Comprehensive energy optimization scheduling method and system based on dynamic characteristics of electrical and thermal networks

Abstract

The present invention belongs to the technical field of coordinated optimization of integrated energy systems, and provides an integrated energy optimization scheduling method and system based on the dynamic characteristics of electrical and thermal networks, comprising the following steps: taking the minimum total operating cost of IES within the day-ahead scheduling period as the objective function, according to the constraints of the power system, the thermal network system, the gas network system and the operation flexibility, analyzing the relationship between the characteristics of the power grid and the dynamic characteristics of the gas and thermal networks, and jointly constructing an integrated energy optimization scheduling model taking into account the sub-hourly operation flexibility; taking the output power of the coupled units as the coupling variable, based on the integrated energy optimization scheduling model taking into account the sub-hourly operation flexibility, adopting a centralized solution method to perform optimization processing, and obtaining the optimal unit combination operation scheduling plan.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated optimization of integrated energy systems, and in particular to an integrated energy optimization scheduling method and system based on the dynamic characteristics of an electrical and thermal network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, the research on optimizing the dispatching and operating flexibility of IES (Integrated Energy System, IES) mainly focuses on optimizing the network structure to improve the system's ability to cope with net load fluctuations and using flexible equipment to improve the system's dispatching flexibility. In terms of providing operational flexibility for the electricity-gas interconnection system, some literature quantified the flexibility provided by the gas network to the power system by analyzing the steady-state gas flow model and the transient gas flow model, and introduced the concept of interlayer pipe storage to evaluate the flexibility of the electricity-gas system. Some literature considered the dynamic characteristics of the gas grid and gas pressure, and coupled the power of the power grid with the natural gas network, indicating that the electricity interconnection The interconnected system has greater flexibility supply. In terms of providing operational flexibility for the electric-heat interconnected system, some literature has established a model of the dynamic characteristics of heat network transmission and the heat storage characteristics of buildings, proving that the heat storage characteristics of the centralized heating system can effectively reduce the amount of wind power curtailment and improve flexibility. Some literature has verified that the dynamic characteristics of heat network transmission have great regulatory potential for participating in the economic operation of the electric-heat IES. Some literature has verified that in the electric-heat system, the inherent flexibility on the user side can provide certain flexibility resources for the system. However, the above-mentioned study only considers the operational flexibility provided by the coupling of two energy flow time scales, and does not simultaneously consider the impact of the coupling of the three energy flow time scales of electricity, gas and heat on the improvement of system operational flexibility.

[0004] In terms of considering the operational flexibility of the electricity-gas-heat interconnected system, some literature has proposed an IES dispatching model that takes into account the dynamic characteristics of the heat and gas networks. By analyzing the relationship between the characteristics of the power grid and the dynamic characteristics of the gas and heat networks, it is concluded that the IES dispatching model can effectively improve the flexibility of system operation and the wind power absorption capacity. Some literature has established an optimized dispatching model that simultaneously considers the dynamic characteristics of the network and demand response, indicating that the dynamic characteristics of the network can improve the flexibility of the system and can more accurately reflect the actual operation of the system. Based on the characteristics of IES gas pipe storage and thermal inertia, some literature has established an IES gas-thermal inertia power support model, indicating that gas-thermal inertia can provide power support as a flexible reserve. The dispatching model proposed by the above research proves that the dynamic characteristics of the gas-heat network can improve the flexibility of the system, but fails to provide a refined mathematical model for the flexibility provided by the dynamic characteristics of the network.

[0005] In summary, as the proportion of renewable energy grid connection increases, it is necessary to break down the barriers between power grids at all levels in order to achieve optimal allocation of resources across the entire network and improve the level of renewable energy consumption. However, current research on providing flexibility resources for electricity-gas-heat interconnected systems mostly uses the method of configuring more flexible devices, without considering the impact of network structure characteristics on system supply flexibility. There is a lack of exploration of the system's existing flexibility resources and consideration of the flexibility provided by the dynamic characteristics of the IES network, and the flexibility demand constraints at the sub-hourly scale are not taken into account. Summary of the invention

