Virtual power plant low-carbon interactive operation optimization method and device

Abstract

The invention is suitable for the technical field of virtual power plant control, and provides a virtual power plant low-carbon interactive operation optimization method and device, and the method comprises the steps: obtaining the data of a production side and a user side of a virtual power plant, determining a dynamic emission factor and an energy attribute cost, establishing a low-carbon demand response mechanism and a double-layer low-carbon scheduling model of the virtual power plant; the upper layer of the model determines the carbon emission quota of the virtual power plant under the optimization target of the highest income, the lower layer introduces a load aggregator, and the excitation response amount of each energy is determined according to the dynamic emission factor and the energy attribute cost; and solving the double-layer low-carbon scheduling model of the virtual power plant, and determining a load demand after response excitation and an updated dynamic carbon emission factor to obtain an optimal low-carbon scheduling scheme. According to the application, the guiding effect of the dynamic carbon emission factor can be maximized, the energy demand of the user side is met while the carbon emission is reduced, the user is guided to adjust the energy consumption behavior, and the purpose of reducing the carbon emission of the system is achieved.

Description

Technical Field

[0001] The present application belongs to the field of virtual power plant control technology, and in particular, relates to a method and device for optimizing low-carbon interactive operation of a virtual power plant. Background Art

[0002] In the context of the "dual carbon" goal, optimizing the energy supply structure and gradually transitioning from traditional energy to clean renewable energy is of great significance to the sustainable development of energy. How to maximize the potential of source-load interaction and market mechanisms in energy conservation and carbon reduction plays a vital role in promoting the low-carbon transformation of the energy system and supporting the "dual carbon" strategy.

[0003] Virtual Power Plant (VPP) can aggregate distributed energy and user-side resources, coordinate the production, conversion, storage, and consumption of various energy forms such as electricity, heat, and gas, and significantly improve the energy efficiency and carbon efficiency of energy supply by virtue of the complementary substitution characteristics between various energy forms. It provides an effective means to solve the problem of clean energy consumption, ensure the balance of supply and demand, and achieve low-carbon operation of energy systems, and has become a current research hotspot. In related research, for example, the patent document with publication number CN116523683A considers the flexible response capability of multiple loads on the demand side to build a demand response model, determine the total carbon emissions of the system, and optimize the minimum carbon emissions and minimum costs based on the user's energy cost, thereby reducing the cost of carbon trading. .

[0004] However, the above method is to guide users to implement carbon emission reduction by participating in carbon market transactions or paying carbon taxes. While reducing carbon emissions, it may not be possible to meet the energy needs of users, which makes the implementation of this method challenging and requires further strategic optimization of low-carbon incentives. Summary of the invention

[0005] The embodiments of the present application provide a method and device for optimizing the low-carbon interactive operation of a virtual power plant, which can adopt a guidance mechanism that coordinates fixed energy prices with dynamic carbon emission factors to maximize the guidance effect of dynamic carbon emission factors, reduce carbon emissions while meeting user-side energy needs, and guide users to adjust their energy use behaviors, thereby achieving the purpose of reducing system carbon emissions.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for optimizing low-carbon interactive operation of a virtual power plant, comprising:

[0008] Obtain energy supply data on the production side of the VPP and energy demand data on the user side;

[0009] Determine dynamic emission factors and energy attribute costs based on energy supply and demand data, and establish a low-carbon demand response mechanism;

[0010] Based on the low-carbon demand response mechanism, a two-layer low-carbon dispatch model based on VPP is established; the upper layer of the two-layer low-carbon dispatch model is used to determine the carbon emission quota of VPP under the optimization goal of the highest benefit; the lower layer of the two-layer low-carbon dispatch model is used to introduce load aggregators (LA) and determine the incentive response amount of each energy source based on dynamic emission factors and energy attribute costs;

[0011] Solve the two-layer low-carbon dispatch model of VPP, determine the load demand after response incentives and the updated dynamic carbon emission factor, and obtain the optimal low-carbon dispatch plan.

[0012] In a second aspect, an embodiment of the present application provides a virtual power plant low-carbon interactive operation optimization device, which executes the virtual power plant low-carbon interactive operation optimization method of the first aspect, including:

[0013] A data acquisition module is used to obtain energy supply data on the production side of the VPP and energy demand data on the user side;

[0014] A low-carbon demand response mechanism building module is used to determine dynamic emission factors and energy attribute costs based on energy supply data and energy demand data, and to establish a low-carbon demand response mechanism;

[0015] The two-layer low-carbon dispatch model building module is used to establish a two-layer low-carbon dispatch model based on VPP based on the low-carbon demand response mechanism; the upper layer of the two-layer low-carbon dispatch model is used to determine the carbon emission quota of the lower VPP under the optimization goal of the highest benefit; the lower layer of the two-layer low-carbon dispatch model is used to determine the incentive response amount of each energy source based on the dynamic emission factor and energy attribute cost;

[0016] The solution module is used to solve the two-layer low-carbon dispatch model of VPP and determine the load demand after responding to the incentive and the updated dynamic carbon emission factor.

[0017] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0018] The low-carbon interactive operation optimization method and device of the virtual power plant in the embodiment of the present application establishes an internal low-carbon demand response mechanism through data from both the supply and demand sides, and then combines with the external carbon quota trading market to establish a VPP two-layer carbon trading architecture to manage carbon emissions and optimize emission reduction from both the supply and demand sides. The upper-layer VPP is the leader in the two-layer optimization, and the two-layer carbon trading model optimizes the response variables of each energy source with the goal of economic optimality. As a follower, the lower-layer load aggregator introduces the load aggregator agent user to participate in carbon trading after receiving the carbon emission factor, and guides the user to adjust the energy use behavior through the incentive-based comprehensive demand response. The output plan on the energy supply side and the incentive plan of the load aggregator interact with each other under the action of the dynamic emission factor, forming a supply and demand interaction, reducing carbon emissions while meeting the energy needs of the user side, and achieving the purpose of reducing the system's carbon emissions. It can effectively reduce the overall carbon emissions of the system and improve the utilization rate of photovoltaics.

