Rural integrated energy system energy balance planning method considering virtual energy storage

Abstract

The invention discloses a rural integrated energy system energy balance planning method considering virtual energy storage, which comprises the following steps: firstly, excavating an electricity-heat-biogas coupling characteristic, and constructing a biogas production link virtual energy storage model and a seasonal gas storage model; the method comprises the following steps: firstly, constructing a multi-time-scale double-layer optimization configuration model considering virtual energy storage, electrochemical energy storage and seasonal energy storage to solve the problem of energy space-time mismatching, then performing model conversion by adopting a KKT condition to obtain a single-layer configuration optimization model, and finally performing calculation solution based on the single-layer configuration optimization model to obtain optimal equipment capacity configuration. According to the method, virtual energy storage and seasonal energy storage in the biogas production link are cooperatively utilized, so that the overall optimization of the energy storage structure and the equipment capacity of the system can be realized, and the whole rural comprehensive energy system has relatively good flexible adjustment capability, relatively high operation economy and resource utilization efficiency and relatively low carbon emission; and a theoretical basis and data support can be provided for economic operation of the rural comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to an energy balance planning method, specifically an energy balance planning method for a rural integrated energy system considering virtual energy storage, and belongs to the technical field of comprehensive energy utilization. Background Art

[0002] As an important part of China's energy consumption, how to optimize the energy structure of the rural integrated energy system and improve the energy utilization efficiency is a concern in the industry. Rural areas are rich in biomass resources, such as crop straws, livestock and poultry manure, etc. These resources can be converted into biogas through biochemical conversion technologies, such as anaerobic fermentation, and then used for power generation or heating. Biogas technology can not only effectively utilize agricultural waste, reduce environmental pollution, but also provide a stable supply of renewable energy and enhance the rural energy self-sufficiency ability. In addition, as a clean and renewable energy form, photovoltaic power generation has broad application prospects in rural areas with good sunshine conditions. Combining photovoltaic power generation with biogas power generation can achieve energy complementarity and optimal allocation, and improve the stability and reliability of the energy system. However, the rural integrated energy system faces many challenges in actual operation. First, the uncertainty of photovoltaic and biogas production leads to fluctuations in clean energy supply, affecting the economic and stable operation of the energy system. Second, the operation time span of the rural energy system is long, and it is necessary to consider the energy supply-demand balance and equipment operation strategies at different time scales. Existing operation modes often lack comprehensive consideration of multi-time scale operation strategies and are difficult to achieve efficient energy utilization and optimal operation of the system. In addition, the installed capacity of photovoltaic power generation equipment in rural areas is increasing continuously, but there is a phenomenon of light abandonment during high-generation periods, resulting in insufficient new energy consumption capacity and inability to flexibly adjust the real-time supply-demand relationship between power generation and electricity consumption. Summary of the Invention

[0003] Aiming at the problems existing in the above-mentioned prior art, the present invention provides an energy balance planning method for a rural integrated energy system considering virtual energy storage, which can improve the operation efficiency and stability of the rural integrated energy system on the premise of realizing multiple energy utilization and flexible material conversion, and at the same time reduce the energy storage cost, and can provide theoretical basis and data support for the economic operation of the rural integrated energy system.

[0004] To achieve the above object, the rural integrated energy system includes a virtual energy storage scheduling part and a seasonal energy storage part in the biogas production link; the virtual energy storage scheduling part in the biogas production link includes an energy conversion device, an energy storage management station, and a virtual energy storage controller. The input end of the energy storage management station is connected to the monitoring point of the energy network, the control input end of the energy storage management station is connected to the virtual energy storage controller, and the regulation signal output end of the virtual energy storage controller is connected to the power regulation end of the energy conversion device. The energy conversion device includes a photovoltaic device, a gas turbine unit, a gas boiler, an electric boiler, and an electric energy storage device; the seasonal energy storage part includes a biogas fermentation tank and a seasonal gas storage tank. The output end of the biogas fermentation tank is connected to the input end of the seasonal gas storage tank, and the output end of the seasonal gas storage tank is connected to the input ends of the gas turbine unit and the gas boiler;

[0005] The energy balance planning method for the rural integrated energy system considering virtual energy storage specifically includes the following steps:

[0006] Step1. Based on the rural integrated energy system framework, construct a virtual energy storage model and a seasonal gas storage model in the biogas production link, and construct constraint relationships;

[0007] Step2. Based on the virtual energy storage model and constraints in the biogas production link and the seasonal gas storage model and constraints, construct a two-layer optimal configuration model considering energy balance, and construct constraint relationships. The two-layer optimal configuration model considering energy balance includes an upper layer model for optimizing investment decisions and day-ahead deterministic scheduling, and a lower layer model for optimizing the intraday output scheduling of various devices;

[0008] Step3. Based on the two-layer optimal configuration model considering energy balance, use the KKT conditions for model conversion, convert the lower layer model into the constraint conditions of the upper layer model, and generate a single-layer configuration optimization model;

[0009] Step4. Based on the single-layer configuration optimization model, perform calculation and solution to obtain the optimal equipment capacity configuration.

