Variable working condition IES optimal scheduling method considering GCT-LTC joint mechanism and PDR mechanism

Abstract

The invention discloses a variable-working-condition IES optimal scheduling method considering a GCT-LTC joint mechanism and a PDR mechanism. The method comprises the steps that S1, a comprehensive energy system comprising power, heat and gas multi-energy complementation is constructed; s2, constructing a variable working condition equipment operation model in the integrated energy system; s3, on the basis of the equipment operation model, constructing a variable working condition IES optimization scheduling model considering a GCT-LTC joint mechanism and a PDR mechanism; and S4, solving the variable working condition IES optimal scheduling model by using a particle swarm algorithm to obtain an operation optimization parameter taking the lowest total operation cost as a target, and performing optimal scheduling of the integrated energy system according to the operation optimization parameter. According to the method, based on a green certificate-stepped carbon transaction joint mechanism, the corresponding optimal scheduling model considering the variable working condition characteristics and requirements of the IES is established with the lowest economic cost as the target, so that the effects of more effective energy conversion and reduced energy waste are achieved, economic operation of the IES is facilitated, and analysis on the overall operation scheduling condition of the IES is more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of power dispatching, and specifically relates to a variable-condition IES (integrated energy system) optimization dispatching method considering a GCT-LTC (green certificate-ladder carbon trading) joint mechanism and a PDR (price-based demand response) mechanism. Background Art

[0002] Given the severe challenges posed by current climate change and economic conditions, building a new power system has become an important requirement and key strategy for implementing my country's new energy security strategy and achieving the goals of "carbon peak and carbon neutrality."

[0003] In this context, the Integrated Energy System (IES) came into being, breaking the limitations of the traditional single energy system and efficiently integrating and optimizing various energy resources. This not only improves energy utilization, but also promotes sustainable development of the environment and economy, and is of great significance to promoting the transformation of my country's energy structure. However, as the problem of energy supply and demand in the world today becomes more prominent, how to reduce greenhouse gas emissions in the power industry, which accounts for a high proportion of carbon emissions, has become a major issue that needs to be solved urgently. To this end, countries around the world have made the construction of a low-carbon and sustainable energy system a key development task and strategic goal. As an important carrier for promoting the decarbonization of the power grid, it is very necessary to deeply explore its equipment emission reduction and system decarbonization strategies.

[0004] Carbon trading mechanism uses economic leverage to include carbon trading costs in its total cost to achieve carbon policy that controls and reduces carbon emissions and is widely used. The paper "Distributed Low-Carbon Economic Dispatch of Integrated Energy System Considering Multiple Independent Operators" considers the carbon trading mechanism in IES. The experimental results show that considering carbon emissions when taking economy as the optimization goal can effectively improve the energy economy of IES. The papers "Low-Carbon Economic Operation Control Strategy of Integrated Energy System under Carbon Emission Trading Mechanism", "Low-Carbon Economic Dispatch of Electricity-Heat-Gas Integrated Energy System Based on Carbon Trading" and "Two-Stage Robust Optimization Model for Day-Ahead Dispatch Considering Carbon Emission Trading" propose to introduce the carbon trading costs calculated in a ladder type into the low-carbon operation model and construct the IES low-carbon economic operation strategy of the ladder type carbon trading mechanism (LTCTM). The related work has more stringently controlled carbon emissions while taking into account the overall economy of the system. The experimental results show that LTCTM has excellent carbon emission reduction effects. In addition, the document "Low-carbon Economic Dispatch of Electricity-Gas-Heat Integrated Energy System Considering Stepped Carbon Trading" proposes constraints on carbon quota trading volume to achieve low-carbon operation of the power system under the influence of uncertain factors such as the carbon trading market and the volatility of renewable energy output.

[0005] The above studies mainly explore the introduction of carbon trading mechanisms into IES optimization strategies to promote low-carbon operation of power systems in a market-driven manner, but they do not fully consider the trend of widespread application of renewable energy in actual system operation. In recent years, my country's policy subsidies and technological progress have complemented each other and jointly promoted the development of the new energy market towards high quality, high efficiency and low cost. The related wind power and photovoltaic power generation industries have grown rapidly, but their inherent uncertainty and volatility have caused a lot of electricity waste.

[0006] The document "Mechanism Design and Impact Analysis of Renewable Energy Quota System" discusses the construction of renewable energy systems at home and abroad, and looks forward to the development direction of renewable energy in my country. The documents "Renewable Energy Quota System and Green Certificate Trading Mechanism Design and Simulation" and "Low-carbon Economic Dispatch of Integrated Energy System Considering Green Certificate-Carbon Trading Mechanism under Carbon Capture Power Plant-Power-to-Gas Joint Operation Mode" analyze the necessity of establishing a green certificate trading market and verify the effectiveness of quota mechanism and GCTM in promoting renewable energy consumption.

[0007] Under the background of dual carbon and subsidy elimination policies, the aforementioned single carbon trading mechanism or GCTM research lacks the joint consideration of the two trading mechanisms and fails to give full play to the system's low-carbon operation capabilities. Therefore, the paper "Green Dispatch of Regional Integrated Energy System Considering Green Certificate-Carbon Equivalence Interaction Mechanism" combines the traditional carbon trading mechanism with green certificates to explore its emission reduction and trading mechanism and construct an IES optimization model. The experimental results show that this method can effectively improve economic and environmental protection. The papers "Optimal Dispatch of Park Integrated Energy System with Flexible Response of Thermal Power under Green Certificate-Carbon Trading Mechanism", "Wind-Fire-Storage Combined Peaking Control Strategy Based on Green Certificate-Carbon Trading Mechanism" and "Economic Operation of Integrated Energy System Considering Green Certificate-Carbon Joint Trading and Demand Response" further introduce the ladder carbon trading and green certificate joint mechanism into IES. By setting different carbon emission stages and corresponding carbon trading prices, it provides enterprises with clear price signals and increasing cost pressures at each stage. The experimental results show that it can more effectively motivate enterprises to reduce carbon emissions and encourage them to take carbon reduction actions in advance. Furthermore, the document "Economic Operation of Integrated Energy System Considering Green Certificate-Carbon Joint Trading and Demand Response" considers demand response (DR) on the load side, and actively adjusts energy usage habits on the energy side to obtain GCTM benefits. The document "Optimal Operation of Integrated Energy System Considering Comprehensive Demand Response and "Dual Carbon" Mechanism" combines LTCTM on the basis of comprehensive DR. Experimental results show that combining DR with carbon trading mechanism can reduce system operating costs and carbon emissions to a greater extent, but it does not consider GCTM.

