-layer capacity configuration system for a multi-energy complementary campus, including a storage medium and a processor;

[0101] The storage medium is used to store instructions;

[0102] The processor is configured to operate according to the instructions to execute the method according to any one of the first aspects.

[0103] Compared with the prior art, the present invention has the following beneficial effects:

[0104] This paper defines the carbon accounting boundaries of the park to accurately calculate the park's carbon emissions and carbon emission reductions, proposes a low-carbon evaluation coefficient for the park based on the park's carbon emission reduction ratio, and uses it as a low-carbon constraint for the park's capacity configuration model; establishes a two-layer capacity configuration model for a multi-energy complementary park, and jointly solves it based on the NSGA-II algorithm and the CPLEX commercial solver, effectively improving the park's carbon reduction performance, fully tapping the park's carbon reduction potential while comprehensively considering the park's economy and supply and demand balance, and promoting the sustainable development of the multi-energy complementary park. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0106] Figure 1 A schematic flow chart of a method for configuring double-layer capacity in a multi-energy complementary park according to an embodiment of the present invention;

[0107] Figure 2 A schematic diagram of the carbon emission accounting boundary of a park in one embodiment of the present invention;

[0108] Figure 3 This is a logical architecture diagram of a dual-layer capacity configuration model for a multi-energy complementary park in one embodiment of the present invention;

[0109] Figure 4 The load power and renewable energy generation power curves of a multi-energy complementary park in one embodiment of the present invention;

[0110] Figure 5 This is a schematic diagram comparing typical daily carbon emissions in four seasons according to an embodiment of the present invention;

[0111] Figure 6 A schematic diagram showing a comparison of installed capacities in an embodiment of the present invention;

[0112] Figure 7 This is a schematic diagram of renewable energy consumption on a typical day in four seasons according to an embodiment of the present invention;

[0113] Figure 8 This is a schematic diagram of equipment utilization on a typical day in four seasons according to an embodiment of the present invention. DETAILED DESCRIPTION

[0114] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0115] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features specified as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0116] Combined with attachment Figure 3 The logic diagram of the double-layer capacity configuration model of a multi-energy complementary park is shown. The logic of the double-layer capacity configuration model of a multi-energy complementary park established by the present invention is as follows: the upper-layer configuration planning model takes the lowest annual comprehensive cost of the park as the optimization goal, and seeks the best configuration scheme to meet the needs of the system and maximize the configuration performance of the micro-energy network. The lower-layer operation optimization model takes the lowest daily operating cost as the optimization goal, considers the proposed low-carbon evaluation coefficient constraint, optimizes the park equipment operation strategy based on the configuration scheme given by the upper-layer model, and updates the equipment output and operating cost to the upper-layer model. Finally, it is solved jointly based on the NSGA-II algorithm and the CPLEX commercial solver.

[0117] Combined with attachment Figure 1 The flowchart of the present invention is shown in further detail for describing the process of establishing the multi-energy complementary park capacity configuration model:

[0118] S1. Define the carbon accounting boundaries of the multi-energy complementary park to accurately calculate the park's carbon emissions and carbon emission reductions, and propose a low-carbon evaluation coefficient for the park based on the park's carbon emission reduction ratio;

[0119] S2. Calculate the transaction cost of green certificates based on the number of green certificates held by the park and the green certificate quota indicator;

[0120] S3. Build a top-level configuration planning model for the multi-energy complementary park, taking the lowest annual comprehensive cost as the optimization goal and considering the maximum capacity limit as a constraint;

[0121] S4. Optimize the multi-energy complementary park's daily operating costs to the minimum, including the green certificate transaction costs, and consider the low-carbon evaluation coefficient as a constraint condition to construct a park lower-level operation optimization model;

[0122] S5. Based on the upper-level configuration planning model of the park and the lower-level operation optimization model of the park, a two-layer capacity configuration model of a multi-energy complementary park is constructed, and the NSGA-II algorithm and the CPLEX commercial solver are used to jointly solve the two-layer capacity configuration model of the multi-energy complementary park to obtain the two-layer capacity configuration parameters of the multi-energy complementary park.

[0123] In a specific implementation of the embodiment of the present invention, the demarcation of the carbon emission accounting boundary of the multi-energy complementary park in step S1 is achieved by:

[0124] The park's electricity sources include electricity purchased from the power grid, combined heat and power (CHP) unit power generation, photovoltaic power generation and wind power generation; the park's heat sources include air source heat pump heating, gas boiler heating and combined heat and power unit heating; the park's cold energy sources include electric chillers and absorption chillers; in addition, the park is also equipped with electric energy storage and heat storage tanks. The park's carbon emissions mainly come from electricity purchases, internal power generation and heating, but the park also takes measures to reduce carbon emissions. One of them is electricity substitution, by using low-carbon electricity to replace high-carbon energy; another measure is to use renewable energy generation, such as photovoltaic and wind power, to reduce carbon emissions and improve energy sustainability. The carbon emission accounting boundaries of multi-energy complementary parks are as follows: Figure 2 shown.

