A mid- to long-term planning approach for an integrated energy system considering carbon reduction constraints and hydrogen energy

Abstract

The present invention relates to a method for medium- and long-term planning of an integrated energy system that takes into account carbon emission reduction constraints and hydrogen energy, including establishing a model of key equipment of the integrated energy system; establishing a dynamic long-term optimization mathematical model of the integrated energy system based on the model of the key equipment; and solving the mathematical model to obtain the optimal equipment configuration and carbon emission path. Compared with the prior art, the present invention, on the one hand, starts from the two directions of total cost and total carbon emissions, establishes an optimization model, and conducts a game between cost and environmental benefits; on the other hand, optimizes the equipment installation situation of the entire planning cycle of the system, thereby providing a reference for decision makers.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system application, and in particular to a long-term planning method for an integrated energy system taking into account carbon emission reduction constraints and hydrogen energy. Background Art

[0002] As energy crisis and environmental problems become increasingly serious, people are paying more and more attention to improving energy efficiency, and many countries have begun to implement large-scale greenhouse gas emission reduction actions. According to relevant surveys, cities consume more than 70% of the world's fossil fuels and cause more than 60% of the world's carbon dioxide emissions. Therefore, optimizing urban energy planning is crucial. In the medium and long term, there is a high degree of uncertainty in the future development of cities, such as reduced investment in renewable energy, changes in energy prices, fluctuations in energy demand, improved equipment power generation efficiency, and equipment degradation. Therefore, in order to provide an economical, reasonable, and reliable energy supply strategy for urban areas, comprehensive, dynamic, and multi-stage long-term planning is required.

[0003] When configuring and replacing equipment in an integrated energy system, parameters such as the power of the alternative equipment are mostly determined in advance. Then, through reasonable configuration of the equipment, it is possible to provide the city with a variety of energy needs such as cooling, heating, and electricity. When the equipment reaches the end of its life, new equipment is arranged to replace it.

[0004] In addition, the current economic planning for urban integrated energy systems mainly focuses on how to reduce the total cost of the system, while ignoring the greenhouse gas emissions of different equipment configurations. Summary of the invention

[0005] In view of the above-mentioned problems that equipment replacement is not flexible enough and carbon emissions are high in the long-term planning of the existing energy system, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: ignoring the excessively high greenhouse gas emissions of the energy system, and not being flexible enough in planning long-term multi-stage equipment installation, and not considering environmental benefits and the flexible changes in various equipment parameters and installation conditions in the future.

[0007] To solve the above technical problems, the present invention can be implemented through the following technical solutions:

[0008] A method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy, the method comprising:

[0009] Establish key equipment models for integrated energy systems;

[0010] Establishing a mathematical model for long-term planning of an integrated energy system based on the models of the key equipment;

[0011] Solving the mathematical model obtains the optimal equipment configuration and carbon emission path.

[0012] Preferably, the model of key equipment of the integrated energy system includes:

[0013] Photovoltaic power generation equipment model, internal combustion engine model, fuel cell model, boiler model, heat pump model, absorption refrigeration unit model, electric refrigeration unit model;

[0014] Energy storage device model, the energy storage device includes a lithium battery, a heat storage tank and a cold storage tank.

[0015] Preferably, the photovoltaic power generation equipment model is expressed as:

[0016]

[0017] in, is the photovoltaic power generation power, G y,d,h is the irradiance, S n is the photovoltaic panel area, is the photoelectric conversion rate, is the correction coefficient, Y represents the number of years of the full planning cycle;

[0018] The internal combustion engine model is expressed as:

[0019]

[0020]

[0021] in, is the natural gas consumption of the internal combustion engine, and are the actual electric power and rated electric power of the internal combustion engine, respectively, ICE and b ICE are the factors affecting the efficiency of internal combustion engines, η el,nom,ICE is the power generation efficiency of the internal combustion engine, is a binary variable, is the internal combustion engine start-stop constraint, is the heating power of the internal combustion engine, η th,nom,ICE The thermal efficiency of the internal combustion engine;

[0022] The fuel cell model is expressed as:

[0023]

[0024]

[0025] in, is the fuel cell hydrogen consumption, and are the actual power and rated power of the fuel cell, respectively,SOFC and b SOFC are the fuel cell efficiency influencing factors, is a binary variable, which is the fuel cell start-stop constraint. is the heating power of the fuel cell, η th,nom,SOFC Heating efficiency for fuel cells;

[0026] The boiler model is expressed as:

[0027]

