Optimal dispatching method of multi-regional virtual power plant for multi-energy complementarity and low carbonization

Abstract

The present invention relates to a multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization, characterized in that it comprises the following steps: step 1, constructing a multi-region VPP architecture organization; step 2, formulating a natural gas production and consumption system operation strategy based on the multi-region VPP architecture organization constructed in step 1, and calculating its power consumption, natural gas production and carbon emission reduction; step 3, based on the natural gas production and consumption system operation strategy formulated in step 2, realizing the power complementarity of multi-region VPP through inter-regional energy interaction, and constructing a multi-region VPP optimization scheduling model with the goal of maximizing the VPP operation benefit; step 4, using a composite differential evolution algorithm to solve the multi-region VPP optimization scheduling model constructed in step 3, thereby realizing the multi-region virtual power plant optimization scheduling for multi-energy complementarity and low carbonization. The present invention can promote the complementary utilization of electric energy, thermal energy and natural gas energy, promote the low-carbon transformation of electric energy, enhance the clean renewable energy absorption capacity, and alleviate the contradiction between renewable energy output and load supply and demand mismatch.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant dispatching, and relates to a multi-region virtual power plant optimization dispatching method, in particular to a multi-region virtual power plant optimization dispatching method for multi-energy complementarity and low carbonization. Background Art

[0002] With the increasing depletion of fossil energy and the increasing prominence of the greenhouse effect, the development of clean and renewable energy and the promotion of energy conservation and emission reduction have become the consensus of countries around the world. In order to promote the low-carbon development of electricity, it is necessary to vigorously develop clean and renewable energy and reduce the emission of various pollutants. In 2020, my country's wind power abandonment reached about 18 billion kWh, and the amount of solar power abandoned reached about 5 billion kWh. The large-scale grid connection of wind power and photovoltaic power indirectly increased the peak-to-valley power difference of the electric load. In response to this problem, relevant departments of the central and state organs have introduced a number of policies and reform measures. Multiple energy complementary models are an important way to further enhance the level of renewable energy consumption. At present, how to improve the utilization rate of renewable energy and enhance the mutual coupling between energy sources has attracted extensive research.

[0003] Power-to-gas (P2G) technology is a new method to achieve load shifting and energy conversion. P2G uses abandoned wind and photovoltaic power to produce easy-to-store natural gas, which is conducive to peak load shaving and valley filling of electricity load and promotes the consumption of renewable energy. Existing literature on P2G research focuses on multi-energy coupling networks, promoting flexible operation of the system, and reducing the amount of wind and photovoltaic power abandoned. However, there are few studies on issues such as P2G raw material costs and raw material supply channels. Carbon capture power plants are an excellent supply channel for CO2 raw materials for P2G equipment. Compared with traditional thermal power plants, carbon capture power plants use carbon capture technology to transform coal-fired thermal power plants, which can significantly reduce carbon emissions and contribute to the low-carbon development of electricity. Carbon capture energy consumption, as an adjustable load, can flexibly respond to the volatility of wind turbine and photovoltaic output. At present, research on power-to-gas and carbon capture power plants rarely involves the "electricity-gas-heat" multi-energy coupling system, and has not yet achieved effective scheduling between "electricity-gas-heat" multi-energy. Under the guidance of our country's policies, large-scale garbage and waste treatment technology has developed rapidly, realizing the resource utilization and reuse of garbage, with huge potential. In 2020, the resource potential of livestock and poultry manure biogas has reached 6% of the annual natural gas consumption in China. The gasification through urban manure treatment can effectively improve the absorption capacity of wind turbines and photovoltaic power generation. With the development of waste incineration treatment and low-emission technology of incineration pollution, the construction of environmentally friendly waste incineration power plants in China has continued to increase, and the energy consumption of flue gas treatment systems accounts for about 25% of their total power generation. By installing a smoke storage device to decouple the waste power generation link from the flue gas treatment link, the absorption capacity of wind turbines and photovoltaic power generation can be further improved by adjusting and translating the power consumption of flue gas treatment. In the face of the situation of high penetration of renewable energy access to the power grid, compared with other traditional power generation units, manure treatment equipment and waste incineration power plants have the advantages of broad controllable space and huge energy saving potential.

[0004] Therefore, promoting the coordinated operation of multiple equipment such as P2G, carbon capture, manure treatment, waste incineration, wind turbines and photovoltaic power generation has obvious advantages. However, due to the geographical dispersion of multiple equipment in different regions, independent operation, lack of coordinated control between each other, and unreasonable resource allocation, there are prominent problems. Virtual power plants (VPPs) can integrate multiple equipment in different regions through advanced information technology to achieve optimized scheduling between regions. At the same time, they have good environmental protection and economy, and can effectively solve the above problems. As an important energy utilization solution, VPP needs to face the threat of grid connection of high-penetration renewable energy. It is urgent to explore the low-carbon economic operation mode of VPP, smooth load fluctuations and improve the ability to absorb renewable energy.

[0005] Existing literature rarely involves the optimal scheduling problem of inter-regional electricity interaction in multi-region VPPs, and the research on single-region VPPs mostly uses high-carbon emission fossil fuel units as controllable units, and rarely adopts a comprehensive scheduling form with environmentally friendly units such as carbon capture and waste incineration power plants as controllable units, and does not consider the impact of manure treatment equipment and P2G operating costs on the optimal scheduling of VPPs.

[0006] After searching, no public documents of the prior art identical or similar to the present invention were found. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a method for promoting the complementary utilization of electric energy, thermal energy and natural gas energy, promoting the low-carbon transformation of electric energy, improving the clean renewable energy absorption capacity, alleviating the contradiction between renewable energy output and load supply and demand, increasing the net profit of virtual power plant operation, reducing the carbon emission intensity of virtual power plants, and fully covering P2G, carbon capture, manure treatment, waste incineration, wind turbines, photovoltaics and other equipment for multi-energy complementarity and low-carbonization of multi-regional virtual power plants.

[0008] A multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization includes the following steps:

[0009] Step 1: Build a multi-region VPP architecture organization;

[0010] Step 2: Based on the multi-regional VPP architecture organization constructed in Step 1, formulate the operation strategy of the natural gas production and consumption system, and calculate its electricity consumption, natural gas production and carbon emission reduction;

[0011] Step 3: Based on the natural gas production and consumption system operation strategy formulated in step 2, the electric energy complementation of multi-regional VPPs is realized through inter-regional energy interaction, and a multi-regional VPP optimization scheduling model is constructed with the goal of maximizing the VPP operation benefits;

[0012] Step 4: Use the composite differential evolution algorithm to solve the multi-regional VPP optimization scheduling model constructed in step 3, and then realize the multi-regional virtual power plant optimization scheduling for multi-energy complementarity and low carbonization.

[0013] Advantages and beneficial effects of the present invention:

[0014] 1. The present invention proposes a multi-regional virtual power plant optimization scheduling method for multi-energy complementarity and low-carbonization. Considering the correlation between various equipment, the present invention proposes an operation strategy for a natural gas production and consumption system composed of multiple units such as carbon capture, P2G and manure treatment, which helps the low-carbon transformation of power generation; considering the coordinated optimization scheduling of multiple energies of "electricity-gas-heat", the present invention proposes a collaborative operation strategy for multiple systems and units such as natural gas production and consumption systems, waste incineration, and renewable energy power generation, which effectively improves the absorption capacity of wind power and photovoltaic power; facing the low-carbon development of energy and multi-energy complementarity, the present invention fully considers multiple factors such as energy market transactions, carbon market transactions, operation of multiple equipment, interruptible loads and environmental pollution, and constructs a multi-regional virtual power plant optimization scheduling model. The proposed optimization scheduling scheme has high universality and good economic benefits, can better improve the absorption capacity of clean renewable energy, alleviate the contradiction between renewable energy output and load supply and demand, achieve supply and demand balance between source and load, promote the complementary utilization of electric energy, thermal energy and natural gas energy, and promote the low-carbon transformation of electric energy, which is of greater academic significance and engineering value.

[0015] 2. The present invention can provide technical support for the dispatching and operation of virtual power plants, solve the threat of high-penetration renewable energy grid connection that virtual power plants need to face, meet the concepts of low carbonization and multi-energy complementarity, and is suitable for scenarios with multiple regions, multiple systems, and multiple devices, and has wide practicality; it can fully consider the correlation between multiple devices, optimize the raw material sources of various types of equipment, reduce the cost expenditure of various types of equipment, and reduce the carbon emissions of various types of equipment; it can solve the problem of difficult scheduling of joint operation of multiple systems and multiple devices, realize the coordinated optimization scheduling of multiple energies of "electricity-gas-heat" and carbon recycling among various systems and equipment, and more effectively improve the absorption capacity of wind power and photovoltaics; it can adapt to the development needs of multi-energy complementarity and low-carbon energy, fully consider energy market and carbon market transactions, joint operation of multiple equipment, interruptible loads and environmental pollution and other factors, and propose a multi-region virtual power plant optimization scheduling strategy. The resulting scheduling scheme has high universality and good economic benefits, and can better improve the absorption capacity of clean renewable energy, alleviate the contradiction between the output of renewable energy and the supply and demand of load, achieve the supply and demand balance between source and load, promote the complementary utilization of electric energy, thermal energy and natural gas energy, and promote the low-carbon transformation of electric energy. It has more academic significance and engineering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the optimal dispatching solution of a multi-region virtual power plant for multi-energy complementarity and low carbonization of the present invention;

[0017] Figure 2 This is a multi-region virtual power plant architecture diagram for multi-energy complementarity and low carbonization of the present invention;

[0018] Figure 3It is the operation principle diagram of the natural gas production and consumption system of the present invention;

[0019] Figure 4 is a flow chart of the improved composite differential evolution algorithm of the present invention;

[0020] Figure 5 is a graph of initial data of each area of ​​the virtual power plant of the present invention;

[0021] Figure 6 It is a comparison diagram of the solution process of various algorithms of the present invention;

