Day-ahead optimal scheduling method for integrated energy system considering gas-heat virtual energy storage characteristics

Abstract

The method comprises the following steps: step 1, a virtual energy storage characteristic model of a heat network pipeline is established, including a transmission time delay model part and a heat loss model; step 2, a virtual energy storage characteristic model of a gas network pipeline is established; step 3, a comprehensive energy system model containing the heat network pipeline and the gas network pipeline is constructed; and step 4, a second-order cone relaxation method is adopted to linearize the virtual energy storage characteristic model of the gas network pipeline, and a solver is used to solve a day-ahead optimization scheduling scenario of the comprehensive energy system model. The method can enhance the coupling degree among various energies of the comprehensive energy system, effectively improve the economic efficiency of system operation and improve the new energy consumption rate.

Description

Technical Field

[0001] This invention relates to the field of microgrid integrated energy system optimization scheduling, and in particular to a day-ahead optimization scheduling method for integrated energy systems that takes into account the characteristics of gas-heat virtual energy storage. Background Technology

[0002] Integrated energy systems refer to new, integrated energy systems that couple multiple energy sources such as electricity, gas, heat, and cooling through multiple stages from "source-grid-load-storage," effectively improving energy utilization efficiency and promoting sustainable energy development while meeting diverse energy demands within the system. The mutual conversion and complementary coordination between various energy flows has become an important measure to improve comprehensive energy utilization efficiency, promote large-scale new energy consumption, and implement the national "dual-carbon" strategy. Scholars have studied the characteristics of multi-energy coupling of electricity, gas, heat, and hydrogen in integrated energy systems, and constructed physical energy storage models for electricity-gas, electricity-heat, and electricity-hydrogen energy.

[0003] The aforementioned studies are all based on steady-state power flow models of heating and natural gas networks, neglecting transmission delays and losses of heat and natural gas in pipelines, with transmission time being almost instantaneous. However, in reality, the transmission speed of electrical energy is close to the speed of light, while the transmission speed of heating and natural gas systems is relatively slow, lasting from minutes to hours. Therefore, the source-load balance of heat and natural gas energy in the network is not instantaneous. The significant difference in transmission speed between electrical and thermal energy also gives them strong complementary characteristics: electrical energy is easy to transmit but difficult to store; however, the transmission characteristics of thermal energy make it easy to store but difficult to transmit, thus exhibiting complementary properties. In other words, the delay from the heat energy side to the user side gives heating network pipelines a natural heat storage characteristic, allowing the heating network to function as a virtual heat storage device.

[0004] A natural gas system consists of gas sources, natural gas pipelines, pressurization stations, and users. Compared to the power system, the natural gas system has a larger transient constant, exhibiting pipeline transmission characteristics with greater inertia and longer transmission times. Natural gas pipelines naturally function as storage devices, and the gas supply network can act as a virtual gas storage facility due to these dynamic characteristics. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a day-ahead optimization scheduling method for integrated energy systems that takes into account the characteristics of gas-heat virtual energy storage. This method can enhance the coupling degree between various energy sources in the integrated energy system, effectively improve the system's operational economy, and increase the renewable energy absorption rate.

[0006] The technical solution adopted in this invention is as follows:

[0007] A comprehensive energy system day-ahead optimization scheduling method considering gas-thermal virtual energy storage characteristics includes the following steps:

[0008] Step 1: Establish a virtual energy storage characteristic model for the heating network pipeline, including a transmission time delay model and a heat loss model; Step 2: Establish a virtual energy storage characteristic model for the gas network pipeline;

[0009] Step 3: Construct a comprehensive energy system model that includes heating network pipelines and gas network pipelines;

[0010] Step 4: Linearize the virtual energy storage characteristic model of the gas network pipeline using the second-order cone relaxation method, and solve the day-ahead optimization scheduling scenario of the integrated energy system model using the solver.

[0011] In step 1,

[0012] (1): The transmission delay model is as follows:

[0013] Because heat energy transfer is slow, a certain time delay is required before the heat source at the primary heating station can supply heat to the user side. This heat transfer delay time is related to the diameter, length, and mass flow rate of the heating medium in the heating network pipeline. The expression for the heat supply delay time of pipeline k is as follows:

[0014]

[0015] In equation (1): τ k ρ represents the energy supply delay time of pipe k; w L represents the density of the heat transfer medium in the pipe. k d represents the length of pipe k; k The diameter of pipe k is represented by m. k This represents the mass flow rate of the heat medium in pipe k.