[0028] in, is the natural gas consumption of boiler, and are the actual thermal power and rated thermal power of the boiler respectively, a GB and b GB are the influencing factors of boiler efficiency, is a binary variable, is the boiler start-stop constraint, η th,nom,GB For boiler working efficiency;

[0029] The heat pump model, absorption refrigeration unit model, and electric refrigeration unit model are expressed as follows:

[0030]

[0031] Among them, I n,eb,y,d,h is the input power of the energy conversion device eb, Q n,eb,y,d,h and are the real-time output power and rated output power of the energy conversion device eb, a eb and b eb is the efficiency influencing factor of the energy conversion equipment eb, is the performance coefficient of the energy conversion device eb, z n,eb,y,d,h is a binary variable that constrains the start and stop of the energy conversion equipment eb, and the energy conversion equipment eb includes heat pumps, absorption refrigeration units and electric refrigeration units;

[0032] The energy storage device model is expressed as:

[0033]

[0034] E n,e4,y,d,h is the real-time storage capacity of energy storage device e4, E n,e4,y,d,h-1 is the energy storage capacity of energy storage device e4 at the last moment, σ e4 is the loss coefficient of energy storage device e4, γ e4 is the energy storage loss coefficient of energy storage device e4, It is a binary variable of the charging state of the energy storage device e4. A value of 1 indicates that the charging of the energy storage device e4 is turned on, and a value of 0 indicates that the charging of the energy storage device e4 is turned off. is a binary variable of the energy storage device e4’s energy release state. A value of 1 indicates that the energy storage device e4 is on for energy release, and a value of 0 indicates that the energy storage device e4 is off for energy release. n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, where the energy storage device e4 includes a lithium battery, a heat storage tank and a cold storage tank;

[0035] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ... D, and h = 1, 2, ... H, where D is the total number of days in a year, and H is the total number of hours in a day.

[0036] Preferably, the energy storage device model satisfies the following constraints:

[0037] E n,e4,y,d-1,H =E n,e4,y,d,0

[0038] E n,e4,y-1,D,H =E n,e4,y,1,0

[0039]

[0040]

[0041] is the maximum allowable charging and discharging coefficient of the energy storage device e4, Rated energy storage capacity of energy storage device e4.

[0042] Preferably, the mid- and long-term planning mathematical model of the integrated energy system includes:

[0043] Models that meet users' cooling, heating and electricity load demand, energy consumption models for primary energy and electricity, total cost models, and carbon emission reduction constraint models.

[0044] Preferably, the model for meeting the user's cooling, heating and electricity load requirements is expressed as:

[0045]

[0046]

[0047]

[0048] Among them, TED y,d,h is the total electricity demand of users, THD y,d,h is the total heat demand of the user, TCD y,d,h The total cooling demand of the user, The power purchased from the power grid, P n,e1,y,d,his the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, is the heat consumption of the absorption refrigeration unit, and are the charging power and discharging power of lithium batteries respectively. and are the charging power and releasing power of the heat storage tank respectively. They are the charging power and discharging power of the cold storage tank respectively. Indicates the power consumption of the heat pump, is the power consumption of the electric refrigeration unit, L n,e3,y,d,h is the cooling power of the cooling device e3.

[0049] Preferably, the energy consumption model for consuming primary energy and electricity is expressed as:

[0050]

[0051]

[0052] in, is the hydrogen consumption volume of the fuel cell, is the volume of hydrogen per unit calorific value, is the hydrogen consumption of the fuel cell, To consume the volume of natural gas, is the volume of natural gas per unit calorific value, F n,e5,y,d,h The amount of gas required for all natural gas consuming equipment e5.

[0053] Preferably, the total cost model is expressed as:

[0054] total cost = C AE +C G&E +C O&M +C CE -RSV

[0055]

[0056]

[0057] C O&M =C FO&M +C VO&M

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] Among them, totalcost is the total cost, C AE is the equipment asset cost, C G&E is the cost of purchasing electricity and gas, C O&M is the system operation and maintenance cost, C FO&M is the fixed operation and maintenance cost, C VO&M is the variable operation and maintenance cost, C CE is the carbon sink purchase cost for the entire planning period from year 1 to year Y, RSV is the residual value of the equipment, is the rated output power of device e, φ n,e is the asset cost per unit power of equipment e, The corresponding units are the price of electricity, natural gas and hydrogen per unit volume. Purchase power for the grid, To consume the volume of natural gas, is the hydrogen consumption volume of the fuel cell, ψ y,e is the annual fixed operation and maintenance cost per unit power of device e, P n,e1,y,d,h is the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, L n,e3,y,d,h is the cooling power of refrigeration equipment e3, CH n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, ω y,e1 ,ω y,e2 ,ω y,e3 ,ω y,e4 The corresponding unit power operation and maintenance costs are the power generation equipment e1, heating equipment e2, refrigeration equipment e3 and energy storage equipment e4. is the unit price of carbon sink, is the carbon sink purchased in year y, ρ n,e is the proportion coefficient of recoverable cost in the remaining life of equipment e, L n,e is the remaining life of equipment e, N e is the device lifespan, It is the capital cost of equipment in the last year of the planning period, i.e., year Y.