[0008] In general, the above work mainly studies carbon trading and green certificates in terms of price mechanisms, policy recommendations and trading models, but there is still a lack of research on their impact on the operating results of IES equipment. In addition, the above-mentioned optimization scheduling models often do not take into account the variable operating conditions of the system, and assume that the operating efficiency of the coupled units remains unchanged, resulting in certain deviations in their input and output, affecting the accuracy of the calculation results. Or when exploring the operation of IES under the variable operating conditions, the control of carbon emissions and the improvement of renewable energy absorption capacity are often not fully considered. At present, few studies can integrate DR compensation mechanisms to achieve peak shaving and valley filling on the demand side, so as to achieve the accuracy, environmental protection and economy of system operation. Summary of the invention

[0009] In view of the above-mentioned deficiencies in the prior art, the variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and DR provided by the present invention improves the accuracy of the equipment model and the renewable energy consumption rate, while reducing carbon emissions, and achieving a more low-carbon, efficient and economical IES operation goal in a dynamic and changeable energy demand environment.

[0010] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a variable working condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism, comprising the following steps:

[0011] S1. Build a comprehensive energy system that includes electricity, heat and gas;

[0012] S2. Constructing the variable-operating-condition equipment operation model in the integrated energy system;

[0013] S3. Based on the equipment operation model, a variable-condition IES optimization scheduling model considering the GCT-LTC joint mechanism and PDR mechanism is constructed;

[0014] S4. Use the particle swarm algorithm to solve the variable operating condition IES optimization scheduling model, obtain the operation optimization parameters with the goal of minimizing the total operation cost, and perform optimal scheduling of the integrated energy system based on the parameters.

[0015] The beneficial effects of the present invention are:

[0016] (1) The IES model based on DEH established in this paper can effectively reflect the variable operating characteristics of the system. The equipment operation results show that considering the variable operating characteristics of the equipment effectively improves the accuracy of the model, makes the scheduling results consistent with the actual operating conditions, and to a certain extent ensures that the equipment operates at a high load rate most of the time, thereby improving the system operation efficiency.

[0017] (2) Both GCTM and LTCTM used in this paper can improve the economy and environmental protection of the system. GCT can encourage IES to actively absorb more renewable energy to obtain benefits, and LTCT can constrain high-carbon emission sources to achieve emission reduction. The combination of the two can effectively achieve the goal of low-carbon and environmental protection. Under this strategy, the total operating cost of the system can be saved by 15.85%, carbon emissions can be reduced by 6.03%, and the renewable energy absorption rate can be increased by 12.31%.

[0018] (3) Based on the combination of two trading mechanisms, this paper considers DR to optimize IES, which effectively smooths the load curve, improves the system operation stability, and alleviates the energy supply pressure of IES to a certain extent, making the system operation more low-carbon and economical. Under the strategy of combining the three mechanisms, the total cost of IES operation decreased by 19.08%, carbon emissions decreased by 12.99%, and the absorption rate increased by 14.39%, effectively coordinating the economy and low-carbon nature of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a variable operating condition IES optimization scheduling method considering a GCT-LTC joint mechanism and DR provided in an embodiment of the present invention.

[0020] Figure 2 A framework diagram of an integrated energy system provided for an embodiment of the present invention.

[0021] Figure 3 A schematic diagram of a traditional EH model provided in an embodiment of the present invention.

[0022] Figure 4 A schematic diagram of a dynamic EH model provided in an embodiment of the present invention.

[0023] Figure 5 This is a fitting curve of equipment efficiency provided by an embodiment of the present invention.

[0024] Figure 6 A schematic diagram of the green certificate mechanism principle provided in an embodiment of the present invention.

[0025] Figure 7 A schematic diagram of the principle of the tiered carbon trading mechanism provided in an embodiment of the present invention.

[0026] Figure 8 A schematic diagram of the GCT-LTCTM joint mechanism principle provided in an embodiment of the present invention.

[0027] Fig. 9 The proportion of each type of load participating in DR constraints provided in the embodiment of the present invention.

[0028] Fig.10 This is a typical daily load optimization result in an embodiment of the present invention.

[0029] Fig.11 is a renewable energy output curve in an embodiment of the present invention.

[0030] Fig.12 This is the electric power optimization operation result in the embodiment of the present invention.

[0031] Fig.13 This is the thermal power optimization operation result in the embodiment of the present invention.

[0032] Fig.14 This is the gas power optimization operation result in the embodiment of the present invention.

[0033] Fig.15 It is the GB operation characteristic in the embodiment of the present invention.

[0034] Fig.16 It is the CHP operation characteristic in the embodiment of the present invention. DETAILED DESCRIPTION

[0035] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0036] The embodiment of the present invention provides a variable working condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism, such as Figure 1 As shown, the following steps are included:

[0037] S1. Build a comprehensive energy system that includes electricity, heat and gas;

[0038] S2. Constructing the variable-operating-condition equipment operation model in the integrated energy system;

[0039] S3. Based on the equipment operation model, a variable-condition IES optimization scheduling model considering the GCT-LTC joint mechanism and PDR mechanism is constructed;

[0040] S4. Use the particle swarm algorithm to solve the variable operating condition IES optimization scheduling model, obtain the operation optimization parameters with the goal of minimizing the total operation cost, and perform optimal scheduling of the integrated energy system based on the parameters.

[0041] In step S1 of the embodiment of the present invention, the comprehensive energy system architecture is constructed as follows: Figure 2As shown, the energy supply part includes the upper-level electricity / heat / gas grid, wind power, photovoltaics, etc. The energy conversion part includes combined heat and power (CHP) units, gas boilers (GB), power to gas (P2G), hydrogen fuel cells (HFC) and other equipment. The energy storage part includes energy storage equipment such as batteries (EES), gas storage tanks (GST), and thermal storage tanks (TST). The energy consumption part includes electricity / heat / gas loads. IES realizes energy coupling between electricity, heat and gas through its conversion equipment, breaking down the barriers between energy sources.