[0022] Figure 7 It is a virtual power plant power curve diagram of scheme 5 of the present invention;

[0023] Figure 8 is a graph of CO2 capture and emission in three areas of the virtual power plant of Scheme 5 of the present invention;

[0024] Fig. 9 It is the operation curve diagram of each unit of the virtual power plant area 1 of solution 5 of the present invention;

[0025] Fig.10 It is an operation curve diagram between virtual power plant regions of Scheme 5 of the present invention. DETAILED DESCRIPTION

[0026] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0027] At present, there are few studies on the optimization scheduling of inter-regional power interaction involving multi-regional virtual power plants (VPPs), and the research on single-region VPPs mostly uses high-carbon emission fossil fuel units as controllable units, and rarely adopts the comprehensive scheduling form of environmentally friendly units such as carbon capture and waste incineration power plants as controllable units, and has not achieved effective scheduling between "electricity-gas-heat" multiple energy sources. To this end, the present invention first proposes a multi-regional VPP structural framework that promotes the coordinated operation of multiple equipment such as power to gas (P2G), carbon capture, manure treatment, waste incineration, wind turbines and photovoltaic power generation; secondly, the carbon capture unit, P2G, manure treatment system, combined heat and power unit (CHP) and gas boiler are combined to form a natural gas production and consumption unit P2G, which uses the captured CO2 as a raw material and jointly meets the natural gas demand of CHP and gas boilers with the manure treatment system. Thirdly, through the coordinated operation of multiple systems and multiple units, the shiftable load characteristics of carbon capture power consumption and flue gas treatment power consumption are used to smooth the output fluctuations of renewable energy, and indirectly realize the flexible use of wind power and photovoltaics. Then, the electric energy complementation of multi-regional VPPs is realized through inter-regional energy interaction, and a multi-regional VPP optimization scheduling model is built with the goal of maximizing the operating benefits of VPPs. Finally, in view of the high-dimensional nonlinearity and difficulty in solving the scheduling model, the group mutation basis vector is introduced into the composite differential evolution algorithm for solution, and a multi-regional virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization is proposed.

[0028] A multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization, such as Figure 1 As shown, the following steps are included:

[0029] Step 1: Build a multi-region VPP architecture organization;

[0030] The multi-regional VPP architecture organization of step 1 analyzes the energy conversion and distribution relationship of the distributed energy system in the VPP through the energy hub; each region of the multi-regional VPP architecture organization includes a corresponding energy hub, and each energy hub includes a carbon capture unit, a renewable energy unit, a natural gas consumption unit, a gas production system, a waste incineration unit, an energy storage unit and a load unit; the load unit includes a fixed load and an interruptible load, and the user needs to pay compensation for cutting off the interruptible load; the natural gas consumption unit and the waste incineration unit are used to jointly meet the heat load demand; the gas production system is used to meet the gas demand of the regional natural gas consumption unit;

[0031] In this embodiment, unlike a single-region VPP, a multi-region VPP can achieve energy complementarity between regions, coordinate the output of energy supply units in each region, and effectively improve the operating income of the VPP. The present invention analyzes the energy conversion and distribution relationship of the distributed energy system in the VPP through an energy hub. Therefore, the multi-region virtual power plant architecture proposed in the present invention for multi-energy complementarity and low carbonization, such as Figure 2 As shown in the figure. Each regional energy hub of the VPP includes carbon capture units (coal-fired generators, carbon capture systems and carbon storage systems), renewable energy units (wind turbines and photovoltaics), natural gas consumption units (cogeneration units and gas boilers), gas production systems (P2G and manure treatment systems), waste incineration units (waste incineration power plants, flue gas treatment systems and smoke storage devices), and energy storage units (electrical energy storage and thermal energy storage). The load units include fixed loads and interruptible loads. Compensation fees must be paid to users for cutting off interruptible loads. The natural gas consumption units and waste incineration power plants jointly meet the heat load demand.

[0032] The combined operation of various energy supply equipment meets the energy demand of the carbon capture system and the flue gas treatment system. By installing a smoke storage device, the waste power generation link and the flue gas treatment link are decoupled, which can more flexibly adapt to the output changes of renewable energy units and smooth the fluctuations of load units. The energy management system formulates control strategies and dispatches the operation of each VPP unit based on information such as energy market prices, renewable energy unit output, and load unit data. When there is an energy shortage in a certain area of ​​the VPP, other areas can supply energy to this area through the inter-regional connection line.

[0033] Step 2: Based on the multi-regional VPP architecture organization constructed in Step 1, formulate the operation strategy of the natural gas production and consumption system, and calculate its electricity consumption, natural gas production and carbon emission reduction;

[0034] The natural gas production and consumption system in step 2 comprises: a carbon capture unit, a gas production system and a natural gas consumption unit;

[0035] The operation strategy of the natural gas production and consumption system is:

[0036] Power-to-gas (P2G) uses the abandoned wind and solar power to electrolyze water to produce hydrogen, and uses the CO2 captured by the carbon capture unit as raw material to produce methane, which is directly supplied to gas consumption units or incorporated into the gas pipeline network; the manure treatment system uses the abandoned wind and solar power to produce biogas and replace natural gas; the gas production system coordinates with the gas pipeline network to meet the gas demand of regional natural gas consumption units; among them, the cogeneration unit meets the regional electricity and heat energy needs; the gas boiler realizes regional heating auxiliary peak regulation.

[0037] The calculation formulas for its electricity consumption, natural gas production and carbon emission reduction are shown below.

[0038] Therefore, the present invention combines the carbon capture unit, P2G, manure treatment system and natural gas consumption unit to form a natural gas production and consumption system. On the one hand, the gas production system can meet the energy needs of the natural gas consumption unit; on the other hand, the remaining part can participate in natural gas market transactions. The natural gas production and consumption system can not only reduce the CO2 storage cost after carbon capture, but also use the abandoned wind and solar power to produce natural gas and incorporate it into the natural gas network, reducing the gas purchase cost of the natural gas consumption unit and realizing load peak shaving and valley filling. The operating principle of the natural gas production and consumption system, such as Figure 3 shown.

[0039] Brief description Figure 3 The meaning of the symbol: Q CG is the CO2 generated by coal-fired power generation units, in tons; Q CE is the CO2 emission from the carbon capture unit to the atmosphere; Q CC is the CO2 capture capacity of the carbon capture system; Q CS is the CO2 storage capacity of the carbon storage system; Q P2G is the amount of CO2 used as raw material for P2G equipment; CWT , P CPV are wind power abandonment and solar power abandonment respectively; V P2G is the gas production capacity of the P2G equipment, in m 3 ; V MTS is the gas production of the manure treatment system, in m 3 .

[0040] (1) Power consumption

[0041] The energy consumption of the natural gas production and consumption system includes the energy consumption of the carbon capture system, the energy consumption of the P2G equipment, and the energy consumption of the manure treatment system, as shown in equation (1). The energy consumption of the carbon capture system includes fixed energy consumption and operating energy consumption. The operating energy consumption mainly comes from the electrical energy consumed by the compressor and the thermal energy consumed by the regeneration tower. The thermal energy demand of the regeneration tower is met by extracting steam from the boiler of the coal-fired generator. The energy consumption of the P2G equipment is met by the abandoned wind and solar power. The manure treatment system gives priority to the abandoned wind and solar power, and the shortfall is met by the power grid.

[0042] P NGP&CS,t,i =P CCS,t,i +P P2G,t,i +P MTS,t,i (1)

[0043] P CCS,t,i =P Const,i +P OP,t,i (2)

[0044]

[0045] Where: P NGP&CS,t,iis the total power of the natural gas production and consumption system in region i during period t; P CCS,t,i , P Const,i , P OP,t,i is the total power, fixed power and operating power of the carbon capture system in region i during period t; P P2G,t,i , P MTS,t,i They are the power of P2G equipment and the power of manure treatment system in region i during period t, and their power is satisfied by the abandoned wind and photovoltaic power, which improves the absorption capacity of wind power and photovoltaic power; η n is the equipment efficiency for treating the nth type of manure; P MTS,n,t,i The power of the equipment for processing the nth type of manure in area i during period t.

[0046] (2) Natural gas production

[0047] Natural gas production V of P2G equipment in region i during period t P2G,t,i The equation is shown below.

[0048] V P2G,t,i =3.6η P2G P P2G,t,i / H g (4)

[0049] In the formula, H g is the calorific value of natural gas, which is 39MJ / m 3 ; η P2G is the conversion efficiency of the P2G device.

[0050] Equivalent natural gas production V of the manure and sewage treatment system MTS in area i during period t MTS,t,i The equation is shown below.

[0051] V MTS,t,i =η MTS P MTS,t,i (5)

[0052] Among them, η FTF It is the conversion efficiency of the manure treatment system.

[0053] (3) Carbon emission reduction

[0054] The carbon emission reduction of the natural gas production and consumption system in region i during period t, that is, the CO2 captured by the carbon capture system, is shown below.

[0055] Q CC,t,i =P OP,t,i / w C (6)

[0056] In the formula, w C is the energy consumption coefficient of CO2 captured by the carbon capture power plant, MWh / ton.

[0057] Total amount of CO2 used as raw material for P2G equipment in region i during period t Q P2G,t,i The equation is shown below.

[0058] Q P2G,t,i =α CO2 η P2G P P2G,t,i (7)

[0059] Where: α CO2 is the CO2 utilization factor of the P2G equipment, tons / MWh.

[0060] The equation for the CO2 storage capacity of the carbon capture system in region i at time t is shown below.

[0061] Q CS,t,i =Q CC,t,i -Q P2G,t,i (8)

[0062] The equation for CO2 emissions to the atmosphere from the natural gas production and consumption system in region i during period t is shown below.

[0063] Q CE,t,i =Q CG,t,i -Q CC,t,i =e CCS P CFG,t,i -Q CC,t,i (9)

[0064] In the formula, e CCS is the CO2 capture factor of the carbon capture system, tonnes / MWh; P CFG,t,i It is the power generated by coal-fired generators.