[0016] The delay time for heat energy transported by a heat pipeline is not necessarily an integer. This invention uses the flow segmentation method to model the delay time of heat energy transport, assuming τ k Between τ k1 and τ k2 Between two integers, τ k2 =τ k1 +1,τ k1 Represents τ k The integer to be taken down; τ k2 Represents τ k The integer rounded up;

[0017] The water temperature at the end of pipe k can be expressed as:

[0018]

[0019] In formula (2): This represents the temperature at the end of pipe k at time t; This represents the temperature at the beginning of pipe k at time t; This represents the temperature at the beginning of pipe k at time t+1;

[0020] (2): The heat loss model is as follows:

[0021] During the transmission of heat through heating pipelines, heat energy is exchanged with the pipeline walls and the external environment, resulting in heat loss. Therefore, the formula for the water temperature at the end of the pipeline (k) is modified as follows:

[0022]

[0023] In equation (3): λ represents the heat transfer coefficient per unit length of the heating network pipe; C w This represents the specific heat capacity of water, 4.2 × 10³ J / (kg·℃). The temperature of the environment surrounding the pipe at time t; m k This represents the mass flow rate of the heat medium in pipe k;

[0024] In step 2, the virtual energy storage characteristic model of the gas network pipeline is as follows:

[0025] 1) The process of natural gas transmission in a natural gas pipeline can be represented by the continuity equation, momentum equation, and state equation. Its simplified model expression is as follows:

[0026]

[0027]

[0028] p = ρZRT (6);

[0029]

[0030] In the above formula: p represents the pressure of the pipeline network node; ρ represents the density of natural gas; ρ0 represents the gas density under standard conditions; v represents the gas velocity; x and t represent the spatial distance and time, respectively; D represents the pipeline diameter; R represents the gas constant; Z represents the average compressibility factor of natural gas; T represents the average temperature of natural gas in the pipeline; δ represents the coefficient of friction.

[0031] 2): The partial differential equations (4) and (5) are transformed into the algebraic form of the Weymouth equations using the finite implicit difference method: Equation (4) is the mass balance equation and Equation (5) is the gas momentum equation. Substituting Equations (6) and (7) into Equations (4) and (5), the steady-state model of the Weymouth equation is shown in Equation (8):

[0032]

[0033]

[0034] Where: Ω plD represents a collection of natural gas pipelines; ij L represents the pipe diameter of pipe ij; ij X represents the length of pipe ij; ij,t p represents the average flow rate of pipe ij at time t; i,t p represents the pressure value at the inlet i of pipe ij at time t; j,t This represents the pressure value at the head end j of pipe ij at time t; This represents the flow rate at the inlet of pipe ij at time t; Let t represent the flow rate at the outlet of pipe ij; i represents the inlet of pipe ij; and j represents the outlet of pipe ij.

[0035] When the virtual energy storage characteristics of the gas network are not considered: the inflow of natural gas equals the outflow. The left side of equation (8) uses the square of the absolute value of the average flow rate to represent the direction of natural gas flow. Using C... M The parameters in expression (8) are excluding the squared pressure term.

[0036] 3): The pipeline storage expression for virtual natural gas energy storage is shown below:

[0037]

[0038]

[0039]

[0040] In the formula: Let be the virtual energy storage capacity of pipeline ij at time t; Let be the amounts of natural gas flowing into and out of pipe ij at time t, respectively. Let R represent the average pressure in pipe ij at time t; R represents the gas constant; T represents the temperature; and Z represents the compressibility coefficient. This represents the virtual energy storage capacity of pipe ij at time t-1.

[0041] In step 3, the objective function of the integrated energy system model is to minimize the total operating cost of the system, which mainly includes electricity purchase cost, operation and maintenance cost, natural gas purchase cost, wind curtailment penalty cost, and nodal pressure difference penalty cost.

[0042] The objective function is expressed as:

[0043]

[0044] f e,t =C e,t ×P t buy,e (16);

[0045] f g,t =Cg,t ×P t buy,g (17);

[0046] f con,t =∑α k P k (18);

[0047] f wind,t =C wind,t ×(P t wind,p -P t wind (19);

[0048] In the above formula: f represents the total operating cost of the system; f e,t f g,t Let f represent the electricity cost and gas cost at time t, respectively; wind,t Table t shows the cost of wind curtailment penalty at time t; f p,t P represents the pressure penalty cost at time t. t buy,e P t buy,g Let f represent the electricity and gas purchased at time t, respectively; con,t C represents the maintenance cost at time t; e,t C g,t C wind,t These represent the electricity purchase price, gas purchase price, and wind curtailment penalty price, respectively; P t wind,p P t wind Represent the predicted and actual wind power output at time t, respectively; α k P represents the maintenance coefficient of each piece of equipment; k This indicates the output of each piece of equipment.

[0049] The constraints include power system constraints, thermal system constraints, and natural gas system constraints.

[0050] ①: Power balance constraint:

[0051] The integrated energy system should ensure that the sum of the output of system equipment (CHP, thermal power, and wind power) equals the load demand during each dispatch period.

[0052] P t CHP +P t buy +P t wind +P t GT -P t HP -Pt P2G =P t load (20);

[0053] In the formula: P t CHP P is the electrical power of CHP at time t; t buy P represents the power purchased at time t. t wind P represents the actual wind power output at time t. t GT P is the electric power of GT at time t; t HP P is the electrical power of HP at time t; t P2G P is the electrical power of HP at time t; t load,e Let t be the electrical load at time t.

