Energy supply adequacy analysis method for time sequence operation simulation integrated energy system

Abstract

A time sequence operation simulation integrated energy system energy supply adequacy analysis method belongs to the technical field of energy, and comprises the following steps: considering the operation characteristic difference of each subsystem and the characteristics of multi-energy coupling equipment, and modeling a power grid, a gas network, a heat supply network and system coupling equipment; considering the constraint relationship among the subsystems, and establishing an electricity-gas-heat comprehensive energy system operation optimization model; and solving and analyzing the operation optimization model of the electricity-gas-heat integrated energy system based on an annual time sequence simulation method of a segmented time sequence and a solution-free rollback mechanism, obtaining operation data and outputting a result. The method plays a supporting role in characteristic analysis of the comprehensive energy system and energy coupling characteristic analysis of the comprehensive energy system under the long-term scale.

Description

Technical Field

[0001] The present invention belongs to the field of energy technology, and in particular relates to a method for analyzing energy supply adequacy of an electricity-gas-heat integrated energy system based on time sequence operation simulation technology. Background Art

[0002] As energy demand continues to grow, environmental pollution and energy crises are becoming increasingly prominent. Vigorously developing renewable energy is an effective way to achieve a low-carbon transformation of the power system. At the same time, achieving multi-energy coupling and interconnected complementarity with natural gas and heating systems can further improve energy efficiency and promote the absorption of renewable energy. The operating characteristics of the various subsystems of the electricity-gas-heat integrated energy system vary significantly, and there are also coupling effects between different energy sources, posing new challenges to the system's energy supply security. As a key component of energy supply security, conducting energy supply adequacy analysis for the electricity-gas-heat integrated energy system is of great significance to its development.

[0003] With the rapid development of society and the economy, the contradiction between the energy supply model, which is primarily based on traditional non-renewable fossil energy, and energy demand and environmental pollution has become increasingly prominent, necessitating a cleaner, more efficient, and more sustainable form of energy utilization. Simultaneously, the penetration rate of renewable energy continues to increase. Traditional isolated energy systems focus solely on optimizing energy utilization within a single system, failing to meet system flexibility requirements. Integrated energy systems (IES) can effectively integrate multiple energy systems, improving operational efficiency and flexibility, and providing solutions for the coordinated planning, optimized operation, and interactive response of various energy sources. They have garnered widespread attention in the energy sector both domestically and internationally. The electricity-gas-heat integrated energy system is a typical example of an integrated energy system. The electricity system enables large-scale, long-distance energy transmission; natural gas and thermal energy are common forms of energy utilization and can be transmitted across regions via pipeline networks. Electricity, natural gas, and heating systems exhibit distinct operational characteristics. Production simulation analysis of these systems can reflect the coupled and complementary nature of integrated energy systems. Due to the large and unpredictable fluctuations in renewable energy and load, traditional production simulation methods based on continuous load curves are insufficient.

[0004] Therefore, the prior art urgently needs a new technical solution to solve the above problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for analyzing the energy adequacy of an integrated energy system by time-series operation simulation, which plays a supporting role in the analysis of the characteristics of the integrated energy system on a long-term scale and the analysis of the energy coupling characteristics of the integrated energy system.

[0006] A method for analyzing energy adequacy of a time-series operation simulation integrated energy system includes the following steps, which are performed in sequence:

[0007] Step 1: Considering the differences in operating characteristics of each subsystem and the characteristics of multi-energy coupling equipment, model the power grid, gas grid, heat grid and system coupling equipment;

[0008] Step 2: Consider the constraints between each subsystem and establish an operation optimization model for the electricity-gas-heat integrated energy system;

[0009] Step 3: Based on the segmented time series and the full-year time series simulation method with no solution rollback mechanism, the operation optimization model of the electricity-gas-heat integrated energy system is solved and analyzed, and the operation data is obtained and the results are output.

[0010] The power grid modeling in step 1 adopts the DC power flow equation; the gas network modeling adopts the pipeline transportation equation, the pipeline material balance equation and the gas state equation; the heat network adopts the thermal conductivity equation; the system coupling equipment modeling includes the power-to-gas model, the cogeneration unit chemical energy to electrical energy and thermal energy model, the gas unit natural gas to electrical energy model and the electric heating power-to-heat energy coupling model.

[0011] The operation optimization model of the electricity-gas-heat integrated energy system described in step 2 is:

[0012]

[0013] Where, T N is the total number of time periods, which is 8760 hours when running the simulation according to the annual time series; N g is the number of thermal power units, k i,s and λ i,s is the slope and intercept of each segment after the linearization of the cost of the i-th thermal power unit, N is the technical output of the i-th thermal power unit at time t; Gas is the number of gas sources, is the unit gas consumption cost, is the gas consumption mass flow rate of the i-th gas source at time t; N W is the number of wind farms, is the wind curtailment penalty coefficient of the i-th wind farm, is the amount of wind curtailment at the i-th wind farm at time t; N L is the number of electrical load nodes, is the load shedding penalty coefficient of the i-th electrical node load, is the load shedding power of the i-th electrical node at time t; N CHP is the number of cogeneration units; and are the electricity production cost coefficient and heat production cost coefficient of the i-th cogeneration unit at time t; and are the electrical output and heat output of the i-th cogeneration unit at time t respectively.