[0125] In a specific implementation of the embodiment of the present invention, the accurate calculation of the park's carbon emissions and carbon emission reductions based on the multi-energy complementary park's carbon accounting boundary includes:

[0126] The park's carbon emissions come from: electricity purchased from the grid, carbon emissions from CHP units, and carbon emissions from gas-fired boiler units. The carbon emissions from electricity purchased from the grid are affected by the amount of electricity purchased and the grid's carbon emission coefficient. An increase in electricity purchased or a higher grid carbon emission factor will lead to an increase in carbon emissions from electricity purchased from the grid. Combined heat and power units have multi-terminal output characteristics, and their power generation-side carbon emissions must be calculated after correction using a heat supply correction coefficient. Carbon emissions from gas-fired boilers are calculated based on their output heating power. The park's carbon emissions are calculated using the following formula:

[0127]

[0128]

[0129]

[0130]

[0131] Where, E buy Carbon emissions from electricity purchases for the park; η buy P is the carbon emission factor of electricity purchased from the grid; buy,t E is the amount of electricity purchased at time t; CHP is the carbon emissions of the CHP unit; η CHP,P is the carbon emission factor of the CHP unit power generation side; P CHP,t is the power generation of the CHP unit gas turbine at time t; W is the unit heat supply correction coefficient; η CHP,H is the carbon emission factor of the heating side of the CHP unit; is the heat-to-electricity ratio of the CHP unit; E GB is the carbon emission of the gas boiler unit; η GB is the carbon emission factor of the gas boiler; H GB,t The gas boiler supplies heat at time t.

[0132] The park considers three ways to reduce carbon emissions: first, renewable energy generation replaces some fossil energy generation; second, power-to-gas units capture CO2; and third, through electricity substitution, using air-source heat pumps to replace some gas boilers for heating, and power-to-gas units to capture CO2 and produce synthesis gas to replace some natural gas as fuel. The park's carbon emission reduction is calculated using the following formula:

[0133] R=R P +R h +R fuel +R P2G

[0134]

[0135]

[0136]

[0137] R P2G =G P2G,t

[0138] Where R p Carbon emission reduction for renewable energy power generation; η P is the carbon emission per unit of electricity in the park; P PV,t 、P WT,t are the power generation of photovoltaic and wind turbines at time t; R h Carbon emission reduction for air source heat pump heating; η h is the carbon emission per unit of heat energy in the park, that is, the ratio of carbon emission generated by heat use to heat use; H air,t is the heating power of the air source heat pump at time t; R fuel is the fuel carbon emission reduction; η gas is the equivalent carbon emission per unit of natural gas; FP2G,t is the output of synthesis gas from the power-to-gas unit at time t; R P2G is the carbon emission reduction of the power-to-gas unit; G P2G,t is the amount of CO2 captured by the power-to-gas unit at time t.

[0139] In a specific implementation of the embodiment of the present invention, the low-carbon evaluation coefficient of the park is proposed based on the carbon emission level and carbon emission reduction ratio of the park, specifically:

[0140] The low-carbon evaluation coefficient of the park is the percentage residual of the ratio of the park's carbon emission reduction to the park's carbon emissions. This parameter can be used as a quantitative indicator to objectively evaluate whether the park's low-carbonization effect can meet the established carbon reduction targets.

[0141]

[0142] Where β is the low-carbon evaluation coefficient; R is the carbon emission reduction of the park; and E is the carbon emission of the park.

[0143] A lower park's low-carbon evaluation coefficient indicates greater success in reducing and improving carbon emissions. This means the park utilizes more environmentally friendly energy to meet its load requirements and implements more carbon reduction projects to increase carbon reductions. Regarding low-carbon targets, parks determine their target low-carbon rating based on their resource endowments and carbon reduction ambitions. They then implement corresponding equipment capacity configuration and operational strategies to ensure the park's low-carbon evaluation coefficient meets its established low-carbon targets. Based on current mainstream research, the definitions of park low-carbon ratings are shown in Table 1.

[0144] Table 1

[0145]

[0146]

[0147] In a specific implementation of the embodiment of the present invention, the calculation process of the green certificate transaction cost in step S2 is specifically as follows:

[0148] By deploying renewable energy units, the park can generate environmentally friendly, green, and clean electricity. Green certificates, earned through renewable energy generation, reflect the green nature of the energy system. A quota coefficient is used to determine the park's green certificate quota, ensuring that the required number of green certificates increases with the park's load. If the park's green certificate holdings exceed the specified quota, the park can sell the excess to generate revenue. Conversely, if the park's green certificate holdings fall below the specified quota, the park must purchase green certificates to meet the quota.

[0149] C t =c t (U b -Ur )

[0150] Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b To stipulate the green certificate quota indicators; U r The number of green certificates held by the park.

[0151]

[0152]

[0153] Where η b P is the quota coefficient of the allocated green certificate quantity; load,t is the electrical load at time t; η t The number of green certificates that can be obtained for each kWh of renewable energy generation; P rew,t is the renewable energy power generation at time t.