[0054] ②: Upper and lower limits of generator output constraints:

[0055]

[0056]

[0057]

[0058]

[0059] In the formula: These are the upper and lower limits of CHP output, respectively. These represent the upper and lower limits of P2G output, respectively. These represent the upper and lower limits of GT output, respectively. These are the upper and lower limits of HP output, respectively.

[0060] ③: Generator set ramping constraints:

[0061]

[0062]

[0063]

[0064] In the formula: These are the minimum and maximum ramp rates for the CHP unit, in kW. These are the minimum and maximum ramp rates for the HP unit, in kW. These are the minimum and maximum ramp rates for the GT unit, respectively, in kW.

[0065] ④: Constraints at the first heat exchange station:

[0066]

[0067] In the formula: t represents the thermal power of the CHP and HP units at time t; Cw represents the specific heat capacity of water; m s The mass flow rate of the first heat exchange station; These are the supply and return water temperatures of the first heating station at time t, respectively.

[0068] ⑤: Constraints of the heat exchange station:

[0069]

[0070] In the formula: Let t be the heat load of the heat exchange station at time t.

[0071] ⑥: Temperature fusion constraints at heating network nodes:

[0072] Hot water at different temperatures flows from different pipes to the same junction and mixes. After mixing at the junction point in the heating network, the hot water flowing into different pipes after mixing has the same temperature. The temperature after the mixing can be expressed as:

[0073]

[0074]

[0075] Where: N + N - These represent the sets of pipes that start and end at point m, respectively. This represents the temperature at the outlet of the supply and return water pipes in pipe m at time t. b,t This represents the mass flow rate of the heat medium at time t; This represents the temperature at the inlet of the water supply pipe m at time t; b represents the temperature at the inlet of the return water pipe of pipe m at time t; b represents the pipe number.

[0076] ⑦: Gas network node flow balance constraints:

[0077] Considering the flow balance at the confluence node, the gas flow continuity equation for any node in the natural gas pipeline network can be expressed as:

[0078]

[0079] In equation (32): Q represents the volume of natural gas injected into node i at time t; i,t Q represents the volume of natural gas flowing into the pipeline at time t. j,t This represents the volume of natural gas flowing out from the end of the pipeline at time t; This represents the volume of the P2G injection pipe at time t; This represents the volume consumed by the load at the end of the pipe at time t; This represents the volume of natural gas consumed by the CHP at the end of the pipeline at time t;

[0080] ⑧: Combined heat and power (CHP) unit:

[0081]

[0082] In equation (33): K represents the calorific value of natural gas; η represents the calorific value of natural gas. CHP For the thermoelectric ratio of CHP operation, ε CHP The gas-to-electricity conversion ratio for CHP operation; This represents the thermal power of CHP at time t; This represents the volume of natural gas consumed by the CHP at the end of the pipeline at time t;

[0083] ⑨: Gas turbine:

[0084]

[0085] In equation (34): η GT For gas turbine operating efficiency; This represents the volume of natural gas consumed by GT at the end of the pipeline at time t;

[0086] ⑩: P2G:

[0087]

[0088] In equation (35): η P2G To improve the efficiency of the electric-to-gas conversion operation; This represents the volume of natural gas consumed by P2G at the end of the pipeline at time t;

[0089] Heat pump:

[0090]

[0091] In the formula: η HB To improve the operating efficiency of electric boilers; This represents the thermal power of HB at time t;

[0092] In step 4, the second-order cone relaxation method is used to linearize the virtual energy storage characteristic model of the gas network pipeline:

[0093] Equation (8) of the Weymouth equation contains absolute value terms and pressure square terms. When using a solver to solve the nonlinear problem of this equation, a non-iterative second-order cone relaxation method is used to linearize the nonlinear problem in the model.

[0094] The gas flow direction in the natural gas network is given in advance during day-ahead scheduling, that is, the gas flow direction is determined. Therefore, the absolute value symbol on the left side of equation (8) can be removed, and the equation can be transformed into a second-order cone inequality and a concave inequality. In addition, in order to prevent excessive relaxation caused by the expansion of the concave inequality, the node gas pressure difference penalty constraint is added during the solution process to tighten it, so as to ensure the solution accuracy. The expressions are shown in equations (37)-(39).

[0095]

[0096] X ij,t ≥C M ·(p i,t -p j,t (38);

[0097]

[0098] In equation (39): f p γ represents the penalty cost for the nodal pressure difference; γ represents the penalty coefficient for the nodal pressure difference.

[0099] In step 4, the day-ahead optimization scheduling scenario of the integrated energy system model is solved, specifically as follows:

[0100] The day-ahead optimization scheduling scenario of the integrated energy system model was solved using MATLAB and the commercial solver Cplex. To illustrate the different impacts of virtual energy storage in gas and heat pipelines on the scheduling results, the following four scenarios were set up for simulation analysis.

[0101] Scenario 1: The virtual energy storage effect of gas and heat is not considered when formulating the scheduling plan, i.e., the constraints of heat pipes and gas pipes in the model are ignored. In this case, the following new constraints are added:

[0102] Gas power balance constraints:

[0103]

[0104] Thermal power balance constraint:

[0105]

[0106] Scenario 2: When formulating the scheduling plan, only the role of thermal virtual energy storage is considered, that is, the gas pipeline constraint is ignored in the optimization model, and the constraint is increased (40).