[0014] The constraints satisfied by the operation optimization model of the electricity-gas-heat integrated energy system are: active power balance constraint of the power system, maximum transmission capacity constraint of branch flow lines, unit ramping constraint, generator start-stop constraint, generator unit spinning standby constraint, pipeline material flow and node gas pressure constraint, and node energy balance constraint.

[0015] The method for solving and analyzing the operation optimization model of the electricity-gas-heat integrated energy system based on the full-year time series simulation method based on the segmented time series and the no-solution rollback mechanism described in step 3 is:

[0016] Sub-step 1: Let b = 0, set the state of the last hour of the day eb-1 as the initial state, and simulate the operation from eb to e+f days;

[0017] Sub-step 2: If there is a solution within the period from eb to e+f days, save the simulation results; if there is no solution, roll back one day, i.e. set b = b+1 and return to sub-step 1;

[0018] Sub-step 3: If the e+fth day is the last day of the simulation cycle, terminate the sequential simulation and output the simulation results of the entire simulation cycle; otherwise, let e=e+f, b=0, and return to sub-step 1.

[0019] Where e is the starting day of the current solution step in the rollback mechanism, b is the number of days rolled back under the current no-solution rollback mechanism, and f is the number of days simulated forward for each step in the time series simulation.

[0020] Through the above-mentioned design scheme, the present invention can bring the following beneficial effects: a method for analyzing the energy supply adequacy of a time-series operation simulation integrated energy system, which supports the analysis of the characteristics of the integrated energy system at a long-term scale and the analysis of the energy coupling characteristics of the integrated energy system; the time-series operation simulation COS can simulate the operation of the power system throughout the year with fine time resolution and detailed operation constraints. It is a core component of tools such as power supply planning, renewable energy consumption, and seasonal energy storage, and is an important analysis method for production simulation of integrated energy systems; using the time-series operation simulation solution method to analyze the integrated energy system will take into account various dynamic factors such as the volatility of renewable energy, the time-varying nature of the load, the changes in the operating status of the equipment, and time coupling constraints in the actual operation of the system, which can more accurately reflect the performance of the integrated energy system in actual operation throughout the year, and provide a scientific basis for system design, operation optimization, decision support, etc.

[0021] The coupling and complementarity of the electricity, gas and heat energy subsystems improves the system's economic benefits and system safety, but may also cause problems to spread across energy subsystems. It is necessary to design a variety of specific scenario analyses such as energy supply shortages and equipment failures, consider a variety of functional adequacy analysis indicators, and analyze the rules based on the timing operation simulation results of the electricity, gas and heat integrated energy system to provide guidance and improvements. Carrying out energy supply adequacy analysis can reveal the impact of multi-energy system coupling on energy supply, which plays a vital role in ensuring energy supply security, revealing system safety mechanisms, and improving system reliability and stability. It is of great significance to promoting energy transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0023] Figure 1 This is a flow chart of a method for analyzing energy adequacy of a time-series simulation integrated energy system according to the present invention.

[0024] Figure 2 This is a schematic diagram of the hot spot load interval of the condensing unit according to a specific implementation method of the time-series operation simulation integrated energy system energy supply adequacy analysis method of the present invention.

[0025] Figure 3 This is a schematic diagram of the rollback mechanism of a specific implementation method of the present invention for analyzing the energy adequacy of a time-series simulation integrated energy system.

[0026] Figure 4 This is a schematic diagram of a solution-free rollback for a specific implementation method of a time-series operation simulation integrated energy system energy supply adequacy analysis method of the present invention.

[0027] Figure 5 This is a schematic diagram of an energy supply adequacy analysis model for a specific implementation method of the present invention for analyzing energy supply adequacy of a time-series operation simulation integrated energy system.

[0028] Figure 6 This is a schematic diagram of the grid topology of an integrated energy system, which is a specific implementation method of the time-series operation simulation integrated energy system energy supply adequacy analysis method of the present invention. DETAILED DESCRIPTION

[0029] A time series operation simulation integrated energy system energy supply adequacy analysis method, such as Figures 1-6 As shown, the following steps are included:

[0030] Step 1: Considering the differences in operating characteristics of each subsystem and the characteristics of multi-energy coupling equipment, model the power grid, gas grid, heat grid and system coupling equipment.

[0031] 1) Grid modeling

[0032] In ultra-high voltage networks, line resistance is much smaller than line reactance and can be ignored. During normal power system operation, node voltages are typically near rated voltages, and the voltage phase angle difference between the two ends of a line is minimal. Therefore, DC power flow equations can be considered for grid modeling.