[0042] In step S2 of the embodiment of the present invention, the variable operating condition equipment operation model includes a DEH model, a variable operating condition IES model based on DEH, an energy conversion equipment model, and an energy storage equipment model.

[0043] In this embodiment, for the construction of the DEH model, the energy hub is defined as a multi-port coupling matrix model, which is used to characterize the multi-input-output relationship of transmission, conversion and storage of various energy forms in a multi-energy system, which can be abstracted as a functional relationship, namely:

[0044] L=f(P)

[0045] Where L is the system output power vector; P is the system input power vector; and f(·) is the transmission, conversion, and storage link that considers various energy forms.

[0046] The classic EH model is as follows Figure 3 As shown, the system in the embodiment of the present invention includes a variety of energy transmission, conversion and storage devices, so a coupling matrix C and an energy storage device attribution matrix S can be used to describe the multi-input and output relationship of the system.

[0047]

[0048] In the formula, α, β, …ω are various energy forms in the system, such as electricity, heat, gas, etc.; E is the actual charging and discharging power vector of the energy storage device. This paper stipulates that the energy released by the device to the system is positive.

[0049] In the above formula, the coupling factor c of C is ij The scheduling factor v of the corresponding device ij and efficiency factor η ij The product of ij =v ij η ij ; The attribution coefficient s in Sij The relationship between the charging / discharging of each energy storage device and the input / output port of the system is represented as: Among them, v ij is the dispatch factor for converting input energy in form i into output energy in form j; η ij is the efficiency factor of the corresponding energy conversion equipment; η i,C , η i,D are the charging and discharging efficiencies of the i-th energy storage device respectively.

[0050] As a classic EH model, it is widely used in modeling research. However, it does not consider the variable operating characteristics of the equipment. Instead, it simplifies the multi-input and output relationship of the system into a linear model and assumes that the efficiency factor η of the conversion equipment is ij is a constant value.

[0051] L=ηVP+SE=CP+SE

[0052] However, in actual operation, the efficiency of most conversion equipment changes with the load rate, and there is a clear functional relationship between the two, η = f(N). For different energy conversion equipment, the specific functional relationship is different, which is called the variable operating condition model of the equipment.

[0053] In this embodiment, the variable operating condition characteristics of the energy conversion equipment are considered in the integrated energy system, and the constant efficiency factor η in the energy hub coupling matrix C is converted into a variable operating condition function relationship, which is expressed as:

[0054]

[0055] In this embodiment, in IES, the input-output relationship of the conversion device based on the classic EH model, that is: out =ηP in

[0056] Where P out Output energy for IES conversion equipment; P in Input energy to the IES conversion device.

[0057] The dynamic input-output relationship of IES after real-time efficiency correction, i.e., the variable working condition IES model based on DEH, is expressed as:

[0058] P out =f(N)P in

[0059] Where L represents the output power vector of different energy forms in the system, c represents the coupling factor, N represents the load rate of the conversion device, P represents the system input power vector of different energy forms in the system, s represents the attribution coefficient, E represents the actual charge and discharge power vector of different energy forms in the system, and the subscripts α, β, …ω represent various energy forms in the system; P out Represents the output energy of IES conversion equipment, P in represents the input energy of the IES conversion device, f(N) represents the functional relationship between the efficiency of the conversion device and the load rate, and N represents the load rate of the conversion device.

[0060] Furthermore, the dynamic DEH model is Figure 4 shown.

[0061] In this embodiment, for most energy conversion equipment, its energy conversion efficiency changes with the load rate, showing nonlinear characteristics, which can be obtained by data fitting. In this embodiment, the energy conversion equipment model includes a CHP unit variable operating condition model, a gas boiler variable operating condition model, a P2G model and a HFC model.

[0062] Specifically, for the variable operating condition model of the CHP unit:

[0063] CHP is a multi-output device that can provide both electricity and heat by burning natural gas. It consists of a gas turbine (GT) and a waste heat boiler (WHB). A fourth-order polynomial is used to fit the relationship between its power generation efficiency and the electric load rate. The heat-to-electricity ratio is expressed as a quadratic function of the electric load rate, that is:

[0064] η CHP =0.06N 4 CHP -0.12N 3 CHP

[0065] -0.15N 2 CHP +0.45N CHP +0.10

[0066] α CHP =4.30N 2 CHP -6.71N CHP +4.22

[0067] Where η CHP is the power generation efficiency of the CHP unit, α CHP is the heat-to-electricity ratio of the CHP unit.

[0068] For the variable operating condition model of gas boiler:

[0069] GB is a device that inputs natural gas and outputs heat energy. Its operating efficiency is linearly related to the load rate, that is:

[0070] η GB =0.18N GB +0.82

[0071] Where η GB is GB efficiency; N GB is the GB load rate.

[0072] The fitting curves of the two devices are as follows Figure 5 As shown in the figure, it can be seen that the power generation efficiency of the CHP unit and the heat generation efficiency of the GB unit are positively correlated with the equipment load rate.

[0073] For P2G model:

[0074] The P2G equipment consists of an electrolyzer (EL) and a methane reactor (MR), and its mathematical model is:

[0075] P EL (t) = η H2 P P2G (t)

[0076]

[0077] Where P EL (t) is the hydrogen power output by EL during period t; η H2 is the EL electric-to-hydrogen conversion efficiency; P P2G (t) is the electric energy consumed by EL during period t; P MR (t) is the natural gas power output by MR during period t; η MR is the MR conversion efficiency; is the hydrogen power input from EL to MR during period t.

[0078] For the HFC model, HFC is a clean energy conversion device that converts hydrogen and oxygen into electricity and water through electrochemical reactions. No harmful gases are produced during the process, namely:

[0079]

[0080] In the formula, is the HFC output electrical and thermal power in period t; is the electrical and thermal efficiency of HFC; is the hydrogen power input into HFC by EL during period t; The upper and lower limits of hydrogen power in the input HFC.