[0065] Step 3: Based on the natural gas production and consumption system operation strategy formulated in step 2, the electric energy complementation of multi-regional VPPs is realized through inter-regional energy interaction, and a multi-regional VPP optimization scheduling model is constructed with the goal of maximizing the VPP operation benefits;

[0066] The specific steps of step 3 include:

[0067] (1) Build a device model:

[0068] ① Gas unit

[0069] P CHP,t,i =V CHP,t,i H g η CHP,e (10)

[0070] H CHP,t,i =V CHP,t,i H g (1-η CHP,e -η CHP,loss ) (11)

[0071] H GB,t,i =V GB,t,i H g η GB (12)

[0072] Where: P CHP,t,i is the electric power of the CHP unit in region i during period t; H CHP,t,i is the thermal power of the CHP unit in region i during period t; V CHP,t,i is the natural gas consumption of CHP units in region i during period t; V GB,t,i is the natural gas consumption of gas boilers in region i during period t; η CHP,e is the electrical efficiency of the CHP unit; η CHP,loss is the CHP unit loss rate; η GB is the gas boiler efficiency; H GB,t,i is the thermal power of the gas boiler in area i during period t.

[0073] ②Energy storage unit

[0074] Considering the self-consumption of the energy storage device, the power equations for electricity and heat storage are shown below.

[0075] S EES,t,i =S EES,t-1,i (1-σ EES,i )+μ EESC,t,i η EESC,i P EESC,t,i +μ EESD,t,i η EESD,i P EESD,t,i (13)

[0076] 0≤μ EESC,t,i +μ EESD,t,i ≤1 (14)

[0077] S TES,t,i =S TES,t-1,i (1-σ TES,i )+μ TESC,t,i η TESC,i Q TESC,t,i +μ TESD,t,i η TESD,i Q TESD,t,i (15)

[0078] 0≤μ TESC,t,i +μ TESD,t,i ≤1 (16)

[0079] Where: S EES,t,i , S TES,t,i is the remaining energy of electric energy storage and thermal energy storage at the end of period t in region i, MWh; P EESC,t,i , P EESD,t,iare the charging and discharging power of the energy storage in region i during period t, positive for charging and negative for discharging; Q TESC,t,i , Q TESD,t,i are the heat storage and heat release powers of the thermal energy storage in region i during period t, positive for heat storage and negative for heat release; η EESC,i , η EESD,i are the charging and discharging efficiencies of the energy storage in region i; η TESC,i , η TESD,i are the heat storage and heat release efficiencies of the thermal energy storage in region i; σ EES,i , σ TES,i are the self-consumption rates of electrical energy storage and thermal energy storage in region i; μ EESC,t,i , μ TESC,t,i Respectively indicate whether the electric energy storage and thermal energy storage are replenishing energy in the i region and the t period, otherwise it is set to 0, and if so, it is set to 1; μ EESD,t,i , μ TESD,t,i They respectively indicate whether the electric energy storage and thermal energy storage release energy in time period t in region i. If not, they are set to 0, and if so, they are set to 1.

[0080] ③ Waste incineration unit

[0081] Waste incineration power plants can meet the power and heat needs of the region at the same time. At present, my country's waste power generation is about 372kWh / ton, and the thermal energy utilization rate is in the range of [18%, 26%]. The total energy W that a waste incineration power plant can provide is WI,i The equation is shown below.

[0082]

[0083] Where: P WI,t,i , H WI,t,i They respectively represent the electric power and thermal power output of the waste incineration power plant in area i during period t; T is the total number of time periods in the scheduling cycle; Δt is the operating time.

[0084] The flue gas generated by the waste incineration power plant flows into the flue gas treatment system through the exhaust pipe. It can be discharged into the atmosphere only after the pollutant gas is removed by the reaction tower. After the introduction of the smoke storage device, the flue gas treatment link and the waste power generation link can be decoupled by adjusting the flue gas diversion ratio entering the reaction tower and the smoke storage device. Therefore, the energy consumption of the flue gas treatment system including the smoke storage device is a controllable load. By controlling the working time of the flue gas treatment system and coordinating and optimizing the operation with other units in the VPP, the VPP scheduling method can be made more flexible. The power equation of the flue gas treatment system is shown below.

[0085] P FG,t,i =w FG (Q FG1,t,i +Q FG2,t,i ) (18)

[0086] Where: PFG,t,i is the flue gas treatment system power in area i at time t; w FG is the power consumption coefficient of the flue gas treatment system; Q FG1,t,i Q is the amount of flue gas flowing into the flue gas treatment system from the exhaust pipe of the waste incineration power plant in area i at time t; FG2,t,i is the amount of smoke flowing into the smoke treatment system from the smoke storage device in area i at time t.

[0087] ④Multi-system and multi-unit collaborative operation

[0088] When multiple systems and units such as the natural gas production and consumption system, waste incineration unit, and renewable energy unit operate in coordination, the carbon capture unit, waste incineration unit, and renewable energy unit jointly meet the energy needs of the carbon capture system and the flue gas treatment system, and the remaining power is connected to the grid.

[0089] P CC,t,i =P CFGCC,t,i +P WICC,t,i +P WTCC,t,i +P PVCC,t,i (19)

[0090] P FG,t,i =P CFGFG,t,i +P WIFG,t,i +P WTFG,t,i +P PVFG,t,i (20)

[0091] Where: P CC,t,i , P FG,t,i are the power of carbon capture system and flue gas treatment system in region i during period t; P CFGCC,t,i , P WICC,t,i , P WTCC,t,i , P PVCC,t,i are the power provided to the carbon capture system by coal-fired generators, waste incineration power plants, wind power, and photovoltaic power plants in region i during period t; P CFGFG,t,i , P WIFG,t,i , P WTFG,t,i , P PVFG,t,i They are the power provided to the flue gas treatment system by coal-fired generators, waste incineration power plants, wind power, and photovoltaic power plants in region i during period t.

[0092] P CFG,t,i =P CFGCC,t,i +P CFGFG,t,i +P CFGN,t,i (twenty one)

[0093] P WI,t,i =P WICC,t,i +P WIFG,t,i +P WIN,t,i (twenty two)

[0094] P WT,t,i=P WTCC,t,i +P WTFG,t,i +P WTN,t,i (twenty three)

[0095] P PV,t,i =P PVCC,t,i +P PVFG,t,i +P PVN,t,i (twenty four)

[0096] Where: P WI,t,i , P WT,t,i , P PV,t,i are the power generation of waste incineration power plant, wind power and photovoltaic power in region i during period t; P CFGN,t,i , P WIN,t,i , P WTN,t,i , P PVN,t,i They are the power of coal-fired generators, waste incineration power plants, wind power and photovoltaic power connected to the grid in region i and period t.

[0097] ⑤Energy interaction between regions

[0098] The interaction of electrical energy and thermal energy can be achieved between the various regions of a multi-region VPP. The energy interaction equation between regions is shown below.

[0099] P IN,t,j,i =μ P,t,i,j P OUT,t,i,j (1-σ P,i,j L P,i,j P OUT,t,i,j ) (25)

[0100] H IN,t,j,i =μ H,t,i,j H OUT,t,i,j (1-σ H,i,j L H,i,j H OUT,t,i,j ) (26)

[0101] Where: P OUT,t,i,j , P IN,t,j,i They are the output power of region i and the input power of region j when there is power interaction between regions ij during period t; H OUT,t,i,j , H IN,t,j,i are the output thermal power of region i and the input thermal power of region j when there is thermal energy interaction between regions ij during period t; P,i,j is the unit distance power loss coefficient between regions ij; σ H,i,j is the heat energy loss coefficient per unit distance between regions ij; L P,i,j , L H,i,j are the distances for transmitting electric energy and heat energy between regions ij, km; μ P,t,i,j , μ H,t,i,jThey respectively indicate whether there is electrical energy and thermal energy interaction between regions ij during time period t. Otherwise, they are set to 0, and if so, they are set to 1.

[0102] (2) Constructing the objective function

[0103] In order to reflect the economic efficiency of VPP, the optimization is carried out with the goal of maximizing the net profit of VPP operation, and the objective function expression is shown in equation (27). Among them, the decision variables include carbon market trading volume, energy market (EM) trading volume, carbon capture unit operating parameters, waste incineration unit operating parameters, gas consumption unit operating parameters, P2G operating parameters, renewable energy unit operating parameters, interruption load, etc.

[0104]

[0105] Where: S is the total number of regions in the VPP. The objective function includes: carbon market income I CM,t,i 、Energy Market Benefits I EM,t,i 、Environmental pollution cost C E,t,i , coal-fired generator fuel cost C CFG,t,i 、Carbon sequestration cost C CS,t,i , Waste incineration power plant cost C WI,t,i 、 Gas consumption unit cost C GC,t,i 、P2G cost C P2G,t,i , wind power and photovoltaic maintenance costs C OM,t,i , Interruption load compensation fee C IL,t,i And so on for 10 parts.

[0106] The various expressions are described in detail as follows:

[0107] ① When the amount of CO2 emitted by the carbon capture unit to the atmosphere is greater than the carbon emission quota Q CO2,t,i When the amount of CO2 emitted by the carbon capture unit to the atmosphere exceeds the carbon emission quota Q CO2,t,i When the amount is small, the remaining carbon emission rights can be sold and corresponding income can be obtained. The carbon trading income equation is shown below.

[0108] I CM,t,i =k C (Q CO2,t,i -Q CE,t,i ) = k C (γ C P CFG,t,i -Q CE,t,i ) (28)

[0109] Where: k C is the carbon trading price, 10,000 yuan / ton; γ C is the carbon emission baseline coefficient, tonnes / MWh.

[0110] ②The revenue expression of the electricity market is shown in equation (29).