[0107] Scenario 3: When formulating the scheduling plan, only the role of gas virtual energy storage is considered, that is, the thermal pipeline constraint is ignored in the optimization model, and the constraint is increased (41).

[0108] Scenario 4: When formulating scheduling plans, the role of virtual energy storage for gas and heat should be considered.

[0109] The steps for comprehensive energy system optimization and scheduling that take into account the characteristics of gas / heat virtual energy storage are as follows: Figure 3 As shown. First, the parameters of the integrated electricity / gas / heat energy system are read. Based on the scheduling time period step, the load forecast curve for the previous 24 hours and the wind power forecast curve are input. Second, considering the virtual energy storage characteristics of the heating pipeline, the equations of the virtual energy storage characteristics of the natural gas pipeline are linearized. Under the premise of meeting the electricity, heat, and gas needs of heating users, an optimization scheduling model is constructed with the objective function of minimizing operating costs and maximizing the renewable energy absorption rate. The commercial solver Cplex is called using MATLAB to solve the model, determining CHP, HP, GT, electricity purchase, gas purchase, and wind power output. Finally, the scheduling plan is output.

[0110] This invention provides a day-ahead optimization scheduling method for integrated energy systems that takes into account the characteristics of gas-thermal virtual energy storage. The technical effects are as follows:

[0111] 1) The integrated energy system active call for gas and heat virtual energy storage economic dispatch method of the present invention can effectively increase the flexibility of IES, effectively optimize equipment output, improve the wind power absorption rate, and reduce the total operating cost of the system.

[0112] 2) This invention uses the MATLAB Cplex solver to solve the problem, and uses the second-order cone relaxation method to linearize the gas pipeline model, reducing the difficulty of solving the problem. In addition, pipeline pressure constraints are added to ensure calculation accuracy.

[0113] 3) The significance of establishing a virtual energy storage model for the heating network pipeline in step 1 of this invention lies in improving the flexibility, reliability, and energy efficiency of the energy system, and promoting the efficient utilization of thermal energy and energy transition. The following are the key implications of establishing the virtual energy storage characteristics of the heating network pipeline:

[0114] ①. Saves construction costs and space: Traditional thermal storage equipment, such as hot water tanks and thermal accumulators, requires independent construction and occupies a large amount of space. Utilizing heating network pipelines for virtual energy storage avoids the cost of building separate storage equipment, saving construction funds. The virtual energy storage model can use existing heating network pipelines for energy storage, requiring no additional land or site. This allows for better utilization of limited space resources.

[0115] ②. Flexibility and scalability: The virtual energy storage model for heating network pipelines can store and release thermal energy by adjusting parameters such as water flow rate, velocity, and temperature. This flexibility allows the energy storage capacity to be expanded or contracted according to demand, resulting in greater flexibility.

[0116] ③. High-efficiency energy utilization: Traditional thermal storage equipment suffers energy losses during heat transmission and storage, such as heat loss and heat delay. However, the virtual energy storage model of the heating network pipeline stores thermal energy in the existing heating network pipeline, reducing losses during energy conversion and improving energy utilization efficiency.

[0117] ④. Simplified Operation and Management: The virtual energy storage model for heating network pipelines integrates the energy storage process into the heating network system, simplifying operation and management. Through pipeline control and monitoring, the storage and release of thermal energy can be adjusted in real time, improving the operational efficiency of the energy storage system.

[0118] Overall, establishing a virtual energy storage model for heating network pipelines offers advantages such as reduced construction costs, flexibility and scalability, efficient energy utilization, simplified operation and management, and optimized space utilization. These advantages help improve the economics and sustainability of thermal energy storage, optimize energy system operation, and further promote the development and utilization of green energy.

[0119] 4) The significance of establishing a virtual energy storage characteristic model for the natural gas network in step 2 of this invention lies in improving the flexibility, reliability, and sustainability of the energy system, and promoting the efficient utilization of natural gas and energy transition. The following are some important implications of establishing a virtual energy storage characteristic model for the natural gas network:

[0120] a. Reduced construction costs: Traditional gas storage facilities require separate gas storage tanks or underground gas storage facilities, which occupy a large area and require additional land and building investment. In contrast, the virtual energy storage feature of natural gas networks can be achieved through existing natural gas pipelines, eliminating the need for separate gas storage facilities and significantly reducing construction costs.

[0121] b. Enhanced Energy System Flexibility: First, traditional gas storage facilities often have limited capacity and are difficult to expand. However, the virtual energy storage feature of natural gas networks allows for flexible expansion of storage capacity through pipeline control and scheduling, adapting to changing demand over different time periods. Furthermore, virtual energy storage can serve as a peak-shaving resource for the energy system, enabling energy dispatching during supply-demand imbalances based on changes in energy demand, thus providing greater flexibility to the energy system.