[0033] Therefore, formula (1) can be simplified as:

[0034]

[0035] Where: x ij is the branch reactance, P ij is the active power and reactive power of the line between node i and node j, Q ij is the reactive power of the line between node i and node j, θ i and θ j are the voltage phase angles at node i and node j, respectively. Equation (1) is a set of linear equations. Applying Kirchhoff's law to node i yields:

[0036]

[0037] For an n-node network, it can be written in matrix form as:

[0038] P SP =Bθ (3)

[0039] Where: P SP is the node active injection power matrix; B is an n×n matrix, and its diagonal elements and non-diagonal elements are as follows.

[0040]

[0041] 2) Gas network modeling

[0042] The fluid motion of natural gas within a pipeline is described by the pipeline momentum equation, the pipeline material balance equation, and the gas state equation. The pipeline momentum equation (i.e., the Navier-Stokes equation) describes the momentum transfer state of natural gas within the pipeline, the material balance equation describes the flow of materials within the natural gas pipeline, and the gas state equation constructs the relationship between natural gas pressure and density using the speed of sound c, as shown in Equations (5)-(7).

[0043]

[0044] p=c 2 ρ (7)

[0045] Where: ρ and ρ αThey represent the gas density at the horizontal plane and at an angle α thereto, in kg / m3; p represents the natural gas pressure, in Pa; g represents the acceleration due to gravity, in m / s2; ω represents the gas flow rate, in m / s; d represents the pipe diameter, in m; and λ represents the friction coefficient.

[0046] Equations (5)-(7) are based on fluid mechanics and use a set of complex partial differential equations to describe the transmission characteristics of natural gas in pipelines. However, the partial differential equations will bring great difficulties to solving the model example and need to be simplified. First, it is assumed that all natural gas pipelines in the natural gas subsystem are at the same height level, that is, α = 0 in Equation (5), g(ρ-ρ a )sinα term can be ignored; at the same time, since the slow flow process in the natural gas pipeline is much smaller than the speed of sound, the convection term Can also be ignored. In addition, The term is a nonlinear term that includes the product of the quadratic term of the natural gas flow rate w and the density ρ. The nonlinear term will greatly increase the complexity of the example. In order to linearize the model, the average natural gas flow rate is used to approximate the quadratic term in the above formula, that is:

[0047]

[0048] After the above simplifications and merging, only three items remain in formula (5):

[0049]

[0050] Note that the natural gas mass flow rate M has the following relationship with the natural gas density ρ, flow velocity ω and pipeline cross-sectional area A:

[0051] M=ρωA (10)

[0052] Where: natural gas material flow rate M, unit is kg / s.

[0053] To facilitate subsequent processing, the gas flow rate ω and gas density ρ in equations (5)-(7) are converted into natural gas material flow M and node pressure p. Specifically, the density ρ in the equation is expressed in terms of pressure p using the state equation, and the material flow M is used to replace the flow rate ω in the momentum equation and pipeline material balance equation. The simplified natural gas momentum transfer equation and pipeline material balance equation can be obtained:

[0054]

[0055]

[0056] After simplifying and transforming the partial differential equations for gas flow, they must be converted to differential form to avoid the added complexity of the model due to differential terms. The Wendroff differential form is a computationally efficient explicit method that does not require solving linear or nonlinear systems of equations while maintaining good stability and second-order accuracy. The present invention employs the Wendroff differential form to approximate the solutions to the partial differential equations (11) and (12).

[0057]

[0058] Where Δx and Δt represent the spatial and time steps respectively. The Wendroff difference form has second-order accuracy and the error is O(Δt 2 +Δx 2 ).

[0059] The present invention sets the spatial step length Δx to be variable. For each section of the pipeline with a length of Δx, the actual situation inside the pipeline does not need to be considered. Therefore, i+1 is replaced by j at the other end of the pipeline, and Δx is replaced by the pipeline length L. ij Substitute (13) into (11) and (12) to get the following expression:

[0060]

[0061] In Equations (14) and (15), the time step Δt is retained in the linearized equations to reflect the dynamic effects of the airflow, which is determined by the specific time scale selected for the model.

[0062] 3) Heat network modeling

[0063] Similar to natural gas systems, heating systems use fluids as the carrier for energy transmission. Therefore, heat transmission also exhibits time-delay characteristics. When considering the dynamics of heat transmission, a dynamic model of the heating system can be established. Without loss of generality, this paper first establishes a dynamic model of the heating network and then simplifies it based on the actual conditions of the integrated energy system being studied.