[0111] I EM,t,i =0.5P EM,t,i (λ EM1,t +λ EM2,t )+0.5|P EM,t,i |(λ EM1,t -λ EM2,t ) (29)

[0112] Where: P EM,t,i is the transaction volume in the energy market in region i during period t, with positive values ​​for electricity sales and negative values ​​for electricity purchases, MWh; EM1,t , EM2,t They are respectively the electricity selling and purchasing prices of VPP in the energy market in region i and period t, RMB 10,000 / MWh.

[0113] ③ In 2020, my country's thermal power generation accounts for about 70.5% of the country's total power generation. The pollutants produced by thermal power generation include CO, CO2, NOX and SO2. Therefore, when VPP purchases electricity from the power grid, it needs to pay the environmental pollution cost, as shown in equation (30).

[0114]

[0115] Where: R is the total number of pollutants; D r,g is the emission coefficient of the rth pollutant, kg / MWh; H r is the environmental coefficient of the rth pollutant, yuan / kg; Y r is the penalty coefficient for the rth pollutant, yuan / kg.

[0116] ④ In order to ensure the accuracy of the model solution, the fuel cost equation of the coal-fired generator considering the valve point effect is shown below.

[0117]

[0118] Where: a i 、b i 、c i d i 、e i are the fuel cost coefficients of coal-fired generators in region i; P CFG,min,i is the lower limit of the output power of coal-fired generators in area i.

[0119] ⑤ At the current stage, the purpose of reducing CO2 emissions is achieved by sealing the captured CO2. Considering that the CO2 storage and transportation distance is long and the cost is high, there are also risks such as leakage of the storage that harms the environment and is prone to explosion. The present invention provides the captured CO2 as a raw material to P2G through the natural gas production and consumption system, realizes carbon recycling, and effectively reduces the amount of CO2 storage. With the continuous advancement of P2G technology, CO2 can be recycled and utilized to a greater extent, reducing carbon storage. The carbon storage cost equation is shown below.

[0120] C CS,t,i =Q CS,t,i k CS (32)

[0121] Q CS,t,i =Q CC,t,i -Q P2G,t,i (33)

[0122] Where: k CS is the CO2 storage cost coefficient, 10,000 yuan / ton.

[0123] ⑥ The cost of the waste incineration power plant refers only to the carbon emission fine, and its expression is as follows.

[0124] C WI,t,i =P WI,t,i k c,t,i (e FG -γ C ) (34)

[0125] Where: e FG is the flue gas emission coefficient of the waste incineration power plant, tons / MWh.

[0126] ⑦ The cost of the natural gas consumption system mainly refers to the cost of purchasing natural gas, and its expression is as follows.

[0127] C GC,t,i =V NGM,t,i k NG (35)

[0128] V BUY,t,i =V CHP,t,i +V GB,t,i -V P2G,t,i (36)

[0129] Where: V NGM,t,i is the total amount of natural gas purchased by region i, m 3 ;k NG is the unit price of natural gas in the natural gas market, 10,000 yuan / m 3 .

[0130] ⑧P2G operation cost is shown in equation (37).

[0131] C P2G,t,i =P P2G,t,i k P2G (37)

[0132] Where: k P2G is the P2G operation cost coefficient, 10,000 yuan / MWh.

[0133] ⑨ The daily maintenance costs of wind power and photovoltaic power are shown in equation (38).

[0134] C OM,t,i =λ1P WT,t,i +λ2P PV,t,i (38)

[0135] In the formula: λ1 and λ2 are the maintenance cost coefficients of wind power and photovoltaic power, respectively, yuan / MWh.

[0136] ⑩ When the user load is interrupted, VPP needs to compensate the user. The interrupted load compensation fee is shown in equation (39).

[0137]

[0138] Where: K is the interruption load level; λ CUT,k is the k-th level interruption load compensation price coefficient, which is positively correlated with the interruption load level, 10,000 yuan / MW; CUT,k,t,i is the total amount of interruption load at level k in area i during period t, MW.

[0139] (3) Constraints

[0140] ① Power balance constraints in each area.

[0141]

[0142] H CHP,t,i +H GB,t,i +H WI,t,i +H TESD,t,i =H HL,t,i +H TESC,t,i (41)

[0143] Where: P EL,t,i , H HL,t,i are the electric load and heat load in area i during period t, MW; P CUT,t,i is the total amount of interruption load at all levels in area i during period t.

[0144] ② Carbon capture unit constraints.

[0145] P CFG,min,i ≤P CFG,t,i ≤P CFG,max,i (42)

[0146] P Const,i ≤P CFGCC,t,i +P WICC,t,i +P WTCC,t,i +P PVCC,t,i ≤P CCS,max,t,i (43)

[0147] P CCS,max,i =w C e CCS P CFG,max,i (44)

[0148] |P CFG,t+1,i -P CFG,t,i |≤ΔP CFG,i (45)

[0149]

[0150] Where: P CFG,max,i is the upper limit of coal-fired generator output power; P CCS,max,i is the upper limit of the operating power consumption of the carbon capture system in region i; ΔP CFG,i is the ramp rate constraint of coal-fired generator output in region i; ΔP OP,i is the ramp rate constraint of the power consumption of the carbon capture system in area i.

[0151] ③ Natural gas consumption unit constraints.

[0152] The electric heat output and ramp constraint equations of the CHP unit are shown below.

[0153] P CHP,min,i ≤P CHP,t,i ≤P CHP,max,i (47)

[0154] H CHP,min,i ≤H CHP,t,i ≤H CHP,max,i (48)

[0155] |P CHP,t+1,i -P CHP,t,i |≤ΔP CHP,i (49)

[0156] Where: P CHP,max,i , P CHP,min,i , H CHP,max,i , H CHP,min,i are the upper and lower limits of the electric output and thermal output of the CHP unit in region i; ΔP CHP,i is the output ramp rate constraint of CHP units in area i.

[0157] The thermal output and ramp constraint equations of the gas boiler are shown below.

[0158] HGB,min,i ≤H GB,t,i ≤H GB,max,i (50)

[0159] |H GB,t+1,i -H GB,t,i |≤ΔH GB,i (51)

[0160] Where: H GB,max,i , H GB,min,i are the upper and lower limits of gas boiler output in area i; ΔH GB,i is the output ramp rate constraint of the gas boiler in area i.

[0161] ④P2G operation constraints

[0162] 0≤P P2G,t,i ≤P P2G,max,i (52)

[0163] Where: P P2G,max,i is the maximum operating power of the P2G device in area i.

[0164] ⑤ Energy storage unit constraints

[0165] The electrical energy storage constraints are shown in equations (53) to (56). Since the thermal energy storage constraints are consistent with the electrical energy storage constraints, they will not be repeated here.

[0166] 0≤P EESC,t,i ≤P EESC,max,i (53)

[0167] P EESD,max,i ≤P EESD,t,i ≤0 (54)

[0168] S EES,min,i ≤S EES,t,i ≤S EES,max,i (55)

[0169] S EES,0,i =S EES,24,i (56)

[0170] Where: P EESC,max,i , P EESD,max,i are the maximum charging and discharging power of the energy storage in area i; S EES,min,i , S EES,max,i are the minimum and maximum capacity of the electric energy storage in area i; S EES,0,i , S EES,24,i They are the initial and final values ​​of the remaining energy storage capacity in area i within one day.

[0171] ⑥Interruption load constraint

[0172] 0≤P CUT,k,t,i ≤RCUT,k,i P EL,t,i (57)

[0173]

[0174] Where: R CUT,k,i is the k-th level load interruption coefficient in area i.

[0175] ⑦ Constraints of waste incineration units

[0176]

[0177] P WI,min,i ≤P WI,t,i ≤P WI,max,i (60)

[0178] |P WI,t+1,i -P WI,t,i |≤ΔP WI,i (61)

[0179] 0.1V WIFG,max,i ≤V WIFG,t,i ≤0.9V WIFG,max,i (62)

[0180] 0≤Q FG3,t,i ≤V L,WIFG,max,i (63)

[0181] Where: W WI,i is the total daily energy output of the waste incineration unit in region i. Generally, according to its installed capacity, the government provides a fixed amount of domestic waste as its fuel, so the total daily energy output of the waste incineration unit is constant; W WI,max,i is the upper limit of the daily total energy output of the waste incineration unit in area i; P WI,max,i , P WI,min,i are the maximum and minimum power output of the waste incineration unit in area i; ΔP WI,i is the output ramp rate constraint of the garbage incineration unit in area i; V WIFG,t,i V is the gas storage capacity of the smoke storage device in area i at time t; WIFG,max,i is the maximum capacity of the smoke storage device in area i; Q FG3,t,i V is the amount of smoke flowing into the smoke storage device at time t in area i; L,WIFG,max,i It is the maximum flow rate of the smoke storage device pipeline equipment.

[0182] ⑧Inter-regional interconnection power constraints

[0183] 0≤P IN,t,j,i ≤P IO,max,i,j (64)

[0184] 0≤P OUT,t,i,j ≤PIO,max,i,j (65)

[0185] Where: P IO,max,i,j is the upper limit of the power transmission between regions ij.

[0186] Step 4: Use the composite differential evolution algorithm to solve the multi-region VPP optimization scheduling model constructed in step 3, and then realize the multi-region virtual power plant optimization scheduling for multi-energy complementarity and low carbonization;

[0187] The multi-region VPP scheduling model for the coordinated operation of multiple systems and units such as natural gas production and consumption systems, waste incineration units, and renewable energy units has the characteristics of high-dimensional nonlinearity and high difficulty in solving. In terms of optimization depth and accuracy, it is difficult to meet the solution requirements using conventional optimization algorithms. To address this problem, the present invention uses Figure 4 The compound differential evolution algorithm with superior performance is used to solve the problem. Since the compound differential evolution algorithm draws on the principle of intermolecular interaction, it cleverly solves the contradiction between optimization speed and depth that is common in evolutionary algorithms.