[0019] The beneficial effects of the above-mentioned second aspect embodiment refer to the beneficial effects of the first aspect embodiment, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 This is an application scenario diagram of the low-carbon interactive operation optimization method and device of a virtual power plant provided in an embodiment of the present application

[0022] Figure 2 It is a flow chart of a low-carbon interactive operation optimization method of a virtual power plant provided in one embodiment of the present application;

[0023] Figure 3 It is a schematic diagram of the interaction between VPP and user information in the low-carbon demand response mechanism provided by an embodiment of the present application;

[0024] Figure 4 It is a schematic diagram of a two-layer low-carbon scheduling framework of a virtual power plant provided in an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of equipment output and carbon emission factor provided in one embodiment of the present application;

[0026] Figure 6 is a schematic diagram of a load curve after response provided by an embodiment of the present application;

[0027] Figure 7It is a schematic diagram of a specific situation of electrical and thermal load transfer provided by an embodiment of the present application;

[0028] Figure 8 is a schematic diagram of the output of each device after the response provided by an embodiment of the present application;

[0029] Fig. 9 is a schematic diagram of optimized loads in various scenarios provided in an embodiment of the present application;

[0030] Fig.10 is a schematic diagram of load transfer and carbon emissions of different photovoltaic capacities provided in one embodiment of the present application;

[0031] Fig.11 It is a structural schematic diagram of a low-carbon interactive operation optimization device for a virtual power plant provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0033] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0034] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0036] References to "an embodiment", "one embodiment" or "some embodiments" described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0037] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present application will be clearly and completely described 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, not 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.

[0038] Reference Figure 1 As shown, it is an application scenario diagram of an embodiment of the low-carbon interactive operation optimization method and device of a virtual power plant of the present invention. VPP optimization scheduling mainly studies the two processes of energy supply and energy demand. On the energy supply side, the integrated virtual power plant purchases electricity and natural gas from the superior energy network, and combines internal photovoltaic (PV), CHP units, electric boilers (EB), power to gas (P2G), gas boilers, electric energy storage and other equipment to build a comprehensive system covering energy supply, conversion and storage to meet the energy needs of users. The structure of VPP is divided into four parts: external energy supply, internal energy equipment, multi-energy load and energy management and control platform.

[0039] Among them, external energy supply: electricity and natural gas are provided to VPP by external power grids and gas grids. Internal energy equipment: energy conversion equipment mainly includes P2G, CHP, and electric boilers, which are used to achieve coordinated utilization and flexible conversion between different energy sources; through battery energy storage, complementary utilization of energy is achieved on a time scale. Multi-energy load: load requirements including electricity, gas and thermal energy. Energy management system: As the "brain" of VPP, the energy management system meets energy demand under the premise of optimal economy and carbon emissions by coordinating and optimizing energy supply, conversion, storage and consumption.

[0040] The optimization method and device are run in the server 101 of the energy management system. The server 101 is connected to a database 102 and multiple terminals 103 through the Internet. The server 101 can be an application server of the energy management system, providing network services and application services, processing processing tasks of the VPP energy management system, etc. The database 102 is used to store files and various table data, etc. The multiple terminals 103 include a collection terminal that can obtain multi-energy load data on the user side and a collection terminal that can obtain energy supply data of internal energy equipment and external energy supply.

[0041] Figure 2 is a flow chart of a virtual power plant low-carbon interactive operation optimization method provided by an embodiment of the present application, referring to Figure 2 The low-carbon interactive operation optimization method of the virtual power plant includes:

[0042] Step 201, obtaining energy supply data on the production side and energy demand data on the user side of the virtual power plant.

[0043] A VPP in a region during the heating season is selected as the research object. On the production side, the VPP is connected to the external power grid and gas grid, and combines internal photovoltaic, gas turbine, CHP units and other units to supply energy to users. On the user side, users are classified into types such as office workers and home users, and then various loads of users are obtained according to the type.

[0044] This embodiment considers the carbon emissions on the VPP production side and the user side respectively. Carbon emissions on the production side can include accounting for direct carbon emissions generated by natural gas combustion, indirect carbon emissions from electricity purchases, and indirect carbon emissions from electric boiler power consumption. Then, it is possible to participate in transactions in the carbon trading market based on these carbon emissions. On the user side, that is, the load side, users do not generate direct carbon dioxide emissions when using loads, but believe that the responsibility for carbon emissions generated by VPP energy production comes from the load side, and calculate the indirect carbon emissions generated by the use of electricity, heat, and gas loads. Then, it is possible to participate in the low-carbon demand response within the VPP based on these carbon emissions.

[0045] In the process of low-carbon demand response within the VPP on the user side, traditional demand-side management mainly affects the user's energy consumption behavior through price mechanisms, such as encouraging users to use electricity during periods of low electricity demand through time-of-use electricity prices, thereby reducing energy consumption during peak hours. However, these mechanisms usually only consider the amount of energy consumption and do not directly involve the management of carbon emissions. In order to more effectively guide users towards low-carbon energy use, the present invention proposes an innovative mechanism that directly integrates carbon emissions into the user's energy costs. Under this mechanism, users not only have to pay for the energy they consume, which constitutes an energy transaction between energy consumption and energy costs, but they must also pay the corresponding carbon emission fees to the VPP based on the carbon emissions generated by their energy use, forming a carbon emission transaction between carbon emissions and carbon emission fees.

[0046] Step 202, based on energy supply data and energy demand data, determine dynamic emission factors and energy attribute costs, and establish a low-carbon demand response mechanism.

[0047] For example, see Figure 3 , step 202 comprises:

[0048] Step 2021, based on the energy supply data and the energy demand data, determine the energy attribute cost, the load forecast of the virtual power plant and the photovoltaic output forecast.

[0049] Energy demand data is carefully sorted out, and energy attribute costs are accurately calculated using advanced algorithm models (such as machine learning, time series analysis, etc.). Energy attribute costs are energy prices for electricity, heat and natural gas, which not only take into account the direct purchase price of energy, but also include factors such as transportation, storage, environmental protection treatment, policy subsidies or taxes to ensure the comprehensiveness and accuracy of cost estimates.

[0050] For VPP load forecasting, it can be achieved by combining historical load data, weather forecasts, socio-economic activity forecasts, user behavior patterns and other multi-dimensional information, and using advanced technologies such as deep learning and artificial intelligence to build a forecasting model. For photovoltaic output forecasting, using meteorological forecast data and real-time operation data of photovoltaic power stations, machine learning algorithms can be used to accurately predict the power generation capacity of photovoltaic power stations in the future.

[0051] Step 2022, based on the load forecast and photovoltaic output forecast of the virtual power plant, day-ahead scheduling is performed to determine the output of each unit.

[0052] Based on the VPP load forecast and PV output forecast results obtained in step 2021, the system enters the day-ahead dispatch stage. In this stage, the requirements for safe and stable operation of the power grid, the performance characteristics of each unit (such as start-up time, ramp rate, minimum stable operation time, etc.), energy cost-effectiveness, and environmental protection policy constraints are first considered to build a multi-objective optimization dispatch model. The multi-objective optimization dispatch model can be implemented by finding the best unit output allocation scheme through optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.).

[0053] Step 2023, determine the dynamic carbon emission factor based on the output of each unit in the scheduling result.