[0064] District heating networks are typically buried underground, with each pipe section typically made of steel and surrounded by a layer of insulation. The state inside the heating network pipes can be described by the heat conduction equation:

[0065]

[0066] Where: ρ w is the medium density; C p is the specific heat capacity of the medium; S k is the cross-sectional area of the pipe k; γ w is the thermal conductivity of the medium; β k is the unit thermal resistance of the pipeline k; is the ambient temperature outside the pipeline k; and are the mass flow rate and medium temperature of pipe k at the time t, which is the distance from the starting point l to the pipe. The second-order term in the formula is caused by the heat conduction effect between adjacent fluid elements. Compared with other coefficients, It is much smaller, and the temperature of hot water along the transmission pipeline will not change suddenly, so the second-order partial derivative can be discarded.

[0067] Simplifying the variables in formula (16), it can also be expressed as follows:

[0068]

[0069] Where: v, c, and ρ are the flow rate, specific heat capacity, mass flow rate and density of the thermal fluid respectively, T a is the ambient temperature outside the pipeline, R is the thermal resistance of the pipe section, t and x are time and position variables respectively.

[0070] Assuming that the heating network is regulated, The mass flow conditions are the same everywhere in the pipeline.

[0071] Equation (16) can be converted into a differential equation with constant coefficients to obtain the general solution:

[0072]

[0073] Where: C1 and C2 are coefficients.

[0074]

[0075] T(0,t)=ψ(t),t≥0 (20)

[0076] Given the above initial state of the pipeline and waiting for the general solution, the temperature at each time and position in the pipeline can be characterized.

[0077] When analyzing based on the hourly scale, it is only required to reflect the constraint relationship between the head and end of each heat network pipeline at a certain time section. This is an extension of the situation in formula (19). The equation is thus simplified to:

[0078]

[0079] Where: T f is the temperature at the beginning of the pipe, T e is the temperature at the end of the pipe, T a is the ambient temperature outside the pipe. The exponential term parameters are determined by the parameter characteristics of each pipe itself.

[0080] 4) Coupled device modeling

[0081] Power-to-gas (P2G) units produce natural gas by consuming electricity, thus transferring energy from the power system to the natural gas system. The relationship between electricity consumption and methane production is as follows:

[0082]

[0083] Where: M P2G P is the natural gas flow converted from the electricity consumed per unit time; P2G is the electrical power consumed by the equipment per unit time; μ P2G is the conversion efficiency of the P2G system; HV is the calorific value of natural gas, usually taken as λ HV =3.4MBtu / MWh.

[0084] Combined heat and power (CHP) units consume the chemical energy of fuel and can simultaneously convert it into electricity and heat, thus providing energy to both the power system and the heating system. The extraction-condensing CHP unit is selected as the analysis object. The thermal load range of a typical extraction-condensing unit is as follows: Figure 2 As shown, the operating conditions of the condensing unit are limited to the rectangular area formed by polygon ABCD.

[0085] The extraction-condensing cogeneration unit has the following constraints:

[0086]

[0087] Where: and are the electrical output and thermal output of the nth cogeneration unit at time t; W max 、W min , Q max and Q min They are the maximum and minimum electric power when the unit is not supplying heat, and the maximum heating capacity and heating capacity of the unit at the time of minimum electric power. k1 and k2 represent the slopes of the AB / CD segment and the BC segment respectively.

[0088] Gas-fired units (GFUs) consume natural gas to produce electricity, enabling energy transmission from the natural gas system to the power system. Using natural gas from the gas grid as fuel for a GFU, the relationship between the natural gas consumed by a GFU and the amount of electricity it can generate can be simplified to a linear relationship:

[0089]

[0090] Where, is the active power provided by the i-th gas generator set at the corresponding node in the power system at time t; η G,iIt represents the energy conversion efficiency of the i-th gas turbine unit, which is determined by factors such as the gas turbine heating design, operating conditions, fuel type and environmental conditions, and the unit is MW·s / kg; M i,t is the natural gas mass flow rate consumed by the i-th gas generator set at the corresponding node in the natural gas system at time t.

[0091] Power-to-heat (P2H) equipment uses electricity to generate heat, achieving energy coupling from the power system to the heating system. The relationship between the power consumption of an electric boiler and its heat output can be simplified to a linear relationship:

[0092]

[0093] Where, is the heat output of the i-th electric boiler; P i EB is the power consumption of the i-th electric boiler; is the electric-to-heat conversion efficiency of the i-th electric boiler, which depends on factors such as the boiler's own structure and operating conditions.

[0094] Step 2: Considering the constraints between subsystems, establish an operation optimization model for the electricity-gas-heat integrated energy system.

[0095] Make full use of multi-energy coupling equipment such as power-to-gas units, cogeneration units, gas units, electric boilers, etc. to achieve coordinated operation and absorption of renewable energy, and enhance the energy coupling characteristics advantages of multi-energy coupling systems.

[0096] The optimization goal of the electricity-gas-heat integrated energy system is to minimize the system operating costs as much as possible.