[0006] In order to solve at least one technical problem existing in the above background technology, the present invention provides a comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network. According to the characteristics of the multi-energy coupling characteristics of electricity, gas and heat and the supply and demand of operational flexibility, a scheduling idea of ​​unified analysis and centralized optimization of the whole network is proposed, which can fully tap the flexibility potential contained in IES and quantify the flexibility resources that can be provided by the dynamic characteristics of the network. Break down the barriers between power grids at all levels, give full play to the interconnection and mutual assistance of multi-energy resources in each layer of the system, provide the ability of system operation flexibility, and leave sufficient reserve to deal with the uncertainty brought by the grid connection of new energy.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides a comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network, comprising the following steps:

[0009] Taking the minimum total operating cost of IES within the day-ahead dispatch period as the objective function, and taking the constraints of power system, heat network system, gas network system and operation flexibility as constraints, the relationship between the characteristics of the power grid and the dynamic characteristics of the gas and heat networks is analyzed, and a comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility is jointly constructed;

[0010] Taking the output power of coupled units as coupling variables and based on the comprehensive energy optimization scheduling model taking into account the sub-hourly operation flexibility, a centralized solution method is adopted to perform optimization processing and obtain the optimal unit combination operation scheduling plan.

[0011] A second aspect of the present invention provides a comprehensive energy optimization dispatching system based on the dynamic characteristics of an electrical and thermal network, comprising:

[0012] The optimization dispatch model construction module is configured as follows: taking the minimum total operating cost of IES within the day-ahead dispatch period as the objective function, and taking the power system constraints, heat network system constraints, gas network system constraints and operation flexibility constraints as constraints, the systems considering the dynamic characteristics of the electrical and heat networks are modeled separately, and a comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility is jointly constructed;

[0013] The operation scheduling scheme acquisition module is configured as follows: taking the output power of the coupled units as the coupling variable, based on the comprehensive energy optimization scheduling model taking into account the sub-hourly operation flexibility, a centralized solution method is used for optimization processing to obtain the optimal unit combination operation scheduling scheme.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned comprehensive energy optimization scheduling method based on the dynamic characteristics of an electrical and thermal network.

[0016] A fourth aspect of the present invention provides a computer device.

[0017] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned comprehensive energy optimization scheduling method based on the dynamic characteristics of an electrical and thermal network are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention aims to address the shortcomings of traditional electricity, gas and heat energy systems that are separated from each other. Such a fragmented operation mode is difficult to achieve a globally optimal scheduling strategy, thus affecting the wind power absorption capacity. In response to the shortcomings of the traditional operation mode, this invention proposes a comprehensive energy optimization scheduling model that takes into account sub-hourly operation flexibility. According to the multi-energy coupling characteristics of electricity, gas and heat and the characteristics of supply and demand of operation flexibility, a scheduling idea of ​​unified analysis and centralized optimization of the entire network is proposed.

[0020] The present invention proposes a comprehensive energy optimization system based on taking into account sub-hourly operational flexibility constraints, models the systems considering the dynamic characteristics of the electrical and thermal networks respectively, takes the output power of the coupled units as the coupling variable, and adopts a centralized solution method to optimize and solve the IES optimization scheduling model that provides operational flexibility considering the dynamic characteristics of the electrical and thermal networks.

[0021] The present invention proposes a comprehensive energy optimization system based on taking into account sub-hourly operational flexibility constraints. For the natural gas grid and power grid models, an incremental piecewise linearization method is used to deal with the non-convexity of the natural gas grid and the power grid, and the model is converted into a mixed integer linear programming problem.

