Electricity, gas and heat integrated energy system construction method introduced into carbon trading market

Abstract

The invention belongs to the field of energy system scheduling, and particularly discloses an electric heat comprehensive energy system construction method introduced into a carbon trading market, aiming at the volatility and uncertainty of wind power and photovoltaic power generation, a wind and light typical scene is extracted by using a generative adversarial network technology; constructing a comprehensive energy system model which comprises a power distribution network, a natural gas network and a thermal network and introduces a carbon trading market; constructing a comprehensive operation cost function according to the power distribution station electricity buying cost, the power distribution station electricity selling income, the gas distribution station gas buying cost, the thermal power plant operation cost, the P2G equipment operation cost and the carbon transaction cost; taking the lowest comprehensive operation cost as a target function of the comprehensive energy system model; operation constraint conditions of the comprehensive energy system model are configured, the operation constraint conditions comprise operation constraint conditions of a power distribution network, a natural gas network and a heat power network, and the operation constraint conditions are configured for the power distribution network by taking a wind and light typical scene as an operation scene. According to the invention, high-efficiency and low-carbon utilization of energy is realized, so that wind and light absorption is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of energy system scheduling, and specifically relates to a method for constructing an electric and thermal integrated energy system that introduces a carbon trading market. Background Art

[0002] As one of the main renewable energy sources, wind power and photovoltaic power generation have significant environmental benefits and can significantly reduce greenhouse gas emissions. However, wind power and photovoltaic power generation are significantly affected by natural conditions, and are intermittent and volatile, which poses great challenges to the safe and stable operation of the power grid, especially in areas with abundant wind and solar resources. How to achieve large-scale absorption has become an urgent problem to be solved. Traditional power systems usually rely on flexible resources such as peak-shaving thermal power units to deal with this volatility, but this is contrary to the goals of clean energy development and carbon emission reduction. Therefore, finding a more efficient wind and solar absorption strategy has become a research hotspot.

[0003] In order to solve such problems, the current solution is to smooth or absorb the fluctuations or uncertainties of new energy through energy storage or demand of the power system. At present, there are also studies on the use of integrated energy systems IES for multi-energy complementarity, connection and coordination of sources, grids, loads and storage, to promote the absorption of new energy and improve the economic efficiency of system operation. However, the current methods do not involve the coordinated optimization of the electricity-gas-heat multi-energy coupled integrated energy system, and the current integrated energy system does not consider the absorption effect of the carbon trading market on new energy equipment. In addition, the current carbon trading mechanism is still based on a single energy system, lacking consideration for the coordinated use of integrated energy systems (including electricity, heat and other energy forms), which to a certain extent limits its role in promoting the absorption of wind and solar. Summary of the invention

[0004] To solve the above problems, the present invention provides a method for constructing an electric and thermal integrated energy system that introduces a carbon trading market, constructs an integrated energy system model including a distribution network, a natural gas network and a thermal network, and introduces a carbon trading market, so as to realize the coordinated utilization of various energy forms such as electricity, heat and gas, and then through the conversion and optimal scheduling between different energy sources within the system, the carbon trading market limits the total carbon emissions and allocates price signals for carbon emission rights, thereby encouraging integrated energy operators to reduce emissions and utilize low-carbon energy, thereby realizing efficient and low-carbon utilization of energy, and promoting the consumption of wind and solar power.

[0005] The technical solution of the present invention is: a method for constructing an electric and thermal integrated energy system introduced into a carbon trading market, comprising the following steps: In view of the volatility and uncertainty of wind and photovoltaic power generation, generative adversarial network technology is used to extract typical wind and solar scenarios; Based on the network topology information of the power distribution network, natural gas network and thermal network, a comprehensive energy system model including the power distribution network, natural gas network and thermal network and introducing the carbon trading market is constructed; The cost of carbon trading in building an integrated energy system; Construct a comprehensive operating cost function based on the electricity purchase cost of the distribution station, the electricity sales revenue of the distribution station, the gas purchase cost of the gas distribution station, the operating cost of the thermal power plant, the operating cost of the P2G equipment, and the carbon trading cost; The objective function of the integrated energy system model is to minimize the comprehensive operating cost; Configure the operating constraints of the integrated energy system model, including the operating constraints of the power distribution network, natural gas network, and thermal network. The operating constraints of the power distribution network are configured based on the typical wind and solar power scenarios.

[0006] In an optional implementation, a typical scene of scenery is extracted using a generative adversarial network technology, specifically including: Based on the wind turbine photovoltaic processing data of each period over the years, the initial basic scenario is generated using the generative adversarial network technology; By comparing and reducing the scenes, representative scenery typical scenes are extracted from the initial basic scenes.

[0007] In an optional implementation, based on the photovoltaic processing data of wind turbines in various periods of the past years, an initial basic scenario is generated using a generative adversarial network technology, specifically including: Step 1: Input the wind turbine photovoltaic output data curves read in various periods of the past years into the generative adversarial network to obtain historical data samples; Step 2: Input historical data samples into the judgement device D as basic data; Step 3, input the read random noise data of wind turbine and photovoltaic output in each period of the past years into the generator G to obtain generated data samples; Step 4: Input the generated data sample into the judgement device D for judgment, and judge the difference between the generated data sample and the basic data. If it is judged as "false", the judgment result is fed back to the generator G, and the generator G is regenerated and then judged by the judgement device D; Step 5, looping step 4 until the judge D determines to be "true", and taking the corresponding wind turbine photovoltaic output as the wind turbine photovoltaic output basic scenario, that is, the initial basic scenario.