[0064] Preferably, the carbon emission reduction constraint model is expressed as:

[0065]

[0066]

[0067]

[0068] in, is the carbon emission in year y, ζ NG and pg are the carbon emission factors of natural gas and power grid, respectively, n,e5,y,d,h The amount of gas required for all natural gas consuming equipment e5, is the electricity consumption, is the amount of carbon sinks purchased in year y, const1 and const2 are constants.

[0069] Preferably, solving the mathematical model to obtain the optimal equipment configuration and carbon emission path includes:

[0070] Iterate to find the global minimum of the total cost:

[0071] min total cost = C AE +C G&E +C O&M +C CE -RSV

[0072] Obtained by the total cost constraint of the cost minimization objective function That is, to find the optimal number of installed units, installation time and installation power of different equipment, and at the same time find the carbon emission curve of the entire planning cycle.

[0073] Compared with the prior art, the present invention has the following advantages:

[0074] On the one hand, the present invention arranges the equipment configuration in the long-term multi-stage dynamic programming of the integrated energy system more flexibly, and on the other hand, takes the carbon emission reduction settings into consideration, thereby establishing a system cost optimal target optimization model to optimize the equipment configuration and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 An energy flow chart of an integrated energy system of a method for long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0076] Figure 2 Hourly load demands of a commercial area on four typical days in a method for long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0077] Figure 3 The real-time electricity price and light radiation of a method for long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0078] Figure 4It is a comparison curve of carbon emissions year by year for four scenarios within a full planning cycle of a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0079] Figure 5 The equipment installation situation of the whole planning cycle in scenario 1 of a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0080] Figure 6 The equipment installation situation of the whole planning cycle in scenario 2 of a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0081] Figure 7 The equipment installation situation of the whole planning cycle in scenario 3 of a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention;

[0082] Figure 8 This is the equipment installation status of the entire planning cycle in Scenario 4 of a medium- and long-term planning method for an integrated energy system considering carbon emission reduction constraints and hydrogen energy in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the following embodiments are merely illustrative in nature, and the present invention is not intended to limit its applicable objects or uses, and the present invention is not limited to the following embodiments.

[0084] Example

[0085] like Figure 1 As shown, this embodiment provides a method for long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy, the method comprising:

[0086] Establish key equipment models for integrated energy systems;

[0087] Establishing a mathematical model for long-term planning of an integrated energy system based on the models of the key equipment;

[0088] Solving the mathematical model obtains the optimal equipment configuration and carbon emission path.

[0089] Among them, the key equipment of the integrated energy system includes energy conversion equipment and energy storage equipment, among which the energy conversion equipment includes photovoltaic power generation equipment, internal combustion engines, fuel cells, boilers, heat pumps, absorption refrigeration units, and electric refrigeration units. The energy storage equipment includes lithium batteries, heat storage tanks and cold storage tanks.

[0090] The following models each device separately:

[0091] (1) Photovoltaic power generation equipment

[0092] When the photovoltaic power generation equipment is working, it is assumed that the power generation of the equipment per hour is proportional to the light radiation and the area of ​​the photovoltaic panel at that time, that is, the power generation of the unit is equal to the light radiation at that moment * the area of ​​the photovoltaic panel * the photoelectric correction coefficient * the photoelectric conversion rate. Therefore, the photovoltaic power generation equipment model is expressed as:

[0093]

[0094] in, is the photovoltaic power generation power, G y,d,h is the irradiance, S n is the photovoltaic panel area, is the photoelectric conversion rate, is the correction coefficient, Y represents the number of years of the full planning cycle;

[0095] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year and H is the total number of hours in a day.

[0096] (2) Internal combustion engine

[0097] When the internal combustion engine equipment is working, the fuel consumption is positively correlated with the real-time electric power and rated electric power of the equipment. Therefore, the internal combustion engine model is expressed as:

[0098]

[0099]

[0100] in, is the natural gas consumption of the internal combustion engine, and are the actual electric power and rated electric power of the internal combustion engine, respectively, ICE and b ICE are the factors affecting the efficiency of internal combustion engines, η el,nom,ICE is the power generation efficiency of the internal combustion engine, is a binary variable, is the internal combustion engine start-stop constraint, is the heating power of the internal combustion engine, η th,nom,ICE The thermal efficiency of the internal combustion engine;

[0101] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year and H is the total number of hours in a day.