[0081] In this embodiment, for the energy storage device model, since the charging and discharging operation of the energy storage device is relatively stable, and the change of the storage energy is mainly related to the self-loss rate of the corresponding device, its charging and discharging efficiency can be regarded as a constant value. Based on this, the energy storage device model in this embodiment is a model that characterizes the charging and discharging energy changes of the energy storage device uniformly constructed for EES, TST and GST devices, which is expressed as:

[0082] W ES,t+1 =W ES,t (1-σ ES )-E ES,t Δt

[0083]

[0084] Where W ES,t , W ES,t+1 are the energy storage capacity of the energy storage device before and after charging and discharging at time t and time t+1 respectively; σ ES E is the self-discharge efficiency of energy storage equipment; ES,t is the actual charging and discharging power of the energy storage device after considering the charging and discharging efficiency at time t, with the energy discharge of the device to the system being positive; Δt is the optimized operation step length; η ES,C , η ES,D They are respectively the charging and discharging efficiency of energy storage devices; They are the upper limits of the charging and discharging power of the equipment respectively; The maximum energy storage capacity of the device.

[0085] In step S3 of the embodiment of the present invention, considering the GCT-LTC joint mechanism means giving priority to the output of new energy and low-carbon units when the integrated energy system is running;

[0086] The model of the GCT-LTC joint mechanism includes a green certificate mechanism model and a ladder carbon trading mechanism model.

[0087] Specifically, in this embodiment, for the Green Certificate Mechanism (GCTM), GCTM is a financial certificate for private enterprises, individual users and other consumers to voluntarily pay extra fees to purchase 1MW of renewable energy, aiming to promote the consumption of renewable energy while alleviating the government's financial pressure; with the gradual maturity of the GCT market, all power generation companies are now required to complete the specified proportion of renewable energy quotas.

[26] The initial renewable energy consumption quota is uniformly allocated by government agencies based on the actual conditions of each enterprise and user. For power generation companies, the consumption index can be equivalent to the proportion of renewable energy power generation in the total electricity, and converted into a corresponding number of green certificates. The corresponding green certificate mechanism principle is as follows Figure 6When the renewable energy indicators consumed by power generation enterprises or users exceed the allocated quota, they can sell surplus green certificates to participate in market transactions to obtain additional profits. On the contrary, if the consumption quota indicators are not met, the corresponding number of green certificates can be purchased in the market to meet the consumption indicators and reduce fines.

[0088] On this basis, the green certificate mechanism model is expressed as follows in this embodiment:

[0089] C GCT =C BG -C SG

[0090]

[0091]

[0092] In the formula, C GCT Represents the purchase cost of the green certificate mechanism C BG Green certificate mechanism income sales income C SG The final transaction amount obtained by the difference between RE (t) represents the output of the new energy unit at time t, μ represents the weight coefficient of renewable energy consumption responsibility, and the new energy unit includes wind power and photovoltaic units. g Indicates the clean energy power generation required by a single green certificate, C g represents the GTC price, T represents the scheduling period, P ebuy (t) represents the amount of electricity purchased by the superior during period t, c v,t It indicates the reduction in power generation corresponding to an increase in unit heat supply when the steam inlet of the CHP unit is constant, P GT,e (t), P GB,h (t) represents the electrical and thermal power output of the CHP unit during period t.

[0093] In this embodiment, the ladder carbon trading mechanism (LTCTM) refers to treating carbon emission rights as a commodity, and using market-based means to encourage enterprises to reduce carbon emissions and optimize production capacity structure. At present, the carbon emission quota method mainly adopts free allocation, which is uniformly allocated by government agencies based on the historical emission data and industry characteristics of each carbon emission source. If the carbon emissions of power generation enterprises are lower than this quota, they will sell the remaining quota to participate in market transactions; otherwise, they will need to purchase the corresponding carbon emission quota. The principle of LTCTM constructed in my country is as follows Figure 7 As shown. That is, if the carbon emission source exceeds its free quota, the price of the carbon emission rights to be purchased will be higher accordingly. This mechanism encourages emission reduction through tiered pricing of carbon trading to achieve stricter environmental control effects.

[0094] On this basis, the ladder carbon trading mechanism model in this embodiment includes the initial carbon emission quota model, the actual carbon emission model, the carbon emission trading amount participating in the carbon trading market, and the ladder carbon trading cost calculation model;

[0095] Among them, the carbon emission sources in IES are mainly upstream thermal power units, GT, GB and gas load. Therefore, the initial carbon emission quota model in this embodiment is expressed as:

[0096]

[0097] In the formula, E IES 、E e,buy 、E GT 、E GB 、E gload They represent the carbon emission quotas of IES, power purchase from higher authorities, GT, GB, and gas load, respectively. ebuy (t) represents the amount of electricity purchased by the superior during period t, χ e , χ h They represent the carbon emission quota per unit of electrical power and per unit of thermal power, respectively, gload is the carbon emission quota per unit gas load consumed, χ e,h Represents the electrical and thermal power conversion parameter, P GT,e (t) represents the power output of the CHP unit during period t, P GT,h (t) represents the thermal power output of GT during period t, P g,Load (t) represents the gas load demand during period t.

[0098] There are many devices involved in carbon emissions in IES. In this embodiment, it is stipulated that the power purchased by the upper level comes from thermal power units. However, most existing studies ignore the direct carbon emissions generated by the gas load during the combustion process and the potential carbon absorption capacity of the MR working process. Therefore, in this embodiment, the amount of CO2 absorbed by the MR equipment and the carbon emissions of the gas load are taken into account in the actual carbon emission model, and the previous model is improved. The actual carbon emission model constructed is expressed as:

[0099]

[0100] In the formula, E IES,a Indicates IES actual carbon emissions, E e,buy,a Indicates the actual carbon emissions of electricity purchased by the superior, E GTGB,a Indicates the total actual carbon emissions of GT and GB, E gload,a Indicates the actual carbon emissions of gas load, E MR,a Indicates the amount of CO2 actually absorbed by MR, P GTGB(t) is the equivalent output power of GT and GB in period t, a1, b1, c1 represent the carbon emission calculation parameters of electricity consumption, a2, b2, c2 represent the carbon emission calculation parameters of natural gas consumption, ξ Represents the equivalent carbon emission parameter per unit gas load, P MR (t) represents the equivalent output power of MR during period t It is the process parameter of MR absorbing CO2.

[0101] Based on the above model, the carbon emission trading amount participating in the carbon trading market in this embodiment is expressed as:

[0102] E IES,t =E IES,a -E IES

[0103] The calculation model of the ladder carbon trading cost is expressed as:

[0104]

[0105] In the formula, C LTCT represents the tiered carbon trading cost, λ represents the carbon trading base price, l represents the length of the carbon emission interval, and κ represents the price increase range.