[0022] The present invention realizes the optimal allocation of resources in the whole network and improves the level of new energy consumption, breaks down the barriers between power grids at various levels, gives full play to the interconnection and mutual assistance of multi-energy resources in various layers of the system, provides the ability of system operation flexibility, and leaves sufficient backup to deal with the uncertainty brought by the connection of new energy to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 IES network structure diagram provided for Example 1 of the present invention;

[0025] Figure 2 A schematic diagram of providing operation flexibility for a thermal power unit provided in Example 1 of the present invention;

[0026] Figure 3(a)-Figure 3(b) A schematic diagram of providing flexibility for the gas-thermal dynamic characteristics provided in Example 1 of the present invention;

[0027] Figure 4 A schematic diagram of a longitudinal section of a heat network pipeline provided in Example 1 of the present invention;

[0028] Figure 5 A schematic diagram of equivalent pipeline storage of a natural gas network provided in Example 1 of the present invention;

[0029] Figure 6 A schematic diagram of incremental piecewise linearization provided in Example 1 of the present invention;

[0030] Figure 7 A schematic diagram of the IES gas heat load and wind power prediction curve provided in Example 1 of the present invention;

[0031] Figure 8 A schematic diagram of the net load of a power grid provided in Embodiment 1 of the present invention;

[0032] Figure 9(a)-Figure 9(b) A schematic diagram of the state change of the heating network provided in Example 1 of the present invention;

[0033] Figure 10(a)-Figure 10(b) A schematic diagram of the change of the gas network state provided in Example 1 of the present invention;

[0034] Figure 11(a)-Figure 11(d) A schematic diagram of system flexibility capacity provided in Example 1 of the present invention;

[0035] Figure 12(a)-Figure 12(b) A schematic diagram of the effect of the flexibility provided in Example 1 of the present invention on system operation. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] Glossary:

[0040] Sub-hourly scale: A smaller scheduling time interval within the hourly scale.

[0041] Embodiment 1

[0042] like Figure 1-2 As shown, this embodiment provides a comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network, including the following steps:

[0043] Step 1: Taking the minimum total operating cost of IES as the objective function, and taking the constraints of power system, heat network system, gas network system and operation flexibility as constraints, a comprehensive energy optimization scheduling model taking into account sub-hourly operation flexibility is constructed;

[0044] Step 2: Taking the conversion power of gas turbines and CHP units as coupling variables, the integrated energy system is optimized as a whole, taking into account the operating costs and the penalty costs of load loss and wind curtailment;

[0045] Step 3: Consider the impact of operational flexibility constraints on system operation and obtain the optimal unit combination operation scheduling plan.

[0046] In step 1, the objective function includes the operating cost and startup cost of conventional units, gas source output cost, power loss load penalty cost, gas loss load penalty cost, heat loss load penalty cost and wind abandonment penalty cost, specifically:

[0047]

[0048] In the formula, Ω well ,Ω ele ,Ω gas ,Ω heat and Ω wtI respectively represent the set of gas source, power network node, natural gas network node, thermal network node and wind turbine; u,t is the startup cost of the thermal power unit; is the output gas flow rate of gas source w at time t (Mm 3 / h); C well is the gas source output cost coefficient; and They are the unsatisfied electrical power (MW) and gas flow (Mm 3 / h) and thermal power (MW); C nsp , C nsg and C nsh are the penalty coefficients for unsatisfied electric power, air flow, and thermal power, respectively; and are the predicted power and actual output of wind turbine p at time t (MW), respectively; is the wind abandonment penalty coefficient; a u 、b u , and c u is the operating cost coefficient of thermal power units.

[0049] The constraints include power system constraints, heat network system constraints, gas network system constraints and operation flexibility constraints.

[0050] The power system constraints specifically include: active power balance constraint (2); upper and lower limit constraints on thermal power unit output power (3); upper and lower limit constraints on wind power unit output power (4); unit ramp rate constraint (5); node power balance and line flow constraint, phase angle constraint (6); unit minimum start and stop time constraint (7);

[0051] The expression is:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, is the load power of node i; P ij,t is the line power connected to node i (MW); and They are the upper and lower limits of the unit’s output respectively; are the upward and downward climbing rates of the unit respectively; θ ij,t is the phase angle difference between nodes i and j (rad); x ij and P ijmax are the reactance and upper limit of power flow of branch ij respectively; and They are the minimum operating time and minimum shutdown time of thermal power units respectively; are the operating and shutdown time of the thermal power units, δ u,t is a 0-1 variable representing the start and stop status of the thermal power unit. are the upward and downward climbing rates of the unit respectively; Ω tu ,Ω chp and Ω gu They are respectively a collection of thermal power units, CHP units and gas turbine units.