[0054] For example, the output of each unit includes the electric output power, thermal output power and gas output power after conversion by the carbon hub. The carbon emission intensity of electricity is the ratio of carbon emissions to electric output power in the same period, and the carbon emission intensity of heat and natural gas is the same. The carbon emission intensity is strongly correlated with the carbon emission factor. As time changes, the output of each unit will change, and the carbon emission factor will also change dynamically. Here, the carbon emission factor needs to be tracked continuously, and the result obtained is the dynamic carbon emission factor.

[0055] The dynamic emission factors include the dynamic carbon emission factor of electricity on the user side, the dynamic carbon emission factor of heat on the user side, and the dynamic carbon emission factor of natural gas on the user side.

[0056] Dynamic carbon emission factor θ of user-side electricity p (t) is expressed as:

[0057]

[0058] Dynamic carbon emission factor θ of user-side heat h (t) is expressed as:

[0059] θ h (t) = θ EH,h (t) (2)

[0060] Dynamic carbon emission factor θ of natural gas on the user side g (t) is expressed as:

[0061] θ g (t) = θ EH,g (t) (3)

[0062] Among them, θ EH,e (t) is the carbon emission intensity of electricity; L e,out (t) is the electrical output power after conversion by the carbon concentrator; P es,dis (t) is the energy storage discharge capacity; θ EH,h (t) is the carbon emission intensity of heat; θEH,g (t) is the carbon emission intensity of natural gas; θ ES (t) is the carbon emission factor of the energy storage equipment during the charging and discharging process; P load is the user's electricity load; t is the time.

[0063] Step 2024, based on the dynamic carbon emission factor and the energy attribute cost, determine the user's energy cost, and send the dynamic carbon emission factor, energy attribute cost and the user's energy cost to the user; the dynamic carbon emission factor, energy attribute cost and the user's energy cost serve as a reference for the user to update his energy demand.

[0064] Carbon emissions are incorporated into the user's energy attribute cost, and the carbon emission fee charged by VPP to users is not for profit, but as a system carbon emission expenditure to encourage users to join the energy conservation and emission reduction action. Then, the user's energy cost will include two parts: one is the traditional energy attribute cost, that is, the cost based on energy consumption; the other is the carbon cost, that is, the cost based on carbon emissions caused by energy consumption.

[0065] Exemplarily, the energy cost of a user includes the comprehensive cost of a unit of electricity used by the user, the comprehensive cost of a unit of heat used by the user, and the comprehensive cost of a unit of natural gas used by the user.

[0066] The comprehensive cost P of the user's unit electricity p (t) is expressed as:

[0067] P p (t) = τ p +P CO2 (t)×θ p (t) (4)

[0068] The comprehensive cost of unit heat used by users P h (t) is expressed as:

[0069] P h (t) = τ h +P CO2 (t)×θ h (t) (5)

[0070] The comprehensive cost P of natural gas used by users g (t) is expressed as:

[0071] P g (t) = τ g +P CO2 (t)×θ g (t) (6)

[0072] Among them, τ p is the energy attribute cost of electricity; τ his the energy attribute cost of heat; τ g is the energy attribute cost of natural gas; P CO2 (t) is the carbon emission price.

[0073] By predicting the energy demand and renewable energy output within the VPP, the dynamic carbon emission factors of electricity, heat and gas are calculated and fed back to users in real time through smart terminal devices. The differences in dynamic carbon emission factors in time and energy types lead to differences in energy costs. Therefore, users can perceive carbon emission factors, carbon emission prices and energy prices (i.e. energy attribute costs) through smart terminal devices. After perceiving the differences in carbon emission factors in different energy sources and different time periods, users can actively adjust their future energy use behaviors and energy use plans.

[0074] Step 2025, receiving the user's updated energy demand, and performing low-carbon scheduling based on the user's updated energy demand.

[0075] After the user actively adjusts the energy consumption plan, the server of the VPP energy management system receives the energy consumption plan published by the user and obtains the user's updated energy demand, thereby performing low-carbon scheduling to meet the user's energy demand.

[0076] Step 2026, after the end of the day, obtain the user's actual energy usage data, and determine the actual carbon emissions of each user based on the user's actual energy usage data and the carbon emission factor of the corresponding period.

[0077] At the end of the day, the actual carbon emissions of each user are calculated by combining the user's actual energy consumption data after low-carbon scheduling and the carbon emission factor of the corresponding period.

[0078] The actual carbon emissions of users The calculation formula is:

[0079]

[0080] Among them, H load (t) is the user's heat load; G load (t) is the gas load of the user; E C T O2 It is the total carbon emissions of the user in the T period. T can be 1 hour as the measurement period or measurement cycle. The user pays the VPP corresponding fees according to the actual energy consumption and carbon emissions to complete energy trading and carbon trading.

[0081] In this embodiment, VPP charges carbon emission fees to users, but the purpose is not to make a profit. The fees should be used as system carbon emission expenses to encourage users to join the energy conservation and emission reduction action. At the same time, the carbon emission measurement of VPP and users adopts hourly accuracy, which is consistent with the time interval of power system dispatch, which is conducive to the connection of multiple markets.

[0082] Implementing a low-carbon demand response mechanism in VPP can effectively motivate users to adopt low-carbon energy consumption behaviors, which theoretically provides an innovative way to promote low-carbon energy consumption. However, the current low-carbon demand response mechanism lacks a clear policy framework, and users have different understandings and acceptance of the concept of carbon trading. In response to the above problems, the present invention allows users to participate in carbon trading through an external regional carbon quota trading market, supplemented by the proposed dynamic carbon emission factor method to accurately measure user carbon emissions and guide energy consumption behaviors, thereby providing users with an operational and effective channel to participate in carbon emission reduction. At the same time, considering that users have small energy consumption and carbon emissions, it is difficult to participate in the carbon quota market with tons as the trading unit, and the large number of users will also bring challenges to the implementation and supervision of transactions. In the following implementation, the present invention introduces a load aggregator as an intermediary to aggregate users' energy consumption and carbon emissions, and participate in market transactions on behalf of users, thereby simplifying the transaction process and reducing management complexity. At the same time, it also provides users with a feasible solution for participating in external carbon market transactions.

[0083] Step 203: Based on the low-carbon demand response mechanism, a two-layer low-carbon scheduling model based on the virtual power plant is established.

[0084] The upper layer of the two-layer low-carbon scheduling model is used to determine the carbon emission quota of the VPP under the optimization goal of highest benefit; the lower layer of the two-layer low-carbon scheduling model is used to introduce load aggregators and determine the incentive response of each energy source based on dynamic emission factors and energy attribute costs.

