Multi-objective optimization method for near-zero-carbon regional integrated energy system

Abstract

The present invention relates to a multi-objective optimization method for a near-zero-carbon regional integrated energy system, and belongs to the field of energy system operation technology. First, the impact of carbon peak and carbon neutrality goals on traditional energy systems is sorted out, and the necessity of using new energy to balance multiple loads is clarified; on this basis, the currently commonly used zero (low) carbon energy planning models in the world and their important characteristics are sorted out to provide a technical framework and theoretical basis for the subsequent establishment of models. Then, an economic evaluation model for a near-zero-carbon integrated energy system is established, which can design and formulate near-zero-carbon integrated energy system design plans and operation plans under different objectives for a given user. The calculation of the model shows that the integrated energy system under environmental constraints can reduce energy consumption and reduce carbon emissions caused by energy consumption. This is the premise and basis for the investment economy of integrated energy projects.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system operation, and specifically relates to a multi-objective optimization method for a near-zero-carbon regional integrated energy system. Background Art

[0002] At present, there have been extensive studies on low-carbon economic planning for integrated energy systems, such as: modeling the integrated energy system of the park from the four subsystems of electricity, heat, cooling and gas, and normalizing the energy storage equipment of these four energy sources into a unified mathematical model. In view of the problem of wind abandonment and high operating costs caused by the "heat-based electricity" of combined heat and power (CHP), a multi-objective optimization scheduling method for regional integrated energy systems based on CHP flexible heat-to-electricity ratio is proposed. Flexible adjustment of CHP heat-to-electricity ratio can effectively promote the grid connection and consumption of wind power and reduce system carbon emissions. The study is based on an integrated energy system with electrical heat and cooling load demand. The life cycle analysis method is used to show that when the low-carbon weight plays a large role, taking into account the carbon emissions of electrical energy storage equipment can effectively reduce the operating cost of the integrated energy system and reduce the total carbon emissions.

[0003] However, these existing studies have not solved the problem of balancing economy and carbon emissions in the planning of zero-carbon energy systems. Therefore, it is necessary to design a multi-objective optimization method for near-zero-carbon regional integrated energy systems to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-objective optimization method for a near-zero-carbon regional integrated energy system to solve the technical problems existing in the above-mentioned prior art. Existing research has not well solved the problem of balancing economy and carbon emissions in the planning of zero-carbon energy systems.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] The multi-objective optimization method of the near-zero-carbon regional integrated energy system includes the following steps:

[0007] S1. Analyze the functional structure of the regional integrated energy system and divide the regional integrated energy system into photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system;

[0008] S2. Establishing photovoltaic power generation model, wind power generation model, energy conversion equipment model and energy storage model respectively according to photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system;

[0009] S3. Considering the limitations of specific equipment in terms of energy supply capacity, optimize the photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model;

[0010] S4. Carry out economic calculations on the optimized photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model.

[0011] The photovoltaic power generation model is as follows:

[0012]

[0013] Among them, η overall The total conversion efficiency of the system, N k is the area of ​​the photovoltaic panel, G t is the solar radiation intensity at time t (W / m 2 ), P STC is the output of the photovoltaic panel under standard test conditions, and Gstc is the light intensity under standard test conditions.

[0014] The wind power generation model is as follows:

[0015]

[0016] Among them, v in 、v r and v out is the cut-in, rated and cut-out characteristic speeds of the wind characteristic curve (m / s); ρ is the air density (1.225kg / m 3 ), A is the rotor area (m 2 ),C P is the wind energy utilization coefficient of the fan, v t is the true wind speed (m / s), P r Rated power of wind turbine (kW).

[0017] The energy conversion equipment model is as follows:

[0018]

[0019] Among them, i,j are the indices of energy types; and represents the energy input and output from device k at time t; represents the efficiency of device k at time point t.

[0020] The energy storage model is as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Among them, δ k，i is the fixed energy loss of the kth energy storage device in use, and the charging and discharging efficiencies are and and Represents the charging and discharging power of the device at time point t; and represents the energy stored in the energy storage device k at time t+1 and t; and Indicates the upper and lower limits of energy storage; and are the maximum charge / discharge power rates of energy storage device k; u k，i It is a binary (0-1) variable to ensure that the charging and discharging processes do not occur at the same time; It means that the energy stored at the beginning of the model calculation is equal to the energy at the end.