[0059] The natural gas system constraints include: node flow balance constraints (8); gas source output constraints (9); and pressure station constraints (10); specifically:

[0060]

[0061]

[0062] p l,t ≤β com p g,t (10)

[0063] In the formula, is the natural gas load flow at node g; η gt and η chp are the conversion efficiency of gas turbine and CHP unit, β com is the compression factor of the compressor; and are the gas flow rates at the pipeline inlet and outlet at time t, respectively, H GV Natural gas has a high calorific value.

[0064] The thermal network constraints include: CHP unit constraints (11)-(12); water supply pipeline temperature constraints (13); heat exchange station constraints (14)-(15); thermal network temperature constraints (16)-(18);

[0065] Specifically:

[0066] P h,t =k chp H h,t (11)

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] In the formula, m h , and They are the hot water flow rate, supply water temperature (°C) and return water temperature (°C) at the outlet of the CHP unit; and are the upper and lower limits of the water supply temperature at the outlet of the CHP unit; H hes,t is the thermal power required by the heat exchange station (MW); m hes , and They are the hot water flow rate, supply water temperature (℃) and return water temperature (℃) at the inlet of the heat exchange station; and They are the upper and lower limits of the return water temperature at the inlet of the heat exchange station respectively; and They represent the pipeline with node h as the end node and the start node respectively; m hl is the water flow in pipe hl; and are the inlet and outlet temperatures of the water supply pipe hl (°C) respectively; and are the inlet and outlet temperatures of the return pipe hl (°C) respectively; and are the temperatures (°C) at the mixing points of the supply and return pipe nodes, respectively.

[0076] Among them, the supply flexibility constraint of the thermal power unit is (20).

[0077]

[0078] In the formula, and are the rising and falling climbing rates of the thermal power units respectively; P u.t is the active power output of the thermal power unit at time t (MW); and are the upper and lower limits of active power output of thermal power units respectively; δ u,t is a 0-1 variable representing the start and stop status of the thermal power unit; Ω tu A collection of thermal power units.

[0079] Among them, Figure 3(a)-Figure 3(b) As shown, the thermal dynamic characteristics provide flexibility constraints including the dynamic hydraulic system model (21)-(35) as:

[0080] The dynamic hydraulic system model is:

[0081] Without considering the temperature loss, the historical pipeline inlet temperature is used to estimate the pipeline end temperature. Figure 4 As shown, where ρ, A kl and L kl Represented as the density of hot water, the cross-sectional area and the length of the pipe respectively. The water in the pipe is divided into blocks of equal mass. The shaded part represents the mass of hot water injected into the pipe over a continuous period of time.

[0082] For example, m t Δt represents the amount of water entering the hot water pipe from t to t+Δt; at the outlet of the pipe, the water volume in the outlet area overlaps due to transmission delay; is the amount of water flowing out of the pipe at time t, including t-γ kl,t and t-φ kl,t The amount of water flowing out of a pipe.

[0083] Among them, t-γ kl,t is the time when the part of hot water that flows out of the pipe at time t flows into the pipe, t-φ kl,t It is the moment when the part of hot water that flows out of the pipe at time t-1 flows into the pipe.

[0084] where γ kl,t and φ kl,t It can be expressed as:

[0085]

[0086]

[0087] In the formula, kl is the hot water pipe number; m kl,b is the working medium flow rate of the hot water pipe kl at time b; n is the retention time of the water flowing out of the pipe at time t in the heating network pipe; m is the retention time of the water flowing out of the pipe at time t+1 in the heating network pipe.

[0088] Therefore, the outlet temperature of the pipe is T t out′ It can be expressed as t-φ kl,tMoment and t-γ kl,t The linear weighting of the three, time and the pipe inlet temperature between these two times, the relationship between the outlet temperature and the inlet temperature of the pipe can be expressed as:

[0089]

[0090]

[0091] In the formula,

[0092]

[0093]

[0094] In the formula, and is the outlet temperature of the water supply pipe and the return pipe without considering the temperature loss (℃); and is the inlet temperature of the water supply pipe and the return pipe (℃); S kl,t From t-φ kl,t The mass of hot water flowing into the pipe from time +1 to time t; R kl,t From t-γ kl,t The mass of hot water flowing into the pipe from time t to time t.