[0008] In an optional implementation, by comparing and reducing scenes, a representative landscape typical scene is extracted from the initial basic scene, specifically including: Step 1: Set the initial basic scene probability of each output are equal; Step 2, Setup As the number of scenes being reduced, calculate any two scenes , The distance between scenes ; Step 3: For any scene , looking for satisfaction The scene is recorded as the target scene , and calculate the product , ; Step 4. Find the smallest , denoted as , looking for satisfaction The scene And the corresponding scene , and the number is counted as indivual; ; Step 5: Update the target scene Probability, update the number of scenes ; ; ; Step 6, return to step 2 and recalculate until the number of scenes that meet the requirements is obtained, thereby obtaining the reduced wind and solar typical daily output curve, that is, the wind and solar typical scene.

[0009] In an optional embodiment, the objective function of the integrated energy system model is:

[0010] In the formula, is the comprehensive operating cost; It is the cost of electricity purchased by the distribution station; is the revenue from electricity sales at the distribution station; is the cost of purchasing gas at the gas distribution station; is the operating cost of the thermal power plant; is the operating cost of the P2G equipment; It is the carbon trading cost of the integrated energy system.

[0011] In an optional embodiment, the operating constraints of the integrated energy system model include grid node balance constraints, gas network node balance constraints, heat network node balance constraints, second-order cone relaxation of power flow calculations, wind turbine operating constraints, photovoltaic operating constraints, power storage device operating constraints, and P2G equipment operating constraints.

[0012] In an optional embodiment, the grid node balance constraint is expressed as,

[0013] In the formula, 、 、 、 、 、 、 、 、 、 Respectively The current power load, the exchange power of the distribution station, the absorption power of the wind turbine, the absorption power of photovoltaics, the power consumption of P2G equipment, the supply power of small gas boilers, the supply power of small thermal power plants, the exchange power of energy storage equipment, the branch outflow power of the grid node, and the branch inflow power of the grid node.

[0014] In an optional implementation, the operation constraint of the wind turbine is that the wind turbine's power consumption is not greater than the maximum power that can be supplied in typical wind and solar scenarios at different time periods.

[0015] In an optional embodiment, the photovoltaic operation constraint is that the photovoltaic power consumption is not greater than the maximum power that can be supplied by typical wind and solar scenarios at different time periods.

[0016] In an optional implementation, the carbon trading costs of building an integrated energy system specifically include: Step 1, calculate the free carbon emission quota by the following formula;

[0017]

[0018]

[0019]

[0020] In the formula, 、 、 、 They are integrated energy systems, electricity purchased from distribution stations, small gas turbines, and free carbon emission quotas for small thermal power plants. ； 、 、 They are distribution station collection, small gas boiler collection, and small thermal power plant collection; Carbon emission quota per unit of electricity supply; Carbon emission quota per unit of heating supply; The power purchased by a single distribution station; 、 They are the power generation and heat supply of a single small gas turbine respectively; 、 They are respectively the power generation and heat supply of a single small thermal power plant; Step 2: Calculate the initial carbon emission quota of the comprehensive energy system including P2G equipment using the following formula: ;

[0021]

[0022] In the formula, P2G equipment is fixed Additional carbon emission quotas obtained; is the carbon capture coefficient of the P2G equipment; is the active power consumed by a single P2G device; Step 3: Calculate the carbon trading cost of the integrated energy system using the following formula: ;

[0023] In the formula, It is the carbon emission of the actual comprehensive energy demand; is the carbon trading price in the trading market; is the step range parameter; is the incentive coefficient of the tiered carbon trading price; It is the compensation coefficient of each tier carbon trading price.

[0024] The method for constructing an electric and thermal integrated energy system by introducing a carbon trading market provided by the present invention has the following beneficial effects compared with the prior art: through the introduction of the carbon trading market mechanism, an electric and thermal coupled integrated energy system is established, and the economic optimization of wind and solar new energy consumption and the integrated energy system is considered; by establishing a minimum comprehensive operating cost objective function, transaction costs, energy purchase and sale costs, and unit operating costs are comprehensively considered; at the same time, through the comprehensive consideration of the balance constraints of the three-network nodes of electricity, gas, and heat, and the operating constraints of multiple energy equipment such as power storage devices and P2G equipment, the operating cost of the overall system is reduced; based on the carbon trading market, by limiting the total carbon emissions and allocating price signals for carbon emission rights, the integrated energy operators are encouraged to reduce emissions and utilize low-carbon energy, so as to achieve efficient and low-carbon utilization of energy, thereby promoting wind and solar consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a flow chart of a method for constructing an electric and thermal integrated energy system for introducing a carbon trading market provided by an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the basic principle of generating adversarial networks.

[0028] Figure 3 It is a basic set of scenes representing the output of wind and solar power.

[0029] Figure 4 It is a schematic diagram of wind and solar output curves on different typical days.

[0030] Figure 5 This is a schematic diagram of the ladder for calculating carbon trading costs.

[0031] Figure 6 It is a schematic diagram of electrical and thermal integrated energy wiring.

[0032] Figure 7 The following are the total cost curves for the four cases.

[0033] Figure 8 This is a schematic diagram comparing the wind and solar output under four cases at node 4.