[0027] The energy supply capacity of specific equipment is limited as follows:

[0028]

[0029]

[0030] Among them, tech refers to the regulations of energy production and conversion; k is a 0-1 variable, indicating whether the equipment is used at a certain moment; and Indicate the maximum and minimum sizes of different devices; is the output of device m at time hr; Cap tech is the maximum installed capacity of the equipment; It is the maximum output of the device.

[0031] Economic accounting takes the annual total cost as the objective function, takes carbon emissions into consideration, or takes both economic efficiency and carbon emissions into consideration.

[0032] When the economic accounting takes the annual total cost as the objective function:

[0033] The optimal configuration of the distributed energy system with multi-energy coordination is obtained by minimizing the total annual cost, as shown in the following formula:

[0034] Min C total =C capital +C O&M +c fuel (10)

[0035]

[0036]

[0037]

[0038] Where, Inv represents the unit investment cost of the equipment; Y represents the life of the equipment; I represents the depreciation rate; OM represents the unit operating cost; dm refers to the number of days in each month; Pf,hr and Pgp,hr represent the fuel price and grid electricity price respectively; C capital represents the investment cost, C O&M represents the operating cost, C fuel represents fuel cost; C aptech Indicates the unit capacity of the equipment, Inv tech Inv represents the unit capacity investment cost of equipment; es and Cap es Indicates the unit investment cost and unit equipment capacity of energy storage; and represents fuel consumption and electricity imports; Pf,hr and Pgp,hr represent hourly fuel prices and grid electricity prices, respectively; represents the amount of energy sold, P bk Indicates the energy selling price; MinC total is the minimum total cost.

[0039] When economic accounting considers the optimal carbon emissions:

[0040] That is, the annual carbon dioxide emissions are the smallest, and the formula is as follows:

[0041]

[0042] Where: c g is the equivalent carbon emission coefficient of the energy consumption of gth type equipment; Δt is the modeling accuracy; Pgt represents the power output of gth type at time t; MinA CE Indicates the minimum CO2 target.

[0043] When economic accounting considers both economic efficiency and carbon emissions, the best situation is:

[0044] Min C total +Min A CE (15).

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] One of the beneficial effects of this plan is that, first of all, the impact of carbon peak and carbon neutrality goals on traditional energy systems is sorted out, and the necessity of using new energy to balance multiple loads is clarified; on this basis, the currently commonly used zero (low) carbon energy planning models and their important characteristics are sorted out in the world, providing a technical framework and theoretical basis for the subsequent establishment of models. Then, an economic evaluation model for a near-zero carbon integrated energy system is established, which can design and formulate a near-zero carbon integrated energy system design and operation plan under different goals for a given user. The calculation of the model shows that the integrated energy system under environmental constraints can reduce energy consumption and reduce carbon emissions caused by energy consumption, which is the premise and basis for the investment economy of integrated energy projects. Under the current energy price and carbon dioxide price policy, the optimal carbon dioxide solution produces the least carbon emissions, but the economy is poor; if carbon dioxide emissions are reduced to zero and carbon neutrality is achieved in the physical sense, the investment required is three times or more than the financial "carbon neutrality". On the basis of considering the carbon emission price, if all carbon dioxide emissions are converted into economic optimal problems by setting carbon prices, then the energy system will still rely on imported natural gas and electricity. Compared with the existing experience abroad, my country's carbon dioxide price is still too low. In order to promote the development of renewable energy and improve the technological level and achieve carbon neutrality, the price of carbon dioxide will inevitably rise further in the future. Compared with the scheme with the best economic efficiency and the scheme with comprehensive consideration of carbon emissions and economy, there is little change in equipment investment and capacity selection between the two. The main change is that the former chooses a larger capacity electric-driven heating technology to better absorb new energy and thus reduce carbon emissions. The integrated energy system can reduce costs and save energy, which is the basis for the commercial operation of the integrated energy system. The reasons for the cost reduction and energy saving of the integrated energy system are: making full use of the peak and valley difference of electricity prices and the cascade utilization of energy to reduce energy losses. The equipment investment cost accounts for a large proportion of the total cost, and excessive equipment capacity will damage the economy; in terms of energy storage, the cost of heat storage is much lower than the cost of electricity storage; peak and valley electricity prices, gas prices, and the actual construction cost of equipment have a greater impact on the optimal design of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the superstructure representation of the zero-carbon energy system of an embodiment of the present application.