[0097]

[0098] Where: T N is the total number of time periods, which is 8760 hours when running the simulation according to the annual time series; N g is the number of thermal power units, k i,s and λ i,s is the slope and intercept of each segment after the linearization of the cost of the i-th thermal power unit, N is the technical output of the i-th thermal power unit at time t; Gas is the number of gas sources, is the unit gas consumption cost, is the gas consumption mass flow rate of the i-th gas source at time t; N W is the number of wind farms, is the wind curtailment penalty coefficient of the i-th wind farm, is the amount of wind curtailment at the i-th wind farm at time t; N L is the number of electrical load nodes, is the load shedding penalty coefficient of the i-th electrical node load, is the load shedding power of the i-th electrical node at time t; N CHP is the number of cogeneration units; and are the electricity production cost coefficient and heat production cost coefficient of the i-th cogeneration unit at time t; and are the electrical output and heat output of the i-th cogeneration unit at time t respectively.

[0099] At any time, the active power in the power system should be balanced:

[0100]

[0101] Where: N g 、N Gas 、N CHP 、N W 、N P2G 、N P2H 、N load are the total number of thermal power units, gas units, cogeneration units, wind farms, power-to-gas units, electric boilers, and electric load nodes; Output of the i-th thermal power unit, gas unit, cogeneration unit, and wind turbine at time t; The electric power consumed by the i-th power-to-gas unit at time t; The electric power consumed by the i-th electric boiler at time t; is the wind power abandoned by the i-th wind turbine at time t; is the load power of the i-th load node at time t; is the load shedding power of the i-th load node at time t.

[0102] Branch power flows are limited by the maximum transmission capacity of the line:

[0103]

[0104] Where: is the upper limit of the transmission capacity of the transmission line km.

[0105] The ramping constraint limits the change in output power of each active generator set within adjacent time intervals. At the same time, the constraint should avoid nonlinearity introduced by absolute value terms and ensure that the power change amplitude during generator startup and shutdown is not affected by ramping and ramping rate constraints. Therefore, in addition to wind turbines, the ramping constraints for other generator sets are considered as follows:

[0106]

[0107] Where: is the upper limit of the ramp rate of the i-th unit, is the upper limit of the ramp rate of the i-th unit. Taking formula (29) as an example, when the thermal power unit is in the shutdown state at the current moment, the increase compared to the previous moment must be less than or equal to The constraint has no effect at this time.

[0108] The active power generation of each generator set should be considered in the unit combination and limited to the output range. The time sequence operation simulation problem is accompanied by the start and stop combination of the units, with the following restrictions:

[0109] P i min ×u i,t ≤P i,t ≤P i max ×u i,t (31)

[0110] Where: P i min and P i max are the maximum and minimum technical outputs of the i-th unit respectively; P i,t is the output of the i-th unit at time t. U i,t is the start / stop status of the i-th unit at time t, which is a 0-1 variable. The value of the unit is 1 when it is on, and 0 when it is off.

[0111] The spinning reserve constraint ensures that the power subsystem has a minimum spinning reserve to avoid unexpected events and forecast errors that affect system operation. In the case of time sequence operation simulation, the spinning reserve constraint of the generator set is considered as follows:

[0112]

[0113] Where: and is the positive and negative reserve requirement of the system in time period t.

[0114] When the generator set is started and stopped, due to the limitations of the unit's own structure, the ramp power has different power constraints than when the unit is in normal operation. The start-stop power constraints are considered as follows:

[0115]

[0116] Where: is the upper limit of the starting power of the i-th unit, is the upper limit of the shutdown power of the i-th unit. Taking formula (35) as an example, when the unit is turned on at the previous moment and shut down at the next moment, the operating power of the unit at the previous moment is required to be no greater than

[0117] After a generator set is started or shut down, it must wait for a certain amount of time to allow various equipment and components to fully operate, cool down, and eliminate residual charges, etc., to avoid damage to the generator set due to frequent starts and stops. The minimum continuous start and stop time required for MW-class thermal power units is usually several hours. The constraints considered are as follows:

[0118] y i,t -z i,t =u i,t -u i,t-1 (36)

[0119] y i,t +z i,t ≤1 (37)

[0120]

[0121] Where: T i on is the minimum continuous operation time of the i-th unit, T i off is the minimum continuous downtime of the i-th unit. i,t and z i,t is a 0-1 variable used to assist the constraint relationship.

[0122] At the source node in a natural gas system, the gas properties and pressure remain stable:

[0123]

[0124] Where: K s is the source node set; p si,0 It is the constant value of the gas pressure at the source node, which is related to the output status, structure and other factors of the gas source node.

[0125] For the intermediate nodes, the node air intake and node air output remain equal at any time, that is, the node material balance constraint:

[0126]

[0127] Where: M DK,t is the natural gas load of node k at time t, including gas consumption by gas turbines, combined heat and power (CHP) turbines, conventional load gas consumption, and power-to-gas (PG) turbine production. (.)k represents the set of natural gas pipelines with end node k, and k(.) represents the set of natural gas pipelines with front node k.