[0010] Furthermore, in Step1, the virtual energy storage model in the biogas production link is expressed as follows:

[0011]

[0012] In the formula: is the fermentation temperature at the t-th time period in the z-th season, representing the state of charge of the virtual energy storage; are the virtual charging and discharging powers of the virtual energy storage in the biogas production link respectively; is the heat transfer power of the external temperature difference; c dig is the specific heat capacity of the material; M dig is the total mass of the material in the fermentation tank.

[0013] Furthermore, the virtual energy storage constraints in the biogas production process include heat transfer constraints and fermentation temperature constraints, which are specifically as follows:

[0014] The heat transfer constraint is expressed as follows:

[0015]

[0016] In the formula: is the ambient temperature at time t; m dig is the hourly feed rate; K i is the comprehensive heat transfer coefficient of each part of the tank top, tank wall and tank bottom, which depends on the heat transfer coefficients of the inner and outer surfaces of the fermentation tank and the thermal conductivity of the tank wall; S i is the heat transfer area of each part of the tank top, tank wall and tank bottom;

[0017] The fermentation temperature constraint is expressed as follows:

[0018]

[0019] In the formula: are the upper and lower limits of the fermentation temperature range on a typical day in the z-th season respectively; are the fermentation temperatures at the beginning and end of a typical day in season z respectively.

[0020] Furthermore, in Step1, the seasonal gas storage model is expressed as follows:

[0021]

[0022] In the formula: are the inlet gas volume and output volume of the gas storage tank at time t; is the biogas volume in the gas storage tank at time t; η gs,los is the self-consumption rate of the gas storage tank; η gs,in 、η gs,out represent the conversion efficiencies when the gas storage tank inlet and outlet gases.

[0023] Furthermore, the seasonal gas storage constraint is expressed as follows:

[0024]

[0025] In the formula: represent the beginning and end times of a typical day in the z-th season respectively; T z is the number of typical days in the z-th season; ε gs,in 、ε gs,out are 0-1 variables, representing the charging and discharging states of the gas storage tank, restricting the gas storage tank from charging and discharging simultaneously; represent the maximum transmission rates of the gas storage tank inlet and outlet gases respectively; are the upper and lower limits of the gas storage tank capacity.

[0026] Further, in Step 2, the objective function of the upper-layer model is expressed as follows:

[0027]

[0028] In the formula: C is the annual average total cost; C inv is the annualized investment cost; is the carbon emission cost of the typical day in the z-th season; is the operating cost of the typical day in the z-th season; D is the number of seasons; C ap,i is the unit capacity investment cost of equipment i; C i is the capacity of equipment i; J is the set of equipment types, where PV, GT, GB, EB, BAT, and SGS are photovoltaic equipment, gas turbine unit equipment, gas boiler equipment, electric boiler equipment, battery equipment, and seasonal gas storage equipment respectively; r is the system discount rate; m is the operating life of the equipment; λ is the unit price of carbon emissions; is the carbon emission of the typical day in the z-th season;

[0029] The objective function of the lower-layer model is expressed as follows:

[0030]

[0031] In the formula: is the system power purchase cost; is the equipment operation and maintenance cost; is the curtailment penalty cost; is the electricity price at time t; is the electricity purchase quantity at time t; c sd,i is the unit operation and maintenance cost of equipment i, P z,t,i is the power of equipment i at time t; is the predicted photovoltaic output value at time t; is the actual photovoltaic output value at time t; and are the costs of unit photovoltaic curtailment and gas curtailment respectively; is the gas curtailment quantity at time t.