[0154] As the number of green certificates increases, the transaction price of green certificates will gradually decrease. The Cournot transaction model of quantity competition is used to describe the transaction price of green certificates. According to the formula of the Cournot model, the green certificate transaction price model can be expressed as follows:

[0155] c t =α t -β t (U r -U b )

[0156] α t =c t,0

[0157]

[0158] Where, α t , β t are the two positive parameters of the inverse price function of the Cournot model; c t,0 is the basic price of green certificate transaction; δ t It is the historical data green certificate transaction price ratio.

[0159] In the embodiment of the present invention, in step S3, the annual comprehensive cost of the multi-energy complementary park is minimized as the optimization goal, and the maximum capacity limit is considered as a constraint condition to construct the park upper-level configuration planning model, specifically:

[0160] The objective function of the upper-level configuration planning model of the park is to minimize the annual comprehensive cost, including equipment investment cost and system operation cost, of which the system operation cost is returned by the lower-level model;

[0161] The corresponding constraint condition of the park upper-level configuration planning model is that the configuration capacity of the park units does not exceed the maximum installed capacity that the park can bear;

[0162] An upper-layer configuration planning model is established according to the objective function and constraint conditions.

[0163] The objective function of the park upper-level configuration planning model is to minimize the annual comprehensive cost, including:

[0164] The objective function of the park upper-level configuration planning model is represented by the following formula:

[0165]

[0166] Where, C, C iv 、C oe,q are annual comprehensive cost, equipment investment cost, and typical day q operating cost respectively; Q is the number of typical days; T q is the number of days corresponding to the qth typical day.

[0167] The equipment investment cost calculation formula is:

[0168]

[0169] Where C iv is the equipment investment cost; c i N is the investment cost of the equipment i per unit capacity to be built; i is the installed capacity of the equipment i to be built; γ is the capital discount rate; r i is the life cycle of the equipment i to be built; N iv The number of types of equipment to be invested and constructed in the park.

[0170] The constraints of the park upper-level configuration planning model are specifically:

[0171] The installed capacity of each equipment to be built shall not exceed the maximum installed capacity limit that the park can bear:

[0172] 0≤N i ≤N i,max

[0173] Where N i is the installed capacity of the equipment i to be built; N i,max It is the maximum installed capacity that the park can bear, that is, the upper limit of the installed capacity of the equipment to be built.

[0174] In a specific implementation of the embodiment of the present invention, in step S4, the optimization goal is to minimize the daily operating cost of the multi-energy complementary park. The daily operating cost of the multi-energy complementary park includes the green certificate transaction cost, and the low-carbon evaluation coefficient is considered as a constraint condition to construct the park lower-level operation optimization model, specifically:

[0175] The objective function of the park's lower-level operation optimization model is to minimize daily operating costs, including electricity purchase costs, fuel costs, equipment maintenance costs, carbon trading costs, and wind and solar curtailment costs;

[0176] The corresponding constraints of the park's lower-level operation optimization model include equipment output constraints, cooling, heating and power balance constraints, energy storage constraints, distribution network power purchase constraints and low-carbon evaluation coefficient constraints;

[0177] A lower-level operation optimization model is established based on the objective function and constraint conditions.

[0178] The objective function of the park lower-level operation optimization model is to minimize the daily comprehensive cost, including:

[0179] The objective function of the park lower-level operation optimization model is represented by the following formula:

[0180] minC oe =C b +C f +C c +C m +C a

[0181] Where C oe 、C b 、C f 、C c 、C m 、C a They are typical daily operating costs, electricity purchase costs, fuel costs, carbon trading costs, maintenance costs and wind and solar power curtailment costs.

[0182] The formula for calculating the electricity purchase cost is:

[0183]

[0184] Where c b,t is the electricity price at time t; P buy,t is the power purchased by the distribution network at time t.

[0185] The fuel cost calculation formula is:

[0186]

[0187] Where c gas is the purchase price of natural gas; G CHP,t , G GB,t are the air intake of the CHP unit and the air intake of the gas boiler at time t, respectively. P2G,t is the amount of synthesis gas produced by the power-to-gas unit at time t.

[0188] The carbon trading cost calculation formula is:

[0189] C c =c cb (ER P2G )

[0190] Where c cb is the carbon trading price; R P2G It is the carbon emission reduction of power-to-gas units.

[0191] The calculation formula for the green certificate transaction cost is:

[0192] C t =c t (U b -U r )

[0193] Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b To stipulate the green certificate quota indicators; U r The number of green certificates held by the park.

[0194] The maintenance cost calculation formula is:

[0195]

[0196] Where c m,i P is the maintenance coefficient of the i-th equipment equivalent to unit power generation; i (t) is the equivalent power generation of equipment i at time t; N m The type of equipment that needs to be operated and maintained.