[0034] Fig. 9 This is a schematic diagram comparing the total energy storage changes and time-of-use electricity prices in Case 2 and Case 4. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0037] The solution to the current wind and solar power consumption is to smooth or absorb the fluctuation or uncertainty of new energy through the energy storage or demand of the integrated energy system (IES), but the current method does not involve the coordinated optimization of the electric-gas-heat multi-energy coupled integrated energy system, and does not consider the problem of the carbon trading market's consumption effect on new energy equipment. This embodiment provides a method for constructing an electric-thermal integrated energy system that introduces a carbon trading market. By introducing the carbon trading market mechanism, an electric-thermal coupled integrated energy system is established, and the economic optimization of wind and solar new energy consumption and the integrated energy system is considered. By establishing a minimum comprehensive operating cost objective function, the transaction cost, the cost of purchasing and selling energy, and the unit operating cost are comprehensively considered. At the same time, by comprehensively considering the balance constraints of the nodes of the three networks of electricity, gas, and heat, and the operating constraints of multiple energy equipment such as power storage devices and P2G devices, the operating cost of the overall system is reduced. Based on the carbon trading market, by limiting the total carbon emissions and allocating price signals for carbon emission rights, the integrated energy operators are encouraged to reduce emissions and use low-carbon energy, so as to achieve efficient and low-carbon utilization of energy, thereby promoting wind and solar power consumption.

[0038] Figure 1 : is a flow chart of a method for constructing an electric and thermal integrated energy system for introducing a carbon trading market provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes the following steps.

[0039] S1, in view of the volatility and uncertainty of wind and photovoltaic power generation, the generative adversarial network technology is used to extract typical wind and solar scenarios.

[0040] S2, based on the network topology information of the power distribution network, natural gas network and thermal network, build a comprehensive energy system model that includes the power distribution network, natural gas network and thermal network, and introduce the carbon trading market.

[0041] S3, the carbon trading costs of building an integrated energy system.

[0042] S4, construct a comprehensive operating cost function based on the electricity purchase cost of the distribution station, the electricity sales revenue of the distribution station, the gas purchase cost of the gas distribution station, the operating cost of the thermal power plant, the operating cost of the P2G equipment, and the carbon trading cost.

[0043] S5, the objective function of the integrated energy system model is to minimize the comprehensive operating cost.

[0044] S6, configure the operating constraints of the integrated energy system model, including the operating constraints of the power distribution network, natural gas network, and thermal network. The power distribution network is configured with the typical wind and solar scenario as the operating scenario.

[0045] The following describes in detail the method for constructing an electric and thermal integrated energy system that introduces a carbon trading market in this embodiment from three major aspects: handling of uncertainty in wind and solar power output, multi-energy coupling relationships in an electric and thermal integrated energy system, and an integrated energy carbon trading market.

[0046] 1. Integrated energy system model under typical wind and solar scenarios Traditional wind and solar power generation prediction methods usually rely on time series analysis or physical models, and it is difficult to accurately capture the impact of complex weather changes on wind and solar power generation. As a deep learning model, the Generative Adversarial Network (GAN) can generate new data samples by learning the probability distribution of historical data, which makes it have great potential in dealing with the randomness and uncertainty of wind and solar power generation output. The GAN model can generate a representative set of wind and solar power generation scenarios by learning a large amount of historical wind and solar power generation data, providing a variety of typical scenarios, and providing a more accurate basis for subsequent energy scheduling optimization. In addition, GAN can also help identify the optimal scheduling strategy under different wind and solar power generation scenarios by generating comparative reduction scenarios, thereby improving the economic benefits of the system. This embodiment uses generative adversarial network technology to extract typical wind and solar scenarios to more accurately simulate and predict the output of new energy, effectively improve the absorption rate of new energy such as wind power and photovoltaics, and reduce the phenomenon of wind and solar abandonment.

[0047] This embodiment uses the generative adversarial network technology to extract typical scenery scenes. First, an initial basic scene is generated, and then the scene is reduced by comparison to obtain a representative typical scenery scene.

[0048] S101, based on the wind turbine photovoltaic processing data in various periods of the past years, the initial basic scenario is generated using the generative adversarial network technology.

[0049] In this example, the most basic components of a generative adversarial network (GAN) are the generator (G) and the discriminator (D). G can analyze the potential probability distribution of real data and generate samples based on the basic characteristics of real data; D can accurately determine whether the input data comes from "fake" data generated by G or real data, output the judgment result and pass it to itself and the generator. The two continuously improve their capabilities through alternating adversarial training and eventually reach a Nash equilibrium. Figure 2 This is a schematic diagram of the basic principle of generating adversarial networks.

[0050] The corresponding relationship between historical comprehensive energy load data and its price is regarded as real data , the complex and difficult-to-quantify distribution relationship between the two is set as , the random noise data at this time It is a simple distribution that can establish a corresponding relationship through the known The generator G needs to process the input data and transform it into a generated data sample with a corresponding relationship that can be mistaken for the real thing. ; At this time, the discriminator D judges the historical data samples and the generated data samples, and the output is and , representing and The probability of being judged as true.

[0051] The loss functions corresponding to the generator G and the discriminator D are as follows: (1) (2) in, is the expectation of the noisy data distribution.