[0048] Figure 2 This is a schematic diagram of campus electric and heating load data according to an embodiment of the present application.

[0049] Figure 3 Schematic diagram of the hourly availability of solar energy (3a) and wind energy (3b) at the project site of an embodiment of the present application.

[0050] Figure 4 The figure is a schematic diagram of the overall steps of the embodiment of the present application. DETAILED DESCRIPTION

[0051] Below in conjunction with the appended Figure 1 -Attached Figure 4 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Existing research has not effectively addressed the issue of balancing economics and carbon emissions in the planning of zero-carbon energy systems.

[0053] Example:

[0054] like Figure 4 As shown in the figure, a multi-objective optimization method for a near-zero-carbon regional integrated energy system is proposed, which includes the following steps:

[0055] S1. Analyze the functional structure of the regional integrated energy system and divide the regional integrated energy system into photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system;

[0056] S2. Establishing photovoltaic power generation model, wind power generation model, energy conversion equipment model and energy storage model respectively according to photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system;

[0057] S3. Considering the limitations of specific equipment in terms of energy supply capacity, optimize the photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model;

[0058] S4. Carry out economic calculations on the optimized photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model.

[0059] The photovoltaic power generation model is as follows:

[0060]

[0061] Among them, η overall The total conversion efficiency of the system, N k is the area of ​​the photovoltaic panel, G t is the solar radiation intensity at time t (W / m 2 ), P STC is the output of the photovoltaic panel under standard test conditions, and Gstc is the light intensity under standard test conditions.

[0062] The wind power generation model is as follows:

[0063]

[0064] Among them, v in 、v r and v out is the cut-in, rated and cut-out characteristic speeds of the wind characteristic curve (m / s); ρ is the air density (1.225kg / m 3 ), A is the rotor area (m 2 ),C P is the wind energy utilization coefficient of the fan, v t is the true wind speed (m / s), P r Rated power of wind turbine (kW).

[0065] The energy conversion equipment model is as follows:

[0066]

[0067] Among them, i,j are the indices of energy types; and represents the energy input and output from device k at time t; represents the efficiency of device k at time point t.

[0068] The energy storage model is as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Among them, δ k，i is the fixed energy loss of the kth energy storage device in use, and the charging and discharging efficiencies are and and Represents the charging and discharging power of the device at time point t; and represents the energy stored in the energy storage device k at time t+1 and t; and Indicates the upper and lower limits of energy storage; and are the maximum charge / discharge power rates of energy storage device k; u k，i It is a binary (0-1) variable to ensure that the charging and discharging processes do not occur at the same time; It means that the energy stored at the beginning of the model calculation is equal to the energy at the end.

[0075] The energy supply capacity of specific equipment is limited as follows:

[0076]

[0077]

[0078] Among them, tech refers to the regulations of energy production and conversion; k is a 0-1 variable, indicating whether the equipment is used at a certain moment; and Indicate the maximum and minimum sizes of different devices; is the output of device m at time hr; Cap tech is the maximum installed capacity of the equipment; It is the maximum output of the device.

[0079] Economic accounting takes the annual total cost as the objective function, takes carbon emissions into consideration, or takes both economic efficiency and carbon emissions into consideration.

[0080] When the economic accounting takes the annual total cost as the objective function:

[0081] The optimal configuration of the distributed energy system with multi-energy coordination is obtained by minimizing the total annual cost, as shown in the following formula:

[0082] Min C total =C capital +C O&M +c fuel (10)

[0083]

[0084]

[0085]

[0086] Where, Inv represents the unit investment cost of the equipment; Y represents the life of the equipment; I represents the depreciation rate; OM represents the unit operating cost; dm refers to the number of days in each month; Pf,hr and Pgp,hr represent the fuel price and grid electricity price respectively; C capital represents the investment cost, C O&M represents the operating cost, C fuel represents fuel cost; Ca ptech Indicates the unit capacity of the equipment, Inv tech Inv represents the unit capacity investment cost of equipment; es and Cap es Indicates the unit investment cost and unit equipment capacity of energy storage; and represents fuel consumption and electricity imports; Pf,hr and Pgp,hr represent hourly fuel prices and grid electricity prices, respectively; represents the amount of energy sold, P bk Indicates the energy selling price; MinC total is the minimum total cost.