[0032] Further, the constraints of the bi-level optimization configuration model considering energy balance include power balance constraints, seasonal energy balance constraints, carbon emission constraints, and equipment investment constraints, which are specifically as follows:

[0033] The power balance constraint is expressed as follows:

[0034]

[0035] In the formula: and They are respectively the power purchase, photovoltaic, gas turbine unit, electric boiler, charging power of electric energy storage, discharging power of electric energy storage, and the magnitude of electric load; and They are respectively the thermal powers of the gas turbine unit, electric boiler, and gas boiler; is the thermal load; and They are respectively the biogas production, biogas consumption of the gas turbine unit, and biogas consumption of the gas boiler;

[0036] The seasonal energy balance constraint is expressed as follows:

[0037]

[0038] In the formula: γ z,e and γ z,h are respectively the electro-thermal energy balance indices for season z; γ z is the comprehensive energy balance index for season z; γ z,set is the energy balance target;

[0039] The carbon emission constraint is expressed as follows:

[0040]

[0041] In the formula: E c,const is the emission reduction target value;

[0042] The equipment investment constraint is expressed as follows:

[0043]

[0044] In the formula: P i,max , H gb,max , E bat,inv , G gs,max respectively represent the investment capacities of different equipment; P i,invmax , H gb ,invmax , E bat,invmax , G gs,invmax respectively represent the maximum investment and construction capacities of different equipment.

[0045] Furthermore, in Step3, when using the KKT conditions for model conversion, the lower-level model is simplified as follows:

[0046]

[0047] In the formula: x is the decision variable of the lower-level model; g z,i (x) ≤ 0 represents the inequality constraint, and m is the number of inequality constraints; h z,j (x) = 0 represents the equality constraint, and n is the number of equality constraints;

[0048] Construct the Lagrangian function of the lower-layer model, which is expressed as follows:

[0049]

[0050] Where: μ z,j is the Lagrange multiplier corresponding to the equality constraint; v z,i is the Lagrange multiplier corresponding to the inequality constraint;

[0051] Use the KKT conditions to replace the lower-layer optimization problem and transform the lower-layer model into the constraint conditions of the upper-layer model. The transformed single-layer configuration optimization model is expressed as follows:

[0052]

[0053] Furthermore, in Step4, the Gurobi solver is used for calculation and solution.

[0054] Compared with the existing technology, the energy balance planning method for the rural integrated energy system considering virtual energy storage first explores the electro-thermal-biogas coupling characteristics, constructs the RIES model (Regional Integrated Energy System Model) considering virtual energy storage in the biogas production link, then establishes a multi-time-scale hybrid energy storage system model considering virtual energy storage, electrochemical energy storage and seasonal energy storage to solve the energy spatio-temporal mismatch problem, then proposes a two-layer configuration optimization model considering seasonal energy balance, then uses the KKT conditions for model conversion to obtain a single-layer configuration optimization model, and finally performs calculation and solution based on the single-layer configuration optimization model to obtain the optimal equipment capacity configuration. The energy balance planning method for the rural integrated energy system considering virtual energy storage can realize the overall optimization of the system energy storage structure and equipment capacity by synergistically using virtual energy storage in the biogas production link and seasonal energy storage, can make the entire rural integrated energy system have better flexible regulation ability, higher operation economy and resource utilization efficiency, and lower carbon emissions, and can provide theoretical basis and data support for the economic operation of the rural integrated energy system. Brief Description of the Drawings

[0055] Figure 1 is the flow chart of the present invention;

[0056] Figure 2 is the operation architecture diagram of the rural integrated energy system in the embodiment of the present invention;

[0057] Figure 3 is the time-of-use electricity price diagram of the embodiment of the present invention;

[0058] Figure 4 is the source-load distribution curve diagram of the embodiment of the present invention, where (a) is the electricity load distribution curve diagram, (b) is the heat load distribution curve diagram, and (c) is the photovoltaic power generation output curve diagram;

[0059] Figure 5 It is a diagram showing the operation results of the electric and thermal power on typical days in four seasons of Scenario 2 of the embodiments of the present invention. Among them, (a) is the diagram of the operation results of the electric power, and (b) is the diagram of the operation results of the thermal power. Specific embodiments

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0061] As Figure 2 shown, the rural integrated energy system includes a virtual energy storage scheduling part for the biogas production link and a seasonal energy storage part; the virtual energy storage scheduling part for the biogas production link includes an energy conversion device, an energy storage management station, and a virtual energy storage controller. The input end of the energy storage management station is connected to the monitoring point of the energy network, the control input end of the energy storage management station is connected to the virtual energy storage controller, the adjustment signal output end of the virtual energy storage controller is connected to the power adjustment end of the energy conversion device, and the energy conversion device includes a photovoltaic device, a gas turbine unit, a gas boiler, an electric boiler, and an electric energy storage device; the seasonal energy storage part includes a biogas fermentation tank and a seasonal gas storage tank. The output end of the biogas fermentation tank is connected to the input end of the seasonal gas storage tank, and the output end of the seasonal gas storage tank is connected to the input ends of the gas turbine unit and the gas boiler.