[0102] (3) Fuel Cell

[0103] When the fuel cell is working, the fuel consumption is positively correlated with the real-time power and rated power of the equipment. Therefore, the fuel cell model is expressed as:

[0104]

[0105]

[0106] in, is the fuel cell hydrogen consumption, and are the actual power and rated power of the fuel cell, respectively, SOFC and b SOFC are the fuel cell efficiency influencing factors, is a binary variable, which is the fuel cell start-stop constraint. is the heating power of the fuel cell, η th,nom,SOFC Heating efficiency for fuel cells;

[0107] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year and H is the total number of hours in a day.

[0108] (4) Boiler

[0109] When the boiler equipment is working, the fuel consumption is positively correlated with the real-time power and rated power of the equipment. Therefore, the boiler model is expressed as:

[0110]

[0111] in, is the natural gas consumption of boiler, and are the actual thermal power and rated thermal power of the boiler respectively, a GB and b GB are the influencing factors of boiler efficiency, is a binary variable, is the boiler start-stop constraint, η th,nom,GB For boiler working efficiency;

[0112] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year and H is the total number of hours in a day.

[0113] (5) Heat pumps, absorption refrigeration units and electric refrigeration units

[0114] The heat pump model, absorption refrigeration unit model, and electric refrigeration unit model are expressed as:

[0115]

[0116] Among them, I n,eb,y,d,h is the input power of the energy conversion device eb, Q n,eb,y,d,h and are the real-time output power and rated output power of the energy conversion device eb, a eb and b eb is the efficiency influencing factor of the energy conversion equipment eb, is the performance coefficient of the energy conversion device eb, z n,eb,y,d,h is a binary variable that constrains the start and stop of the energy conversion equipment eb, and the energy conversion equipment eb includes heat pumps, absorption refrigeration units and electric refrigeration units;

[0117] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year and H is the total number of hours in a day.

[0118] (6) Energy storage equipment, including lithium batteries, thermal storage tanks and cold storage tanks

[0119] The energy storage device model is expressed as:

[0120]

[0121] E n,e4,y,d,h is the real-time storage capacity of energy storage device e4, E n,e4,y,d,h-1 is the energy storage capacity of energy storage device e4 at the last moment, σ e4 is the loss coefficient of energy storage device e4, γ e4 is the energy storage loss coefficient of energy storage device e4, It is a binary variable of the charging state of the energy storage device e4. A value of 1 indicates that the charging of the energy storage device e4 is turned on, and a value of 0 indicates that the charging of the energy storage device e4 is turned off. is a binary variable of the energy storage device e4’s energy release state. A value of 1 indicates that the energy storage device e4 is on for energy release, and a value of 0 indicates that the energy storage device e4 is off for energy release. n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, where the energy storage device e4 includes a lithium battery, a heat storage tank and a cold storage tank;

[0122] Energy storage needs to be continuous, that is, the energy storage in the first hour of the first day of the year is equal to the energy storage at the end of the last hour of the last day of the previous year, and the energy storage in the first hour of the day is equal to the energy storage in the last hour of the previous day. Energy storage and energy release cannot be carried out at the same time and must be subject to the maximum value constraint. Therefore, the energy storage device model meets the following constraints:

[0123] E n,e4,y,d-1,H =E n,e4,y,d,0

[0124] E n,e4,y-1,D,H =E n,e4,y,1,0

[0125]

[0126]

[0127] is the maximum allowable charging and discharging coefficient of the energy storage device e4, Rated storage capacity of energy storage device e4;

[0128] The subscripts n, y, d, and h represent equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ... D, and h = 1, 2, ... H, where D is the total number of days in a year, and H is the total number of hours in a day.

[0129] On the basis of the establishment of the above equipment models, a mathematical model for the long-term planning of the integrated energy system is established, including:

[0130] Models that meet users' cooling, heating and electricity load demand, energy consumption models for primary energy and electricity, total cost models, and carbon emission reduction constraint models.