[0197] The formula for calculating the cost of curtailed wind and solar power is:

[0198]

[0199] Where c a P is the penalty coefficient for curtailing wind and solar power; PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; PV,max,t 、P WT,max,t are the upper limits of photovoltaic and wind turbine output at time t respectively;

[0200] The constraints of the park's lower-level operation optimization model include cooling, heating, and electricity power balance constraints, equipment output constraints, energy storage constraints, power purchase constraints, and low-carbon evaluation coefficient constraints;

[0201] The cooling, heating and electric power balance constraints are:

[0202]

[0203] Where, Pdischarge,t P is the energy storage discharge power at time t; charge,t H is the energy storage charging power at time t; discharge,t H is the heat storage tank heat release power at time t; charge,t P is the heat storage tank charging power at time t; CHP,t is the power generation capacity of the CHP unit at time t; P PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; buy,t P is the power purchased at time t; load,t P is the electric load power at time t; ER,t P is the power consumption of the refrigerator at time t; AIR,t is the power consumption of the air source heat pump unit at time t; H GB,t is the heating power of the gas boiler at time t; H AIR,t is the heating power of the air source heat pump at time t; H load,t is the heat load power at time t; H AR,t is the heat consumption power of the absorption refrigerant at time t; C AR,t C is the cooling power of the absorption chiller at time t; ER,t C is the cooling power of the electric refrigerator at time t; load,t is the cooling load power at time t;

[0204] The device output constraint is obtained by the following formula:

[0205]

[0206]

[0207] In the formula, j is the equipment to be built, k is other equipment, P j,t is the equipment output of the jth equipment to be put into construction at time t, P k,t The output of the kth other device at time t, N j is the installed capacity of the jth equipment to be built, P k,max is the maximum output of the kth other device; ΔP CHP , ΔH GB These are the ramp power limits for CHP units and gas boilers respectively.

[0208] The energy storage constraint is obtained by the following formula:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214] Where, P charge,t 、P discharge,t are the charging and discharging amounts of the electric energy storage respectively; are the maximum charge and discharge rate of electric energy storage; N PS Configure capacity for electric energy storage; H charge,t 、H discharge,t They are charging and discharging of thermal energy storage respectively; are the maximum charge and discharge rates of thermal energy storage; N HS Allocate capacity for thermal energy storage; S HS,t is the charge state of the thermal energy storage at time t; S PS,t S is the state of charge of the energy storage at time t; HS,min 、S HS,max are the minimum and maximum values ​​of the thermal energy storage charge state respectively; S PS,min 、S PS,max are the minimum and maximum values ​​of the state of charge of the energy storage, respectively; W HS,t 、W PS,t are the thermal energy storage and electrical energy storage at time t respectively; σ HS , σ PS are the self-loss rates of thermal energy storage and electrical energy storage respectively; ρ HS,charge , ρ PS,charge are the thermal energy storage charging and discharging efficiency; ρ PS,charge , ρ PS,discharge are the energy storage charging and discharging efficiency respectively.

[0215] The power purchase constraints of the distribution network are obtained through the following formula:

[0216] 0≤P buy,t ≤P buy,max

[0217] Where, P buy (t) is the purchased electric power at time t; P buy,max The upper limit of power purchase for the distribution network.

[0218] The low-carbon evaluation coefficient constraint is obtained by the following formula:

[0219] 0≤β≤β aim

[0220] Where, β is the low-carbon evaluation coefficient of the park on that day; β aim It is the low-carbon evaluation target of the park, that is, the maximum low-carbon evaluation coefficient.

[0221] In the embodiment of the present invention, in step S5, a two-layer capacity configuration model of a multi-energy complementary park is constructed based on the upper-layer configuration planning model of the park and the lower-layer operation optimization model of the park, and the NSGA-II algorithm is used in conjunction with the CPLEX commercial solver to solve the two-layer capacity configuration model of the multi-energy complementary park. This is achieved by:

[0222] Based on the upper configuration planning model and lower operation optimization model, a two-layer capacity configuration model of a multi-energy complementary park is constructed taking into account low-carbon constraints and green certificate trading. The logical structure is as follows: Figure 3 As shown in the figure, the upper-level configuration planning model determines the optimal system configuration, finding the best configuration solution to meet system requirements and maximize the micro-energy grid's performance. The lower-level operation optimization model calculates the optimal operation solution for the system based on the configuration provided by the upper-level configuration planning model. This model considers factors such as the system's real-time load demand, energy supply, and system configuration to determine the optimal operation strategy to ensure efficient system operation and energy utilization. The upper-level configuration planning model is solved using the NSGA-II algorithm, while the lower-level operation optimization model is a 0-1 mixed integer linear programming problem, solved in Matlab using the YALMIP toolbox and the commercial CPLEX solver.