[0085] Exemplarily, step 203 includes:

[0086] A VPP two-tier carbon trading architecture is established based on a low-carbon demand response mechanism and a regional carbon quota trading market; the regional carbon quota trading market is used for load aggregators to aggregate users’ energy consumption and carbon emissions and participate in market transactions on behalf of users.

[0087] For example, the load aggregator in this application is a multi-energy load aggregator (MELA). There is no difference between LA and MELA mentioned in this embodiment. After introducing the load aggregator, the two-layer low-carbon dispatch framework of the established VPP is first introduced, such as Figure 4As shown in the figure, the upper layer is VPP, which receives carbon quotas issued by the carbon quota trading market to supply electricity, heat and gas energy, calculates the actual carbon emissions generated by operation, and participates in carbon trading according to the surplus or shortage of carbon quotas. The Multi Energy Load Aggregator (MELA) is introduced in the lower layer to provide energy to users, and at the same time, an incentive-based comprehensive demand response contract is signed with users to guide users to change their energy behavior. The incentive agreement clarifies the compensation fees provided for the user's load response. MELA participates in the external carbon trading market as an independent entity, receives carbon quotas issued by the carbon trading market, and receives the dynamic carbon emission factor signal of VPP, and participates in carbon trading by calculating carbon emissions based on actual energy consumption.

[0088] exist Figure 4 Specifically, VPP predicts the output of distributed photovoltaic power and the demand for various energy loads, regulates the start and stop and operating status of energy conversion equipment and energy storage equipment, and purchases electricity and natural gas from external power grids and gas grids, and optimizes the output of each part considering the carbon emission trading cost to achieve VPP low-carbon economic scheduling, and calculates the dynamic carbon emission factor of each energy source based on the scheduling results. After receiving the dynamic carbon emission factor and time-of-use electricity price information, the lower-level MELA comprehensively considers the energy purchase cost and carbon trading cost, issues a response task to the user, and then sends the load demand to the upper-level VPP. VPP re-schedules the day-ahead based on the updated energy demand, and feeds back the new carbon emission factor to the lower-level MELA. Through repeated iterative optimization of the upper and lower layers, supply and demand interaction is achieved to achieve the optimal plan for both parties.

[0089] Next, based on the VPP two-tier carbon trading architecture, a VPP-based two-tier low-carbon scheduling model is established.

[0090] Exemplarily, the upper layer of the two-layer low-carbon scheduling model is the photovoltaic VPP low-carbon scheduling model.

[0091] The energy system of the present invention is in the form of a community integrated energy system (Community Integrated Energy System, CIES), which includes two parts: power supply and heat supply. Among them, the power supply part is composed of a cogeneration unit and wind power and photovoltaic equipment, and the heating part is composed of a cogeneration unit and an electric boiler. In addition, the system can purchase electricity and natural gas from external power grids and natural gas grids. In order not to affect the upper power grid, photovoltaic and wind power are only consumed internally and are not returned to the upper power grid. Therefore, when the output of wind power and photovoltaic power is greater than the load, wind and light will be abandoned.

[0092] First, the CIES production-side constraints of the PV VPP low-carbon dispatch model are analyzed as follows:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] P grid.min ≤P grid (t)≤P grid.max (13)

[0099] P grid.down ≤P grid (t)-P grid (t-1)≤P grid.up (14)

[0100] 0≤P IDR (t)≤P PV (t)+P WT (t)-P load (t)-P GB (t), (15)

[0101] P PV (t)+P WT (t)-P load (t)-P GB (t)>0

[0102] P grid (t)+P WT (t)+P PV (t)+P GT (t) = P load (t)+P EB (t) (16)

[0103] H HE (t)+H EB (t) = H load (t) (17)

[0104] L g,out (t)+G net (t) = G load (t) (18)

[0105] Among them, P PV (t) represents the photovoltaic output power, Indicates the upper limit of photovoltaic output power; P WT (t) represents the photovoltaic output power, G represents the upper limit of photovoltaic output power, and the uncertainty of renewable energy unit output is simulated by the predicted value and prediction error. GT (t), P GT (t) and H HE (t) are the natural gas power consumed by the micro gas turbine GT, the electric power output by GT and the waste heat power recovered by the waste heat boiler HE; I GT (t) is a variable of 0 or 1, I GT (t) = 1 means GT is in the power-on state, I GT (t) = 0 means GT is in shutdown state; a, b, c are fuel consumption coefficients; λ GT is the output electric-to-heat power ratio of GT; η GT is the heat recovery efficiency of HE. They are the output power P of GT respectively. GT (t) upper and lower limits; They are GT climbing power P GT (t)-P GT The upper and lower limits of (t-1). EB (t) is the output thermal power of the electric boiler EB, P EB (t) is the power consumption of EB; η EB is the power consumption efficiency of EB; is the upper limit of EB’s output thermal power. grid.max , P grid.min The power purchased by the power grid is P grid (t) Maximum and minimum limits; P grid.up , P grid.down They are the climbing power P of the power grid respectively. grid (t)-P grid The upper and lower limits of (t-1). IDR (t) is the power of incentive-based demand response (IDR), P GB (t) is the power consumption of the gas boiler GB; H load (t) is the gas load; L g,out (t) is the output power of natural gas after conversion by the carbon concentrator; G net (t) is the amount of natural gas purchased from the natural gas grid; G load (t) is the amount of gas load.

[0106] For example, the upper layer of the two-layer low-carbon dispatch model calculates the operating cost and carbon trading cost, performs day-ahead dispatch, determines the dynamic carbon emission factors of various energy sources, and passes the carbon emission factors and energy prices to the lower layer MELA. The optimization target with the highest benefit, maxE, is expressed as:

[0107]

[0108] Among them, C sell (t) represents the revenue from energy sales; C grid (t) represents the cost of purchasing electricity from the power grid; C op (t) represents the operating cost; C P (t) is the penalty cost of abandoning light; C CO2 is the carbon trading cost; t is the time, and T is the time period of time t.

[0109] The various physical quantities in the formula are expressed as:

[0110]

[0111] C grid (t) = P grid (t)c g,b (t) (21)

[0112]

[0113] C P (t) = P abandon (t)×W p ,P abandon (t)>0 (23)

[0114] C CO2 =β(E CO2 -D CO2 ) (twenty four)

[0115] Among them, P load , H load , G load are the user's electricity load, thermal load and natural gas load respectively; c g,b (t) represents the price of electricity purchased by VPP from the external power grid; P CHP (t) is the power consumption of the CHP cogeneration unit; P GF (t) is the power consumption of the gas boiler GF; P P2G (t) is the power consumption of the power-to-gas unit P2G; P es,cha (t) is the charging capacity of the energy storage device; P es,dis (t) is the discharge capacity of the energy storage device; a CHP , a GF , a EB , a P2G , a S They represent the operating cost coefficients of the combined heat and power unit CHP, gas boiler GF, electric boiler EB, power-to-gas unit P2G and energy storage device S respectively; P abandon (t) is the amount of abandoned photovoltaic power, Wp represents the penalty price for abandoned light; when VPP participates in the external carbon quota trading market, D CO2 is the VPP quota; β is the carbon trading price.