[0062] As Figure 1 shown, the method for energy balance planning of the rural integrated energy system considering virtual energy storage specifically includes the following steps:

[0063] Step1. Based on the framework of the rural integrated energy system, construct a virtual energy storage model for the biogas production link and a seasonal gas storage model, and construct constraint relationships.

[0064] The virtual energy storage model for the biogas production link is expressed as follows:

[0065]

[0066] In the formula: is the fermentation temperature at time t in the z-th season, representing the state of charge of the virtual energy storage; are respectively the virtual charge and discharge powers of the virtual energy storage (DVES) in the biogas production link; is the heat transfer power due to the external temperature difference; c dig is the specific heat capacity of the material; M dig is the total mass of the material in the fermentation tank.

[0067] The constraints of the virtual energy storage in the biogas production link include heat transfer constraints and fermentation temperature constraints, which are specifically as follows:

[0068] The heat transfer constraint is expressed as follows:

[0069]

[0070] In the formula: is the environmental temperature at time period t; m dig is the feed rate per hour; K i is the comprehensive heat transfer coefficient of each part of the tank top, tank wall and tank bottom, which depends on the heat transfer coefficients of the inner and outer surfaces of the fermentation tank and the heat conduction coefficient of the tank wall; S i is the heat transfer area of each part of the tank top, tank wall and tank bottom, m 2 .

[0071] The fermentation temperature constraint is expressed as follows:

[0072]

[0073] In the formula: are the upper and lower limits of the fermentation temperature range on a typical day in the z-th season respectively; are the fermentation temperatures at the beginning and end of a typical day in season z respectively.

[0074] The seasonal gas storage model is expressed as follows:

[0075]

[0076] In the formula: are the gas inlet volume and output volume of the gas storage tank at time period t; is the amount of biogas in the gas storage tank at time period t; η gs,los is the self-consumption rate of the gas storage tank; η gs,in and η gs,out represent the conversion efficiencies when the gas storage tank is inletting and outlettng gas.

[0077] The seasonal energy storage device (SGS) is used to smooth the seasonal source-load fluctuations, then the seasonal gas storage constraint is expressed as follows:

[0078]

[0079] In the formula: represent the beginning and end times of a typical day in the z-th season respectively; T z is the number of typical days in the z-th season; ε gs,in and ε gs,out are 0-1 variables, representing the charging and discharging states of the gas storage tank, restricting that the gas storage tank cannot be charged and discharged simultaneously; represent the maximum transmission rates of the gas inlet and outlet of the gas storage tank respectively; are the upper and lower limits of the gas storage tank capacity.

[0080] Step2. Based on the virtual energy storage model and constraints of the biogas production link and the seasonal gas storage model and constraints, construct a two-layer optimal configuration model considering energy balance, and construct the constraint relationship.

[0081] The two - layer optimal configuration model considering energy balance includes an upper - layer model for optimizing investment decisions and day - ahead deterministic scheduling, and a lower - layer model for optimizing the intraday output scheduling of various devices, as follows:

[0082] The objective function of the upper - layer model is expressed as follows:

[0083]

[0084] In the formula: \(C\) is the annual average total cost; \(C\) inv is the annualized investment cost; is the carbon emission cost of the typical day in the \(z\) - th season; is the operating cost of the typical day in the \(z\) - th season; \(D\) is the number of seasons; \(C\) ap,i is the unit - capacity investment cost of device \(i\); \(C\) i is the capacity of device \(i\); \(J\) is the set of device types, where PV, GT, GB, EB, BAT, and SGS are photovoltaic devices, gas turbine unit devices, gas boiler devices, electric boiler devices, battery devices, and seasonal gas storage devices respectively; \(r\) is the system discount rate; \(m\) is the operating life of the device; \(\lambda\) is the unit price of carbon emissions; is the carbon emission of the typical day in the \(z\) - th season.

[0085] The objective function of the lower - layer model is expressed as follows:

[0086]

[0087] In the formula: is the system's electricity purchase cost; is the device operation and maintenance cost; is the penalty cost for energy curtailment; is the electricity price at time \(t\); is the electricity purchase quantity at time \(t\); \(c\) sd,i is the unit operation and maintenance cost of device \(i\), \(P\) z,t,i is the power of device \(i\) at time \(t\); is the predicted photovoltaic output value at time \(t\); is the actual photovoltaic output value at time \(t\); and are the costs of unit photovoltaic curtailment and gas curtailment respectively; is the gas curtailment quantity at time \(t\).