[0188] The specific steps of step 4 include:

[0189] Each iteration of the compound differential evolution algorithm will be sorted to divide the individuals of the population into dominant and inferior populations. The DE / rand / 1 mutation strategy shown in formula (66) is used for the dominant population, and the DE / best / 1 mutation strategy shown in formula (67) is used for the inferior population to enhance the directionality of evolution.

[0190] Y i,G =X D3,G +F(X D1,G -X D2,G ) (66)

[0191] Y i,G =X best,G +F(X D1,G -X D2,G ) (67)

[0192] Where: X best,G is the best vector for individuals in the Gth generation; X D1,G , X D2,G , X D3,G They represent the D1, D2 and D3 vectors of the randomly selected individuals of the Gth generation, and the three vectors are all different; F is the variation factor; Y i,G It is the intermediate vector of individuals generated in the mutation operation.

[0193] In summary, in the mutation operation of the dominant community in the late evolution period, the mutation difference phasor X in formula (67) D1,G -XD2,G gradually approaches 0, which leads to the problem that the evolution is still prone to mutation stagnation and premature convergence, resulting in a significant decline in population diversity. Therefore, a group mutation base vector is introduced to improve the traditional composite differential evolution algorithm to address the deficiency of insufficient population diversity in the later stage of evolution. The specific process is as follows.

[0194] (1) Assume that each generation of individual vectors is n-dimensional. The original population X G of the G-th generation and the intermediate mutant population Y G of the same generation are mixed to obtain a mixed population V G+1 with a population size twice that of the original. The individuals in V G+1 are composed of the carbon market trading volume, energy market trading volume, operating parameters of the carbon capture unit, operating parameters of the waste incineration unit, operating parameters of the gas consumption unit, P2G operating parameters, operating parameters of the renewable energy unit, interruption load, etc. in each region.

[0195]

[0196] (2) For the individuals in the mixed population V G+1 , use the YALMIP toolbox in MATLAB to solve the objective function equation (27) of the multi-region VPP optimal scheduling model, and take the calculated VPP operation revenue as the fitness evaluation value. Sort the values from high to low, and select the top n best individuals to form the population X G+1 of the (G + 1)-th generation. Sort them and select the top m best individuals to form the group mutation base vector B G+1 = [X 1,G+1 , X 2,G+1 , …, X m,G+1 , where m < n. Here, X 1,G+1 is the X best,G+1 in equation (67).

[0197] (3) In the group mutation base vector B G+1 , randomly select an individual vector as the mutation base vector in each mutation process, and increase the diversity of the mutation base vector through an improved differential evolution mutation mechanism. The relevant expression is shown in equation (69).

[0198] Y i,G+1 = Rand(B G+1 ) + F(X D1,G+1 - X D2,G+1 + X D1,D2,G+1 ) (69)

[0199] where Rand(B G ) is to randomly select an individual vector from the group of mutation base vectors B G .

[0200] The new generation of populations can be obtained through crossover and selection of improved compound differential evolution. After continuous iteration, the optimal VPP operating income individuals are finally converged, that is, the multi-region virtual power plant optimization scheduling scheme for multi-energy complementarity and low carbonization. The introduction of group mutation basis vectors in the traditional compound differential evolution algorithm improves the defect of insufficient population diversity in the late evolution period, so that the population diversity can be better maintained throughout the evolution process. While taking into account the optimization speed, the algorithm can avoid falling into the local optimal solution, improve the global deep optimization ability, and improve the algorithm performance.

[0201] Step 5. The multi-regional virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization of the present invention will build a mathematical model and constraints of the multi-regional virtual power plant based on the MATLAB platform and YALMIP toolbox in an environment where the CPU is Inter(R)Core(TM)i7-8250U with a main frequency of 1.8GHz, and use the improved composite differential evolution algorithm proposed in the present invention to solve it.

[0202] The present invention will be further described below by the best embodiment of the present invention:

[0203] The best implementation example takes the distributed integrated energy system in some urban areas of Tianjin as an example, establishes a multi-regional virtual power plant optimization scheduling model, and uses the improved composite differential evolution algorithm of the present invention to solve the optimization scheduling model, thereby obtaining a multi-regional virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization.

[0204] 1) Basic data of the example

[0205] The VPP system consists of three regions. The electrical load, thermal load, wind power and photovoltaic power of each region, and the transaction price of VPP in the energy market are as follows: Figure 5 Each area of ​​the VPP contains a carbon capture unit, a P2G system, a CHP unit, a gas boiler, a waste incineration unit, and a renewable energy unit. The parameters of each unit are shown in Table 1. The pollutant gas emission parameters are shown in Table 2.

[0206] Table 1 Parameters of each unit of the energy hub

[0207]

[0208]

[0209] Table 2 Pollutant gas emission parameters

[0210] Polluted gas Emission factor (kg / MWh) Environmental factor (yuan / kg) Penalty coefficient (yuan / kg) NOx 0.619 6.464 1.616 CO2 184.083 0.019 0.008 CO 0.17 0.808 0.129 SO2 0.001 4.848 0.808

[0211] 2) Scheme design and analysis of results of each scheme

[0212] In order to measure the impact of the natural gas production and consumption system operation strategy, power-to-gas equipment, inter-regional energy interaction, multi-system and multi-unit coordinated operation, improved compound differential evolution algorithm, and participation in the energy market and carbon trading market on the VPP operating income, the present invention designs 5 schemes, as shown in Table 3. Scheme 1 and Scheme 2 do not implement the natural gas production and consumption system operation strategy, and all captured CO2 is sealed. In Scheme 1 and Scheme 2, there is no inter-regional energy interaction, and each region is optimized independently. In Scheme 3, multiple systems and multiple units operate independently, and wind power and photovoltaic power are all connected to the grid, and no energy is provided to the carbon capture system and flue gas treatment system. Scheme 4 uses the traditional compound differential evolution algorithm to solve the model.

[0213] Table 3 Design and construction methods of five VPP solutions

[0214] Solution No. 1 2 3 4 5 Operation strategy of natural gas production and consumption system × × √ √ √ P2G Devices × √ √ √ √ Inter-regional energy interaction × × √ √ √ Multi-system and multi-unit collaborative operation √ √ × √ √ Improved compound differential evolution algorithm √ √ √ × √ Participating in the energy market √ √ √ √ √ Participating in the carbon trading market √ √ √ √ √

[0215] Five schemes are used to build VPP optimization scheduling models and solve them. The cost and benefit results of the five VPP schemes are compared, as shown in Table 4. The optimal scheduling results of the five VPP schemes are compared, as shown in Table 5. The operation results of the carbon capture system and flue gas treatment system of the five VPP schemes are compared, as shown in Table 6. The solution process of multiple algorithms is compared, as shown in Table 7. Figure 6 shown.

[0216] Table 4 Comparison of cost and benefit results of five VPP schemes

[0217]

[0218]

[0219] Table 5 Comparison of optimal scheduling results of five VPP schemes

[0220]

[0221] Table 6 Comparison of the operation results of the carbon capture system and flue gas treatment system of the five VPP schemes

[0222]

[0223] Through Tables 4 to 6 and Figure 6 Through comprehensive comparative analysis, we can draw the following conclusions.

[0224] 1) Compared with Scheme 1, P2G improves the wind and solar power consumption capacity of Scheme 2, reducing the cost of wind and solar power abandonment by 853,200 yuan, as shown in the sixth row of Table 4. Due to the lack of P2G, it is impossible to consume the abandoned wind and solar power to provide natural gas for gas-fired units, resulting in the gas purchase cost of Scheme 1 being significantly higher than other schemes, as shown in the fourth row of Table 4. Therefore, P2G can improve the consumption capacity of wind power and photovoltaic power, reduce the cost of purchasing natural gas, and make the VPP operating income of Scheme 2 higher than that of Scheme 1, increasing by 1.1195 million yuan, as shown in the last row of Table 4.

[0225] 2) Compared with Scheme 2, Scheme 5 uses 32t of CO2 as raw material for P2G equipment by implementing the operation strategy of the natural gas production and consumption system, as shown in the sixth and seventh rows of Table 5. The cost of CO2 storage is saved, which reduces the P2G cost of Scheme 5 by 12,900 yuan compared with Scheme 2, as shown in the fifth row of Table 4. At the same time, the carbon trading income of Scheme 5 increases by 56,900 yuan compared with Scheme 2, as shown in the tenth row of Table 4. The carbon capture amount of Scheme 5 increases by 182.5t compared with Scheme 2, as shown in the sixth row of Table 5. This is because the CO2 emitted by the carbon capture power plant in Scheme 2 is not directly stored, and the storage cost of CO2 is considered in the optimization, which restricts the carbon capture amount of Scheme 2.

[0226] 3) Further comparison with Scheme 2 shows that Scheme 5 can realize that the light-load area can supply energy to the heavy-load area through energy interaction between VPP areas. Since the light-load area supplies power to the heavy-load area by increasing the output power of the coal-fired generator, the output power of the coal-fired generator increases by 1915MW, as shown in the first row of Table 5, and the corresponding cost increases by 797,000 yuan, as shown in the first row of Table 4. Therefore, the energy market purchase of Scheme 5 is reduced by 2201MW, as shown in the fourth row of Table 5, and the corresponding energy market revenue increases by 1032,900 yuan, as shown in the eleventh row of Table 4. Therefore, by implementing the natural gas production and consumption system operation strategy and realizing energy interaction between regions, the VPP operation revenue of Scheme 5 is higher than that of Scheme 2, increasing by 259,800 yuan, as shown in the last row of Table 4.

[0227] 4) Compared with Scheme 3, Scheme 5 can flexibly dispatch power resources through the coordinated operation of multiple systems and multiple units, reducing carbon emissions and wind and solar power abandonment by 1,156 tons and 47.33MW, respectively, as shown in the fifth and last rows of Table 5. At the same time, carbon trading revenue increased by 692,900 yuan, as shown in the tenth row of Table 4, and the cost of wind and solar power abandonment was reduced by 333,600 yuan, as shown in the sixth row of Table 4. Therefore, the joint operation of multiple equipment can effectively reduce the energy cost of carbon capture systems and flue gas treatment systems, improve carbon capture, wind power and photovoltaic absorption capacity, and VPP economy. Finally, the VPP operating income of Scheme 5 is higher than that of Scheme 3, increasing by 652,200 yuan, as shown in the last row of Table 4.