[0223] In the embodiment of the present invention, the simulation parameters are as follows: the cooling, heating and electricity load curves of typical days in four seasons and the renewable energy output forecast curves. Figure 4 The relevant parameters of each device in the multi-energy complementary park are shown in Tables 2 to 11, with a discount rate of 0.08; the maximum power purchased from the grid is 1000kW, and the time-of-use electricity price is shown in Table 12; the park's low-carbon target is a low-carbon park, and the low-carbon evaluation coefficient constraint is set at 60%; the carbon trading price is 0.267 yuan / kg; the green certificate quota coefficient is 0.35, the green certificate trading price is 220 yuan / book, and the green certificate conversion coefficient is 1 book / MWh; the natural gas thermal combustion value is 9.78kWh / m3, which is equivalent to carbon emissions of 1.8kg / m 3 The purchase price is 2.45 yuan / m 3 The thermoelectric conversion coefficient is 3600 MJ / MW·h. The number of populations in the genetic algorithm is 40, the number of iterations is 400, the crossover rate is 0.9, and the mutation rate is 0.2.

[0224] Table 2 CHP related parameters

[0225]

[0226] Table 3 Air source heat pump related parameters

[0227]

[0228] Table 4 Photovoltaic related parameters

[0229]

[0230]

[0231] Table 5 Absorption chiller related parameters

[0232]

[0233] Table 6 Wind power related parameters

[0234]

[0235] Table 7 Electric Refrigerator Related Parameters

[0236]

[0237] Table 8 Electric energy storage related parameters

[0238]

[0239] Table 9 Gas boiler related parameters

[0240]

[0241]

[0242] Table 10 Power-to-gas related parameters

[0243]

[0244] Table 11 Thermal energy storage related parameters

[0245]

[0246] Table 12 Time-of-use electricity prices

[0247]

[0248] Furthermore, after solving the proposed dual-layer capacity configuration problem of the multi-energy complementary park through model optimization, the optimal results are shown in Table 13 below:

[0249] Table 13 Planning costs

[0250]

[0251] Because the model incorporates low-carbon evaluation coefficient constraints and establishes a green certificate trading mechanism, the park deploys higher-capacity renewable energy units to ensure low-carbon operation and obtain green certificates exceeding its quota. Furthermore, the lower economic performance of renewable energy units compared to traditional units leads to high investment costs, reaching 18.6339 million yuan, or approximately 46.31%. Furthermore, annual operating costs, including carbon trading costs, green certificate trading costs, fuel costs, electricity purchase costs, maintenance costs, and wind and solar power curtailment, are also significant items, accounting for a significant portion of the total cost, reaching 21.6033 million yuan, or approximately 53.69%. Carbon trading costs and green certificate trading costs are strongly correlated with environmental impact and low-carbon development. Carbon trading costs reached 5.0743 million yuan, while green certificate trading costs were -401,700 yuan, indicating that the park's green certificate acquisition exceeded its quota, indicating that it generated a certain amount of income from green certificate trading. Fuel costs and electricity purchase costs totaled RMB 13.576 million and RMB 1.4537 million, respectively, covering the project's energy supply. Because the park's cooling, heating, and electricity loads are largely met by gas-fired boilers and CHP units, the park still incurs significant fuel costs to purchase fossil fuels for these traditional units. However, electricity purchase costs are lower than other costs due to the park's ample on-site generators.

[0252] Furthermore, the carbon emission of the multi-energy complementary park was analyzed to obtain the carbon emission, carbon emission reduction and low carbon evaluation coefficient of each season. Figure 5 shown.

[0253] Depend on Figure 5 It can be seen that due to the large cooling and electricity loads in summer, carbon emissions are the highest among the four seasons, reaching 68,253.49 kg. Therefore, although renewable energy power generation resources are also relatively abundant in summer, its high carbon emissions still lead to a high low-carbon evaluation coefficient, which just meets the set low-carbon evaluation coefficient constraint of 60.00%. At the same time, thanks to the abundant wind and light resources and relatively moderate load demand, the carbon emission reductions on typical days in spring and autumn are only 1.58% and 8.43% less than those in summer, respectively, while carbon emissions are reduced by 33.64% and 34.89%, and their low-carbon evaluation coefficients are 40.67% and 43.74%, respectively. In terms of typical days in autumn, although its carbon emissions have decreased significantly compared to summer, its carbon emission reductions are also greatly reduced due to the lack of renewable energy resources, and its low-carbon evaluation coefficient is also 60.00%.

[0254] Furthermore, the equipment capacity configuration of the multi-energy complementary park is analyzed to obtain the installed capacity of photovoltaic, wind power, CHP, gas boiler, electric energy storage, thermal energy storage, air source heat pump and power-to-gas unit in the planning results. Figure 6 shown.