[0088] The constraints of the two - layer optimal configuration model considering energy balance include power balance constraints, seasonal energy balance constraints, carbon emission constraints, and device investment constraints, as follows:

[0089] The power balance constraint is expressed as follows:

[0090]

[0091] In the formula: and are respectively the power of purchasing electricity, photovoltaic, gas turbine unit, electric boiler, charging power of electric energy storage, discharging power of electric energy storage, and the magnitude of electric load; and are respectively the thermal power of the gas turbine unit, electric boiler, and gas boiler; is the thermal load; and are respectively the biogas production, biogas consumption of the gas turbine unit, and biogas consumption of the gas boiler.

[0092] is the balance index representing clean energy. Considering the outputs of photovoltaic, gas turbine unit, and gas boiler as clean energy, the corresponding seasonal energy balance constraint is shown as follows, that is, the energy balance index of each season should be greater than the given value.

[0093]

[0094] In the formula: γ z,e and γ z,h are respectively the electro-thermal energy balance indices in season z; γ z is the comprehensive energy balance index in season z; γ z,set is the energy balance target.

[0095] The carbon emission constraint is expressed as follows:

[0096]

[0097] In the formula: E c,const is the emission reduction target value.

[0098] Since investment resources (such as funds, land, infrastructure) are limited, to ensure the practical operability of the operation plan, the equipment investment constraint is set as follows:

[0099]

[0100] In the formula: P i,max 、H gb,max 、E bat,inv 、G gs,max represent the investment capacities of different equipment respectively; P i,invmax 、H gb ,invmax 、E bat,invmax 、G gs,invmax represent the maximum investment and construction capacities of different equipment respectively.

[0101] Step3. Based on the bi-level optimal configuration model considering energy balance, the KKT (Karush-Kuhn-Tucker) condition is used for model transformation.

[0102] Simplify the lower - layer model as follows:

[0103]

[0104] In the formula: \(x\) is the decision variable of the lower - layer model; \(g\) z,i (x) ≤ 0 represents the inequality constraint, and \(m\) is the number of inequality constraints; \(h\) z,j (x) = 0 represents the equality constraint, and \(n\) is the number of equality constraints.

[0105] Construct the Lagrangian function of the lower - layer model, which is expressed as follows:

[0106]

[0107] In the formula: \(\mu\) z,j is the Lagrange multiplier corresponding to the equality constraint; \(v\) z,i is the Lagrange multiplier corresponding to the inequality constraint.

[0108] Use the KKT conditions to replace the lower - layer optimization problem, transform the lower - layer model into the constraint conditions of the upper - layer model, and the transformed single - layer configuration optimization model is expressed as follows:

[0109]

[0110] Step4. Calculate and solve based on the single - layer configuration optimization model to obtain the optimal equipment capacity configuration.

[0111] The following further explains the energy - balance planning method for the rural integrated energy system considering virtual energy storage in combination with an embodiment.

[0112] Taking a state - owned farm enterprise in Jiangsu as an example, the architecture diagram of the integrated energy system of this farm enterprise is as Figure 2 shown. Select typical cycles within four seasons, with a typical daily scheduling step of 1h and 90 typical days in each season. The parameters of the fermenter are shown in Table 1 below, and the parameters of the investment equipment are shown in Table 2 below. Among them, equipment PV represents photovoltaic equipment, equipment GT represents gas turbine unit equipment, equipment GB represents gas boiler equipment, equipment EB represents electric boiler equipment, equipment SGS represents seasonal gas storage equipment, equipment BAT represents electric energy storage equipment. The gas storage tank selects 5Mpa compressed storage, the basic price of carbon trading is set at 0.25 yuan / kg, and the unit electric - heat carbon emission quota coefficients are 0.798kg / kWh and 0.385kg / kWh respectively. The system discount rate is set at 0.1. The time - of - use electricity price and source - load distribution are shown in Figure 3 、 Figure 4 . The computing platform configuration for the example verification is an R7 8845HS CPU. Model it based on the Matlab 2023b simulation platform and call the Gurobi commercial solver for solution.

[0113] Table 1 Fermenter Parameters

[0114]

[0115] Table 2 Investment Equipment Parameters

[0116]

[0117] In the northern part of Jiangsu Province in China, the four seasons are distinct. The photovoltaic power generation output curves for typical days in each season of the embodiment are as Figure 4 (shown in Figure (c)). It can be analyzed from the figure that the photovoltaic power generation output is the largest in summer and the smallest in winter. The photovoltaic power generation output in spring and autumn is similar; the photovoltaic power generation output is large during the day and zero at night. Among the air temperatures on typical operating days in each season, the temperature is the highest in summer, reaching about 20°C. Winter specifically refers to November and December. The temperature is low, but the maintenance period during shutdown in extremely cold weather is not considered. The air temperature in spring and autumn fluctuates, and the temperature difference between day and night is obvious.