[0131] Among them, the model that meets the user's cooling, heating and electricity load requirements is expressed as:

[0132] The total demand is equal to (or greater than) the power of the energy released by each energy conversion device + energy storage device - the power of the energy consumed and stored by each device. The mathematical expression is:

[0133]

[0134]

[0135]

[0136] Among them, TED y,d,h is the total electricity demand of users, THD y,d,h is the total heat demand of the user, TCD y,d,h The total cooling demand of the user, The power purchased from the power grid, P n,e1,y,d,h is the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, is the heat consumption of the absorption refrigeration unit, and are the charging power and discharging power of lithium batteries respectively. and are the charging power and releasing power of the heat storage tank respectively. They are the charging power and discharging power of the cold storage tank respectively. Indicates the power consumption of the heat pump, is the power consumption of the electric refrigeration unit, L n,e3,y,d,h is the cooling power of the cooling device e3.

[0137] The energy consumption model for consuming primary energy and electricity is expressed as:

[0138] Fuel consumption is equal to the energy required by the equipment * the volume of fuel required per kWh calorific value, where the fuel includes hydrogen and natural gas. Therefore, the specific mathematical expression is:

[0139]

[0140]

[0141] in, is the hydrogen consumption volume of the fuel cell, is the volume of hydrogen per unit calorific value, is the hydrogen consumption of the fuel cell, To consume the volume of natural gas, is the volume of natural gas per unit calorific value, F n,e5,y,d,h The amount of gas required for all natural gas consuming equipment e5.

[0142] The total cost model is expressed as:

[0143] Total cost equals equipment asset cost C AE + Cost of purchasing electricity and gas C G&E +System operation and maintenance cost C O&M + Carbon sink purchase cost C for the entire planning period from year 1 to year Y CE - Equipment residual value RSV, mathematical expression is:

[0144] totalcost=C AE +C G&E +C O&M +C CE -RSV

[0145] Among them, the equipment asset cost C AE It is equal to the asset cost of all equipment e in the whole planning cycle. The asset cost of each equipment is the equipment asset investment cost of the equipment in each year during its life cycle. The equipment asset investment cost of a single equipment in each year is equal to the rated installed power * unit asset cost, which is specifically expressed as:

[0146]

[0147] Cost of purchasing gas for power supply C G&EIt is equal to the sum of the total electricity purchase cost and gas purchase (hydrogen and natural gas) cost during the entire planning period, which can be expressed as:

[0148]

[0149] System operation and maintenance cost C O&M Equal to the fixed operation and maintenance cost C FO&M +Variable operation and maintenance costs C VO&M ,in:

[0150] Fixed operation and maintenance cost C FO&M It is equal to the rated installed power of the equipment * unit operation and maintenance cost. The variable operation and maintenance cost is equal to the actual working power of each device * unit power operation and maintenance cost, expressed as:

[0151] C O&M =C FO&M +C VO&M

[0152]

[0153]

[0154] The carbon sink purchase cost of the entire planning cycle from year 1 to year Y is equal to the sum of the carbon sink purchase costs in each year of the entire cycle. The carbon sink purchase cost of each year is equal to the amount of carbon sink purchased * carbon sink, expressed as:

[0155]

[0156] Equipment residual value RSV is equal to the rated installed power of each equipment in the planning period * unit asset cost at the end of the planning period * residual value coefficient * remaining life / total life cycle, expressed as:

[0157]

[0158]

[0159] Among them, totalcost is the total cost, C AE is the equipment asset cost, C G&E is the cost of purchasing electricity and gas, C O&M is the system operation and maintenance cost, C FO&M is the fixed operation and maintenance cost, C VO&M is the variable operation and maintenance cost, C CE is the carbon sink purchase cost for the entire planning period from year 1 to year Y, RSV is the residual value of the equipment, is the rated output power of device e, φ n,e is the asset cost per unit power of equipment e, The corresponding units are the price of electricity, natural gas and hydrogen per unit volume. Purchase power for the grid, To consume the volume of natural gas, is the hydrogen consumption volume of the fuel cell, ψ y,e is the annual fixed operation and maintenance cost per unit power of device e, P n,e1,y,d,h is the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, L n,e3,y,d,h is the cooling power of refrigeration equipment e3, CH n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, ω y,e1 ,ω y,e2 ,ω y,e3 ,ω y,e4 The corresponding unit power operation and maintenance costs are the power generation equipment e1, heating equipment e2, refrigeration equipment e3 and energy storage equipment e4. is the unit price of carbon sink, is the carbon sink purchased in year y, ρ n,e is the proportion coefficient of recoverable cost in the remaining life of equipment e, L n,e is the remaining life of equipment e, N e is the device lifespan, It is the capital cost of equipment in the last year of the planning period, i.e., year Y.

[0160] The carbon reduction constraint model is expressed as:

[0161]

[0162]

[0163]

[0164] in, is the carbon emission in year y, ζ NG and pg are the carbon emission factors of natural gas and power grid, respectively, n,e5,y,d,h The amount of gas required for all natural gas consuming equipment e5, is the electricity consumption, is the amount of carbon sinks purchased in year y, const1 and const2 are constants.