[0255] Depend on Figure 6 As can be seen, within the constraints of the low-carbon assessment coefficient, the park has configured relatively low capacities of CHP units and gas-fired boiler units to meet its low-carbon goals. The CHP unit is configured at 3224 kW, representing 53.73% of its maximum installed capacity, and the gas-fired boiler unit is configured at 1840 kW, representing 30.67% of its maximum installed capacity. Furthermore, in conjunction with the introduction of a green certificate trading mechanism, the park has deployed a large number of photovoltaic and wind turbines to generate clean electricity and obtain green certificates, representing 91.39% and 89.02% of their maximum installed capacity, respectively. Furthermore, the high load demand on typical summer days necessitates the deployment of a large number of energy supply units. However, during the relatively low load seasons of spring and autumn, when the park experiences resource redundancy, the park has deployed 611 kW of thermal energy storage and 974 kW of electrical energy storage to facilitate energy transfer during peak resource demand. A 319 kW power-to-gas unit has also been deployed to reduce carbon emissions while absorbing the excess renewable energy in spring and autumn, ensuring that the park operates within its low-carbon assessment targets.

[0256] Furthermore, the renewable energy absorption rate of the multi-energy complementary park is analyzed, and the renewable energy absorption rate is defined as the ratio of the actual output of the renewable energy unit to the maximum predicted output under its current installed capacity:

[0257]

[0258] Where λ rew,i is the i-th renewable energy consumption rate, P i,t is the operating power of the i-th renewable energy at time t; P i,t,max is the maximum operating power of the i-th renewable energy at time t.

[0259] Obtain the renewable energy consumption situation of the park on a typical daily and annual time scale in different seasons. Figure 7 The wind power absorption rate is 100% in all seasons, and the photovoltaic absorption rate is 100% in spring, summer, and winter. Only on typical days in autumn is there some curtailment of solar power, with a photovoltaic absorption rate of 99.8%. The overall absorption rate is 99.9%, indicating that most of the redundant resources can be transferred through energy storage equipment and absorbed by power-to-gas units.

[0260] Furthermore, the equipment utilization rate of the multi-energy complementary park is analyzed, and the equipment utilization rate is defined as the ratio of the annual equivalent power of the equipment to the invested power:

[0261]

[0262] Obtain the equipment utilization rate of the park on a typical day in four seasons. Figure 8As shown. Overall, air-source heat pump utilization was high, reaching 83.47% in summer and 83.79% in winter, respectively. This indicates that air-source heat pumps operate near maximum capacity on typical days in summer and winter. CHP units also maintained a high utilization rate, reaching approximately 85.32% in summer. However, due to the large proportion of renewable energy generation in spring and autumn, CHP unit utilization was lower than in summer and winter. Simultaneously, due to the decrease in CHP unit utilization in spring and autumn, gas boiler unit utilization increased compared to summer. In winter, due to the large heat load demand, gas boiler utilization was the highest of all seasons, reaching 11.48%. Electricity and thermal energy storage utilization was low in all seasons. Power-to-gas units operated only in spring and autumn, when resources were redundant, at 4.17% and 12.27%, respectively.

[0263] Example 2

[0264] Based on the same inventive concept as Example 1, the present invention provides a dual-layer capacity configuration system for a multi-energy complementary campus, including a storage medium and a processor;

[0265] The storage medium is used to store instructions;

[0266] The processor is configured to operate according to the instruction to execute the method according to any one of embodiment 1

[0267] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0268] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0269] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0270] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0271] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

[0272] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for configuring double-layer capacity in a multi-energy complementary park, characterized in that: include: Based on the carbon accounting boundary of the multi-energy complementary park, calculate the park's carbon emissions and carbon emission reductions; Calculate the park's low-carbon evaluation coefficient based on the park's carbon emissions and carbon emission reductions; Calculate the transaction cost of green certificates based on the number of green certificates held by the park and the green certificate quota indicators; Taking the lowest annual comprehensive cost of the multi-energy complementary park as the optimization goal and considering the maximum capacity limit as the constraint condition, the upper-level configuration planning model of the park is constructed; Taking the lowest daily operating cost of the multi-energy complementary park as the optimization goal, the daily operating cost of the multi-energy complementary park includes the green certificate transaction cost, and considering the low-carbon evaluation coefficient as a constraint condition, constructing the park's lower-level operation optimization model; Based on the upper-layer configuration planning model of the park and the lower-layer operation optimization model of the park, a double-layer capacity configuration model of the multi-energy complementary park is constructed, and the double-layer capacity configuration model of the multi-energy complementary park is solved to obtain the double-layer capacity configuration parameters of the multi-energy complementary park; The calculation formula for the green certificate transaction cost is: C t =c t (U b -U r ) c t =a t -b t (U r -U b ) α t ＝c t,0 Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b is the green certificate quota indicator; U r is the number of green certificates held by the park, η b P is the quota coefficient of the allocated green certificate quantity; load,t is the electric load at time t; η t The number of green certificates that can be obtained for each kWh of renewable energy generation; P rew,t is the renewable energy power generation at time t, α t , β t are the two positive parameters of the inverse price function of the Cournot model; c t,0 is the basic price of green certificate transaction; δ t The historical data green certificate transaction price ratio; The method for constructing the park upper-level configuration planning model includes: The objective function of constructing the park upper-level configuration planning model is to minimize the annual comprehensive cost, which includes the equipment investment cost and the system operation cost, wherein the system operation cost is returned by the park upper-level configuration planning model; Constructing a corresponding constraint condition for the upper-level configuration planning model of the park, wherein the constraint condition is that the configuration capacity of the park units does not exceed the maximum installed capacity that the park can bear; Based on the objective function and constraints, an upper-level configuration planning model is established to determine the configuration capacity of each device to be built in the multi-energy complementary park, and the capacity is sent to the lower-level operation optimization model of the park as the equipment output constraint; The method for constructing the park lower layer operation optimization model includes: Constructing an objective function for the park's lower-level operation optimization model, wherein the objective function is to minimize daily operating costs, which include electricity purchase costs, fuel costs, equipment maintenance costs, carbon trading costs, and wind and solar curtailment costs; Constructing constraints for the park's lower-level operation optimization model, including equipment output constraints, cooling, heating, and electricity power balance constraints, energy storage constraints, distribution network power purchase constraints, and low-carbon evaluation coefficient constraints; Establish a lower-level operation optimization model based on the objective function and constraints, determine the operating output of each device in the multi-energy complementary park, and return the equipment operation strategy and system operation cost to the upper-level configuration planning model; The method of constructing a dual-layer capacity configuration model for a multi-energy complementary park based on the upper-layer configuration planning model and the lower-layer operation optimization model of the park, solving the dual-layer capacity configuration model for the multi-energy complementary park, and obtaining the dual-layer capacity configuration parameters of the multi-energy complementary park includes: The NSGA-II algorithm is used to solve the upper-level configuration planning model of the campus, determine the optimal configuration of the system, and find the best configuration solution to meet system requirements and maximize the configuration performance of the micro-energy network; The CPLEX solver is used to solve the park's lower-level operation optimization model. The lower-level operation optimization model calculates the optimal operation plan of the system based on the optimal configuration given by the upper-level configuration planning model. The lower-level operation optimization model considers the system's real-time load demand, energy supply, and system configuration to determine the best operation strategy.