[0087] When economic accounting considers the optimal carbon emissions:

[0088] That is, the annual carbon dioxide emissions are the smallest, and the formula is as follows:

[0089]

[0090] Where: c g is the equivalent carbon emission coefficient of the energy consumption of gth type equipment; Δt is the modeling accuracy; Pgt represents the power output of gth type at time t; MinA CE Indicates the minimum CO2 target.

[0091] When economic accounting considers both economic efficiency and carbon emissions, the best situation is:

[0092] Min C total +Min A CE (15).

[0093] Taking the school as the research object, based on the analysis of the operating characteristics of various energy supply and energy storage equipment, the typical daily load of each season was used as input to simulate the daily operation of the campus. The model adopts multi-objective optimization, takes annual total cost and carbon dioxide emissions as optimization targets, and gives the optimal equipment capacity and operation strategy of the system under different targets.

[0094] Case Study:

[0095] The specific scenario will build an efficient and intelligent campus demonstration project with a total planning area of ​​about 10 square kilometers and a campus capacity of more than 40,000 people. The case analysis provides the basic performance of the optimized integrated energy system, and the required input items include terminal energy demand information, resource availability, energy prices and technical and economic characteristics of equipment.

[0096] The campus-level data of the campus for a certain year is used as a benchmark for further analysis. At the same time, in order to maintain generality, the campus load is input based on a typical week, with 168 hours per week, and a total of 2016 data points for the whole year. Figure 2 shows the typical load of the campus. The maximum amount of local wind and solar energy available is as follows: Figure 3 shown.

[0097] Energy commodities are an important type of commodity in the country, and their prices are determined by the local government where the project is located. The current price of natural gas used for power generation and heating in the region is 2.29 yuan / m3 The price of hydrogen is 0.6 yuan / kWh; the price of biomass energy is 0.4 yuan / kg. The grid electricity adopts the time-of-use electricity price, with an average of 0.824 yuan / kWh.

[0098] The carbon emission factor of electricity and the price of carbon emission determine the equipment selection of the subsequent technical solution. According to relevant literature, the carbon emission factor of electricity in this report is 712g / kWh, the carbon emission factor of natural gas is 184g / kWh, biomass is regarded as renewable energy, and the price of carbon dioxide is 30 yuan per ton.

[0099] The technical and economic data of distributed energy technology include efficiency, unit investment, operation and maintenance cost and life span.

[0100] The technical and economic data collected, compared, sorted and processed are listed in Table 1 and Table 2 according to energy production technology and energy storage technology. The investment cost, operation and maintenance cost, efficiency, life span, etc. of energy technology will change with time. The annual inflation rate in this article is 4.5%. It should be noted that this article aims to provide an optimization design method for a multi-energy coordinated distributed energy system, but it cannot ensure the accuracy of technical and economic data. Model users can modify the technical and economic data according to actual conditions, so as to obtain results that are more in line with the actual project.

[0101] Table 1 Parameters of alternative equipment

[0102]

[0103]

[0104] Table 2 Technical and economic data of energy storage technology

[0105]

[0106] The above distributed energy system takes into account the optimal economy, the optimal carbon emissions, and the optimal economy and carbon dioxide emissions at the same time, totaling different equipment selection schemes under three circumstances.

[0107] The results of the model calculation show that under the current policy, the most economically optimal configuration mode and investment scale of the integrated energy system are shown in Tables 3 and 4. Among the many alternative technologies, the optimization design model only selected several of them, including two types of gas equipment (gas internal combustion engine and gas boiler), solar photovoltaic, wind turbine, ground source heat pump, heat storage, and power storage device. The scheme did not select biomass boilers with low heating efficiency, and the more expensive hydrogen fuel cells and electric heating technology. The model believes that the main heat load depends on natural gas-driven equipment to balance, namely CHP and natural gas boilers, and the cost of natural gas equipment and imported natural gas accounts for about 40% of the total cost; secondly, due to the high efficiency of geothermal technology (COP = 3), its heating cost is actually lower than that of natural gas heating when the electricity price is low and the wind is abundant, so it is also one of the important heating options under the conditions of optimal economy.