[0118] To verify the feasibility and effectiveness of the proposed energy balance planning method for rural integrated energy systems considering virtual energy storage, the following three scenarios are set for comparison.

[0119] Scenario 1: Adopt the traditional carbon trading mechanism and only consider electrochemical energy storage;

[0120] Scenario 2: On the basis of Scenario 1, additionally consider the characteristics of virtual energy storage (DVES) in the biogas production link;

[0121] Scenario 3: On the basis of Scenario 2, add seasonal gas storage equipment (SGS) to achieve energy balance adjustment between seasons.

[0122] The scenario comparison is set as shown in Table 3 below.

[0123] Table 3 Scenario Comparison Settings

[0124]

[0125] Capacity configuration analysis: Use the proposed energy balance planning method for rural integrated energy systems considering virtual energy storage to solve the optimized configuration model of the electricity-thermal-biogas multi-energy coupling rural integrated energy system in the embodiment. The optimized capacity configuration results under different scenarios are shown in Table 4 below.

[0126] Table 4 Optimized Configuration Results under Different Scenarios

[0127]

[0128] As can be seen from Table 4, in Scenario 1 without considering virtual energy storage, the system needs to configure 1800 kW of batteries to meet the dynamic regulation requirements. In Scenario 2, virtual energy storage can effectively improve the collaborative complementarity ability of electricity-biogas energy, reducing the demand for battery capacity to 1200 kWh. When seasonal energy storage devices are further added in Scenario 3, the long-term energy storage capacity of the system is significantly enhanced, which can ensure the long-term stable operation of the gas turbine unit. This increases the capacity of the gas turbine unit to 700 kW. At the same time, under the action of the electricity-heat-biogas collaborative mechanism, the surplus photovoltaic power can effectively increase the biogas production through the electric boiler, and the photovoltaic capacity also increases. Through the comparative analysis of Table 4, it can be seen that the proposed two-layer optimization configuration model considering energy balance in the rural integrated energy system energy balance planning method with virtual energy storage can realize the overall optimization of the system energy storage structure and equipment capacity by synergistically using virtual energy storage in the biogas production link and seasonal energy storage. Compared with Scenario 1 and Scenario 2, the proposed two-layer optimization configuration model considering energy balance in the rural integrated energy system energy balance planning method with virtual energy storage is more balanced in equipment investment. In the configuration of energy storage equipment, mainly gas storage tanks are configured, with a configuration of 6720 m 3 , and a small amount of batteries are configured as auxiliary, with a configuration capacity of 500 kW. With the support of a reasonable configuration of the energy storage system and energy conversion equipment, the entire rural integrated energy system can have good flexible regulation ability and can achieve load following the source to a certain extent.

[0129] Economic analysis: The economic cost results under different scenarios are shown in Table 5 below.

[0130] Table 5 Economic indicators under different scenarios

[0131]

[0132] As can be seen from Table 5, compared with Scenario 1, the investment cost, operation cost, and carbon trading cost in Scenario 2 decreased by 3.17%, 14.19%, and 11.31% respectively, and the total cost decreased by 10.23%. It can be seen that after considering the virtual energy storage characteristics of the fermenter, the operation economy of the system is improved. Compared with Scenario 2, the total cost in Scenario 3 decreased by 6.35%. Among them, the operation cost decreased by 695,600 yuan, the investment cost increased by 409,000 yuan, and the carbon trading cost decreased by 125,800 yuan, indicating that the long-term operation benefits brought by seasonal gas storage can effectively offset the increase in upfront investment. From the comparison results shown in Table 5, it can be seen that the proposed two-layer optimization configuration model considering energy balance in the rural integrated energy system energy balance planning method with virtual energy storage can realize the coordinated improvement of operation economy and resource utilization efficiency by introducing virtual energy storage and seasonal gas storage technologies.

[0133] Low-carbon analysis: The carbon emission changes in different scenarios and seasons are shown in Table 6 below. The carbon emission change refers to the increase or decrease in carbon emissions in the current scenario compared to the base scenario, and Scenario 1 gives the base carbon emissions.