[0228] 5) Scheme 5 uses the improved composite differential evolution algorithm, and scheme 4 uses the conventional composite differential evolution algorithm. Figure 8 By comparison, it can be seen that Scheme 4 using the conventional compound differential evolution algorithm basically converged at around 650 generations, while Scheme 5 using the improved compound differential evolution algorithm of the present invention was still optimizing at this time and basically converged at around 1150 generations. It is proved that the improved compound differential evolution algorithm of the present invention can improve the defect of insufficient population diversity in the late stage of evolution, and can better maintain population diversity throughout the evolutionary process. While taking into account the optimization speed, the ability of the algorithm to jump out of the local optimum is improved, continuous deep optimization is achieved, and the algorithm performance is improved. As can be seen from Table 4, the VPP operating income of Scheme 5 increased by 149,300 yuan compared with Scheme 4, and the VPP operating income value obtained by using the improved compound differential evolution algorithm of the present invention is more considerable.

[0229] In summary, implementing the operation strategy of the natural gas production and consumption system, introducing P2G equipment, realizing the coordinated operation of multiple systems and multiple units and energy interaction between regions can improve the operating income of VPP, reduce the carbon emissions of VPP, more effectively utilize internal resources and optimize the energy structure, and enhance the clean energy consumption capacity of VPP. The improved composite differential evolution algorithm proposed in the present invention can realize continuous deep optimization and greatly improve the optimization performance of the algorithm.

[0230] 3) Analysis of optimization results of VPP design 5

[0231] Through the above analysis, it can be seen that the VPP scheme 5 constructed according to the multi-regional virtual power plant optimization scheduling model for multi-energy complementarity and low carbonization proposed in the present invention is superior to other schemes in terms of operating income, carbon emissions, renewable energy absorption capacity, etc. Therefore, this section only analyzes the optimization scheduling results within and between regions of VPP scheme 5, and further explains the effectiveness and superiority of the multi-regional virtual power plant optimization scheduling model for multi-energy complementarity and low carbonization proposed in the present invention.

[0232] (1) Analysis of regional optimization scheduling results

[0233] Based on the results of the optimal dispatch, taking Area 1 of Scheme 5 as an example, the optimal dispatch results in the region are analyzed. The coal-fired generators, interrupted loads, remaining capacity of electric energy storage, and charging and discharging power in the three regions of Scheme 5 are shown in Figure 2. Figure 7 The CO2 emissions and capture in the three regions in Scheme 5 are shown in Figure 8 The power consumption of the carbon capture system in area 1 in Scheme 5, the power consumption of the flue gas treatment system, the amount of wind and solar power abandoned and the power of the electric load shared by each unit, the thermal load and the thermal load power shared by each unit, the remaining capacity of the thermal energy storage device, and the power output of the waste incineration power plant are shown in Figure 5. Fig. 9shown.

[0234] During the period from 1:00 to 7:00, wind power output is large and load demand is small. Figure 7 (a) It can be seen that the output of coal-fired generators in each area does not change with the load, and maintains the minimum output power to meet the load demand. Figure 8 , Fig. 9 From (a) to (b), we can see that most of the wind power output is provided to the carbon capture system and the flue gas treatment system. The power consumption of these two systems is relatively large, making the CO2 emissions in each region during this period approximately zero. Fig. 9 (c) It can be seen that the amount of wind and photovoltaic power that is abandoned is mainly concentrated in this period. Since flexible adjustment units such as coal-fired generators and waste incineration power plants are limited in their adjustment capacity to meet constraints such as unit output, ramp rate, carbon capture units and waste incineration units, the amount of wind and photovoltaic power that is abandoned is generated. At this time, on the one hand, the P2G equipment absorbs most of the abandoned wind and photovoltaic power. On the other hand, the carbon capture power consumption and flue gas treatment power consumption, which are transferable loads, are transferred to this period, which further improves the absorption capacity of wind power and photovoltaic power, alleviates the contradiction between renewable energy power generation and electricity load demand, and achieves peak shaving and valley filling. Combined with Fig. 9 (c) to (e) show that, since the electrical load is small and the thermal load is high during this period, the thermal load demand is basically met by the gas boiler, and the heat storage device is in a heat release state. Figure 7 From (c) to (d), it can be seen that the energy storage is in a charging state during this period.

[0235] During 8:00-9:00, the electricity load increases and the wind power output decreases. Figure 7 (a) Figure 8 , Fig. 9 From (a) to (b), we can see that in order to meet the power load demand, the carbon capture power plant increases its output. At the same time, the carbon capture energy consumption and flue gas treatment energy consumption are transferred to other time periods, and the overall trend during this period is reduced to increase the net output of the carbon capture power plant. Fig. 9 (d) It can be seen that in order to solve the problem of rapid decrease in thermal load and increase in electrical load, the output of the gas boiler decreases rapidly and the output of the CHP unit increases rapidly.

[0236] During the period from 10:00 to 17:00, the electricity load and the electricity price in the energy market increased compared with the previous period. Figure 7 (a) and Figure 9(f) show that in order to meet the power load demand, coal-fired generators continue to increase their output power, and the output power and grid-connected power of waste incineration power plants increase. Since there is an upper limit to the total daily power generation of waste incineration power plants, the output power cannot be increased throughout the entire period. At the same time, the interrupted load increases accordingly with the changing trend of power load and wind and solar power output, and the amount of electricity purchased in the energy market also increases significantly. Combined with Figure 8 and Fig. 9 As shown in (a) to (b) and (f), the carbon capture power consumption and flue gas treatment power consumption provided by each unit remain at a low level, resulting in higher carbon emissions during this period. However, during the period from 15:00 to 17:00, the flue gas treatment power consumption increased slightly. This is because the wind power and photovoltaic output levels are high, which can meet more energy needs of the flue gas treatment system. As shown in Figure 9 (d) to (e), the heat load is low and the electric load is large during this period. The heat load is basically provided by the CHP unit to relieve the pressure of the electric load. The power storage system is in a discharge state, and the heat storage system is in a heat storage state.

[0237] During the period from 18:00 to 20:00, the electricity load decreased slightly and the wind power output increased. Fig. 9 (b) It can be seen that the power consumption of the flue gas treatment system is at a medium level and most of it is provided by wind power. Fig. 9 From (d) to (e), we can see that as the electrical load decreases and the thermal load increases, the output of the CHP unit continues to decrease, the gas boiler meets most of the thermal load demand, and the heat storage device is in a heat release state during this period to alleviate the heating pressure.

[0238] During the period from 21:00 to 22:00, wind power output increases. Figure 7 (a) Figure 8 , Fig. 9 (a) and (f) show that during this period, the power output of coal-fired generators and the grid-connected power of waste incineration power plants increase, and the carbon capture power consumption is transferred to this period to smooth out the fluctuation of wind power output. Fig. 9 (d) It can be seen that the heat load is large at this time and the heat storage system is in a heat release state.

[0239] During the period from 23:00 to 24:00, wind power output first decreased and then increased compared with the previous period. Figure 7 (a) Figure 8 , Fig. 9 (a) to (b) It can be seen that the power output of coal-fired generators first increases and then decreases with the fluctuation of wind power output, the power consumption of the carbon capture system first decreases and then increases accordingly, and the energy consumption of flue gas treatment is at a medium level first and then increases significantly. As the carbon capture power consumption and flue gas treatment power consumption of the transferable load are transferred to this period, the power provided by wind power to the carbon capture system and flue gas treatment system first decreases and then increases significantly.

[0240] (2) Analysis of inter-regional optimization scheduling results

[0241] According to the optimization scheduling results, the transaction volume of each regional energy market and the inter-regional electric energy interaction power in Scheme 5 are shown in Figure 10.

[0242] Depend on Figure 7 and Fig.10It can be seen that during the period of 1:00-6:00, the electricity price in the energy market is low, the energy storage systems in each region are in a charging state, and region 1 sells the remaining electricity to the energy market. During the period of 7:00-8:00, the power of inter-regional electric energy interaction is 0, and each region meets the load demand in the region by increasing the power output of coal-fired generators or the purchase of electricity from the energy market. During the period of 9:00-16:00, the electricity price in the energy market is high, the energy storage system is discharged, the interruption load increases, and the power output of coal-fired generators increases. Some regions meet the load demand in the region by purchasing electricity from the energy market. Region 3 supplies energy to region 1 by continuously increasing the power output of coal-fired generators to reduce the energy market purchase of region 1. During the period of 17:00-22:00, region 3 mainly supplies energy to regions 1 and 2 by increasing the power output of coal-fired generators to reduce the energy market purchase of regions 1 and 2. During the period of 23:00-24:00, as the load of each region decreases, the energy storage system of each region is in a charging state, and the interruption load is reduced.

[0243] Therefore, through the coordinated operation of multiple systems and multiple units and the energy interaction between regions, energy complementarity and flexible scheduling between regions can be achieved. Fully dispatch energy storage resources to participate in system optimization scheduling, and use the time-shiftable and adjustable characteristics of carbon capture power consumption and flue gas treatment power consumption to establish a demand response mechanism. When the output of renewable energy sources such as wind power and photovoltaic power fluctuates, through the demand response mechanism, while reducing the cost and carbon emissions of carbon capture units and waste power generation units, coordinate the coordinated operation of multiple systems and multiple units to achieve supply and demand balance between sources and loads and flexible coordinated optimization scheduling. At the same time, it solves the problem of volatility of renewable energy output, improves the absorption capacity of wind power and photovoltaic power, and alleviates the contradiction between the output of renewable energy and the demand for electricity load to a certain extent. P2G equipment can use the abandoned wind and photovoltaic power to produce natural gas to meet the energy demand of gas consumption units, which can further improve the absorption capacity of wind power and photovoltaic power. It optimizes the energy structure of VPP and improves the overall operation efficiency of VPP.