[0116] For example, the carbon quota of VPP only considers the use of CHP, GF and purchased electricity. Then, the carbon quota of the system can be expressed as:

[0117]

[0118] Among them, E CHP (t) is the carbon emission of CHP; E GF (t) is the carbon emission of GF; E grid (t) is the carbon emissions corresponding to the electricity purchased by VPP from the external power grid.

[0119] VPP carbon emission quota D CO2 It is expressed as:

[0120]

[0121] Among them, σ CHP is the carbon emission quota coefficient of cogeneration; σ GF is the quota coefficient of gas boiler; σ grid are the quota coefficients of power purchase from the power grid; P CHP (t) is the power consumption of the cogeneration unit; P GF (t) is the power consumption of the gas boiler; P grid (t) is the power purchased by the power grid; t is the time, and T is the time period of time t.

[0122] Exemplarily, the lower layer of the two-layer low-carbon scheduling model is the MELA low-carbon operation model.

[0123] The lower-level MELA sets the incentive response amount of each energy source based on the carbon emission factor and energy price, encourages users to change their energy consumption behavior, and gives users a certain degree of economic subsidies. The constraints of the two-level low-carbon scheduling model include the range constraints and dissatisfaction constraints of the incentive response amount of each energy source.

[0124] The stimulus response of each energy source should be within the user response potential range. The range constraint of the stimulus response of each energy source is expressed as:

[0125] 0≤P k,idr (t)≤P k,max k∈{e,h,g} (27)

[0126] Among them, P k,idr (t) is the demand response of the kth energy source; P k,maxis the maximum response potential of the kth energy; e is electric energy; h is thermal energy; g is natural gas energy.

[0127] At the same time, in order to ensure the user's energy comfort, the dissatisfaction constraint is expressed as:

[0128]

[0129] in, represents the user's dissatisfaction with the kth energy at time t; is the upper limit of user dissatisfaction with the k-th energy source.

[0130] The calculation method of dissatisfaction is expressed as:

[0131]

[0132] Among them, θ k (t) is the degree of initiative of the user to participate in demand response DR by using the kth energy source at time t. The degree of initiative of the user to participate in DR is inversely proportional to its value; q k,idr is the subsidy price of the k-th energy at time t.

[0133] The optimization goal of the two-layer low-carbon dispatch model is to minimize the total cost of the load aggregator LA; the optimization goal of the two-layer low-carbon dispatch model is minC LA It is expressed as:

[0134] minC LA =C buy +C CEF +C idr (30)

[0135] Among them, C buy The cost of purchasing electricity from the superior; C CEF is the carbon trading cost; C idr Subsidize the costs of incentivized demand response.

[0136]

[0137] Among them, q e,IES ,q h,IES (t), q g,IES (t) are the prices of electricity, heat and gas purchased by MELA from the grid operator at time t. Price power can be understood as the price of purchasing unit power; q e,idr ,q h,idr ,q g,idr P is the incentive compensation price for electricity, heat and gas issued by MELA to users; e1,load (t), P h1,load (t), P g1,load (t) is the total amount of electricity, heat and gas load purchased; Pe,idr (t), P h,idr (t), P g,idr (t) is the total amount of electricity, heat and gas load of users after response; E LA is the carbon emissions of LA; E Q is the carbon quota of LA; CEF is the carbon trading price. Both the upper and lower layers of the two-layer low-carbon dispatch model adopt an improved step-by-step carbon pricing mechanism.

[0138] LA's quota for participating in the carbon trading market Q It is expressed as:

[0139]

[0140] Among them, E quote,e 、E quote,h 、E quote,g are the carbon quota coefficients for electricity, heat and gas respectively.

[0141] In this embodiment, VPP predicts the output of distributed photovoltaic power and the demand for each energy load, regulates the start and stop and operating status of energy conversion equipment and energy storage equipment, and purchases electricity and natural gas from external power grids and gas grids, and optimizes the output of each part considering the carbon emission transaction cost to achieve VPP low-carbon economic scheduling, and calculates the dynamic carbon emission factor of each energy source according to the scheduling results. After receiving the dynamic carbon emission factor and time-of-use electricity price information, the lower-level MELA comprehensively considers the energy purchase cost and carbon transaction cost, issues a response task to the user, and then sends the load demand to the upper-level VPP. VPP re-schedules the day-ahead based on the updated energy demand, and feeds back the new carbon emission factor to the lower-level MELA. Next, it is necessary to achieve supply and demand interaction through repeated iterative optimization of the upper and lower layers to achieve the optimal plan for both parties.

[0142] Step 204, solve the two-layer low-carbon dispatch model of the virtual power plant, determine the load demand after the response incentive and the updated dynamic carbon emission factor, so as to obtain the optimal low-carbon dispatch plan.

[0143] The upper and lower layers of the two-layer low-carbon scheduling model are coupled through carbon emission factors and energy demand. It is a dual-objective planning problem similar to the Stackelberg game. The upper layer VPP is the leader in the two-layer optimization, and the decision variables are the output power of the power grid and natural gas grid, the output coefficient of the energy conversion module, and the energy storage charge and discharge capacity. The lower layer MELA, as a follower, optimizes the response variables of each energy source with the goal of economic optimality after receiving the carbon emission factor. It can be seen from the two-layer optimization model that both the upper and lower layers are quadratic programming problems, and they can be solved using the solver. The solution steps are as follows.

[0144] Step 1: Initialize the load curve and input data such as unit parameters and line parameters.

[0145] Step 2: Use VPP to optimize economic scheduling for the upper layer of the two-layer low-carbon scheduling model and calculate the dynamic carbon emission factors of various energy sources.

[0146] Step 3: The lower layer of the two-layer low-carbon dispatch model uses MELA to respond to demand with the dynamic carbon emission factors of various energy sources as guiding signals, and constructs and solves the optimal dispatch model of MELA to determine the load demand after the response incentive.

[0147] Step 4: Based on the updated load demand, re-perform the low-carbon economic dispatch of the VPP, and send the new dynamic carbon emission factors of various energy sources to the lower layer MELA of the two-layer low-carbon dispatch model.

[0148] Step 5: Repeat steps 2 to 4 until the preset convergence condition is reached.

[0149] In this embodiment, VPP and MELA are continuously iterated to finally find the optimal solution, determine the load demand after the response incentive and the updated dynamic carbon emission factor, and obtain the optimal low-carbon scheduling plan.