[0052] From the functional relationship, we can see that when The smaller the value of is, the corresponding The larger is the value, the closer the data generated by the generator is to the real data; similarly, when The larger the value of The smaller the value of is, the better the corresponding discrimination effect is. The smaller the value, the better the optimization goal of G. The larger the value, the better the optimization goal of D. and If they are mutually confrontational, the GAN minimization and maximization game model established is as follows: (3) The goals of the generator and the discriminator are mutually antagonistic and contradictory. In the above formula, we first maximize , and then minimize from the generator's perspective , that is, the minimization and maximization game process of GAN. Figure 3 It is a basic scene set representing the wind and solar output. Figure 3 (a) is the basic scenario set of wind power output, Figure 3 (b) is the basic scenario set of photovoltaic output.

[0053] Specifically, the initial basic scene is generated through the following steps.

[0054] Step 1: Input the wind turbine photovoltaic output data curves read in various periods of the past years into the generative adversarial network to obtain historical data samples.

[0055] Step 2: Input historical data samples into the judgement device D as basic data.

[0056] Step 3: Input the read random noise data of wind turbine and photovoltaic output in each period of each year into the generator G to obtain generated data samples.

[0057] Step 4: Input the generated data sample into the judgement device D for judgment to determine the difference between the generated data sample and the basic data. If it is judged as "false", the judgment result is fed back to the generator G, and the generator G is regenerated and then handed over to the judgement device D for judgment.

[0058] Step 5, looping step 4 until the judge D determines to be "true", and taking the corresponding wind turbine photovoltaic output as the wind turbine photovoltaic output basic scenario, that is, the initial basic scenario.

[0059] S102, extracting representative scenery typical scenes from the initial basic scenes by comparing and reducing the scenes.

[0060] Step 1: Set the initial basic scene probability of each output Are equal.

[0061] For example, (4) Step 2, Setup As the number of scenes being reduced, calculate any two scenes ， The distance between scenes .

[0062] The scene distance in this embodiment is the Kantorovich distance.

[0063] (5) Step 3: For any scene , looking for satisfaction The scene is recorded as the target scene , and calculate the product , (6) Step 4. Find the smallest , denoted as , looking for satisfaction The scene And the corresponding scene , and the number is counted as indivual.

[0064] (7) Step 5: Update the target scene Probability, update the number of scenes ; (8) (9) Step 6, return to step 2 and recalculate until the number of scenes that meet the requirements is obtained, thereby obtaining the reduced wind and solar typical daily output curve, that is, the wind and solar typical scene. Figure 4 This is a schematic diagram of wind and solar output curves on different typical days. Figure 4 (a) is the typical daily wind turbine output curve. Figure 4 (b) is the typical daily photovoltaic output curve.

[0065] 2. Consider the integrated energy system of wind and solar power consumption.

[0066] The basic features of the electric-thermal integrated energy system of this embodiment mainly include: (1) highly coupled multiple energy forms such as electricity, gas and heat, which can simultaneously meet the coordinated optimization of multiple energy sources and improve the flexibility of the integrated energy system; (2) due to the phenomenon that the peak and valley of each energy load will be staggered at different times of the day, the mutual conversion of energy between the power supply, heat supply and gas supply systems can greatly smooth the fluctuation of the electric and heat load, so that the equipment of the integrated energy system is in the efficient operation range for a long time, increase the operating life of the equipment and reduce costs; (3) the excess electric energy generated during the valley period of the power supply system can be fully utilized for heating or large-scale storage, which can not only improve the utilization efficiency of facilities, but also increase the consumption of excess electric energy; through the coordination and cooperation between various energy systems, the comprehensive utilization efficiency of energy can be improved and the consumption of renewable energy can be promoted; (4) compared with independently operated energy systems, the integrated energy system can provide uninterrupted supply to important users when an accident occurs in the power system, natural gas system or thermal system, and improve the system's ability to quickly recover from failures, meet the cascade utilization of multiple energy sources, and improve the reliability and safety of the integrated energy system.

[0067] This embodiment constructs a comprehensive operating cost function based on the electricity purchase cost of the distribution station, the electricity sales revenue of the distribution station, the gas purchase cost of the gas distribution station, the operating cost of the thermal power plant, the operating cost of the P2G equipment, and the carbon trading cost, and takes the lowest comprehensive operating cost as the objective function of the comprehensive energy system model.

[0068] 1. Objective function.

[0069] Under the premise of satisfying the constraints of each unit in the integrated energy system, the operation objective function is to minimize the comprehensive operation cost per unit time. The objective function includes transaction costs taking into account user satisfaction, energy purchase and sale costs, and unit operation costs: (10) In the formula, is the comprehensive operating cost; It is the cost of electricity purchased by the distribution station; is the revenue from electricity sales at the distribution station; is the cost of purchasing gas at the gas distribution station; is the operating cost of the thermal power plant; is the operating cost of the P2G equipment; It is the carbon trading cost of the integrated energy system.

[0070] Exemplarily, the total duration of the scheduling time is 24 hours, and the unit duration is 1 hour.

[0071] 2. Constraints.

[0072] The operation constraints of the comprehensive energy system model in this embodiment include the operation constraints of the power distribution network, natural gas network, and thermal network, among which the power distribution network is configured with the typical wind and solar scenario as the operation scenario. Specifically, it includes the grid node balance constraints, gas network node balance constraints, thermal network node balance constraints, second-order cone relaxation of power flow calculation, wind turbine operation constraints, photovoltaic operation constraints, power storage device operation constraints, and P2G equipment operation constraints.