[0108] In the economically optimal solution, most of the electricity demand is met by grid imports, energy storage, and wind power generation; among them, the electricity import fee from the grid accounts for about 15% of the annualized cost, the investment in wind farms accounts for nearly 30%, and the two costs are close to 50% of the annualized cost of the project. The investment in all other equipment that does not use fossil fuels as input energy accounts for only 3% of the annualized cost. The reason is that under the condition that economy is the optimization goal, the optimization design model tends to choose lower-cost equipment among the same type of equipment. Natural gas and its equipment have low unit costs, and wind power generation has a small marginal cost, so it is favored by the model. On the other hand, the unit capacity cost of equipment with a small investment ratio is high, and the current price of carbon dioxide is only 30 yuan / ton. Therefore, it is difficult to rely on reducing the consumption of fossil energy to improve the economy of these equipment in disguise, so they are not selected as the main equipment.

[0109] Table 3 Equipment selection for the optimal economic solution

[0110]

[0111]

[0112] After careful analysis, it was found that the model not only stored some wind power and electricity from the grid during certain periods through electric energy storage, but also used it to drive heat pumps to meet the heating needs of the project. This is different from the previous study that the economic efficiency of natural gas heating is higher than that of electric heating at all times. The main reason is that the introduction of wind power in this case makes the marginal cost of self-generated electricity in the park and the grid electricity price during the trough period much lower than the cost of using natural gas for power generation. At the same time, the thermal efficiency of ground source heat pumps is much higher than all other heating technologies. The combination of multiple factors has created competition with natural gas equipment in terms of economy. However, considering the strong intermittent nature of wind power and the high price of power grid during peak hours, natural gas heating technology still seems to have certain economic value. In the future, with the decline in wind power costs and energy storage costs, the improvement of the decarbonization level of the grid, the improvement of heat pump technology, the improvement of demand-side management, the rise in carbon prices and other changes in a series of factors, natural gas equipment may eventually withdraw from the historical stage of park-level zero (low) carbon projects.

[0113] Table 5 Equipment selection for optimal carbon dioxide emission solution

[0114]

[0115] The results of the model calculation (Table 5 and Table 6) show that the implementation of the optimal solution for carbon dioxide emissions is a feasible optimal solution that uses new energy to meet all the electric and thermal loads of the campus. In other words, this solution is a new energy micro-energy network that operates off-grid. Compared with the results of the economic optimum, it can be seen that with the reduction of carbon emissions, the relevant wind power generation capacity and energy storage capacity have increased significantly, and the cost has risen rapidly. The cost of reducing carbon emissions in this case to zero is about three times that of the economic optimum solution, among which the annualized cost of wind farms, hydrogen fuel cells and electric / thermal energy storage exceeds 20 million, accounting for about 70% of the total annualized cost. Using new energy technology for independent power generation and turning the campus into an "autonomous micro-energy network" seems to be the best answer from the perspective of carbon emissions. However, the annualized cost of this solution exceeds 30 million. If calculated based on the actual operation of the project for 30 years, the total investment is close to 1 billion. In practice, the pressure on investors is even greater.

[0116] Table 6 Annualized cost of the optimal carbon dioxide emission solution

[0117]

[0118] On the other hand, from a technical point of view, this solution has high requirements for load-side management in actual operation and is more suitable for use in parks with lower load safety requirements. The main reason is that an energy system that relies entirely on new energy will be greatly affected by the instability of new energy, and there may be a source-load imbalance, which also explains why the system is equipped with 100MWh and 50MWh of electric and thermal energy storage, respectively. However, as mentioned above, the operation of these energy storages requires accurate judgment of the load, but in real life it is difficult to make a 100% accurate prediction of the load for the next hour, so there is a risk that energy scheduling cannot be carried out in time to meet the load.