[0244] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0245] 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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.

[0246] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization, characterized by: The following steps are involved: Step 1: Build a multi-region VPP architecture organization; Step 2: Based on the multi-regional VPP architecture organization constructed in Step 1, formulate the operation strategy of the natural gas production and consumption system, and calculate its electricity consumption, natural gas production and carbon emission reduction; Step 3: Based on the natural gas production and consumption system operation strategy formulated in step 2, the electric energy complementation of multi-regional VPPs is realized through inter-regional energy interaction, and a multi-regional VPP optimization scheduling model is constructed with the goal of maximizing the VPP operation benefits; Step 4: Use the composite differential evolution algorithm to solve the multi-region VPP optimization scheduling model constructed in step 3, and then realize the multi-region virtual power plant optimization scheduling for multi-energy complementarity and low carbonization; The specific steps of step 3 include: (1) Build a device model: ① Gas unit P.S CHP,t,i NV CHP,t,i H g η CHP,e (10) H CHP,t,i =V CHP,t,i H g (1st) CHP,e -or CHP,loss ) (11) H GB,t,i =V GB,t,i H g η GB (12) Where: P CHP,t,i is the electric power of the CHP unit in region i during period t; H CHP,t,i is the thermal power of the CHP unit in region i during period t; V CHP,t,i is the natural gas consumption of CHP units in region i during period t; V GB,t,i is the natural gas consumption of gas boilers in region i during period t; η CHP,e is the electrical efficiency of the CHP unit; η CHP,loss is the CHP unit loss rate; η GB is the gas boiler efficiency; H GB,t,i is the thermal power of the gas boiler in area i during period t; ②Energy storage unit Considering the self-consumption of the energy storage device, the power equations for electricity and heat storage are as follows; S EES,t,i =S EES,t-1,i (1-p EES,i )+μ EESC,t,i or EESC,i P EESC,t,i +m EESD,t,i or EESD,i P EESD,t,i (13) 0≤μ EESC,t,i +m EESD,t,i ≤1 (14) S TES,t,i =S TES,t-1,i (1-p TES,i )+μ TESC,t,i or TESC,i Q TESC,t,i +m TESD,t,i or TESD,i Q TESD,t,i (15) 0≤μ TESC,t,i +m TESD,t,i ≤1 (16) Where: S EES,t,i , S TES,t,i is the remaining energy of electric energy storage and thermal energy storage at the end of period t in region i, MWh; P EESC,t,i , P EESD,t,i are the charging and discharging power of the energy storage in region i during period t, positive for charging and negative for discharging; Q TESC,t,i , Q TESD,t,i are the heat storage and heat release powers of the thermal energy storage in region i during period t, positive for heat storage and negative for heat release; η EESC,i , η EESD,i are the charging and discharging efficiencies of the energy storage in region i; η TESC,i , η TESD,i are the heat storage and heat release efficiencies of the thermal energy storage in region i; σ EES,i , σ TES,i are the self-consumption rates of electrical energy storage and thermal energy storage in region i; μ EESC,t,i , μ TESC,t,i Respectively indicate whether the electric energy storage and thermal energy storage are replenishing energy in the i region and the t period, otherwise it is set to 0, and if so, it is set to 1; μ EESD,t,i , μ TESD,t,i They respectively indicate whether the electric energy storage and thermal energy storage in region i and period t release energy, otherwise they are set to 0, and if so, they are set to 1; ③ Waste incineration unit The waste incineration power plant can meet the power supply and heating needs of the region at the same time; the total energy W that the waste incineration power plant can provide WI,i The equation is shown below; Where: P WI,t,i , H WI,t,i They represent the electric power and thermal power output of the waste incineration power plant in region i during period t; T is the total number of periods in the scheduling cycle; Δt is the operating time; The power equation for the flue gas treatment system is as follows: P FG,t,i =w FG (Q FG1,t,i +Q FG2,t,i ) (18) Where: P FG,t,i is the flue gas treatment system power in area i at time t; w FG is the power consumption coefficient of the flue gas treatment system; Q FG1,t,i Q is the amount of flue gas flowing into the flue gas treatment system from the exhaust pipe of the waste incineration power plant in area i at time t; FG2,t,i is the amount of smoke flowing into the smoke treatment system from the smoke storage device in area i at time t; ④Multi-system and multi-unit collaborative operation When the multi-system and multi-unit of the natural gas production and consumption system, the waste incineration unit, and the renewable energy unit operate in coordination, the carbon capture unit, the waste incineration unit, and the renewable energy unit jointly meet the energy demand of the carbon capture system and the flue gas treatment system, and the remaining power is connected to the grid; P CC,t,i =P CFGCC,t,i +P WICC,t,i +P WTCC,t,i +P PVCC,t,i (19) P FG,t,i =P CFGFG,t,i +P WIFG,t,i +P WTFG,t,i +P PVFG,t,i (20) Where: P CC,t,i , P FG,t,i are the power of carbon capture system and flue gas treatment system in region i during period t; P CFGCC,t,i , P WICC,t,i , P WTCC,t,i , P PVCC,t,i are the power provided to the carbon capture system by coal-fired generators, waste incineration power plants, wind power, and photovoltaic power plants in region i during period t; P CFGFG,t,i , P WIFG,t,i , P WTFG,t,i , P PVFG,t,i They are the power provided to the flue gas treatment system by coal-fired generators, waste incineration power plants, wind power, and photovoltaic power generation in region i during period t; P CFG,t,i =P CFGCC,t,i +P CFGFG,t,i +P CFGN,t,i (21) P WI,t,i =P WICC,t,i +P WIFG,t,i +P WIN,t,i (22) P WT,t,i =P WTCC,t,i +P WTFG,t,i +P WTN,t,i (23) P PV,t,i =P PVCC,t,i +P PVFG,t,i +P PVN,t,i (24) Where: P WI,t,i , P WT,t,i , P PV,t,i are the power generation of waste incineration power plant, wind power and photovoltaic power in region i during period t; P CFGN,t,i , P WIN,t,i , P WTN,t,i , P PVN,t,i They are the power of coal-fired generators, waste incineration power plants, wind power, and photovoltaic grid-connected power in region i during period t; ⑤Energy interaction between regions The interaction of electric energy and thermal energy can be realized between each area of ​​the multi-area VPP. The energy interaction equation between areas is as follows: P IN,t,j,i =μ P,t,i,j P OUT,t,i,j (1-σ P,i,j L P,i,j P OUT,t,i,j ) (25) H IN,t,j,i =μ H,t,i,j H OUT,t,i,j (1-p H,i,j L H,i,j H OUT,t,i,j ) (26) Where: P OUT,t,i,j , P IN,t,j,i They are the output power of region i and the input power of region j when there is power interaction between regions ij in period t; H OUT,t,i,j , H IN,t,j,i are the output thermal power of region i and the input thermal power of region j when there is thermal energy interaction between regions ij during period t; P,i,j is the unit distance power loss coefficient between regions ij; σ H,i,j is the heat energy loss coefficient per unit distance between regions ij; L P,i,j , L H,i,j are the distances for transmitting electric energy and heat energy between regions ij, km; μ P,t,i,j , μ H,t,i,j They respectively indicate whether there is electrical energy and thermal energy interaction between regions ij during time period t, otherwise they are set to 0, and if so, they are set to 1; (2) Constructing the objective function The optimization is carried out with the goal of maximizing the net profit of VPP operation. The objective function expression is shown in equation (27): Where: S is the total number of regions in the VPP; the objective function includes the carbon market income I CM,t,i 、Energy Market Benefits I EM,t,i 、Environmental pollution cost C E,t,i , coal-fired generator fuel cost C CFG,t,i 、Carbon sequestration cost C CS,t,i , Waste incineration power plant cost C WI,t,i , Gas consumption unit cost C GC,t,i 、P2G cost C P2G,t,i , wind power and photovoltaic maintenance costs C OM,t,i , Interruption load compensation fee C IL,t,i ; in: ①The carbon trading revenue equation is as follows: I CM,t,i =k C (Q CO2,t,i -Q CE,t,i )=k C (γ C P CFG,t,i -Q CE,t,i ) (28) Where: k C is the carbon trading price, 10,000 yuan / ton; γ C is the carbon emission baseline coefficient, tonnes / MWh; ② The revenue expression of the electricity market is shown in equation (29): I EM,t,i =0.5P EM,t,i (l EM1,t +λ EM2,t )+0.5|P EM,t,i |(l EM1,t -l EM2,t ) (29) Where: P EM,t,i is the transaction volume in the energy market in region i during period t, with positive values ​​for electricity sales and negative values ​​for electricity purchases, MWh; EM1,t , EM2,t They are the electricity selling and purchasing prices of VPP in the energy market in region i and period t, 10,000 yuan / MWh; ③ When VPP purchases electricity from the grid, it needs to pay the environmental pollution cost, as shown in equation (30); Where: R is the total number of pollutants; D r,g is the emission coefficient of the rth pollutant, kg / MWh; H r is the environmental coefficient of the rth pollutant, yuan / kg; Y r is the penalty coefficient for the rth pollutant, yuan / kg; ④The fuel cost equation of coal-fired generator considering valve point effect is as follows; Where: a i , b i 、c i ,d i 、e i are the fuel cost coefficients of coal-fired generators in region i; P CFG,min,i is the lower limit of coal-fired generator output power in region i; ⑤The carbon storage cost equation is as follows: C CS,t,i =Q CS,t,i k CS (32) Q CS,t,i =Q CC,t,i -Q P2G,t,i (33) Where: k CS is the CO2 storage cost coefficient, 10,000 yuan / ton; ⑥ The cost of waste incineration power plants refers only to carbon emission fines, and its expression is as follows: C WI,t,i JP WI,t,i k c,t,i (e FG -γ C ) (34) Where: e FG is the flue gas emission coefficient of the waste incineration power plant, tons / MWh; ⑦ The natural gas consumption system cost is expressed as follows: C GC,t,i =V NGM,t,i k NG (35) V BUY,t,i =V CHP,t,i +V GB,t,i -V P2G,t,i (36) Where: V NGM,t,i is the total amount of natural gas purchased by region i, m 3 ;k NG is the unit price of natural gas in the natural gas market, 10,000 yuan / m 3 ; ⑧P2G operation cost is shown in equation (37); C P2G,t,i =P P2G,t,i k P2G (37) Where: k P2G is the P2G operation cost coefficient, 10,000 yuan / MWh; ⑨ The daily maintenance costs of wind power and photovoltaic power are shown in equation (38); C OM,t,i =λ1P WT,t,i +λ2P PV,t,i (38) Where: λ1 and λ2 are the maintenance cost coefficients of wind power and photovoltaic power, respectively, RMB / MWh; ⑩ When the user load is interrupted, VPP needs to compensate the user; the interruption load compensation fee is shown in equation (39); Where: K is the interruption load level; λ CUT,k is the k-th level interruption load compensation price coefficient, which is positively correlated with the interruption load level, 10,000 yuan / MW; CUT,k,t,i is the total amount of interruption load at level k in area i during period t, MW; (3) Constraints ① Power balance constraints in each area; H CHP,t,i +H GB,t,i +H WI,t,i +H TESD,t,i =H HL,t,i +H TESC,t,i (41) Where: P EL,t,i , H HL,t,i are the electric load and heat load in area i during period t, MW; P CUT,t,i is the total amount of interrupted load at all levels in region i during period t; ② Carbon capture unit constraints; P CFG,min,i ≤P CFG,t,i ≤P CFG,max,i (42) P Const,i ≤P CFGCC,t,i +P WICC,t,i +P WTCC,t,i +P PVCC,t,i ≤P CCS,max,t,i (43) P CCS,max,i =w C e CCS P CFG,max,i (44) |P CFG,t+1,i -P CFG,t,i |≤ΔP CFG,i (45) Where: P CFG,max,i is the upper limit of coal-fired generator output power; P CCS,max,i is the upper limit of the operating power consumption of the carbon capture system in region i; ΔP CFG,i is the ramp rate constraint of coal-fired generator output in region i; ΔP OP,i is the ramp rate constraint of the power consumption of the carbon capture system in area i; ③ Natural gas consumption unit constraints; The electric heat output and ramp constraint equations of the CHP unit are shown below; P CHP,min,i ≤P CHP,t,i ≤P CHP,max,i (47) H CHP,min,i ≤H CHP,t,i ≤H CHP,max,i (48) |P CHP,t+1,i -P CHP,t,i |≤ΔP CHP,i (49) Where: P CHP,max,i , P CHP,min,i , H CHP,max,i , H CHP,min,i are the upper and lower limits of the electric output and thermal output of the CHP unit in region i; ΔP CHP,i is the output ramp rate constraint of CHP units in area i; The thermal output and ramp constraint equations of the gas boiler are shown below; H GB,min,i ≤H GB,t,i ≤H GB,max,i (50) |H GB,t+1,i -H GB,t,i |≤ΔH GB,i (51) Where: H GB,max,i , H GB,min,i are the upper and lower limits of gas boiler output in area i; ΔH GB,i is the output ramp rate constraint of gas boiler in area i; ④P2G operation constraints 0≤P P2G,t,i ≤P P2G,max,i (52) Where: P P2G,max,i is the maximum operating power of P2G equipment in area i; ⑤ Energy storage unit constraints The electrical energy storage constraints are shown in equations (53) to (56). Since the thermal energy storage constraints are consistent with the electrical energy storage constraints, they will not be repeated here; 0≤P EESC,t,i ≤P EESC,max,i (53) P EESD,max,i ≤P EESD,t,i ≤0 (54) S EES,min,i ≤S EES,t,i ≤S EES,max,i (55) S EES,0,i =S EES,24,i (56) Where: P EESC,max,i , P EESD,max,i are the maximum charging and discharging power of the energy storage in area i; S EES,min,i , S EES,max,i are the minimum and maximum capacity of the electric energy storage in area i; S EES,0,i , S EES,24,i are the initial and final values ​​of the remaining capacity of the electric energy storage in area i within one day; ⑥Interruption load constraint 0≤P CUT,k,t,i ≤R CUT,k,i P EL,t,i (57) Where: R CUT,k,i is the k-th level load interruption coefficient in area i; ⑦ Constraints of waste incineration units P WI,min,i ≤P WI,t,i ≤P WI,max,i (60) |P WI,t+1,i -P WI,t,i |≤ΔP WI,i (61) 0.1V WIFG,max,i ≤V WIFG,t,i ≤0.9V WIFG,max,i (62) 0≤Q FG3,t,i ≤V L,WIFG,max,i (63) Where: W WI,i is the total daily energy output of the waste incineration unit in area i; W WI,max,i is the upper limit of the daily total energy output of the waste incineration unit in area i; P WI,max,i , P WI,min,i are the maximum and minimum power output of the waste incineration unit in area i; ΔP WI,i is the output ramp rate constraint of the garbage incineration unit in area i; V WIFG,t,i V is the gas storage capacity of the smoke storage device in area i at time t; WIFG,max,i is the maximum capacity of the smoke storage device in area i; Q FG3,t,i V is the amount of smoke flowing into the smoke storage device in area i at time t; L,WIFG,max,i The maximum flow rate of the smoke storage device pipeline equipment; ⑧Inter-regional interconnection power constraints 0≤P IN,t,j,i ≤P IO,max,i,j (64) 0≤P OUT,t,i,j ≤P IO,max,i,j (65) Where: P IO,max,i,j is the upper limit of the power transmission between regions ij.