[0073] 1) Grid node balance constraints The electric power in the grid node balance constraint is based on the unit time length of a single electric node as the balance scale, and satisfies the 24h balance constraint of each node one by one: (11) In the formula, 、 、 、 、 、 、 、 、 、 Respectively The current power load, the exchange power of the distribution station, the absorption power of the wind turbine, the absorption power of photovoltaics, the power consumption of P2G equipment, the supply power of small gas boilers, the supply power of small thermal power plants, the exchange power of energy storage equipment, the branch outflow power of the grid node, and the branch inflow power of the grid node.

[0074] 2) Gas network node balance constraints The gas volume in the gas network node balance constraint is based on the unit time of a single electrical node as the balance scale, and satisfies the 24h balance constraint of each node one by one: (12) In the formula, is the gas load gas volume; is the volume of gas exchanged at the gas distribution station; is the volume of gas produced by the P2G device; is the volume of gas consumed by the gas turbine; is the exchange gas volume of the gas storage equipment; is the outflow gas volume of the branch of the gas network node; It is the volume of gas flowing into the branch of the gas network node.

[0075] 3) Heat network node balance constraints The heat load in the heat network node balance constraint is based on the unit time of a single electrical node as the balance scale, and satisfies the 24h balance constraint of each node one by one: (13) In the formula, is the heat load; is the heating power of the waste heat boiler; It is the heating power of a small thermal power plant.

[0076] 4) Second-order cone relaxation for power flow calculation The power loss in the branch is obtained by calculating the difference between the power injected into the branch start node and the power flowing out of the branch end node. The upper and lower limits of the power injected into the branch start node and the power flowing out of the branch end node are as follows: (14) In the formula, is the lower limit of branch node switching power; is the injected power at the starting node of the branch; is the upper limit of the switching power of the branch node; is the outflow power of the branch start node; is the power loss in the branch line.

[0077] Since the reactive power in the branch power flow solution formula is much smaller than the active power, if the two are squared and added together, the proportion of reactive power will be smaller, so the formula can be simplified to formula (16): (15) (16) In the formula, is the reactive power flowing into the branch node; is the reference voltage of the power grid; is the branch equivalent resistance.

[0078] Since equation (16) has a square term when calculating the branch active power, it needs to be convexly optimized, as shown in the following equation: (17) 5) Fan operation constraints The actual output of the wind turbine must be less than or equal to the maximum output that the wind turbine can supply at different time periods. That is, the operation constraint of the wind turbine is that the wind turbine's power consumption is not greater than the maximum power that can be supplied by typical wind and solar scenarios at different time periods. The calculation method of the wind turbine's power consumption rate is as follows: (18) (19) In the formula, is the actual output of the fan; is the maximum output that the fan can provide; is the fan absorption rate.

[0079] 6) Operational constraints of photovoltaics The actual output of photovoltaic power generation must be less than or equal to the maximum output that photovoltaic power generation can provide at different time periods. That is, the operation constraint of photovoltaic power generation is that the power consumption of photovoltaic power generation is not greater than the maximum power that can be supplied by typical wind and solar power scenarios at different time periods. The calculation method of wind turbine power consumption rate is as follows: (20) (twenty one) In the formula, is the actual output of photovoltaics; is the maximum output that photovoltaic can provide, is the fan absorption rate.

[0080] 7) Operation constraints of power storage devices The maximum capacity range of the power storage equipment and the output or energy storage in each period are restricted: (twenty two) In the formula, It is the lower limit of the total energy storage capacity of the power storage device; is the energy storage capacity of the energy storage device in the current period; It is the upper limit of the total energy storage capacity of the power storage device; It is the maximum power limit of the power storage device output; It is the exchange power between the energy storage device and the power grid; It is the maximum power upper limit of the power storage device input; It is the lower limit of the maximum power input of the power storage device; is the input power of the power storage device in the current period; It is the lower limit of the maximum power output of the power storage device; It is the output power of the energy storage device in the current period.

[0081] 8) Operational constraints of P2G devices P2G equipment consumes excess power from the power grid and converts it into gas from the gas grid: (twenty three) In the formula, is the volume of gas produced by the P2G device; yes The heat coefficient generated by the complete combustion of natural gas is converted to 35590KJ / m3; is the conversion efficiency of P2G, which is taken as 0.6 here; It is the electrical power generated by the P2G device.

[0082] Since P2G equipment has high requirements for operating conditions, the efficiency of gas production under different electrical powers is different, and P2G equipment will incur high maintenance costs when it is deactivated, this embodiment defines the following operating ranges of P2G equipment: (twenty four) In the formula, and They are the upper and lower limits of P2G equipment operation; It is the maintenance cost of P2G equipment; It is the P2G equipment maintenance cost coefficient under different electric powers.

[0083] 3. Electrical and thermal coupled integrated energy carbon trading market The carbon trading model includes three important elements: initial carbon emission quota, actual carbon emission and carbon trading cost. This embodiment implements the construction of carbon trading cost of integrated energy system through the following steps.

[0084] Step 1: Calculate the free carbon emission quota.

[0085] The power generation of the CHP unit is converted into equivalent heat supply, and the carbon emission quota is allocated according to the total equivalent heat supply. The free carbon emission quota is calculated by the following formula.