[0119] The main difference between the optimal CO2 emission scheme and the economic optimal scheme is whether CO2 can be converted into economic cost through "carbon price" and thus play a role in energy conservation and emission reduction; in the economic optimal scheme, the carbon price is included in the final total cost at 30 yuan per ton; while in the CO2 emission optimal scheme, the economic efficiency of carbon emissions is ignored, and reducing actual CO2 emissions becomes a "hard requirement" of the model. The results show that if you want to achieve an energy system that is truly completely dependent on new energy, the CO2 emission optimal scheme needs to pay twice as much as the economic optimal scheme. The huge price difference between the two schemes is actually the future scenario of the carbon trading market: carbon prices still have a lot of room to rise. If the price of CO2 in the carbon trading market remains at a low level, stakeholders will often choose to pay for emitting CO2 to achieve "carbon neutrality" in a financial sense, rather than investing in the development of new energy technologies to achieve "carbon neutrality" in a physical sense.

[0120] Table 7 Equipment combination for optimal economic performance and carbon dioxide emissions

[0121]

[0122]

[0123] Table 8 Annualized cost of the optimal solution for economic performance and carbon dioxide emissions

[0124]

[0125] From the optimal solution of economy and carbon emissions (the weight of economy and carbon emissions is 5:5) (Table 7 and Table 8), it can be seen that, under the condition of balancing economy and carbon emissions, as investment increases, the total carbon emissions will decrease with the amount of imported natural gas and electricity. Compared with the optimal solution for economy, the investment in wind farms has increased, but the configuration of electric energy storage has not changed significantly. However, the capacity of natural gas boilers has decreased and the capacity of electric thermal technology has increased, indicating that the newly added wind power is mainly used to balance the thermal load of the case. On the other hand, compared with the optimal solution for carbon dioxide emissions, this solution reduces the reliance on expensive hydrogen fuel cells, while at the same time, based on economic considerations, some natural gas equipment can be appropriately used to achieve an effective balance between reducing carbon dioxide emissions and economy.

[0126] By comparing the above three options, the study found that in winter, relying on excess wind power or power grids during low electricity prices for heating is a future development trend. This is also a huge advantage of wind power compared to photovoltaic power generation: in winter, the demand for heating is high, and the lack of sunshine makes it difficult to use solar thermal technology to heat most parts of my country; but wind energy is often unaffected in winter, and the average wind speed even exceeds that in summer. In the face of the requirement to effectively reduce carbon emissions, wind power may be used to replace other primary energy sources for heating in the future.

[0127] Among the three options, under the current policy, the option with the best economic performance has obvious advantages. The reason is that its investment cost is relatively low and it has achieved "carbon neutrality" in a financial sense. In the future, as carbon prices rise, combined options that consider both goals at the same time, or even more expensive optimal options for carbon dioxide emissions, may gradually be accepted by people. Here, the research team makes a simple calculation to help readers better understand the impact of carbon prices on option selection:

[0128] In the economic scheme, the carbon emission fee is 212,000 yuan / year, equivalent to 5,300 tons of carbon dioxide per year, while the dual-target scheme has an annual carbon dioxide emission of 3,500 tons, reducing carbon emissions by 1,800 tons. At the same time, compared with the economic scheme, the economic and carbon emission joint optimal design scheme has an annual investment increase of 510,000 yuan / year, equivalent to 283 yuan per ton of carbon dioxide; far higher than the current market situation of about 30 yuan per ton of carbon dioxide. The carbon dioxide optimal scheme emits 0 tons of carbon dioxide, and the investment is 20.1 million yuan / year more than the economic optimal scheme, equivalent to 3,792 yuan per ton of carbon dioxide; at the same time, this scheme invests 15 million yuan / year more than the joint optimal scheme, equivalent to 4,285 yuan per ton of carbon dioxide.

[0129] Taking into account that various parameters may change in the future, the above calculation results may fluctuate within a certain range, but it is sufficient to prove that carbon prices will greatly affect the choice of the optimal "carbon neutrality" plan: conservatively speaking, if the carbon price in the future exceeds 200 yuan per ton, then the optimal carbon dioxide emissions plan will be more competitive than the economic plan. If the carbon price is close to 4,000 yuan per ton, then achieving true zero carbon emissions from a technical point of view, that is, adopting the optimal design plan of both economy and carbon emissions, will be more attractive than achieving carbon emissions in a financial sense.