[0128] The pipe flow rate at the load node is given by the mass flow rate at the end of the pipe connected to it. The constraint form is as follows:

[0129]

[0130] Where: represents the mass flow of load node j at time t; M ij,t It represents the mass flow rate of the pipe connected to the load node close to the load side at time t.

[0131] There should also be upper and lower limit constraints for pipeline material flow and node air pressure:

[0132]

[0133] Where: and Indicates the upper and lower limits of the air pressure at node i, and Indicates the upper and lower limits of the flow rate at the head end of pipe ij, and Indicates the upper and lower limits of the flow rate at the end of pipeline ij.

[0134] For any heating node, the inflow is equal to the outflow.

[0135] Im=m q (46)

[0136] Where: I is the node-branch correlation matrix of the heating network; m is the pipeline flow vector; m q Inject traffic into the node.

[0137] When the medium flows in the heating network, friction causes pressure head loss. However, similar to Kirchhoff's voltage law, in a closed-loop heating network, the sum of the pressure head losses of the medium is zero. The constraint form is as follows:

[0138] h f =K h m|m| (47)

[0139] ∑h f =0 (48)

[0140] Where: h f is the pressure head loss vector of the medium; K h is the resistance coefficient matrix of the pipeline, which is determined by the pipeline flow rate and the internal parameters of the pipeline.

[0141] The node heat power balance constraint describes the relationship between the source and load node pipeline flow, temperature difference and heat power.

[0142]

[0143] Where: Q load and Q source are the thermal powers of load and heat source nodes respectively; Cp is the specific heat capacity of the medium; and is the supply water temperature and return water temperature of the load node; and are the supply water temperature and return water temperature of the source node.

[0144] Based on the mass regulation mode, all branches should operate at their rated mass flow, and the node energy balance constraints are as follows:

[0145]

[0146] Where: is the temperature of hot water flowing from the i-th pipe into the n-th node; is the temperature of hot water flowing from the nth node into the jth pipe; It is the set of all pipes directly connected to the nth node and with water flowing in or out.

[0147] The temperatures of nodes in the supply and return pipes are subject to upper and lower limits:

[0148]

[0149] Where: and Indicates the upper and lower limits of the hot water temperature in the water supply pipe at node n, and Indicates the upper and lower limits of the hot water temperature in the return pipe at node n.

[0150] Step 3: Perform solution analysis based on the full-year time series simulation method with segmented time series and no solution rollback mechanism, obtain operation data and output results.

[0151] The unsolvable rollback mechanism in the full-year time series simulation can be divided into multiple sub-steps. The rollback mechanism flow chart is as follows: Figure 3 shown.

[0152] Sub-step 1: Let b = 0, set the state of the last hour of the day eb-1 as the initial state, and simulate the operation from eb to e+f days;

[0153] Sub-step 2: If there is a solution within the period from eb to e+f days, save the simulation results; if there is no solution, roll back one day, i.e. set b = b+1 and return to sub-step 1;

[0154] Sub-step 3: If the e+fth day is the last day of the simulation cycle, terminate the sequential simulation and output the simulation results of the entire simulation cycle; otherwise, let e=e+f, b=0, and return to sub-step 1.

[0155] Where e is the starting day of the current solution step in the rollback mechanism, b is the number of days rolled back under the current no-solution rollback mechanism, and f is the number of days simulated forward for each step in the time series simulation.

[0156] For full-year time series simulations, a segmented time series approach, combined with a rollback mechanism, accelerates model solution speed while avoiding unsolvable problems. It was also noted that varying the number of short periods used to segment the full-year time series results in varying total solution time and rollback times, leading to varying accuracy of the model results.

[0157] Taking the start-up and shutdown of a unit between two short cycles as an example, the rollback mechanism can be used to deal with the unsolvable situation as follows: Figure 4 As shown in the figure. The unit was shut down during the last two moments of the previous short cycle. Due to the minimum start-stop time constraint, the unit must still be shut down at the start of the next short cycle. Because the next cycle may experience increased load or a sudden drop in renewable energy output, the unit must be immediately turned on to achieve a balanced solution for the integrated energy system. This conflicts with the minimum start-stop time constraint. Therefore, we roll back from m = 0 to m = 2, at which point the unit can be turned on at the start of the next short cycle, ensuring system balance.

[0158] Step 4: Based on the output results, combined with the energy supply adequacy index of the electricity-gas-heat integrated energy system, analyze under multiple scenario examples

[0159] The energy supply adequacy analysis model used is as follows: Figure 5 As shown in the figure, a time-series operation simulation is conducted based on system modeling and design scenarios, and the solution results are evaluated. The adequacy indicator is extended from daily and annual load loss to daily and annual load loss rates. Multiple indicators, such as coupling equipment utilization, new energy consumption rate, system operating costs, and solution time, are also set to analyze and evaluate the operation of the integrated energy system.