[0086] (25) (26) (27) (28) In the formula, 、 、 、 They are free carbon emission quotas for integrated energy systems, electricity purchased from distribution stations, small gas turbines, and small thermal power plants; 、 、 They are distribution station collection, small gas boiler collection, and small thermal power plant collection; The carbon emission quota per unit of power supply is 0.728; The carbon emission quota per unit of heating supply is 0.102; The power purchased by a single distribution station; 、 They are the power generation and heat supply of a single small gas turbine respectively; 、 They are respectively the power generation and heat supply of a single small thermal power plant.

[0087] Step 2: Calculate the initial carbon emission quota of the integrated energy system including P2G equipment.

[0088] The essence of the carbon trading model is to calculate the difference between the initial carbon quota and the actual carbon emissions. If the difference is greater than zero, the integrated energy system can sell this unused carbon emission quota through the carbon trading market; conversely, if the difference is less than zero, the integrated energy market can purchase the missing carbon emission quota through the carbon trading market to ensure that the carbon emissions of the integrated energy meet the standards.

[0089] In the carbon trading model, this embodiment takes into account the role of P2G equipment in comprehensive energy. The absorption effect of P2G equipment enables the integrated energy system to obtain additional carbon quotas due to the operation of P2G equipment: (29) (30) In the formula, It is the initial carbon emission quota of the comprehensive energy system taking into account P2G equipment. P2G equipment is fixed Additional carbon emission quotas obtained; is the carbon capture coefficient of the P2G equipment; It is the active power consumed by a single P2G device.

[0090] Step 3: Calculate the carbon trading cost of the integrated energy system.

[0091] For the calculation of transaction costs of carbon emissions, this embodiment adds a step-by-step reward and punishment calculation model. Different intervals correspond to different reward and punishment ladder prices. is positive, corresponding to the carbon emissions of the integrated energy system at this time being greater than the actual carbon emission quota, and it is necessary to purchase carbon quotas from the carbon trading market. According to the ladder price model, the greater the demand, the higher the unit carbon quota price, and the higher the cost at this time; It is negative, corresponding to the carbon emissions of the integrated energy system at this time being less than the actual carbon emission quota. At this time, the carbon quota can be sold to the carbon trading market to obtain profits. According to the ladder pricing model, the greater the demand, the higher the unit carbon quota price, and the greater the profit.

[0092] (31) In the formula, It is the carbon emission of the actual comprehensive energy demand; is the carbon trading price in the trading market; is the step range parameter; is the incentive coefficient of the tiered carbon trading price; It is the compensation coefficient of each tier carbon trading price.

[0093] According to the carbon trading cost calculation model, a carbon trading cost calculation ladder diagram can be drawn. Figure 5 This is a schematic diagram of the carbon trading cost calculation ladder. Figure 5 It can be concluded that the carbon trading cost is directly related to carbon emissions. If carbon emissions are less than carbon emission quotas, additional profits can be obtained by selling remaining carbon emission quotas; if carbon emissions are greater than carbon emission quotas, additional carbon quotas need to be purchased through the carbon trading market to meet policy requirements.

[0094] 4. Example settings

[0095] The electrical and thermal integrated energy model used in this embodiment is based on a 14-node power grid-5-node gas grid-1-node heat grid for example analysis. Different nodes are equipped with different energy supply equipment and coupling devices. The specific wiring is as follows: Figure 6 shown.

[0096] In order to verify the effectiveness of the energy storage-carbon trading optimization scheduling strategy for the integrated energy system considering wind and solar power consumption proposed in this invention. The calculation data is based on the wind turbine and photovoltaic output mapping relationship data model after the reduction of the case in this embodiment. In order to compare the multi-objective optimization results of the total operating cost of the wind turbine system, system carbon emissions, and photovoltaic consumption rate of the electric and thermal integrated energy system under different cases, this embodiment sets the following cases: Case 1: Electricity and heat integrated energy system model based on traditional time-of-use energy prices.

[0097] Case 2: Based on Case 1, add an electric energy storage system.

[0098] Case 3: Based on Case 1, join the comprehensive energy carbon trading market.

[0099] Case 4: Based on Case 1, join the electric energy storage and integrated energy carbon trading market.

[0100] The following table 1 shows the cost statistics under different cases, in units of

[0101] Table 1: Cost statistics under different cases

[0102] Figure 7 shows the total cost curves for four cases.

[0103] from Figure 7 It can be seen from the total cost curves of cases 1-4 that the cost fluctuation trends within 24 hours in the four cases are consistent, and the trends are consistent with the overall trends of electricity load and gas load. From Table 1, it can be seen that the total cost of case 2 is reduced by 1.40398 million yuan compared with case 1; the total cost of case 3 is reduced by 69.82 million yuan compared with case 1. It can be seen that simply adding energy storage system or joining carbon trading market in the integrated energy system can reduce the total cost of system operation. Among them, the impact of energy storage system on total cost is greater, which is reduced by about 5.403% compared with the traditional model, while the reduction ratio of simply joining carbon trading market is limited, only 0.269%. In case 4, which adds energy storage and carbon trading market, the total cost is reduced by 1.5908 million yuan, or 6.122%, compared with case 1. The cost reduction of case 4 is greater than the sum of the cost reduction of cases 2 and 3. It can be seen that the combination of energy storage and carbon trading market has achieved the effect of "1+1>2" in reducing the total cost of system operation.