[0165] Finally, solving the mathematical model to obtain the optimal equipment configuration and carbon emission path includes:

[0166] Iterate to find the global minimum of the total cost:

[0167] min total cost = C AE +C G&E +CO&M +C CE -RSV

[0168] Obtained by the total cost constraint of the cost minimization objective function That is, to find the optimal number of installed units, installation time and installation power of different equipment, and at the same time find the carbon emission curve of the entire planning cycle.

[0169] Reference Figure 2 to Figure 8 ,Below, a development block in Shanghai that adopts an integrated energy system is selected as ,verification.

[0170] Reference Figure 2 The load demand data for a commercial area in a developing block in Shanghai. The data is selected as a typical day in one of the four seasons of the year, with a step length of 1 hour and a day of 24 hours. Figure 2 (a), (b), (c), and (d) are the demand curves for typical days in spring, summer, autumn, and winter. Considering the actual life, the commercial and residential areas of the community in the initial period are 5446m 2 and 1200m 2 ,Table 1 shows the planned area for the community expansion, and the user demand is positively correlated with the community area.

[0171] Table 1 Block expansion

[0172]

[0173] As living standards improve, unit load increases at a rate of 1% per year. Due to the improvement of technology, the operating efficiency of each energy conversion equipment increases year by year at different rates: PV, EHP, ABC at 2% per year, ICE, GB at 1% per year, EC, SOFC at 3% per year. Other specific parameters are shown in Table 2. The price of electricity and natural gas remains unchanged, and the carbon emission factor of the power grid decreases by 1.5% per year.

[0174] Table 2. Equipment parameters

[0175]

[0176] In this embodiment, four scenarios are set:

[0177] Scenario 1: This scenario is the baseline scenario, where no carbon emission reduction constraints are set and everything remains the same.

[0178] Scenario 2: The initial year is the same as the baseline scenario, with the carbon emissions at the end of the planning period set at 50% of the initial year.

[0179] Scenario 3: The initial year is the same as the baseline scenario, with the overall carbon emissions for the entire planning period set at 50% of the baseline scenario.

[0180] Scenario 4: The initial year is the same as the baseline scenario, with the carbon emissions in the last year of the planning period set at 50% of the initial year, and the overall carbon emissions for the entire planning period set at 50% of the baseline scenario.

[0181] According to the cooling, heating and electricity load data and related equipment parameters of the integrated energy community, a mixed integer linear programming method is used to establish a mathematical model for the medium- and long-term planning of the integrated energy system. The model is solved with economy as the optimization goal to obtain a solution set with a more economical system and a more reasonable equipment configuration. This example is solved by calling the Cplex solver in MATLAB.

[0182] Taking the commercial area as an example, refer to Table 3 and Figure 4 The long-term dynamic programming model solution for the block considering carbon emission reduction and hydrogen shows that the total cost of the system has a certain conflict with the carbon emission limit. If the overall carbon emissions are to be reduced, the total cost of the system will increase accordingly. At this time, how the system operates depends on the comprehensive consideration of the decision makers. Among them, the comparison between scenario one and scenario two shows that the cost of achieving the carbon emission reduction target at the end of the setting period increased by 41.7%, and the total carbon emissions decreased by 12.5%. The economic efficiency of this setting method is poor; the comparison between scenario one and scenario three shows that the cost of achieving the carbon emission reduction target at the end of the setting period increased by 35.2%, and the total carbon emissions decreased by 50%. It can be seen that the economic efficiency of scenario three is better than that of scenario two; the comparison between scenario three and scenario four shows that when the overall carbon emission reduction constraints are the same, it is more economical not to set the carbon emission reduction constraints at the end of the planning period, but the carbon emission curve of the entire planning period of scenario four is more in line with development needs, safer and more conservative, and avoids acting too hastily.

[0183] Table 3. Total cost and total carbon emissions data in four scenarios

[0184] scene Total cost (yuan) Total carbon emissions (tons) Scenario 1 1353982131 651078.63 Scenario 2 1918445647 575951.25 Scenario 3 1830215416 325532.8 Scenario 4 2127332565 325532.8

[0185] Carbon emissions depend on energy usage, which is closely related to equipment configuration. Figure 6 The equipment installation situation of the entire planning cycle is shown, so as to further analyze how to achieve a win-win situation in the game between cost and environment. By comparing scenario one and scenario three, it is found that under the constraint of emission reduction in the last year, a fuel cell SOFC will be installed. The characteristic of this equipment is to use hydrogen as input fuel to achieve zero emission, but the price of hydrogen is high and the equipment cost is high. The above two scenarios both constrain the reduction of carbon emissions in the last year and the cost optimization as a constraint, so there is a sudden drop in carbon emissions in the last year. For the overall emission reduction constraint, photovoltaic power generation equipment is preferred because of its long life and can reduce emissions in all stages, especially when the equipment has a high initial efficiency. In addition, a comprehensive analysis shows that the more stringent the carbon emission constraints are, the more likely it is that natural gas equipment will be used for equipment installation, because the carbon emission factor of natural gas is smaller than the emission factor of purchasing electricity from the power grid.