2. The multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization according to claim 1 is characterized by: The multi-regional VPP architecture organization of step 1 analyzes the energy conversion and distribution relationship of the distributed energy system in the VPP through the energy hub; each region of the multi-regional VPP architecture organization includes a corresponding energy hub, and each energy hub includes a carbon capture unit, a renewable energy unit, a natural gas consumption unit, a gas production system, a waste incineration unit, an energy storage unit and a load unit; the load unit includes a fixed load and an interruptible load; the natural gas consumption unit and the waste incineration unit are used to jointly meet the heat load demand.

3. The multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization according to claim 1 is characterized by: The natural gas production and consumption system in step 2 includes: a carbon capture unit, a gas production system and a natural gas consumption unit; the gas production system includes: P2G and a manure treatment system to meet the gas demand of the regional natural gas consumption unit; the natural gas consumption unit includes: a cogeneration unit and a gas boiler; The operation strategy of the natural gas production and consumption system is: Power-to-gas (P2G) uses the abandoned wind and solar power to electrolyze water to produce hydrogen, and uses the CO2 captured by the carbon capture unit as raw material to produce methane, which is directly supplied to gas consumption units or incorporated into the gas pipeline network; the manure treatment system uses the abandoned wind and solar power to produce biogas and replace natural gas; the gas production system coordinates with the gas pipeline network to meet the gas demand of regional natural gas consumption units; among them, the cogeneration unit meets the regional electricity and heat energy needs; the gas boiler realizes regional heating auxiliary peak regulation.

4. The multi-region virtual power plant optimization scheduling method for multi-energy complementarity and low carbonization according to claim 1 is characterized by: The specific steps of step 4 include: Each iteration of the compound differential evolution algorithm will be sorted to divide the individuals in the population into dominant and inferior populations; the DE / rand / 1 mutation strategy shown in formula (66) is used for the dominant population, and the DE / best / 1 mutation strategy shown in formula (67) is used for the inferior population to enhance the directionality of evolution; Y i,G =X D3,G +F(X D1,G -X D2,G ) (66) Y i,G =X best,G +F(X D1,G -X D2,G ) (67) Where: X best,G is the best vector for individuals in the Gth generation; X D1,G , X D2,G , X D3,G They represent the D1, D2 and D3 vectors of the randomly selected individuals of the Gth generation, and the three vectors are all different; F is the variation factor; Y i,G It is the intermediate vector of individuals generated in the mutation operation; The specific process is as follows; (1) Assume that the individual vector of each generation is n-dimensional, and the original population X of the Gth generation G and the intermediate mutant population Y of this generation G Mix to get a mixed population V with a population size of twice G+1 , V G+1 The individual units are composed of the carbon market transaction volume, energy market transaction volume, carbon capture unit operation parameters, waste incineration unit operation parameters, gas consumption unit operation parameters, P2G operation parameters, renewable energy unit operation parameters, and interruption load in each region; (2) For the individuals in the mixed population V G+1 Use the YALMIP toolbox of MATLAB to solve the objective function equation (27) of the multi-region VPP optimal scheduling model, and take the calculated VPP operation revenue as the fitness evaluation value. Sort them from high to low, and select the top n best individuals to form the population X of the (G + 1)-th generation G+1 , sort them and select the top m best individuals to form the group mutation base vector B G+1 = [X 1,G+1 , X 2,G+1 , …, X m,G+1 , where m < n; among them, X 1,G+1 is the X in equation (67) best,G+1 ; (3) In the group variation basis vector B G+1 In , individual vectors are randomly selected as the mutation basis vectors in each mutation process, and the diversity of the mutation basis vectors is increased through the improved differential evolution mutation mechanism. The relevant expression is shown in equation (69); Y i,G+1 =Rand(B G+1 )+F(X D1,G+1 -X D2,G+1 +X D1,D2,G+1 ) (69) Among them, Rand (B G+1 ) is in the variant basis vector group B G+1 Randomly select an individual vector from; The new generation of population can be obtained through crossover and selection of improved compound differential evolution. After continuous iteration, it finally converges to obtain the individual with the best VPP operating profit, that is, the optimal scheduling plan for multi-regional virtual power plants for multi-energy complementarity and low carbonization.

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