[0106] In this embodiment, in the optimization operation of IES, the GCT-LTC joint mechanism enables the system to give priority to the output of new energy and low-carbon units, promoting low-carbon economic operation. Figure 8 shown.

[0107] In step S3 of the embodiment of the present invention, the PDR (Price Demand Response) mechanism refers to guiding users to adjust their energy consumption periods by implementing time-of-use prices; users respond to price adjustments and transfer part of their peak loads to flat and valley periods, thereby achieving the purpose of "peak shaving and valley filling".

[0108] Different types of loads have different sensitivities to the same electricity price signal. In addition to fixed loads, PDR loads are divided into curtailable loads (CL) and shiftable loads (SL). In this embodiment, the three types of load constraints account for Fig. 9 shown.

[0109] Based on this, the model of the PDR mechanism in this embodiment includes a CL model and a SL model;

[0110] Among them, in the CL model, CL chooses whether to reduce its own load by comparing the price changes in the current period before and after DR. Therefore, the demand response characteristics are described by the price demand elasticity matrix B(t,x), and the load change that can be reduced after the demand response is calculated based on it. It is expressed as:

[0111]

[0112] In the formula, represents the initial load reduction at time t, B CL,t (t,x) represents the diagonal matrix of the price demand elasticity of the load that can be reduced, b t,x represents the elasticity coefficient in the price demand elasticity matrix B(t,x), ρ x represents the price at time x, represents the initial price at time x, represents the load change at time t after demand response, represents the initial load at time t, Δρ x It represents the price change at time x after the demand response.

[0113] In the SL model, SL refers to the load that users can flexibly adjust their working hours according to their own energy needs. Using the peak, flat and valley time-of-use price as a signal, users can be guided to transfer the load during peak hours to the flat and valley hours. Similarly, the price demand elasticity matrix is ​​used to describe the change in load that can be transferred after demand response.

[0114]

[0115] In the formula, represents the initial transferable load at time t; B SL,t (t,x) is the transferable load price demand elasticity matrix.

[0116] In step S3 of the embodiment of the present invention, a variable operating condition IES optimization scheduling model is constructed with the goal of minimizing the sum of various energy costs, operation and maintenance costs, GCT costs, and LTCT costs, which is expressed as:

[0117] F=min(C buy -C sell +C om +C LTCT +C GCT )

[0118]

[0119]

[0120] In the formula, C buy Represents the purchase cost of various types of energy, Csell represents the revenue from selling system energy, C om represents the system operation and maintenance cost, C GCT represents the GCT cost, C LTCT represents the LTCT cost, and Respectively represent the operation and maintenance costs of system equipment, renewable energy and system energy storage equipment, ρ e,t represents the real-time electricity price during period t, ρ g , h is the unit price of gas energy and heat energy, P wt,t , P pv,t , P GT,t , P WHB,t and P GB,t They represent the output power of wind power, photovoltaic power, GT, WHB and GB in period t, P gbuy,t represents the energy purchased from the upper main network during period t, Γ e,t represents the unit electricity price of the upper grid during period t, P hsell,t P represents the heat sold in period t, esell,t represents the electricity sales in period t, P hsell,t P represents the heat sold in period t, EL,t represents the EL output during period t, P MR,t represents the MR output during period t, P HFC,t represents the HFC output during period t, W EES,t Indicates the energy stored / released by the battery during period t, W GST,t Indicates the storage / release energy of the gas tank during period t, W TST,t represents the storage / release energy of the heat storage tank during period t, m represents the unit operation and maintenance cost of each equipment, and the subscripts EL, MR, GT, WHB, GB, HFC, wt, pv, EES, GST and TST represent various types of equipment.

[0121] In this embodiment, the constraints of the variable operating condition IES optimization scheduling model include energy conversion equipment constraints, energy storage equipment constraints, system energy balance constraints, and tie line power balance constraints;

[0122] The energy conversion equipment constraints include P2G equipment power constraints and HFC Equipment Power Constraints and CHP and GB equipment power constraints In the formula, Indicates the minimum hydrogen power output by EL, P EL (t) represents the hydrogen power output by EL during period t, Indicates the maximum hydrogen power output by EL, P MR (t) is the natural gas power output by MR during period t, represents the minimum natural gas power output by MR, represents the maximum natural gas power output by MR, Indicates the upper and lower limits of hydrogen power input into HFC, represents the hydrogen power input from EL into HFC during period t, P out Indicates the capacity of the device. Indicates the maximum capacity of the device;

[0123] The energy storage device constraints are:

[0124] W ES,t+1 =W ES,t (1-σ ES )-E ES,t Δt

[0125]

[0126] Where W ES,t , W ES,t+1 They represent the energy storage capacity of the energy storage device before and after charging and discharging at time t and time t+1, respectively, ES Represents the self-discharge efficiency of energy storage equipment, E ES,t It represents the actual charging and discharging power of the energy storage device after considering the charging and discharging efficiency at time t, with the energy discharge of the device to the system as positive, Δt represents the optimized operation step, η ES,C , η ES,D They represent the charging and discharging efficiency of energy storage devices, Respectively represent the upper limit of the device's charging and discharging power. Indicates the maximum energy storage capacity of the device;

[0127] The system energy balance constraint is:

[0128]

[0129] Where L represents the output power vector of different energy forms in the system, c represents the coupling factor, N represents the load rate of the conversion device, P represents the system input power vector of different energy forms in the system, s represents the attribution coefficient, E represents the actual charge and discharge power vector of different energy forms in the system, and the subscripts α, β, …ω represent various energy forms in the system;

[0130] The tie line power balance constraint is:

[0131]

[0132] Where P line,t Indicates that the tie line can pass power, Indicates the maximum power that the tie line can pass.

[0133] In this embodiment, the model constructed above belongs to a mixed integer nonlinear programming problem. The existing methods such as linearization are used to process its nonlinear and multivariable models, and then the traditional mathematical optimization methods are used to solve them. This processing method requires simplifying the model, which is easy to cause errors in the solution results. In order to avoid this problem, in this embodiment, the particle swarm algorithm that can effectively process nonlinear models is used to solve the optimization target, which can adapt to different constraints and requirements while retaining the nonlinear characteristics of the model, and quickly find a better solution.

[0134] The embodiment of the present invention provides a specific example analysis of the above-mentioned variable operating condition IES optimization scheduling.