[0150] In one embodiment, in order to verify the effectiveness of the low-carbon interactive operation optimization method of the virtual power plant, a specific simulation experiment is set up.

[0151] A VPP in a region during the heating season was selected as the research object. The VPP is connected to the external power grid and gas grid, and combines internal photovoltaic, gas turbine, CHP units and other units to supply energy to users. The users are divided into two types: office workers and home users, of which 80 are office workers and 20 are home users. The equipment operating parameters and energy consumption curves of the two types of users are obtained. In view of the uncertainty of VPP photovoltaic output and user load, a large number of photovoltaic output scenarios are simulated by Monte Carlo method, and reduced to typical photovoltaic output scenarios based on K-means scenario clustering. The external carbon trading price is 50 yuan / t, the carbon emission factor of natural gas is 0.19kg / m3, the carbon emission factor of the power grid is 0.5703kg / kwh, the carbon emission factor of CHP power supply is 0.392kg / kwh, the carbon emission factor of CHP heating is 0.212kg / kwh, the carbon emission factor of electric boiler is 0.728kg / kwh, the power supply price is fixed at 0.5 yuan / kWh, the heating price is 0.2 yuan / kWh, and the user carbon emission fee is 1 yuan / kg.

[0152] This embodiment is simulated in Matlab R2021b with a cycle of 24 hours and a step size of 1 hour. The improved particle swarm algorithm is used to solve the upper-level VPP low-carbon economic dispatch model, and the Ipopt solver is used to solve the lower-level user low-carbon demand response model.

[0153] Calculate the output and carbon emission factors of each device such as Figure 5 As shown. For power supply equipment, during the period of high photovoltaic output (e.g. 10-15 o'clock), the power demand is mainly met by photovoltaic power generation, and the carbon emission factor of electricity is correspondingly low. Therefore, the carbon emission cost of electricity during this period is the lowest. The carbon emission factor curve can be used to guide users to transfer their power load to this period to use cleaner electricity, while helping VPP promote consumption.

[0154] Since there is a close coupling relationship between electricity and thermal energy, the carbon emission factor of thermal energy will be directly affected by the amount of photovoltaic power generation. When the photovoltaic power generation is high, the power supply of the CHP unit will be reduced accordingly. Due to the cogeneration characteristics of the CHP unit, its heating capacity will also decrease. At this time, the electricity consumed by the electric boiler mainly comes from photovoltaics, so the carbon emission intensity is low, and the thermal carbon emission factor during this period reaches the minimum value of the day.

[0155] During the evening peak period (17:00-20:00), electricity demand surges, which is mainly met by the power grid and fossil fuel units, causing the carbon emission factor of electricity to rise to the peak. At the same time, the large power output of the CHP unit also means that its thermal output increases. Since the carbon emission intensity of CHP is lower than that of gas boilers, its thermal energy supply at this time leads to a relatively low carbon emission factor for heat. Therefore, the dynamic change of the carbon emission factor will encourage users to reduce electricity consumption and increase thermal energy utilization during periods with higher carbon emission costs, so as to achieve optimization of energy use and peak shaving and valley filling.

[0156] The energy storage device is charged at the time when the photovoltaic output is the largest (13-14 o'clock), when the carbon emission factor of electricity is 0, so the internal carbon potential of the energy storage after charging is 0. During the period of high load (21-22 o'clock), the energy storage device is discharged, and the carbon emission factor of the output electricity is 0, which can be regarded as clean energy, thus causing the dynamic electricity carbon emission factor to decrease during this period.

[0157] The sources of natural gas are only the natural gas grid and P2G equipment, and its dynamic carbon emission factor is affected by the amount of natural gas output by P2G. When the photovoltaic output is high, P2G converts carbon dioxide into natural gas, forming carbon emission reduction in the form of carbon sinks, and the dynamic carbon emission factor is low at this time. However, due to the limited installed capacity of P2G equipment, the impact on the dynamic carbon emission factor is also small, with a maximum decrease of only 0.04kg / m3. Therefore, the dynamic carbon emission factor of natural gas has a weak guiding effect on the user's energy usage sequence.

[0158] After the dynamic carbon emission factor is released to the user, the user responds to it. The load curve after the response is as follows: Figure 6As shown in the figure. According to the simulation results, the electric load has achieved peak load shifting. In the period from 11 to 14, the electric load has increased due to the low carbon emission factor of electricity, while the load has been reduced in other periods. The thermal load has been reduced overall through reduction and transfer. Since the emission factor of natural gas has changed little, the gas load has also changed little. A small part of the gas load has been transferred to the period of P2G output, but the transfer amount is small.

[0159] Figure 7 The specific situation of the transfer of electricity and heat loads is shown in detail. Among them, the electricity load from 6 to 7 o'clock and 17 to 23 o'clock is transferred to the 8 to 15 period, during which the photovoltaic output reaches the highest. At the same time, the exchange of electricity and heat energy also occurs: in the 2-6 period when the heat energy carbon emission factor is lower, part of the convertible heat load is converted into electricity load; and in the 17-24 period when the electricity energy carbon emission factor is lower, part of the heat load is converted into electricity load.

[0160] The user publishes the load demand after the response to the VPP, and the VPP performs low-carbon dispatch again, such as Figure 8 After the implementation of low-carbon demand response, photovoltaic output was absorbed, which shows that under the influence of dynamic carbon emission factors, the adjustment of users' electricity consumption behavior and the output dispatch on the energy supply side have formed an effective linkage, which promotes the full utilization of renewable energy.

[0161] In order to verify the supply and demand effectiveness of the proposed method, the following five scenarios are set up, as shown in Table 1.

[0162] Table 1 Simulation conditions of five scenarios

[0163]

[0164] Solve the optimized load curves for each scenario and compare them with scenario 1 as the original scenario. The optimized loads for each scenario are as follows: Fig. 9 The simulation results are shown in Table 2.

[0165] Table 2 Simulation results indicators

[0166]

[0167] Depend on Fig. 9 It can be seen that scenario 2 considers time-of-use energy prices to guide LA regulation. LA does not bear carbon emission responsibility, and its changes are only related to energy prices, that is, LA transfers in load during low-price periods and transfers out and reduces load during high-price periods.

[0168] Scenario 3 considers carbon trading guided by fixed electricity prices and dynamic carbon emission factors. By observing its load adjustment, it can be seen that its load adjustment behavior is obviously correlated with the dynamic carbon emission factor curve. LA switches in load during periods with lower carbon emission factors and switches out and reduces load during periods with higher carbon emission factors.