2. The method for configuring double-layer capacity in a multi-energy complementary park according to claim 1, characterized in that: The calculation formula for the park's carbon emissions is: E=E buy +E CHP +E GB Where, E is the carbon emissions of the park, E buy Carbon emissions from electricity purchases for the park; η buy P is the carbon emission factor of electricity purchased from the grid; buy,t E is the electricity purchased at time t; CHP is the carbon emissions of the CHP unit; η CHP,P is the carbon emission factor of the CHP unit power generation side; P CHP,t is the power generation of the CHP unit gas turbine at time t; W is the unit heat supply correction coefficient; η CHP,H is the carbon emission factor of the heating side of the CHP unit; is the heat-to-electricity ratio of the CHP unit; E GB is the carbon emission of the gas boiler unit; η GB is the carbon emission factor of the gas boiler; H GB,t The heat supply of the gas boiler at time t; The calculation formula for the park's carbon emission reduction is: R=R P +R h +R fuel +R P2G R P2G =G P2G,t Where R is the carbon emission reduction of the park, R p Carbon emission reduction for renewable energy power generation; η P is the carbon emission per unit of electricity in the park; P PV,t 、P WT,t are the power generation of photovoltaic and wind turbines at time t; R h Carbon emission reduction for air source heat pump heating; η h is the carbon emission per unit of heat energy in the park, that is, the ratio of carbon emission generated by heat use to heat use; H air,t is the heating power of the air source heat pump at time t; R fuel is the fuel carbon emission reduction; η gas is the equivalent carbon emission per unit of natural gas; F P2G,t is the output of synthesis gas from the power-to-gas unit at time t; R P2G is the carbon emission reduction of the power-to-gas unit; G P2G,t is the amount of CO2 captured by the power-to-gas unit at time t.

3. The method for configuring double-layer capacity in a multi-energy complementary park according to claim 2, characterized in that: The calculation formula of the park's low-carbon evaluation coefficient is: Where β is the low-carbon evaluation coefficient; R is the carbon emission reduction of the park; and E is the carbon emission of the park.

4. The method for configuring double-layer capacity in a multi-energy complementary park according to claim 1, characterized in that: The park upper configuration planning model includes an objective function and constraints; The objective function is: Where, C, C iv 、C oe,q are annual comprehensive cost, equipment investment cost, and typical day q operating cost respectively; Q is the number of typical days; T q is the number of days corresponding to the qth typical day; System operating costs; The equipment investment cost calculation formula is: Where C iv is the equipment investment cost; c i N is the investment cost of the equipment i per unit capacity to be built; i is the installed capacity of the equipment i to be built; γ is the capital discount rate; r i is the life cycle of the equipment i to be built; N iv The number of types of equipment to be invested and constructed in the park; The constraint condition includes: the installed capacity of each equipment to be built does not exceed the maximum installed capacity limit that the park can bear, which is expressed as: 0≤N i ≤N i,max Where N i is the installed capacity of the equipment i to be built; N i,max It is the maximum installed capacity that the park can bear, that is, the upper limit of the installed capacity of the equipment to be built.