[0095] The dynamic thermal system model is:

[0096] Taking into account the heat loss of hot water during the transmission process, the pipe outlet temperature should be corrected using the Sukhov temperature drop formula:

[0097]

[0098]

[0099] In the formula, and are the outlet temperatures of the water supply pipe and the return pipe after taking into account the temperature loss (℃); T t am is the ambient temperature; J kl,t is the temperature drop coefficient; kl and c are the thermal conductivity of the hot water pipe and the specific heat capacity of water respectively.

[0100] In this embodiment, a constant mass flow and variables temperature (CFVT) control mode is adopted. Under this regulation mode, the thermal model and hydraulic model of the heating network can be decoupled, and γ kl,t ,φ kl,t , S kl,tand R kl,t are all constants determined by pipeline parameters and hot water flow, so the model of the heating network dynamic system is linear.

[0101] In the heat network system supply flexibility model:

[0102] The pipeline transmission delay and heat loss will produce a certain temperature difference, which will passively store or release energy, which can be used to adjust the output of the CHP unit and improve the flexibility of the power system. Figure 3(a) shows the process of passive heat storage and heat release in the pipeline, ΔH c,t and ΔH d,t They represent the energy stored and released by the heat network pipeline at the sub-hour time scale, respectively, indicating that the delay of the heat network pipeline has the ability of bidirectional regulation and rapid response at the sub-hour time scale. The ability of the heat network pipeline to provide operational flexibility in real-time operation is closely related to the heat storage and release state of the pipeline, and is constrained by the upper and lower limits of the actual pipeline temperature and the heat storage and release operation time.

[0103] In actual working conditions, the operation status of the heat network pipeline is divided into two states: heat storage and heat release, and meets the following requirements:

[0104] c hot,t +d hot,t ≤1 (29)

[0105] Where: c hot,t and d hot,t It is a binary 0-1 variable, which represents the heat storage and release status of the heat network pipeline.

[0106] When the heat output of the heating network system is balanced with the heat load, the heating network pipeline can increase the output of the CHP unit by storing heat, thereby providing upward flexibility; and reduce the output of the CHP unit by releasing heat, thereby providing downward flexibility. Based on the principle of maximum satisfaction of the heat load, the flexibility capacity provided by the heating network pipeline at the sub-hour time scale cannot exceed the maximum heat storage and release capacity of the pipeline during this time period. Therefore:

[0107]

[0108] Where: Indicates the heat release capacity of the heat network pipeline (MW), which must meet Indicates the heat storage capacity of the heat network pipeline (MW), which must meet k chp is the thermoelectric conversion ratio.

[0109] On a sub-hourly time scale, the maximum heat release power and maximum heat storage power available for the heat network pipeline are expressed as:

[0110]

[0111] Where: H chp,t and H hload,t They are the heat output of the CHP unit and the heat load demand of the heating network respectively.

[0112] Considering the capacity and ramp rate limitations of the CHP units, the flexibility of the heat supply network system can be further expressed as:

[0113]

[0114] In the formula, and are the upward and downward climbing rates of the CHP unit respectively; P h,t is the power output of the CHP unit at time t (MW); and They are the upper and lower limits of the power output of the CHP unit respectively; Ω chp It is a collection of CHP units.

[0115] Under actual working conditions, the temperature of the heating network pipeline needs to be maintained within certain upper and lower limits.

[0116]

[0117] Where: T kl,t represents the pipe temperature at time t (℃), and Respectively represent the upper and lower limits of the pipe temperature.

[0118] In order to avoid the pipeline storing or releasing heat for a long time, which causes the pipeline temperature to continue to run at a high or low level, it is necessary to set the maximum heat storage time and heat release time constraints. Assuming that the pipeline is in the heat release state at the initial moment, starts to store heat at time t3, and turns to heat release at time t4, then:

[0119]

[0120]

[0121] Where: and They are the longest heat release time and the longest heat storage time of the heating network pipeline respectively.