[0169] Scenario 4 considers carbon trading guided by time-of-use electricity prices and dynamic carbon emission factors. Due to the coordinated guidance of electricity prices and carbon prices, its load adjustment behavior is related to both dynamic carbon emission factors and time-of-use electricity price curves. Compared with scenario 3, the energy cost is high during the noon period, so the electric load transfer is relatively small. During the evening peak period, affected by the peak electricity price, the energy cost is greatly increased, so the electric load is significantly reduced.

[0170] Since the guidance methods are the same for scenarios 3 and 5, and scenarios 4 and 6, the load trends are similar. As can be seen from Table 2, compared with scenarios 3 and 4, scenarios 5 and 6 have improved various indicators through continuous iterations on the supply and demand sides, thus proving the effectiveness of the interaction between supply and demand.

[0171] Three scenarios with PV installed capacities of 100kW, 160kW, and 200kW are set to study the impact of PV penetration on system carbon emissions and costs. For the three penetration rates, simulations are performed according to scenario 5 and scenario 6. The simulation results are shown in the figure below. Fig.10 .

[0172] Before the two-layer iterative optimization, the carbon emission factors of electricity in the three photovoltaic penetration scenarios were the same during the period when the photovoltaic output was 0. During the photovoltaic output period, the output of other equipment decreased, and the dynamic carbon emission factor of electricity decreased, making the dynamic carbon emission factor show an overall downward trend. The higher the photovoltaic penetration rate, the more the dynamic carbon emission factor decreases, and the stronger the guiding effect on the load.

[0173] After two-layer iterative optimization, the carbon emission factors under the three photovoltaic penetration scenarios changed in most periods. This is because after users responded to the carbon emission factors, the load also changed during the period when photovoltaics were not working, causing changes in the dynamic carbon emission factors. The higher the photovoltaic penetration rate, the lower the dynamic carbon emission factor, and the carbon emissions of LA will be reduced accordingly, thereby obtaining higher economic benefits in the carbon market and reducing overall costs.

[0174] It can be seen from Table 3 that when the photovoltaic penetration rate is too high, it is difficult to absorb the electricity during peak hours. At this time, the method of scenario 5 is better than scenario 6. Since it uses a fixed energy price, the price change comes entirely from the change in the dynamic carbon emission factor curve. Therefore, during the photovoltaic peak period, the electricity price is extremely low, so a large amount of energy is transferred in. Scenario 6 adopts a guidance method that combines time-of-use electricity prices with carbon emission factors. The electricity cost during the photovoltaic high temperature period is higher than the valley electricity price. Therefore, the role of guiding the load to participate in absorption is not obvious enough, and the absorption effect is poor. Therefore, for VPPs with extremely high photovoltaic penetration rates, a guidance mechanism that coordinates fixed energy prices and dynamic carbon emission factors can be used to maximize the guidance effect of dynamic carbon emission factors.

[0175] Table 3 Simulation results of different photovoltaic capacities

[0176]

[0177] Through simulation, the guiding role of dynamic emission factors on users, the effectiveness of supply and demand interaction, and the impact of photovoltaic penetration on the regulation effect are analyzed to verify that the proposed method can effectively reduce the overall carbon emissions of the system.

[0178] In summary, the low-carbon interactive operation optimization method of the virtual power plant provided in the embodiment of the present application establishes an internal low-carbon demand response mechanism through data from both the supply and demand sides, and then combines with the external carbon quota trading market to establish a VPP two-layer carbon trading architecture to manage carbon emissions and optimize emission reduction from both the supply and demand sides. The upper-layer VPP is the leader in the two-layer optimization, and the two-layer carbon trading model optimizes the response variables of each energy source with the goal of economic optimality. As a follower, the lower-layer LA introduces load aggregators as agents to participate in carbon trading after receiving the carbon emission factor. At the same time, it guides users to adjust their energy use behaviors through incentive-based comprehensive demand response. The output plan on the energy supply side and the incentive plan of the load aggregator interact with each other under the action of dynamic emission factors, forming supply and demand interaction, reducing carbon emissions while meeting the energy needs of the user side, and achieving the purpose of reducing system carbon emissions. It can effectively reduce the overall carbon emissions of the system and improve the utilization rate of photovoltaics.

[0179] See also Fig.11 An embodiment of the present application provides a low-carbon interactive operation optimization device for a virtual power plant, which executes a low-carbon interactive operation optimization method for a virtual power plant as in the above embodiment, including a data acquisition module 301, a low-carbon demand response mechanism construction module 302, a feature reconstruction module 303 and a feature processing and prediction output module 304.

[0180] The data acquisition module 301 is used to acquire the energy supply data of the production side and the energy demand data of the user side of the virtual power plant;

[0181] A low-carbon demand response mechanism building module 302 is used to determine dynamic emission factors and energy attribute costs based on energy supply data and energy demand data, and establish a low-carbon demand response mechanism;

[0182] The double-layer low-carbon dispatch model construction module 303 is used to establish a double-layer low-carbon dispatch model based on a virtual power plant based on a low-carbon demand response mechanism; the upper layer of the double-layer low-carbon dispatch model is used to determine the carbon emission quota of the virtual power plant under the optimization goal of the highest benefit; the lower layer of the double-layer low-carbon dispatch model is used to determine the incentive response amount of each energy source according to the dynamic emission factor and the energy attribute cost;

[0183] The solution module 304 is used to solve the double-layer low-carbon dispatch model of the virtual power plant to determine the load demand after responding to the incentive and the updated dynamic carbon emission factor.

[0184] It should be noted that although several units / modules or sub-units / modules of the low-carbon interactive operation optimization device of the virtual power plant are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be concretized in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for concretization.

[0185] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0186] The present application also provides a determination machine readable storage medium, in which determination machine execution instructions are stored. When the processor executes the determination machine execution instructions, the low-carbon interactive operation optimization method of the virtual power plant provided in the above embodiment of the present application is implemented.

[0187] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0188] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A low-carbon interactive operation optimization method for a virtual power plant, characterized in that: include: Obtain energy supply data on the production side of the virtual power plant and energy demand data on the user side; Based on the energy supply data and the energy demand data, determine dynamic emission factors and energy attribute costs, and establish a low-carbon demand response mechanism; Based on the low-carbon demand response mechanism, a two-layer low-carbon dispatch model based on a virtual power plant is established; the upper layer of the two-layer low-carbon dispatch model is used to determine the carbon emission quota of the virtual power plant under the optimization goal of maximum benefit; the lower layer of the two-layer low-carbon dispatch model is used to introduce load aggregators and determine the incentive response amount of each energy source according to the dynamic emission factor and the energy attribute cost; Solve the two-layer low-carbon scheduling model of the virtual power plant, determine the load demand after response incentives and the updated dynamic carbon emission factor, and obtain the optimal low-carbon scheduling solution.