[0122] c. Improve energy efficiency: Through the virtual energy storage characteristics of natural gas networks, efficient energy conversion and utilization can be achieved during the energy conversion and utilization process. Excess electricity can be converted into natural gas and stored without the need for additional gas storage equipment, thus avoiding energy waste and improving energy utilization efficiency.

[0123] d. Facilitating Energy Transition: Establishing virtual energy storage capabilities for natural gas networks helps drive energy transition towards renewable and low-carbon energy sources. It enables better integration of renewable energy, natural gas, and electricity systems, promoting multi-energy complementarity and synergistic development.

[0124] In conclusion, establishing virtual energy storage capabilities for natural gas networks can improve the flexibility, reliability, and sustainability of energy systems, promoting efficient energy utilization and energy transition. This is of great significance for achieving sustainable development of clean energy and establishing low-carbon, intelligent energy systems. Attached Figure Description

[0125] Figure 1 This is a schematic diagram of the structure of a microgrid integrated energy system in an industrial park.

[0126] Figure 2 This is a schematic diagram of the heating network pipeline and gas network pipeline structure of the present invention.

[0127] Figure 3 This is a flowchart of the solution process for the IES optimized scheduling model in this invention. Detailed Implementation

[0128] A day-ahead optimization scheduling method for integrated energy systems considering the virtual energy storage characteristics of gas and heat is proposed. First, virtual energy storage characteristic models of the heat network and gas network are established. A heating pipeline model is constructed using a flow segmentation method, and a gas network pipeline model with added pipeline pressure penalty constraints is also constructed. Second, a day-ahead optimization scheduling model for the integrated energy system is established, comprising a 6-node heat network pipeline and a 3-node gas network pipeline system. A second-order cone relaxation method is used to linearize the gas network pipeline model. Finally, the day-ahead optimization scheduling scenarios for the integrated energy system are solved using the Yalmip+cplex solver in Matlab simulation software. Numerical examples demonstrate that the proposed scheduling method is feasible and can enhance the coupling between various energy sources in the integrated energy system, effectively improving system operating economy and increasing the renewable energy absorption rate.

[0129] A comprehensive energy system optimization and scheduling method that takes into account gas-thermal virtual energy storage:

[0130] The first step is to obtain the overall load and hourly load demand data of the integrated energy system;

[0131] The second step is to set constraints based on the load and hourly load demand data from the first step; the constraints include at least the maximum load constraint, the energy supply equipment operation constraint, the virtual energy storage equipment operation constraint, and the power balance constraint.

[0132] The third step is to establish an objective function based on the constraints in the second step and the virtual energy storage characteristics of the gas-heat pipeline, with the goal of minimizing the total operating cost.

[0133] The fourth step involves constructing a planning optimization model and an operation scheduling model based on the objective function in the third step, and optimizing the comprehensive energy considering the virtual energy storage characteristics of the gas-heat pipeline. The optimization model uses equipment output as a variable to analyze the optimization effect of virtual energy storage characteristics under four different scenarios.

[0134] An example of applying this invention to an integrated microgrid energy system in an industrial park:

[0135] First, the system structure of the integrated energy system of the industrial park microgrid is given, the equipment coupling and energy flow direction are clarified, and the heterogeneous energy flow transmission characteristics are analyzed.

[0136] Secondly, a virtual energy storage model of heat and gas energy flow is constructed, and the integrated energy system optimization and scheduling method of the present invention, which takes into account gas and heat virtual energy storage, is applied.

[0137] Finally, a second-order cone programming linearization method for optimizing the nonlinear virtual energy storage model is proposed, and a solver is applied to solve it.

[0138] (I): Explanation of the integrated energy system structure in this invention:

[0139] The architecture of the integrated energy system in this invention is as follows: Figure 1 As shown, the park obtains its energy supply for production and other purposes through wind power, external electricity purchases, and external gas purchases.

[0140] The integrated energy system in this invention consists of an energy supply network, energy conversion equipment, and energy loads, etc. The energy supply network includes an electric system, a heating system, a gas supply system, etc.

[0141] Energy conversion equipment includes electrothermal coupling equipment, electrical coupling equipment, etc.

[0142] The electricity generated by renewable energy sources is converted into various forms of energy through energy conversion equipment, including combined heat and power (CHP), heat pumps (HP), power to gas (P2G), and gas turbines (GT).

[0143] Electrical energy differs significantly from heat and gas energy in terms of transmission speed. Electrical energy travels at near the speed of light, while heat and gas energy exhibit high inertia and prolonged transmission time. In this context, heat and gas energy pipelines naturally possess storage properties. Therefore, leveraging these dynamic characteristics, heat and gas energy can serve as virtual energy storage devices. The structures of heat network pipelines and gas network pipelines are as follows... Figure 2 As shown.

[0144] (II): The integrated energy system optimization and scheduling method considering gas thermal virtual energy storage based on the present invention:

[0145] The integrated energy system scheduling optimization of this industrial park optimizes the output of equipment within the system, aiming to minimize the total operating cost while also considering the effect of renewable energy consumption. The total cost includes electricity purchase cost, operation and maintenance cost, natural gas purchase cost, wind curtailment penalty cost, and nodal pressure difference penalty cost.