[0104] The biggest contribution to the total cost reduction in Case 2 and Case 4 is the reduction in electricity purchase costs. Due to the electricity balance equation, the output of wind power and photovoltaic power replaces the purchased electricity output, so the consumption rate will increase.

[0105] Table 2 below shows the wind and solar power consumption rates under four cases.

[0106] Table 2: Wind and solar power consumption rates under different cases

[0107] From Table 2, we can see that in case 2 compared with case 1, the wind turbine absorption rate at node 4 increased from 82.12% to 94.85%, the wind turbine at node 14 increased from 78.85% to 94.53%, and the photovoltaic at node 8 increased from 90.14% to 96.72%, with an increase of 12.73%, 15.68%, and 6.58%, respectively. The addition of the electric energy storage system has greatly improved the absorption rate of wind and solar power. Although wind turbines and photovoltaics have large fluctuations, energy storage can be used as a load when the load is low or the wind and solar power output is in excess, and as a power source when the load is peak or the wind and solar power output is low, to smooth out fluctuations.

[0108] Compared with Case 1, Case 4 has a significant improvement in the absorption rates of wind turbines No. 4 and No. 14 and photovoltaic No. 8, which are 11.59%, 15.22% and 6.52% respectively. However, the improvement rates are slightly lower than those of Case 2. The reason is that the carbon trading market is taken into account in Case 4. Since the gas purchase process will also consume carbon quotas, the gas purchase cost of Case 4 is lower than that of Case 1 and Case 2. Due to energy coupling and balance, the reduction in gas purchase will lead to an increase in electricity purchase. Therefore, the electricity purchase cost of Case 4 is 83,220 yuan more than that of Case 2, and the corresponding electricity purchase has increased by 10.548WM. Since excessive electricity purchase will affect the absorption rate of new energy, the wind and solar absorption rate in Case 4 has been slightly reduced.

[0109] In Case 3 compared with Case 1, the absorption rates of wind turbines No. 4 and No. 14 and photovoltaic No. 8 increased only to 1.69%, 2.00% and 0.30% respectively. The reason is that although the carbon trading strategy is adopted, the structure of the system has not changed. Therefore, it is difficult to achieve a substantial reduction in the process of re-optimizing the lowest total cost. As a result, the remaining costs in the system are difficult to be further reduced, and the improvement in the absorption rate of new energy is also very limited.

[0110] Table 3 below shows the total carbon emissions under the four cases.

[0111] Table 3: Total carbon emissions under different cases

[0112] Table 4 below compares the amount of electricity purchased by distribution stations in the four cases.

[0113] Table 4: Electricity purchased by distribution stations under different cases

[0114] From Table 3, we can see that the carbon emissions of Case 2 are reduced by 15,780 tons compared with Case 1, Case 3 is reduced by 26,733 tons compared with Case 1, and Case 4 is reduced by 17,657 tons compared with Case 1.

[0115] The reduction in carbon emissions in Case 2 is due to the role of electric energy storage, which leads to an increase in the consumption rate of new energy, and thus a reduction in the overall electricity purchase. As shown in Table 4, Case 2 has the lowest electricity purchase, which is 119.01MW lower than Case 1, or 42.720%. Since the system objective function is the total operating cost, the operating costs of each unit will be gambled during the optimization process. Therefore, in Case 2, the TP operating cost is higher, that is, the TP output is the highest, so the carbon emission reduction is the lowest in Cases 2, 3, and 4.

[0116] Case 3 has the largest reduction in carbon emissions. Although according to the previous analysis, Case 3 has limited reduction in total costs and limited increase in new energy consumption rate, the total carbon emissions are the lowest among the four cases because only the carbon trading market is used and the game process of ES for cost optimization is not considered.

[0117] Case 4 takes energy storage into consideration by taking the carbon trading market into consideration, and its carbon emissions are between Case 2 and Case 3, which is 1,368,739 tons.

[0118] Figure 8 This is a comparison diagram of wind and solar output under four cases of node 4. Fig. 9 This is a schematic diagram comparing the total energy storage changes and time-of-use electricity prices in Case 2 and Case 4. Figure 8 as well as Fig. 9 It can be seen that the total power of the electric energy storage is affected by the wind and solar output, as well as the power of the distribution station. At 8h and 17h, the wind turbine output at node 14 is low, and the photovoltaic output at node 8 is small. At this time, the electric energy storage outputs power to the system, reaching the lowest point of energy storage. Between 12h and 14h, although the wind turbine output is low, the photovoltaic output is at a peak. At this time, the electric energy storage has a certain recovery, which is a peak period of electric energy storage.

[0119] It can be seen from the fact that the total energy of electric energy storage fluctuates with the new energy units that electric energy storage reflects the output characteristics of wind turbine photovoltaics to a certain extent, smoothes the fluctuations of wind turbine photovoltaics to a certain extent, and improves the absorption rate of wind turbine photovoltaics.