[0135] In this embodiment, Figure 2 The IES topology shown in the figure is used as the object for simulation analysis. The scheduling cycle T is specified as 24h, and the time step t is 1h; the technical and cost parameters of the main units of the system are shown in Table 1; the parameters of the energy storage system are shown in Table 2; the electricity, heat, and gas load levels before and after participating in DR are shown in Table Fig.10 As shown; wind and light renewable energy output curves are as follows Fig.11 As shown; the energy consumption peak, flat and valley time periods of the system corresponding to each load and the prices purchased and sold to the superior energy network are shown in Table 3; in terms of LTCT, the carbon emission quota and carbon emission calculation parameters are shown in Table 4, the unit carbon trading base price λ is 252 yuan / t, the carbon emission interval length l is 95t, and the price increase range κ is 25%; in terms of GCTM, the renewable energy consumption responsibility coefficient μ is 15%, the unit price of the green certificate is 128.6 yuan / book, and the clean energy power generation E required by a single green certificate g is 1MWh.

[0136] Table 1 IES main equipment parameters

[0137]

[0138] Table 2 IES energy storage equipment parameters

[0139]

[0140] Table 3 Peak and valley time-sharing prices for load purchase and sale

[0141]

[0142]

[0143] Table 4 Carbon emission coefficient of relevant equipment

[0144]

[0145] In order to increase the consumption rate of new energy, reduce carbon emissions, and enhance economic efficiency, this paper sets up five different operation scenarios for comparative analysis under the premise that all kinds of mechanisms can be traded smoothly, among which scenario 5 is the method proposed in this paper. The information of the comparison scenarios is shown in Table 5.

[0146] Table 5 Five typical scene settings

[0147]

[0148] In this embodiment, the operation indicators such as economy, environment and renewable energy consumption rate of five scenarios are evaluated, and the optimization results are shown in Table 6;

[0149] Table 6 Optimization results of each scenario

[0150]

[0151] (1) Analysis of economic benefits

[0152] In order to verify the impact of each mechanism on the economic benefits of IES, the economic costs under the five scenarios in Table 6 are compared and analyzed.

[0153] In terms of total cost, scenario 5 decreased by 23.58%, 10.96%, 12.78%, and 6.68% respectively compared with scenarios 1-4, of which the energy purchase cost decreased by 11.92%, 2.65%, -0.02%, and 0.29% respectively, and the operation and maintenance cost increased by 17.31%, 17.02%, 13.94%, and 13.41% respectively. In addition, the GCT and LTCT benefits of scenario 5 increased by -15.70% and 87.34% respectively compared with scenario 4. It can be seen that since scenario 1 does not consider any mechanism, when the system is at peak load, the energy supply pressure is relatively large, which leads to a high energy purchase cost in this scenario. At the same time, due to the lack of additional transaction subsidies, the total cost is huge.

[0154] Based on this, Scenario 2-4 introduces GCTM and LTCTM. By buying and selling green certificates and carbon emission rights through the two mechanisms alone or in combination, the IES's revenue channels are broadened, corresponding cost subsidies are obtained, economic pressure is alleviated, and total operating costs are effectively reduced.

[0155] In addition, scenario 5 takes DR into consideration based on scenario 4. Fig. 9 It can be seen that DR guides users to change their energy consumption behavior through prices, reducing or transferring electricity, heat and gas loads during peak hours to off-peak hours, alleviating energy supply pressure while improving system flexibility, obtaining the economic benefits of GCT, LTCT and DR, and minimizing total costs.

[0156] (2) Analysis of environmental benefit results

[0157] In order to verify the impact of each mechanism on the environmental benefits of IES, the carbon emissions and renewable energy consumption rates under the five scenarios in Table 6 are compared and analyzed.

[0158] In terms of total system carbon emissions, scenario 5 decreased by 14.93%, 11.23%, 9.22% and 8.01% respectively compared with scenarios 1 to 4. Although the carbon emissions of scenarios 2 and 3 have improved to a certain extent compared with scenario 1 without the mechanism, it can be seen from the data that the ability of a single mechanism to constrain system carbon emissions is relatively limited, showing the limitations of a single mechanism in constraining system carbon emissions.

[0159] Scenarios 4 and 5 combine multiple mechanisms on the basis of single mechanism scenarios 2 and 3. Specifically, scenario 4 combines LTCTM with GCTM. On the one hand, green certificates are used to ensure that the system operating costs are at a low level, thereby improving the utilization rate of wind and solar power and reducing carbon emissions generated by the use of IES energy equipment. On the other hand, the total carbon emissions of the system are further constrained through the carbon trading mechanism. For gas load carbon emissions, the carbon emission quota coefficient is the same. If the total load remains unchanged, the carbon emissions remain unchanged. Therefore, the gas load carbon emissions of scenarios 1 to 4 are the same, but for scenario 5, the gas load carbon emissions have decreased by 2.95% compared with before DR. It can be seen that when the system reduces or transfers the gas load after considering DR, it can better play its advantages of peak shaving and valley filling and improve the environmental benefits of the system.

[0160] (3) Analysis of renewable energy absorption capacity

[0161] In terms of overall renewable energy absorption capacity, scenario 5 increases by 12.61%, 8.60%, 5.98%, and 1.85% respectively compared to scenarios 1 to 4. The gradual increase in absorption rate further verifies the positive correlation between emission reduction effect and absorption capacity.

[0162] In summary, by comparing the operating indicators under different scenarios, it can be seen that each mechanism has different degrees of positive impact on the economic and environmental benefits of IES. In particular, scenario 5, which comprehensively considers the GCT, LTCT and DR mechanisms, can maximize the optimization of the load curve, reduce the total system cost, increase the renewable energy absorption rate, constrain carbon emissions, and improve the environmental protection of the system under the coordinated operation of multiple mechanisms, providing an effective way for the green development of IES.

[0163] In order to further highlight the impact of the green certificate-ladder carbon trading joint mechanism and demand response on the optimal dispatch of the variable-condition integrated energy system, the operation and dispatch results of scenario 5 are analyzed. The power balance of electricity, heat and gas is as follows: Figures 12 to 14 As shown; GB, CHP equipment operating characteristics see Figures 15 to 16 .