[0146]

[0147] In the formula: f represents the total operating cost of the system, f e,t f g,t Let P represent the electricity and gas purchase costs at time t. t buy ,e P t buy,g Let f be the amount of electricity and gas purchased at time t. con,t f represents the maintenance cost at time t. w,t This represents the cost of wind curtailment penalty at time t;

[0148] f e,t =C e,t ×P t buy,e

[0149] f g,t =C g,t ×P t buy,g

[0150] f con,t =∑α k P k

[0151] f wind,t =C wind,t ×(P t wind,p -P t wind )

[0152]

[0153] C e,t C g,t C wind,t These represent the electricity purchase price, gas purchase price, and wind curtailment penalty price, respectively.

[0154] P t wind,p P t wind These represent the predicted and actual wind power output at time t, respectively.

[0155] α kP represents the maintenance coefficient of each device. k This indicates the output of each piece of equipment.

[0156] The main constraints are as follows:

[0157] ①: Power balance constraint:

[0158] The integrated energy system should ensure that the sum of the output of system equipment (CHP, thermal power, wind power, etc.) equals the load demand of each scheduling period.

[0159] P t CHP +P t buy +P t wind +P t GT -P t HP -P t P2G =P t load

[0160] In the formula: P t CHP P is the electrical power of CHP at time t; t buy P represents the power purchased at time t. t wind P represents the actual wind power output at time t. t GT P is the electric power of GT at time t; t HP P is the electrical power of HP at time t; t P2G P is the electrical power of HP at time t; t load,e Let t be the electrical load at time t.

[0161] ②: Upper and lower limits of generator output constraints:

[0162]

[0163]

[0164]

[0165]

[0166] In the formula: These are the upper and lower limits of CHP output, respectively. These are the upper and lower limits of P2G output, respectively. These are the upper and lower limits of GT output, respectively; These are the upper and lower limits of HP output, respectively;

[0167] ③: Generator set ramping constraints:

[0168]

[0169]

[0170]

[0171] In the formula: These are the minimum and maximum ramp rates for the CHP unit, in kW. These are the minimum and maximum ramp rates for the HP unit, in kW. These are the minimum and maximum ramp rates for the GT unit, in kW.

[0172] ④: Constraints at the first heat exchange station:

[0173]

[0174] In the formula: t represents the thermal power of the CHP and HP units at time t; Cw represents the specific heat capacity of water; m s The mass flow rate of the first heat exchange station; Let t be the supply and return water temperatures of the first heating station.

[0175] ⑤: Constraints of the heat exchange station:

[0176]

[0177] In the formula: Let t be the heat load of the heat exchange station at time t.

[0178] ⑥: Temperature fusion constraints at heating network nodes:

[0179]

[0180]

[0181] Where: N + N - These represent the sets of pipes that start and end at point m, respectively. This represents the temperature at the outlet of the water supply and return pipes in pipe m at time t.

[0182] ⑦: Gas network node flow balance constraints:

[0183]

[0184] In the formula: Q represents the volume of natural gas injected into node i at time t; i,t Q represents the volume of natural gas flowing into the pipeline at time t. j,t This represents the volume of natural gas flowing out from the end of the pipeline at time t; This represents the volume of the P2G injection pipe at time t; This represents the volume of GT at the end of the pipe and the volume consumed by the load at time t.

[0185] (III): The optimized scheduling method in this invention, which takes into account gas-thermal virtual energy storage, is applied to the scheduling process and solution method of integrated energy systems:

[0186] Regarding the scheduling process, this invention sets up four operating scenarios: scheduling of integrated energy systems without considering the virtual energy storage characteristics of gas and heat, considering the virtual energy storage characteristics of heat, considering the virtual energy storage characteristics of gas, and considering the virtual energy storage characteristics of gas and heat, to illustrate the impact of the virtual energy storage characteristics of gas and heat pipelines on the optimized scheduling of integrated energy systems.

[0187] In Scenario 1, the virtual energy storage effect of gas and heat is not considered when formulating the scheduling plan; that is, the virtual energy storage characteristics of gas and heat pipelines in the model are ignored. In this case, the following new constraint, namely the steady-state equilibrium constraint, is added:

[0188] 1) Gas balance constraint:

[0189]

[0190] 2) Thermal equilibrium constraint:

[0191]

[0192] Scenario 2 and Scenario 3 respectively ignore the virtual energy storage constraints of gas and heat, and add corresponding steady-state constraints.

[0193] In terms of the solution, the optimization problem at the scheduling level is a nonlinear optimization problem. First, the nonlinear constraints involved are linearized using the second-order cone relaxation method. Then, the mature commercial solver CPLEX is called on the YALMIP platform in the MATLAB environment to solve the problem.