[0120] The above disclosure is only a preferred embodiment of the present invention, but the present invention is not limited thereto. Any non-creative changes that can be thought of by a person skilled in the art, as well as several improvements and modifications made without departing from the principle of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for constructing an electric and thermal integrated energy system that introduces a carbon trading market. It is characterized in that The following steps are involved: In view of the volatility and uncertainty of wind and photovoltaic power generation, generative adversarial network technology is used to extract typical wind and solar scenarios; Based on the network topology information of the power distribution network, natural gas network and thermal network, a comprehensive energy system model including the power distribution network, natural gas network and thermal network and introducing the carbon trading market is constructed; The cost of carbon trading in building an integrated energy system; Construct a comprehensive operating cost function based on the electricity purchase cost of the distribution station, the electricity sales revenue of the distribution station, the gas purchase cost of the gas distribution station, the operating cost of the thermal power plant, the operating cost of the P2G equipment, and the carbon trading cost; The objective function of the integrated energy system model is to minimize the comprehensive operating cost; Configure the operating constraints of the integrated energy system model, including the operating constraints of the power distribution network, natural gas network, and thermal network. The operating constraints of the power distribution network are configured based on the typical wind and solar power scenarios.

2. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 1, It is characterized in that Generative adversarial network technology is used to extract typical scenery scenes, including: Based on the wind turbine photovoltaic processing data of each period over the years, the initial basic scenario is generated using the generative adversarial network technology; By comparing and reducing the scenes, representative scenery typical scenes are extracted from the initial basic scenes.

3. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 2, It is characterized in that Based on the wind turbine photovoltaic processing data of each period over the years, the initial basic scenario is generated using the generative adversarial network technology, including: Step 1: Input the wind turbine photovoltaic output data curves read in various periods of the past years into the generative adversarial network to obtain historical data samples; Step 2: Input historical data samples into the judgement device D as basic data; Step 3, input the read random noise data of wind turbine and photovoltaic output in each period of the past years into the generator G to obtain generated data samples; Step 4: Input the generated data sample into the judgement device D for judgment, and judge the difference between the generated data sample and the basic data. If it is judged as "false", the judgment result is fed back to the generator G, and the generator G is regenerated and then judged by the judgement device D; Step 5, loop step 4 until the judge D determines to be "true", and use the corresponding wind turbine photovoltaic output as the wind turbine photovoltaic output basic scenario, that is, the initial basic scenario.

4. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 3, It is characterized in that By comparing and reducing the scenes, representative scenery scenes are extracted from the initial basic scenes, including: Step 1: Set the initial basic scene probability of each output are equal; Step 2, Setup As the number of scenes being reduced, calculate any two scenes , The distance between scenes ; Step 3: For any scene , looking for satisfaction The scene is recorded as the target scene , and calculate the product , ； Step 4. Find the smallest , denoted as , looking for satisfaction The scene And the corresponding scene , and the number is counted as indivual; ； Step 5: Update the target scene Probability, update the number of scenes ; ； ； Step 6, return to step 2 and recalculate until the number of scenes that meet the requirements is obtained, thereby obtaining the reduced wind and solar typical daily output curve, that is, the wind and solar typical scene.

5. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 1, It is characterized in that The objective function of the integrated energy system model is: In the formula, is the comprehensive operating cost; It is the cost of electricity purchased by the distribution station; is the revenue from electricity sales at the distribution station; is the cost of purchasing gas at the gas distribution station; is the operating cost of the thermal power plant; is the operating cost of the P2G equipment; It is the carbon trading cost of the integrated energy system.

6. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 5, It is characterized in that The operating constraints of the integrated energy system model include grid node balance constraints, gas network node balance constraints, heat network node balance constraints, second-order cone relaxation of power flow calculations, wind turbine operation constraints, photovoltaic operation constraints, power storage device operation constraints, and P2G equipment operation constraints.

7. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 6, It is characterized in that The grid node balance constraint is expressed as, In the formula, , , , , , , , , , Respectively The current power load, the exchange power of the distribution station, the absorption power of the wind turbine, the absorption power of photovoltaics, the power consumption of P2G equipment, the supply power of small gas boilers, the supply power of small thermal power plants, the exchange power of energy storage equipment, the branch outflow power of the grid node, and the branch inflow power of the grid node.

8. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 7, It is characterized in that The operating constraint of the wind turbine is that the power consumption of the wind turbine is not greater than the maximum power that can be supplied in typical wind and solar scenarios at different time periods.

9. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to claim 8, It is characterized in that The operating constraint of photovoltaics is that the power consumption of photovoltaics is not greater than the maximum power that can be supplied in typical wind and solar scenarios at different time periods.

10. The method for constructing an electric and thermal integrated energy system for introducing a carbon trading market according to any one of claims 5 to 9, It is characterized in that The carbon trading costs of building an integrated energy system include: Step 1, calculate the free carbon emission quota by the following formula; In the formula, , , , They are free carbon emission quotas for integrated energy systems, electricity purchased from distribution stations, small gas turbines, and small thermal power plants; , , They are distribution station collection, small gas boiler collection, and small thermal power plant collection; Carbon emission quota per unit of electricity supply; Carbon emission quota per unit of heating supply; The power purchased by a single distribution station; , They are the power generation and heat supply of a single small gas turbine respectively; , They are respectively the power generation and heat supply of a single small thermal power plant; Step 2: Calculate the initial carbon emission quota of the comprehensive energy system including P2G equipment using the following formula: ; In the formula, P2G equipment is fixed Additional carbon emission quotas obtained; is the carbon capture coefficient of the P2G equipment; is the active power consumed by a single P2G device; Step 3: Calculate the carbon trading cost of the integrated energy system using the following formula: ; In the formula, It is the carbon emission of the actual comprehensive energy demand; is the carbon trading price in the trading market; is the step range parameter; is the incentive coefficient of the tiered carbon trading price; It is the compensation coefficient of each tier carbon trading price.

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