[0164] analyze Fig. 9 and Fig.12 It can be seen that the peak-to-valley difference of IES electric load is reduced after the DR mechanism. During the low electricity price and low load period from 24:00 to 6:00, it is the peak period of wind power generation at night, and wind resources are abundant. Therefore, the continuous power generation of wind power is given priority at this time, which helps to reduce the impact on the environment. And because the cost of purchasing electricity during this period is relatively low, the insufficient part of the electric load can be purchased directly from the superior power grid, and batteries, HFC and CHP supply power to the load to improve the reliability and flexibility of the system.

[0165] During the relatively stable power load periods of 7:00-9:00, 15:00-16:00 and 21:00-23:00, wind power generation gradually weakens, but photovoltaic output gradually increases. At this time, the power load is provided by photovoltaic, wind power, CHP and HFC equipment. In the time periods of 10:00-14:00 and 17:00-20:00 when the power price and power load are higher, the comprehensive cost of purchasing electricity from the upper power grid is higher than the power generation and operation cost of CHP and HFC. This period mainly relies on photovoltaic power generation, CHP and HFC for energy supply, and the insufficient part is purchased from the upper power grid. During the period of high system load and high electricity price, energy equipment always maintains a large power output, achieving high-efficiency operation while ensuring the balance of IES supply and demand, reducing the system's power purchase cost.

[0166] analyze Fig. 9 and Fig.13 It can be seen that after the DR mechanism, the peak-to-valley difference of the IES heat load is reduced, and the heat supply is mainly provided by CHP, GB and HFC units. During the peak and normal periods of heat load, HFC and CHP have a large output, which can generate heat energy and electricity to meet the demand for electric heat energy. GB serves as a supplement to ensure the stable operation of the system, and there is still redundant heat to sell after meeting the demand, to obtain corresponding benefits and reduce the total cost of IES. During the low load period of 24:00-4:00, the energy supply structure is not much different. TST performs heat charging at this time to play a peak regulation role.

[0167] analyze Fig. 9 and Fig.14It can be seen that the peak-to-valley difference of IES gas load is reduced after the DR mechanism. The system gas load demand is mainly maintained by purchasing gas from the superior gas network, and only part of the power is output by the gas tank and P2G equipment during a relatively short period of time. Analysis of the electricity and heat operation results shows that CHP and HFC, as the main equipment to maintain the balance of electricity and heat, consume a large amount of natural gas in the process of power supply and heating, and the electricity and heat demand is higher than the gas load demand, resulting in a large pressure on the power supply and heating of the system. Therefore, purchasing natural gas is the main means. In the period of 23:00-5:00, the gas load is in a low demand period, and the electricity price is at the valley price. The sum of the operating cost of the P2G equipment and the carbon reduction benefits brought by the MR synthesis is lower than the gas price. Therefore, at this time, the gas load demand is jointly supplied by P2G and the superior gas network.

[0168] Under the actual operating conditions of the system, the equipment operating efficiency changes with the load rate. Therefore, this paper considers the variable operating characteristics of the equipment in IES operation to reduce the impact of the load rate on the actual operating results. Fig.15 and 16 It can be seen that the actual operating efficiency of GB and CHP equipment is higher than the rated efficiency most of the time, and they are in a high load rate state. This shows that the system has significantly improved its model accuracy under such operating conditions, energy conversion is more efficient, energy waste is reduced, and it is beneficial to the economic operation of IES, making the analysis of the overall operation and scheduling of IES more accurate.

[0169] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0170] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A variable-condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism, characterized in that: The following steps are involved: S1. Build a comprehensive energy system that includes electricity, heat and gas; S2. Construct the variable-condition equipment operation model in the integrated energy system; S3. Based on the equipment operation model, a variable operating condition IES optimization scheduling model considering the GCT-LTC joint mechanism and PDR mechanism is constructed; S4. Use the particle swarm algorithm to solve the variable operating condition IES optimization scheduling model, obtain the operation optimization parameters with the goal of minimizing the total operation cost, and perform optimal scheduling of the integrated energy system based on the parameters.

2. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 1 is characterized in that: In the step S2, the variable operating condition equipment operation model includes a DEH model, a variable operating condition IES model based on DEH, an energy conversion equipment model, and an energy storage equipment model; The DEH model is a variable operating condition function relationship corresponding to the variable operating condition characteristics of the energy conversion equipment, which is expressed as: The variable operating condition IES model based on DEH is expressed as: P out =f(N)P in Where L represents the output power vector of different energy forms in the system, c represents the coupling factor, N represents the load rate of the conversion device, P represents the system input power vector of different energy forms in the system, s represents the attribution coefficient, E represents the actual charge and discharge power vector of different energy forms in the system, and the subscripts α, β, …ω represent various energy forms in the system; P out Represents the output energy of IES conversion equipment, P in represents the input energy of the IES conversion device, f(N) represents the functional relationship between the efficiency of the conversion device and the load rate, and N represents the load rate of the conversion device; The energy conversion equipment model includes a CHP unit variable operating condition model, a gas boiler variable operating condition model, a P2G model and an HFC model; the energy storage equipment model is a model uniformly constructed for EES, TST and GST equipment to characterize the changes in charging and discharging energy of the energy storage equipment.

3. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 1 is characterized in that: In step S3, considering the GCT-LTC joint mechanism means giving priority to the output of new energy and low-carbon units when the integrated energy system is running; The model of the GCT-LTC joint mechanism includes a green certificate mechanism model and a ladder carbon trading mechanism model.

4. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 3 is characterized in that: The green certificate mechanism model is expressed as: C GCT =C BG -C SG In the formula, C GCT Represents the purchase cost of the green certificate mechanism C BG Sales income with green certificate mechanism income C SG The final transaction amount obtained by the difference between RE (t) represents the output of the new energy unit at time t, μ represents the weight coefficient of renewable energy consumption responsibility, E g Indicates the clean energy power generation required by a single green certificate, C g represents the GTC price, T represents the scheduling period, P ebuy (t) represents the amount of electricity purchased by the superior during period t, c v,t It indicates the reduction in power generation corresponding to an increase in unit heat supply when the steam inlet of the CHP unit is constant, P GT,e (t), P GB,h (t) represents the electrical and thermal power output of the CHP unit during period t.

5. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 3 is characterized in that: The ladder carbon trading mechanism model includes an initial carbon emission quota model, an actual carbon emission model, a carbon emission trading amount participating in the carbon trading market, and a ladder carbon trading cost calculation model; The initial carbon emission quota model is expressed as: In the formula, E IES 、E e,buy 、E GT 、E GB 、E gload They represent the carbon emission quotas of IES, power purchase from higher authorities, GT, GB, and gas load, respectively. ebuy (t) represents the amount of electricity purchased by the superior during period t, χ e , χ h They represent the carbon emission quota per unit of electrical power and per unit of thermal power, respectively, gload is the carbon emission quota per unit gas load consumed, χ e,h Represents the electrical and thermal power conversion parameter, P GT,e (t) represents the power output of the CHP unit during period t, P GT,h (t) represents the thermal power output of GT during period t, P g,Load (t) represents the gas load demand during period t. The actual carbon emission model is expressed as: In the formula, E IES,a Indicates IES actual carbon emissions, E e,buy,a Indicates the actual carbon emissions of electricity purchased by the superior, E GTGB,a Indicates the total actual carbon emissions of GT and GB, E gload,a Indicates the actual carbon emissions of gas load, E MR,a Indicates the amount of CO2 actually absorbed by MR, P GTGB (t) is the equivalent output power of GT and GB in period t, a1, b1, c1 represent the carbon emission calculation parameters of electricity consumption type, a2, b2, c2 represent the carbon emission calculation parameters of natural gas consumption type, ξ Represents the equivalent carbon emission parameter per unit gas load, P MR (t) represents the equivalent output power of MR during period t MR is the process parameter for absorbing CO2; The carbon emission trading amount participating in the carbon trading market is expressed as: AND IES,t =And IES,a -AND IES The ladder carbon trading cost calculation model is expressed as: In the formula, C LTCT represents the tiered carbon trading cost, λ represents the carbon trading base price, l represents the length of the carbon emission interval, and κ represents the price increase rate.

6. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 1 is characterized in that: In step S3, the PDR mechanism refers to guiding users to adjust energy consumption periods by implementing time-of-use prices; the model of the PDR mechanism includes a CL model and a SL model; In the CL model, the demand response characteristics are described by the price demand elasticity matrix B(t,x), and the load change that can be reduced after the demand response is calculated based on the price demand elasticity matrix B(t,x). It is expressed as: In the formula, represents the initial load reduction at time t, B CL,t (t,x) represents the diagonal matrix of the price demand elasticity of the load that can be reduced, b t,x represents the elasticity coefficient in the price demand elasticity matrix B(t,x), ρ x represents the price at time x, represents the initial price at time x, represents the load change at time t after demand response, represents the initial load at time t, Δρ x It represents the price change at time x after the demand response; In the SL model, the change in load that can be transferred after demand response is described by the price demand elasticity matrix In the formula, represents the initial transferable load at time t; B SL,t (t,x) is the transferable load price demand elasticity matrix.

7. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 1 is characterized in that: In step S3, with the goal of minimizing the sum of various energy costs, operation and maintenance costs, GCT costs, and LTCT costs, a variable operating condition IES optimization scheduling model is constructed, which is expressed as: F=min(C buy -C sell +C om +C LTCT +C GCT ) In the formula, C buy Represents the purchase cost of various types of energy, C sell represents the revenue from selling system energy, C om represents the system operation and maintenance cost, C GCT represents the GCT cost, C LTCT represents the LTCT cost, and Respectively represent the operation and maintenance costs of system equipment, renewable energy and system energy storage equipment, ρ e,t represents the real-time electricity price during period t, ρ g , h is the unit price of gas energy and heat energy, P wt,t , P pv,t , P GT,t , P WHB,t and P GB,t They represent the output power of wind power, photovoltaic power, GT, WHB and GB in period t, P gbuy,t represents the energy purchased from the upper main network during period t, Γ e,t represents the unit electricity price of the upper grid during period t, P hsell,t P represents the heat sold in period t, esell,t represents the electricity sales in period t, P hsell,t P represents the heat sold in period t, EL,t represents the EL output during period t, P MR,t represents the MR output during period t, P HFC,t represents the HFC output during period t, W EES,t Indicates the energy stored / released by the battery during period t, W GST,t Indicates the storage / release energy of the gas tank during period t, W TST,t represents the storage / release energy of the heat storage tank during period t, m represents the unit operation and maintenance cost of each equipment, and the subscripts EL, MR, GT, WHB, GB, HFC, wt, pv, EES, GST and TST represent various types of equipment.

8. The variable operating condition IES optimization scheduling method considering the GCT-LTC joint mechanism and the PDR mechanism according to claim 7 is characterized in that: The constraints of the variable operating condition IES optimization scheduling model include energy conversion equipment constraints, energy storage equipment constraints, system energy balance constraints and tie line power balance constraints; The energy conversion equipment constraints include P2G equipment power constraints and HFC Equipment Power Constraints and CHP and GB equipment power constraints In the formula, Indicates the minimum hydrogen power output by EL, P EL (t) represents the hydrogen power output by EL during period t, Indicates the maximum hydrogen power output by EL, P MR (t) is the natural gas power output by MR during period t, represents the minimum natural gas power output by MR, represents the maximum natural gas power output by MR, Indicates the upper and lower limits of hydrogen power input into HFC, represents the hydrogen power input from EL into HFC during period t, P out Indicates the capacity of the device. Indicates the maximum capacity of the device; The energy storage device constraints are: W ES,t+1 =W ES,t (1-p ES )-E ES,t Δt Where W ES,t , W ES,t+1 They represent the energy storage capacity of the energy storage device before and after charging and discharging at time t and time t+1, respectively, ES Represents the self-discharge efficiency of energy storage equipment, E ES,t It represents the actual charging and discharging power of the energy storage device after considering the charging and discharging efficiency at time t, with the energy discharge of the device to the system as positive, Δt represents the optimized operation step, η ES,C , η ES,D They represent the charging and discharging efficiency of energy storage devices, Respectively represent the upper limit of the device's charging and discharging power. Indicates the maximum energy storage capacity of the device; The system energy balance constraint is: Where L represents the output power vector of different energy forms in the system, c represents the coupling factor, N represents the load rate of the conversion device, P represents the system input power vector of different energy forms in the system, s represents the attribution coefficient, E represents the actual charge and discharge power vector of different energy forms in the system, and the subscripts α, β, …ω represent various energy forms in the system; The tie line power balance constraint is: Where P line,t Indicates that the tie line can pass power, Indicates the maximum power that the tie line can pass.

Images