[0194] The present invention provides a comprehensive energy system optimization and scheduling method that incorporates gas-thermal virtual energy storage, and its technical effects are as follows:

[0195] Table 1 Comparison of gas and thermal virtual energy storage characteristics in four scenarios

[0196]

[0197]

[0198] As shown in Table 1 above, Scenario 4 takes into account the characteristics of gas and heat virtual energy storage, which improves the system's flexibility and adjustment capabilities, increases the wind power absorption rate, and effectively reduces the total operating cost of the system. Compared with Scenario 1, which does not take into account the characteristics of gas and heat virtual energy storage, the total operating cost of the system decreases by 4.47%. The research results show that the day-ahead optimization scheduling of the integrated energy system that takes into account the characteristics of gas and heat virtual energy storage proposed in this invention is feasible.

Claims

1. A method for day-ahead optimal dispatch of integrated energy system (IES) considering gas-heat virtual energy storage (GHVES), characterized in that Includes the following steps: Step 1: Establish a virtual energy storage characteristic model for the heating network pipeline, including a transmission time delay model and a heat loss model; Step 2: Establish a virtual energy storage characteristic model for the gas network pipeline; Step 3: Construct a comprehensive energy system model that includes heating network pipelines and gas network pipelines; Step 4: Linearize the virtual energy storage characteristic model of the gas network pipeline using the second-order cone relaxation method, and solve the day-ahead optimization scheduling scenario of the integrated energy system model using the solver; In step 2, the virtual energy storage characteristic model of the gas network pipeline is as follows: 1) The process of natural gas transmission in a natural gas pipeline can be represented by the continuity equation, momentum equation, and state equation. Its simplified model expression is as follows: （4）； （5）； （6）； （7）； In the above formula: p Indicates the pressure at a node in the pipeline network; Indicates the density value of natural gas; This indicates the gas density under standard conditions. v Indicates gas flow rate; x and t These represent spatial distance and time, respectively; D represents the pipeline diameter; R represents the gas constant; Z represents the average compressibility factor of natural gas; and T represents the average temperature of the natural gas in the pipeline. Indicates the coefficient of friction; 2) The partial differential equations (4) and (5) are transformed into the algebraic form of the Weymouth equations using the finite implicit difference method: Equation (4) is the mass balance equation, and equation (5) is the gas momentum equation. Substituting equations (6) and (7) into equations (4) and (5), the steady-state model of the Weymouth equation is shown in equation (8): （8）； （9）； In the formula: Represents a collection of natural gas pipelines; Indicates pipeline ij The diameter of the pipe; Indicates pipeline ij The length of the pipe; express t time ij Average flow rate in the pipeline; express t Time Pipeline ij Head i The pressure value; express t Time Pipeline ij Head j The pressure value; express t Time Pipeline ij Traffic flow at the entrance; express t Time Pipeline ij Flow rate at the exit; Indicates pipeline ij At the entrance; Indicates pipeline ij Exit; When the virtual energy storage characteristics of the gas network are not considered: the inflow of natural gas equals the outflow; the left side of equation (8) uses the square of the absolute value of the average flow rate to represent the direction of natural gas flow; The parameters in equation (8) are excluding the squared pressure term; 3) The pipeline storage expression for virtual natural gas energy storage is shown below: （10）； （11）； （12）； In the formula: for t Time Pipeline ij The amount of virtual energy storage in the pipeline; , They are respectively t Inflow and outflow pipes at all times ij Natural gas volume; for t Time Pipeline ij The average pressure; Represents the gas constant; Indicates temperature; Indicates the compression factor; express t -1 time pipeline ij The amount of virtual energy storage in the pipeline; In step 4, the Weymouth equation (8) contains an absolute value term and a pressure square term. When using a solver to solve the nonlinear problem of this equation, a non-iterative second-order cone relaxation method is used to linearize the nonlinear problem in the model. The gas flow direction in the natural gas network is given in advance during day-ahead scheduling, that is, the gas flow direction is determined, so that the absolute value sign on the left side of equation (8) can be removed and the equation can be transformed into a second-order cone inequality and a concave inequality; in addition, the node gas pressure difference penalty constraint is added during the solution process to tighten it, as shown in equations (37)-(39). (37)； (38)； (39)； In equation (39): This represents the penalty cost for the pressure difference at the nodes; This represents the penalty coefficient for the pressure difference at the nodes.

2. The day-ahead optimization scheduling method for integrated energy systems considering gas-heat virtual energy storage characteristics as described in claim 1, characterized in that: In step 1, (1) The transmission delay model is as follows: The heat transfer delay time is related to the diameter, length, and mass flow rate of the heating medium in the heating network pipes; k The expression for the power supply delay time is as follows: （1）； In formula (1): Indicates pipeline k The power supply delay time; Indicates the density of the heat transfer medium in the pipe; Indicates pipeline k Length; Indicates pipeline k The diameter; Indicates pipeline k The mass flow rate of the heat medium; The time delay of heat energy transport is modeled using the flow segmentation method, assuming... Between and Between two integers, , express The integer rounded down; express The integer rounded up; Then the pipeline k The terminal water temperature can be expressed as: （2）； In formula (2): This represents the temperature at the end of pipe k at time t; This represents the temperature at the beginning of pipe k at time t; This represents the temperature at the beginning of pipe k at time t+1; (2) The heat loss model is as follows: During the transmission of heat through heating pipes, heat energy is exchanged with the pipe walls and the external environment, resulting in heat loss. Therefore, the pipes... k The formula for terminal water temperature has been modified and is expressed as follows: （3）； In equation (3): λ represents the heat transfer coefficient per unit length of the heating network pipe; This indicates the specific heat capacity of water; This represents the temperature of the environment surrounding the pipe at time t; This represents the mass flow rate of the heat medium in pipe k.