[0134] Table 6 Carbon emission changes in different scenarios and seasons

[0135]

[0136] As can be seen from Table 6, after considering virtual energy storage, the carbon emissions in Scenario 2 decreased by 8.3%. The carbon reduction is mostly concentrated in spring, autumn, and winter, with carbon emissions decreasing by 12.2%, 11.7%, and 8.6% respectively. In summer, due to the relatively high fermentation temperature, the carbon emissions increased slightly by 2.6%. In Scenario 3, by introducing seasonal energy storage equipment, flexible scheduling of biogas during the day was achieved, thus effectively replacing traditional energy. The carbon emissions in each season decreased by 16.4%, 7.4%, 20.2%, and 12.4% respectively, and the overall carbon emissions decreased by 14.4%. From the comparison results shown in Table 6, it can be seen that the proposed two-layer optimization configuration model considering energy balance in the rural integrated energy system energy balance planning method considering virtual energy storage has significant effects on carbon reduction.

[0137] Analysis of scheduling results: Taking the operation results of the daily electro-thermal power in the four seasons of Scenario 2 as an example, as Figure 5 shown, after considering virtual energy storage, the fermentation temperature changes continuously with the ambient temperature. During the low electricity price periods from 0:00 to 8:00 and 16:00 to 24:00, the fermentation temperature rises, and all the newly generated biogas is used for heat production by the gas boiler. During the high electricity price period from 9:00 to 16:00, the fermentation temperature naturally decreases, reducing the heating pressure. Most of the newly generated biogas is used for the gas boiler, and a small part is used for the gas turbine unit for power generation and heat production. Thus, it can be seen that the virtual energy storage model can achieve efficient conversion of different energies by flexibly regulating the energy flow direction, improving the system operation efficiency and flexibility.

[0138] As can be seen from the above, the rural integrated energy system energy balance planning method considering virtual energy storage can improve the operation efficiency and stability of the rural integrated energy system while realizing multiple utilization of energy and flexible conversion of substances, and at the same time reduce the energy storage cost, which can provide a theoretical basis and data support for the economic operation of the rural integrated energy system.

Claims

1. A method for energy balance planning of a rural integrated energy system taking into account virtual energy storage, characterized in that: The rural comprehensive energy system includes a virtual energy storage dispatching part and a seasonal energy storage part in the biogas production link; the virtual energy storage dispatching part in the biogas production link includes energy conversion equipment, an energy storage management station and a virtual energy storage controller, the input end of the energy storage management station is connected to the monitoring point of the energy network, the control input end of the energy storage management station is connected to the virtual energy storage controller, the adjustment signal output end of the virtual energy storage controller is connected to the power adjustment end of the energy conversion equipment, and the energy conversion equipment includes photovoltaic equipment, gas turbine units, gas boilers, electric boilers and electric energy storage equipment; the seasonal energy storage part includes a biogas fermentation tank and a seasonal gas storage tank, the output end of the biogas fermentation tank is connected to the input end of the seasonal gas storage tank, and the output end of the seasonal gas storage tank is connected to the input end of the gas turbine unit and the gas boiler; The energy balance planning method of the rural integrated energy system taking into account virtual energy storage specifically includes the following steps: Step 1. Based on the rural integrated energy system framework, construct a virtual energy storage model for biogas production and a seasonal gas storage model, and establish constraint relationships; Step 2. Based on the virtual energy storage model and constraints of the biogas production link and the seasonal gas storage model and constraints, a two-layer optimization configuration model considering energy balance is constructed, and a constraint relationship is constructed. The two-layer optimization configuration model considering energy balance includes an upper model for optimizing investment decisions and day-ahead deterministic scheduling and a lower model for optimizing the intraday output scheduling of various types of equipment; Step 3. Based on the two-layer optimization configuration model considering energy balance, the KKT condition is used for model conversion, the lower-layer model is converted into the constraint conditions of the upper-layer model, and a single-layer configuration optimization model is generated; Step 4. Perform calculations based on the single-layer configuration optimization model to obtain the optimal equipment capacity configuration.

2. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 1 is characterized in that: In Step 1, the virtual energy storage model of the biogas production process is expressed as follows: Where: is the fermentation temperature in the zth season and period t, representing the state of charge of the virtual energy storage; They are the virtual charging and discharging powers of the virtual energy storage in the biogas production stage; Transfer heat power to the external temperature difference; c dig is the specific heat capacity of the material; M dig is the total mass of the material in the fermentation tank.

3. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 2 is characterized in that: The virtual energy storage constraints in the biogas production process include heat transfer constraints and fermentation temperature constraints, as follows: The heat transfer constraint is expressed as follows: Where: is the ambient temperature during period t; m dig is the feed rate per hour; K i is the comprehensive heat transfer coefficient of the tank top, tank wall and tank bottom, which depends on the heat transfer coefficient of the inner and outer surfaces of the fermentation tank and the thermal conductivity of the tank wall; S i is the heat transfer area of ​​the tank top, tank wall and tank bottom; The fermentation temperature constraint is expressed as follows: Where: are the upper and lower limits of the fermentation temperature range on a typical day in the zth season; are the fermentation temperatures at the beginning and end of a typical day in season z, respectively.

4. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 1 is characterized in that: In Step 1, the seasonal gas storage model is expressed as follows: Where: is the air intake and output of the gas storage tank during period t; is the amount of biogas in the gas storage tank during period t; η gs,los is the self-consumption rate of the gas tank; η gs,in , η gs,out Indicates the conversion efficiency of the gas tank when it is inletting and outlet.

5. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 4 is characterized in that: The seasonal gas storage constraint is expressed as follows: Where: Respectively represent the start and end time of the typical day of the zth season; T z is the number of typical days in the zth season; ε gs,in , ε gs,out It is a 0-1 variable, indicating the filling and deflation status of the gas tank, and restricts the gas tank from being filled and deflated at the same time; They represent the maximum transmission rates of gas in and out of the gas tank respectively; The upper and lower limits of the gas tank capacity.

6. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 1, characterized in that: In Step 2, the objective function of the upper model is expressed as follows: J∈(PV,GT,GB,EB,BAT,SGS) Where: C is the average annual total cost; C inv is the annualized investment cost; is the typical daily carbon emission cost in the zth season; is the operating cost of a typical day in the zth season; D is the number of seasons; C ap,i is the unit capacity investment cost of equipment i; C i is the capacity of device i; J is the set of device types, where PV, GT, GB, EB, BAT, and SGS are photovoltaic devices, gas turbine equipment, gas boiler equipment, electric boiler equipment, battery equipment, and seasonal gas storage equipment, respectively; r is the system discount rate; m is the operating life of the equipment; λ is the unit price of carbon emissions; is the carbon emissions on a typical day in the zth season; The objective function of the lower model is expressed as follows: Where: The cost of purchasing electricity for the system; Equipment operation and maintenance costs; Penalty costs for abandoned energy; is the electricity price in period t; is the amount of electricity purchased during period t; c sd,i is the unit operation and maintenance cost of equipment i, P z,t,i is the power of device i in period t; The predicted photovoltaic output value during period t; is the actual photovoltaic output value during period t; and are the unit cost of abandoned solar power and abandoned gas, respectively; is the amount of abandoned gas in period t.

7. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 6 is characterized in that: The constraints of the two-level optimization configuration model considering energy balance include power balance constraints, seasonal energy balance constraints, carbon emission constraints, and equipment investment constraints, as follows: The power balance constraint is expressed as follows: Where: and They are electricity purchase, photovoltaic, gas turbine unit, electric boiler, electric energy storage charging power, electric energy storage discharging power and electric load size; and are the thermal power of the gas turbine unit, electric boiler and gas boiler respectively; is the heat load; and They are biogas production, biogas combustion in gas turbine units and biogas combustion in gas boilers; The seasonal energy balance constraint is expressed as follows: c z ≥c z,set Where: γ z,e and γ z,h are the electricity and heat energy balance index of season z; γ z is the comprehensive energy balance index of season z; γ z,set To achieve energy balance goals; The carbon emission constraint is expressed as follows: Where: E c,const is the emission reduction target value; The equipment investment constraint is expressed as follows: Where: P i,max , H gb,max 、E bat,inv , G gs,max Respectively represent the investment capacity of different equipment; P i,invmax , H gb,invmax 、E bat,invmax , G gs,invmax They represent the maximum investment and construction capacity of different equipment respectively.

8. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 1, characterized in that: In Step 3, when the KKT condition is used for model conversion, the lower model is simplified as follows: st.g z,i (x)≤0,i=1,2,3,...,m h z,j (x)=0,j=1,2,3,...,n Where: x is the decision variable of the lower model; g z,i (x)≤0 represents an inequality constraint, m is the number of inequality constraints; h z,j (x) = 0 represents an equality constraint, and n is the number of equality constraints; The Lagrangian function of the underlying model is constructed as follows: Where: μ z,j is the Lagrange multiplier corresponding to the equality constraint; v z,i is the Lagrange multiplier corresponding to the inequality constraint; Use KKT conditions to replace the lower-level optimization problem and transform the lower-level model into the constraint conditions of the upper-level model. The transformed single-layer configuration optimization model is expressed as follows: st.g z,i (x)≤0 h z,j (x)=0 v z,i g z,i (x)=0 v z,i ≥0。 9. The energy balance planning method for a rural integrated energy system taking into account virtual energy storage according to claim 1, characterized in that: In Step 4, the Gurobi solver is used for calculation and solution.

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