[0186] Therefore, it can be seen from the above that there are many ways to constrain carbon emission reduction at this stage. Among them, the economic efficiency of constraints on the overall situation is better, while the economic efficiency of constraints on a certain year is poor, but it also has its advantages; as a medium- and long-term planning method for an integrated energy system with cost optimization as the objective function, considering carbon emission reduction constraints and hydrogen energy use, it realizes the optimization of equipment installation and configuration at the micro level on the basis of ensuring environmental benefits, which can not only improve the economic benefits of users, but also improve the social benefits of energy conservation and emission reduction, and it promotes the optimal scheduling of integrated energy equipment and its application in some new directions.

[0187] The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments can also be implemented in various other ways, and various omissions, substitutions, and changes can be made without departing from the technical concept of the present invention.

Claims

1. A method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy, characterized in that: The method includes: Establish key equipment models for integrated energy systems; Establishing a mathematical model for long-term planning of an integrated energy system based on the models of the key equipment; Solving the mathematical model to obtain the optimal equipment configuration and carbon emission path; Among them, the models of key equipment of the integrated energy system include: Photovoltaic power generation equipment model, internal combustion engine model, fuel cell model, boiler model, heat pump model, absorption refrigeration unit model, electric refrigeration unit model; An energy storage device model, wherein the energy storage device includes a lithium battery, a heat storage tank and a cold storage tank; The mathematical model for medium- and long-term planning of the integrated energy system includes: Meet the user's cooling, heating and electricity load demand model, energy consumption model of primary energy and electricity, total cost model, and carbon emission reduction constraint model; The model for meeting the user's cooling, heating and electricity load requirements is expressed as: Among them, TED y,d,h is the total electricity demand of users, THD y,d,h is the total heat demand of the user, TCD y,d,h The total cooling demand of the user, The power purchased from the power grid, P n,e1,y,d,h is the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, is the heat consumption of the absorption refrigeration unit, and are the charging power and discharging power of lithium batteries respectively. and are the charging power and releasing power of the heat storage tank respectively. They are the charging power and discharging power of the cold storage tank respectively. Indicates the power consumption of the heat pump, is the power consumption of the electric refrigeration unit, L n,e3,y,d,h is the cooling power of the cooling device e3; The energy consumption model for consuming primary energy and electricity is expressed as: in, is the hydrogen consumption volume of the fuel cell, is the volume of hydrogen per unit calorific value, is the hydrogen consumption of the fuel cell, To consume the volume of natural gas, The volume of natural gas per unit calorific value, F n,e5,y,d,h The amount of gas required for all equipment that consumes natural gas e5; The total cost model is expressed as: totalcost=C AE +C G&E +C O&M +C CE -RSV C O&M =C FO&M +C VO&M Among them, total cost is the total cost, C AE is the equipment asset cost, C G&E is the cost of purchasing electricity and gas, C O&M is the system operation and maintenance cost, C FO&M is the fixed operation and maintenance cost, C VO&M is the variable operation and maintenance cost, C CE is the carbon sink purchase cost for the entire planning period from year 1 to year Y, RSV is the residual value of the equipment, is the rated output power of device e, φ n,e is the asset cost per unit power of equipment e, The corresponding units are the price of electricity, natural gas and hydrogen per unit volume. Purchase power for the grid, To consume the volume of natural gas, is the hydrogen consumption volume of the fuel cell, ψ y,e is the annual fixed operation and maintenance cost per unit power of device e, P n,e1,y,d,h is the power supply of the power generation equipment e1, Q n,e2,y,d,h Heating power for heating equipment e2, L n,e3,y,d,h is the cooling power of refrigeration equipment e3, CH n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, ω y,e1 ,ω y,e2 ,ω y,e3 ,ω y,e4 The corresponding unit power operation and maintenance costs are the power generation equipment e1, heating equipment e2, refrigeration equipment e3 and energy storage equipment e4. is the unit price of carbon sink, is the carbon sink purchased in year y, ρ n,e is the proportion coefficient of recoverable cost in the remaining life of equipment e, L n,e is the remaining life of equipment e, N e is the device lifespan, is the capital cost of equipment in the last year of the planning period, i.e., year Y; The carbon reduction constraint model is expressed as: in, is the carbon emission in year y, ζ NG and pg are the carbon emission factors of natural gas and power grid, respectively, n,e5,y,d,h The amount of gas required for all natural gas consuming equipment e5, is the electricity consumption, is the amount of carbon sinks purchased in year y, const1 and const2 are constants.