5. The method for configuring double-layer capacity of a multi-energy complementary park according to claim 1, characterized in that: The objective function of the park lower level operation optimization model is: minC oe =C b +C f +C t +C c +C m +C a Where C oe 、C b 、C f 、C c 、C t 、C m 、C a They are typical daily operating costs, electricity purchase costs, fuel costs, carbon trading costs, green certificate trading costs, maintenance costs and wind and solar power curtailment costs; The formula for calculating the electricity purchase cost is: Where C b is the electricity purchase cost, c b,t is the electricity price at time t; P buy,t The power purchased by the distribution network at time t; The fuel cost calculation formula is: Where C f is the fuel cost, c gas is the purchase price of natural gas; G CHP,t , G GB,t are the air intake of the CHP unit and the air intake of the gas boiler at time t, respectively. P2G,t is the amount of synthesis gas produced by the power-to-gas unit at time t; The carbon trading cost calculation formula is: C c =c cb (E-R P2G ) Where C c is the carbon trading cost, c cb is the carbon trading price; R P2G Carbon emission reduction of power-to-gas units; The calculation formula for the green certificate transaction cost is: C t =c t (U b -U r ) Where C t is the transaction cost of green certificates; c t is the green certificate transaction price; U b To stipulate the green certificate quota indicators; U r The number of green certificates held for the park; The maintenance cost calculation formula is: Where C m is the maintenance cost, c m,i P is the maintenance coefficient of the i-th equipment equivalent to unit power generation; i,t is the equivalent power generation of equipment i at time t; N m Types of equipment that require operation and maintenance; The formula for calculating the cost of curtailed wind and solar power is: Where C a is the cost of curtailing wind and solar power, c a P is the penalty coefficient for curtailing wind and solar power; PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; PV,max,t 、P WT,max,t are the upper limits of photovoltaic and wind turbine output at time t respectively; The constraints of the park's lower-level operation optimization model include cooling, heating, and electricity power balance constraints, equipment output constraints, energy storage constraints, power purchase constraints, and low-carbon evaluation coefficient constraints; The cooling, heating and electric power balance constraints are: Where, P discharge,t P is the energy storage discharge power at time t; charge,t H is the energy storage charging power at time t; discharge,t is the heat storage tank heat release power at time t; H charge,t P is the heat storage tank charging power at time t; CHP,t is the power generation capacity of the CHP unit at time t; P PV,t is the photovoltaic power generation power at time t; P WT,t P is the wind power generation power at time t; buy,t P is the power purchased at time t; load,t P is the electric load power at time t; ER,t P is the power consumption of the refrigerator at time t; AIR,t is the power consumption of the air source heat pump unit at time t; H GB,t is the heating power of the gas boiler at time t; H AIR,t is the heating power of the air source heat pump at time t; H load,t is the heat load power at time t; H AR,t is the heat consumption power of the absorption refrigerant at time t; C AR,t C is the cooling power of the absorption chiller at time t; ER,t C is the cooling power of the electric refrigerator at time t; load,t is the cooling load power at time t; The equipment output constraints are: In the formula, j is the equipment to be built, k is other equipment, P j,t P is the output of the jth equipment to be built at time t, k,t The output of the kth other device at time t, N j is the installed capacity of the jth equipment to be built, P k,max is the maximum output of the kth other device; ΔP CHP , ΔH GB They are the ramp power limits for CHP units and gas boilers; The energy storage constraint is: Where, P charge,t 、P discharge,t are the charging and discharging amounts of the electric energy storage respectively; are the maximum charge and discharge rate of electric energy storage; N PS Configure capacity for electric energy storage; H charge,t 、H discharge,t They are charging and discharging of thermal energy storage respectively; are the maximum charge and discharge rates of thermal energy storage; N HS Allocate capacity for thermal energy storage; S HS,t is the charge state of the thermal energy storage at time t; S PS,t is the state of charge of the energy storage at time t; S HS,min 、S HS,max are the minimum and maximum values ​​of the thermal energy storage charge state respectively; S PS,min 、S PS,max are the minimum and maximum values ​​of the state of charge of the energy storage, respectively; W HS,t 、W PS,t are the thermal energy storage and electrical energy storage at time t respectively; σ HS , σ PS are the self-loss rates of thermal energy storage and electrical energy storage respectively; ρ HS,charge , ρ PS,charge are the thermal energy storage charging and discharging efficiency; ρ PS,charge , ρ PS,discharge are the charging and discharging efficiency of electric energy storage; The power purchase constraints of the distribution network are: 0≤P buy,t ≤P buy,max Where, P buy,t is the purchased electric power at time t; P buy,max The upper limit of power purchase for the distribution network; The low carbon evaluation coefficient constraint is: 0≤β≤β aim Where, β is the low-carbon evaluation coefficient of the park on that day; β aim It is the low-carbon evaluation target of the park, that is, the maximum low-carbon evaluation coefficient.

6. A dual-layer capacity configuration system for a multi-energy complementary park, characterized in that: including storage media and processors; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-5.

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