[0160] In scenarios such as failures or maintenance, some thermal power units must be shut down during maintenance schedules. Conventional power systems must maintain power balance by curtailing wind and load. However, due to the inherent storage characteristics of the gas grid and the thermal inertia of the heating system, coupled devices can be used to supplement energy supply to the power system. The annual load loss rate is defined to measure the compensation of load shortfalls over the entire year. The expression is as follows:

[0161]

[0162] Where: is the load of all power nodes in the 8760 period, The actual load shedding at all power nodes during the 8760 hour period. A smaller value indicates less load shedding, and a more secure power system energy supply.

[0163] To further consider the specific load loss time, the daily load loss rate can be considered. On this basis, the annual load loss distribution diagram under different scenarios is analyzed to find the relationship between the load loss problem and the specific time node. The daily load loss rate formula is as follows:

[0164]

[0165] Where: is the load of all power nodes in the selected period of one day, The actual load shedding across all power nodes during the selected day. A smaller value indicates less load shedding, and a more secure power system.

[0166] Conventional power systems may need to curtail wind power during periods of high wind power generation due to real-time power balancing requirements. In this case, the energy storage characteristics of the gas and thermal energy subsystems can be utilized to convert excess wind power into the heating system or natural gas system, thus avoiding wind curtailment. The new energy absorption rate is defined as follows:

[0167]

[0168] Where: is the total output of all wind turbines during the 8760 period, The actual wind curtailment rate of all wind turbines during the 8760 period. The larger the value, the higher the wind power absorption rate and the better the system energy supply adequacy.

[0169] The higher the utilization rate of multi-energy coupling equipment in the system, the better the economic benefits. To describe the degree of coupling and complementarity of the integrated energy system, the comprehensive utilization rate is defined as the ratio of the usage of each coupling equipment to its total installed capacity, as shown in the following formula:

[0170]

[0171] Where: and These are the theoretical maximum outputs of gas-fired units, power-to-gas units, combined heat and power (CHP) units, the thermal portion of CHP units, and electric boilers, respectively. Assuming the total installed capacity of multi-energy coupling equipment is fixed, larger values in different scenarios indicate higher utilization of the coupling equipment and greater coupling and complementary effects in the integrated energy system.

[0172] A specific embodiment: the system grid topology is as follows: Figure 6 As shown in Figure 1, a 24-node power subsystem, a 13-node natural gas subsystem, and a 16-node heating subsystem are coupled. The analysis is based on the annual electricity, gas, and heat load curves. The detailed configuration parameters for the coupled devices in this example are shown in Table 1.

[0173] Table 1 Detailed parameters of coupling equipment

[0174]

[0175] In order to verify the effectiveness of the proposed model, it is combined with multiple scenarios for comparison in terms of energy supply adequacy. The comparison scenarios are introduced as follows.

[0176] Scenario 1: Baseline operation. In this scenario, the multi-energy system operates in a coupled manner, with primary and secondary peaks in electricity load in winter and summer. The heating network does not operate during the non-heating season, and there are periods of high wind power generation.

[0177] Scenario 2: Independent operation. The power system, natural gas system, and heating system operate in a decoupled manner.

[0178] Scenario 3: Equipment failure. Due to a sudden failure of coupled equipment such as a gas turbine unit or a cogeneration unit, the energy flow path between subsystems is blocked.

[0179] Scenario 4: Maintenance Operation. Thermal power units undergo equipment maintenance in turn, and each thermal power unit is forced to shut down during the maintenance period.

[0180] Scenario 5: Load scale difference. The electric load is compared with the baseline operating state and scaled within a certain ratio.

[0181] Table 2 Comparison of indicators in multiple scenarios

[0182]

[0183]

[0184] Based on the above indicators, scenarios 1-4 were measured separately, and the results are shown in Table 2. As can be seen from the table, the coupled operation of the multi-energy system (scenarios 1, 3, and 4), regardless of whether it is in normal operation or not, can significantly increase the renewable energy absorption rate and reduce the power system load loss rate. This is because when the power system operates independently (scenario 2), the only response to wind power fluctuations is to start and stop or adjust the thermal power units. These adjustment methods are limited and subject to minimum start and stop times and ramp constraints, resulting in poor system flexibility. After the coupled operation of the multi-energy system, during periods of high wind power generation, wind energy that cannot be absorbed by the power system can be transferred to the natural gas subsystem and the heating subsystem through power-to-gas units and electric boilers, while meeting branch capacity constraints. In addition to changing the primary form of energy consumption in the two systems, it also utilizes the pipe-storage characteristics of the gas grid system, similar to those of energy storage devices, to couple with the complementary power system to deal with high wind power generation or source-load imbalances.

[0185] It was also found that the comprehensive utilization rate of coupled devices in Scenario 1 was lower than in Scenarios 3 and 4. The degree of coupled device utilization depends on multiple factors, including the objective function setting, case scale, and case structure. However, the increased utilization rate of coupled devices in scenarios such as maintenance and failures means that in extreme environments, the low flexibility of independent subsystems makes it more difficult to maintain system balance and real-time operation, requiring more power sources from other energy subsystems through multi-energy coupled devices.