2. The low-carbon interactive operation optimization method of a virtual power plant according to claim 1, characterized in that: The determining of dynamic emission factors and energy attribute costs based on the energy supply data and the energy demand data, and establishing a low-carbon demand response mechanism, includes: Determining the energy attribute cost, the load forecast of the virtual power plant, and the photovoltaic output forecast based on the energy supply data and the energy demand data; Based on the load forecast and photovoltaic output forecast of the virtual power plant, day-ahead scheduling is performed to determine the output of each unit; Determine the dynamic carbon emission factor based on the output of each unit in the dispatch results; Based on the dynamic carbon emission factor and the energy attribute cost, determine the energy cost of the user, and send the dynamic carbon emission factor, the energy attribute cost and the energy cost of the user to the user; the dynamic carbon emission factor, the energy attribute cost and the energy cost of the user are used as a reference for the user to update the energy demand; receiving the energy demand updated by the user, and performing low-carbon scheduling based on the energy demand updated by the user; After the end of the day, the actual energy usage data of the users is obtained, and based on the actual energy usage data of the users and the carbon emission factors of the corresponding time period, the actual carbon emissions of each user are determined.

3. The low-carbon interactive operation optimization method of a virtual power plant according to claim 2, characterized in that: The dynamic emission factors include the dynamic carbon emission factor of user-side electricity, the dynamic carbon emission factor of user-side heat, and the dynamic carbon emission factor of user-side natural gas; The dynamic carbon emission factor θ of the user-side electricity p (t) is expressed as: The dynamic carbon emission factor θ of the user-side heat h (t) is expressed as: i h (t)=θ EH,h (t) The dynamic carbon emission factor θ of the natural gas at the user side g (t) is expressed as: i g (t)=θ EH,g (t) Among them, θ EH,e (t) is the carbon emission intensity of electricity; L e,out (t) is the electrical output power after conversion by the carbon concentrator; P es,dis (t) is the energy storage discharge capacity; θ EH,h (t) is the carbon emission intensity of heat; θ EH,g (t) is the carbon emission intensity of natural gas; θ ES (t) is the carbon emission factor of the energy storage equipment during the charging and discharging process; P load is the user's electricity load; t is the time.

4. The low-carbon interactive operation optimization method of a virtual power plant according to claim 3, characterized in that: The energy cost of the user includes the comprehensive cost of the user's unit electricity, the comprehensive cost of the user's unit heat and the comprehensive cost of the user's unit natural gas; The comprehensive cost P of the user's unit electricity usage p (t) is expressed as: P p (t)=τ p +P CO2 (t)×θ p (t) The comprehensive cost P of the user using unit heat h (t) is expressed as: P h (t)=τ h +P CO2 (t)×θ h (t) The user's comprehensive cost per unit of natural gas is P g (t) is expressed as: P g (t)=τ g +P CO2 (t)×θ g (t) Among them, τ p is the energy attribute cost of electricity; τ h is the energy attribute cost of heat; τ g is the energy attribute cost of natural gas; P CO2 (t) is the carbon emission price.

5. The low-carbon interactive operation optimization method of a virtual power plant according to claim 1, characterized in that: Based on the low-carbon demand response mechanism, a two-layer low-carbon dispatch model based on a virtual power plant is established, including: Based on the low-carbon demand response mechanism and the regional carbon quota trading market, a two-tier carbon trading framework for virtual power plants is established; the regional carbon quota trading market is used by load aggregators to aggregate users’ energy consumption and carbon emissions and participate in market transactions on behalf of users; Based on the two-layer carbon trading architecture of the virtual power plant, a two-layer low-carbon scheduling model based on the virtual power plant is established.

6. The low-carbon interactive operation optimization method of a virtual power plant according to claim 1, characterized in that: The optimization target maxE with the highest benefit is expressed as: Among them, C sell (t) represents the revenue from energy sales; C grid (t) represents the cost of purchasing electricity from the power grid; C op (t) represents the operating cost; C P (t) is the penalty cost of abandoning light; C CO2 is the carbon trading cost; t is the time, and T is the time period of time t.

7. The low-carbon interactive operation optimization method of a virtual power plant according to claim 1, characterized in that: The carbon emission quota of the virtual power plant is D CO2 It is expressed as: Among them, σ CHP is the carbon emission quota coefficient of cogeneration; σ GF is the quota coefficient of gas boiler; σ grid are the quota coefficients of power purchase from the power grid; P CHP (t) is the power consumption of the cogeneration unit; P GF (t) is the power consumption of the gas boiler; P grid (t) is the power purchased by the power grid; t is the time, and T is the time period of time t.

8. The low-carbon interactive operation optimization method of a virtual power plant according to claim 1, characterized in that: The optimization goal of the two-layer low-carbon dispatch model is to minimize the total cost of the load aggregator; the optimization goal of the two-layer low-carbon dispatch model is minC LA It is expressed as: minC LA =C buy +C CEF +C idr Among them, C buy The cost of purchasing electricity from the superior; C CEF is the carbon trading cost; C idr Subsidize the costs of incentivized demand response.

9. The low-carbon interactive operation optimization method of a virtual power plant according to any one of claims 1 to 8, characterized in that: The constraints of the two-layer low-carbon scheduling model include range constraints and dissatisfaction constraints of the incentive response amount of each energy source; The range constraint of the stimulus response quantity of each energy source is expressed as: 0≤P k,idr (t)≤P k,max k∈{e,h,g} Among them, P k,idr (t) is the demand response of the kth energy source; P k,max is the maximum response potential of the kth energy; e is electric energy; h is thermal energy; g is natural gas energy; The unsatisfactory constraint is expressed as: in, represents the user's dissatisfaction with the kth energy at time t; is the upper limit of user dissatisfaction with the k-th energy source.

10. A virtual power plant low-carbon interactive operation optimization device, using the virtual power plant low-carbon interactive operation optimization method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to acquire energy supply data on the production side of the virtual power plant and energy demand data on the user side; A low-carbon demand response mechanism building module, used to determine a dynamic emission factor and energy attribute cost based on the energy supply data and the energy demand data, and to establish a low-carbon demand response mechanism; A two-layer low-carbon dispatch model construction module is used to establish a two-layer low-carbon dispatch model based on a virtual power plant based on a low-carbon demand response mechanism; the upper layer of the two-layer low-carbon dispatch model is used to determine the carbon emission quota of the virtual power plant under the optimization goal of the highest benefit; the lower layer of the two-layer low-carbon dispatch model is used to determine the incentive response amount of each energy source according to the dynamic emission factor and the energy attribute cost; A solution module is used to solve the double-layer low-carbon dispatch model of the virtual power plant to determine the load demand after responding to the incentive and the updated dynamic carbon emission factor.

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