[0130] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A multi-objective optimization method for a near-zero-carbon regional integrated energy system, characterized in that: The following steps are involved: S1. Analyze the functional structure of the regional integrated energy system and divide the regional integrated energy system into photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system; S2. Establishing photovoltaic power generation model, wind power generation model, energy conversion equipment model and energy storage model respectively according to photovoltaic power generation system, wind power generation system, energy conversion system and energy storage system; S3. Considering the limitations of specific equipment in terms of energy supply capacity, optimize the photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model; S4. Perform economic calculations on the optimized photovoltaic power generation model, wind power generation model, energy conversion equipment model, and energy storage model; The photovoltaic power generation model is as follows: Among them, η overall The total conversion efficiency of the system, N k is the area of ​​the photovoltaic panel, G t is the solar radiation intensity at time t, W / m 2 , P STC is the output of the photovoltaic panel under standard test conditions, and Gstc is the light intensity under standard test conditions; The wind power generation model is as follows: Among them, v in 、v r and v out is the cut-in, rated and cut-out characteristic speeds of the wind characteristic curve, m / s; ρ is the air density, 1.225 kg / m 3 , A is the rotor area, m 2 ,C P is the wind energy utilization coefficient of the fan, v t is the true wind speed, m / s, P r Rated power of wind turbine, kW; The energy conversion equipment model is as follows: Among them, i,j are the indices of energy types; and represents the energy input and output from device k at time t; represents the efficiency of device k at time point t; The energy storage model is as follows: Among them, δ k，i is the fixed energy loss of the kth energy storage device in use, and the charging and discharging efficiencies are and and represents the charging and discharging power of the device at time point t; and represents the energy stored in the energy storage device k at time t+1 and t; and Indicates the upper and lower limits of energy storage; and are the maximum charge / discharge power rates of energy storage device k; u k，i It is a binary 0-1 variable to ensure that the charging and discharging processes do not occur at the same time; It means that the energy stored at the beginning of the model calculation is equal to the energy at the end; Economic accounting takes annual total cost as the objective function, takes carbon emissions into consideration, or takes both economic efficiency and carbon emissions into consideration. When the economic accounting takes the annual total cost as the objective function: The optimal configuration of the distributed energy system with multi-energy coordination is obtained by minimizing the total annual cost, as shown in the following formula: My C total =C capital +C O&M +c fuel (10) Where, Inv represents the unit investment cost of the equipment; Y represents the life of the equipment; I represents the depreciation rate; OM represents the unit operating cost; dm refers to the number of days in each month; Pf,hr and Pgp,hr represent the fuel price and grid electricity price respectively; C capital represents the investment cost, C O&M represents the operating cost, C fuel Cap represents fuel cost; tech Indicates the unit capacity of the equipment, Inv tech Inv represents the unit capacity investment cost of equipment; es and Cap es Indicates the unit investment cost and unit equipment capacity of energy storage; and represents fuel consumption and electricity imports; Pf,hr and Pgp,hr represent hourly fuel prices and grid electricity prices, respectively; represents the amount of energy sold, P bk Indicates the energy selling price; Min C total is the minimum total cost; When economic accounting considers the optimal carbon emissions: That is, the annual carbon dioxide emissions are the smallest, and the formula is as follows: Where: c g is the equivalent carbon emission coefficient of the energy consumption of gth type equipment; Δt is the modeling accuracy; Pgt represents the power output of gth type at time t; MinA CE represents the minimum CO2 target; When economic accounting considers both economic efficiency and carbon emissions, the best situation is: MinC total +MinA CE (15).

2. The multi-objective optimization method for a near-zero-carbon regional integrated energy system according to claim 1 is characterized in that: The energy supply capacity of specific equipment is limited as follows: Among them, tech refers to the regulations of energy production and conversion; k is a 0-1 variable, indicating whether the equipment is used at a certain moment; and Indicate the maximum and minimum sizes of different devices; is the output of device m at time hr; Cap tech is the maximum installed capacity of the equipment; It is the maximum output of the device.

Images