[0186] Scenario 5 studies the impact of load size on various indicators of the integrated energy system, and the relevant results are shown in Table 3. As the load size decreases, the annual load loss generally shows a downward trend. This is because the reduction in load size reduces the peak demand and relatively increases the system capacity, thereby increasing the flexibility of the integrated energy system and making it easier to achieve energy coupling and complementarity through various energy subsystems. In addition, the new energy consumption rate, coupling equipment utilization rate, and system operating costs also show a downward trend. As the load size decreases, it is easier for the power system itself to meet the balance during load fluctuations. At the same time, it was found that as the load size decreases, the solution time of the example is still reduced. This is because the solver's search space for the solution is reduced, or certain constraints become easier to meet, reducing the number of iterations.

[0187] Table 3 Differences in indicators under different load scales in scenario 5

[0188]

[0189] Based on the above analysis, the model proposed in this paper describes more details of integrated energy system modeling and adequacy analysis, and reveals the operating characteristics of each link and their coordination relationship. The simulation results prove the effectiveness of the proposed optimization method.

[0190] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing energy adequacy of a time-series operation simulation integrated energy system, characterized by: The process includes the following steps, which are performed in sequence: Step 1: Considering the differences in operating characteristics of each subsystem and the characteristics of multi-energy coupling equipment, model the power grid, gas grid, heat grid and system coupling equipment; Step 2: Consider the constraints between each subsystem and establish an operation optimization model for the electricity-gas-heat integrated energy system; Step 3: Based on the segmented time series and the full-year time series simulation method with no solution rollback mechanism, the operation optimization model of the electricity-gas-heat integrated energy system is solved and analyzed, and the operation data is obtained and the results are output.

2. The method for analyzing energy adequacy of a time-series operation simulation integrated energy system according to claim 1 is characterized by the following steps: The power grid modeling adopts the DC power flow equation; the gas network modeling adopts the pipeline transportation equation, the pipeline material balance equation and the gas state equation; the heat network adopts the thermal conductivity equation; the system coupling equipment modeling includes the power-to-gas model, the cogeneration unit chemical energy to electrical energy and thermal energy model, the gas unit natural gas to electrical energy model and the electric heating power-to-heat energy coupling model.

3. The method for analyzing energy adequacy of a time-series operation simulation integrated energy system according to claim 1 is characterized by: The operation optimization model of the electricity-gas-heat integrated energy system described in step 2 is: Where, T N is the total number of time periods, which is 8760 hours when running the simulation according to the annual time series; N g is the number of thermal power units, k i,s and λ i,s is the slope and intercept of each segment after the linearization of the cost of the i-th thermal power unit, N is the technical output of the i-th thermal power unit at time t; Gas is the number of gas sources, is the unit gas consumption cost, is the gas consumption mass flow rate of the i-th gas source at time t; N W is the number of wind farms, is the wind curtailment penalty coefficient of the i-th wind farm, is the amount of wind curtailment at the i-th wind farm at time t; N L is the number of electrical load nodes, is the load shedding penalty coefficient of the i-th electrical node load, is the load shedding power of the i-th electrical node at time t; N CHP is the number of cogeneration units; and are the electricity production cost coefficient and heat production cost coefficient of the i-th cogeneration unit at time t; and are the electrical output and heat output of the i-th cogeneration unit at time t respectively.

4. The method for analyzing energy adequacy of a time-series operation simulation integrated energy system according to claim 3 is characterized by: The constraints satisfied by the operation optimization model of the electricity-gas-heat integrated energy system are: active power balance constraint of the power system, maximum transmission capacity constraint of branch flow lines, unit ramping constraint, generator start-stop constraint, generator unit spinning standby constraint, pipeline material flow and node gas pressure constraint, and node energy balance constraint.

5. The method for analyzing energy adequacy of a time-series operation simulation integrated energy system according to claim 1 is characterized by: The method for solving and analyzing the operation optimization model of the electricity-gas-heat integrated energy system based on the full-year time series simulation method based on the segmented time series and the no-solution rollback mechanism described in step 3 is: Sub-step 1: Let b = 0, set the state of the last hour of the day eb-1 as the initial state, and simulate the operation from eb to e+f days; Sub-step 2: If there is a solution within the period from eb to e+f days, save the simulation results; if there is no solution, roll back one day, i.e. set b = b+1 and return to sub-step 1; Sub-step 3: If day e+f is the last day of the simulation cycle, terminate the sequential simulation and output the simulation results of the entire simulation cycle; otherwise, let e=e+f, b=0, and return to sub-step 1; Where e is the starting day of the current solution step in the rollback mechanism, b is the number of days rolled back under the current no-solution rollback mechanism, and f is the number of days simulated forward for each step in the time series simulation.

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