3. The day-ahead optimization scheduling method for integrated energy systems considering gas-thermal virtual energy storage characteristics according to claim 1, characterized in that: In step 3, the objective function of the integrated energy system model is to minimize the total operating cost of the system, which mainly includes electricity purchase cost, operation and maintenance cost, natural gas purchase cost, wind curtailment penalty cost, and nodal pressure difference penalty cost. The objective function is expressed as: （15）； （16）； （17）； （18）； （19）； In the above formula: f This represents the total operating cost of the system; , They are respectively represented as t The cost of purchasing electricity and gas at any given time; surface t Constantly incurring the cost of wind curtailment penalties; express t Time-based air pressure penalty cost; , They are respectively represented as t Real-time electricity and gas purchase volume; express t Constant maintenance costs; , , These represent the electricity purchase price, gas purchase price, and wind curtailment penalty price, respectively. , They represent t Real-time wind power forecast output and actual output; Indicates the maintenance coefficient of each piece of equipment; This indicates the output of each piece of equipment.

4. The day-ahead optimization scheduling method for integrated energy systems considering gas-thermal virtual energy storage characteristics according to claim 3, characterized in that: The constraints of the integrated energy system model include power system constraints, thermal system constraints, and natural gas system constraints: ① Power balance constraints: The integrated energy system should ensure that the sum of the output of system equipment (CHP, thermal power, and wind power) equals the load demand during each dispatch period. （20）； In the formula: for t The electrical power of CHP at that moment; for t Real-time power purchase capacity; for t Real-time wind power output; for t The electric power of GT at time t; for t The power consumption of HP at any given moment; for t The power consumption of HP at any given moment; for t Electrical load at any given moment; ② Upper and lower limits of generator output: （21）； （22）； （23）； （24）； In the formula: , These are the upper and lower limits of CHP output, respectively. , These represent the upper and lower limits of P2G output, respectively. , These represent the upper and lower limits of GT output, respectively. , These are the upper and lower limits of HP output, respectively. ③ Generator set ramping constraints: （25）； （26）； （27）； In the formula: , These are the minimum and maximum ramp rates for the CHP unit, in kW. , These are the minimum and maximum ramp rates for the HP unit, in kW. , These are the minimum and maximum ramp rates for the GT unit, in kW. ④ Constraints of the first heat exchange station: （28）； In the formula: , They are respectively t Thermal power of CHP and HP units at all times; This is the specific heat capacity of water; The mass flow rate of the first heat exchange station; , They are respectively t The constant water supply and return temperatures at the primary heating station; ⑤ Constraints of heat exchange station: （29）； In the formula: for t The heat load of the heat exchange station at all times; ⑥ Temperature fusion constraints at heating network nodes: Hot water at different temperatures flows from different pipes to the same junction and mixes. After mixing at the junction point in the heating network, the hot water flowing into different pipes after mixing has the same temperature. The temperature after the mixing can be expressed as: （30）； （31）； In the formula: , Representing respectively m A collection of pipes with a starting point and an ending point; , express t time m Temperature at the outlet of the water supply and return pipelines; express t The mass flow rate of the heat transfer medium at any given time; express t time m Temperature at the inlet of the piped water supply pipeline; express t time m Temperature at the inlet of the return water pipe; Indicates the pipe number; ⑦ Gas network node flow balance constraints: Considering the flow balance at the confluence node, the gas flow continuity equation for any node in the natural gas pipeline network can be expressed as: （32）； In equation (32): express t Injecting nodes at all times i The volume of natural gas purchased; express t The volume of natural gas flowing into the pipeline at any given time express t The volume of natural gas flowing out from the end of the pipeline at any given time; express t The volume of the P2G injection pipe at any given time; , express t The volume consumed by the load at the end of the pipeline at any given time (GT); express t The volume of natural gas consumed by CHP at the end of the pipeline at any given time; ⑧ Combined heat and power (CHP) unit: （33）； In equation (33): K These are the calorific values ​​of natural gas; The thermoelectric ratio for CHP operation. The gas-to-electricity conversion ratio for CHP operation; express t Thermal power of CHP at any given time; express t The volume of natural gas consumed by CHP at the end of the pipeline at any given time; ⑨ Gas turbine: （34）； In equation (34): For gas turbine operating efficiency; express t The volume of natural gas consumed at the end of the pipeline, GT, at any given time; ⑩P2G: (35)； In equation (35): η P2G To improve the efficiency of electric-to-gas conversion; express t The volume of natural gas consumed by the P2G at the end of the pipeline at any given time; Heat pump: (36)； In the formula: To improve the operating efficiency of electric boilers; express t The thermal power of HB at any given time.

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