2. According to claim 1, a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy is characterized in that: The photovoltaic power generation equipment model is expressed as: in, is the photovoltaic power generation power, G y,d,h is the irradiance, S n is the photovoltaic panel area, is the photoelectric conversion rate, is the correction coefficient, Y represents the number of years of the full planning cycle; The internal combustion engine model is expressed as: in, is the natural gas consumption of the internal combustion engine, and are the actual electric power and rated electric power of the internal combustion engine, respectively, ICE and b ICE are the factors affecting the efficiency of internal combustion engines, η el,nom,ICE is the power generation efficiency of the internal combustion engine, is a binary variable, is the internal combustion engine start-stop constraint, is the heating power of the internal combustion engine, η th,nom,ICE The thermal efficiency of the internal combustion engine; The fuel cell model is expressed as: in, is the fuel cell hydrogen consumption, and are the actual power and rated power of the fuel cell, respectively, SOFC and b SOFC are the fuel cell efficiency influencing factors, is a binary variable, which is the fuel cell start-stop constraint. is the heating power of the fuel cell, η th,nom,SOFC Heating efficiency for fuel cells; The boiler model is expressed as: in, is the natural gas consumption of boiler, and are the actual thermal power and rated thermal power of the boiler respectively, a GB and b GB are the influencing factors of boiler efficiency, is a binary variable, is the boiler start-stop constraint, η th,nom,GB For boiler working efficiency; The heat pump model, absorption refrigeration unit model, and electric refrigeration unit model are expressed as follows: Among them, I n,eb,y,d,h is the input power of the energy conversion device eb, Q n,eb,y,d,h and are the real-time output power and rated output power of the energy conversion device eb, a eb and b eb is the efficiency influencing factor of the energy conversion equipment eb, is the performance coefficient of the energy conversion device eb, z n,eb,y,d,h is a binary variable that constrains the start and stop of the energy conversion equipment eb, and the energy conversion equipment eb includes heat pumps, absorption refrigeration units and electric refrigeration units; The energy storage device model is expressed as: E n,e4,y,d,h is the real-time storage capacity of energy storage device e4, E n,e4,y,d,h-1 is the energy storage capacity of energy storage device e4 at the last moment, σ e4 is the loss coefficient of energy storage device e4, γ e4 is the energy storage loss coefficient of energy storage device e4, It is a binary variable of the charging state of the energy storage device e4. A value of 1 indicates that the charging of the energy storage device e4 is turned on, and a value of 0 indicates that the charging of the energy storage device e4 is turned off. is a binary variable of the energy storage device e4’s energy release state. A value of 1 indicates that the energy storage device e4 is on for energy release, and a value of 0 indicates that the energy storage device e4 is off for energy release. n,e4,y,d,h is the charging power of the energy storage device e4, DIS n,e4,y,d,h is the energy release power of the energy storage device e4, where the energy storage device e4 includes a lithium battery, a heat storage tank and a cold storage tank; The subscripts n, y, d, and h represent the equipment installed in the nth year, the yth year, the dth day, and the hth hour, respectively, where d = 1, 2, ...D, and h = 1, 2, ...H, where D is the total number of days in a year, and H is the total number of hours in a day.

3. A method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy according to claim 2, characterized in that: The energy storage device model meets the following constraints: AND n,e4,y,d-1,H =And n,e4,y,d,0 AND n,e4,y-1,D,H =And n,e4,y,1,0 is the maximum allowable charging and discharging coefficient of the energy storage device e4, Rated energy storage capacity of energy storage device e4.

4. According to claim 1, a method for medium- and long-term planning of an integrated energy system considering carbon emission reduction constraints and hydrogen energy is characterized in that: Solving the mathematical model to obtain the optimal equipment configuration and carbon emission path includes: Iterate to find the global minimum of the total cost: min total cost=C AE +C G&E +C O&M +C CE -RSV Obtained by the total cost constraint of the cost minimization objective function That is, to find the optimal number of installed units, installation time and installation power of different equipment, and at the same time find the carbon emission curve of the entire planning cycle.

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