[0122] The dynamic characteristics of the natural gas network include:

[0123] Natural gas has a large inertial time constant and is compressible during transmission. Natural gas pipelines have a certain storage capacity, so there is a flow difference between the beginning and end of the pipeline. The equivalent storage diagram of the natural gas network is as follows: Figure 5 As shown in the figure, the shaded area represents the “equivalent pipe stock” of the pipeline, Δx gl and Dgl are the length and diameter of the pipe, respectively, and p g,t and p l,t are the gas pressures at the inlet and outlet of the pipeline at time t (bar), is the average pressure in the pipeline at time t; and is the gas flow rate at the pipeline inlet and outlet at time t (Mm 3 / h), is the average flow rate of the pipeline at time t. Therefore, the pipeline storage capacity constraints are shown in equations (36) and (37); the pipeline flow constraints are shown in equations (38) and (39).

[0124]

[0125]

[0126]

[0127]

[0128] Where M gl , t is the storage of pipeline gl at time t (Mm 3 ), R is the gas constant, T gas is the natural gas temperature; Z is the natural gas compression factor; ρ0 is the natural gas density under standard conditions; F gl is the friction coefficient of the pipeline gl; and are the upper and lower limits of the flow rate of pipeline gl respectively. It is obtained by formula (36) under steady-state conditions.

[0129] The role of pipeline storage capacity in the natural gas system is similar to that of power system reserve, which can be used to buffer the fluctuation of natural gas load. In order to ensure that there is enough adjustment space in the next scheduling cycle, the pipeline storage capacity after one cycle is restored to the initial value, leaving a certain adjustment margin for the next scheduling cycle, as shown in formula (40), which is similar to the principle of energy storage device.

[0130]

[0131] In the formula, Ω gline A collection of gas network pipelines.

[0132] The gas grid supply flexibility model includes:

[0133] The time delay characteristics of natural gas transmission result in a certain amount of pipeline storage capacity in the natural gas pipeline. The storage and release of the pipeline storage capacity can be used to adjust the output of the gas turbine unit, thereby improving the operational flexibility of the power system.

[0134] Figure 3(b) shows the process of natural gas pipeline storage and release. c,t and ΔG d,t They represent the gas power stored and released by the natural gas pipeline at the sub-hourly scale. The pipeline inventory of the natural gas pipeline has the ability to adjust bidirectionally and quickly at the sub-hourly scale. The ability of the gas network to provide operational flexibility in real-time operation is closely related to the gas storage and discharge status of the pipeline, and is constrained by the upper and lower limits of the natural gas pipeline pressure and the storage and discharge operation time.

[0135] In actual working conditions, the operation status of natural gas pipelines is divided into two states: gas storage and gas release, and must meet the following requirements:

[0136] c gas,t +d gas,t ≤1 (41)

[0137] Where: c gas,t and d gas,t It is a binary 0-1 variable, which represents the storage and release status of the natural gas pipeline.

[0138] When the import and export flows of the natural gas pipeline reach a balance, the natural gas pipeline can provide upstream flexibility by releasing natural gas and increasing the available gas volume of the gas turbine; it can provide downstream flexibility by storing natural gas and reducing the available gas volume of the gas turbine. In order to maximize the demand for gas load, the flexibility provided by the natural gas pipeline on a sub-hourly time scale cannot exceed the maximum storage and release capacity of the pipeline during that time period. So:

[0139]

[0140] Where: and They are the maximum gas storage and maximum gas release power (MW), which must meet and

[0141] On a sub-hourly time scale, the maximum gas release power and maximum gas storage power available in a natural gas pipeline can be expressed as:

[0142]

[0143] Where: H GV Natural gas has a high calorific value.

[0144] The flexibility provided by the natural gas system is also limited by the capacity and ramp rate of the gas turbines of the coupled units. Therefore, the flexibility of the natural gas system supply is:

[0145]

[0146] Where: and are the upward and downward climbing rates of the gas turbine respectively; P n,t is the output of gas turbine n at time t (MW); and are the upper and lower limits of gas turbine output respectively; Ω gt A collection of gas turbine units.

[0147] In actual operation, the pipeline pressure must be maintained within a certain upper and lower limit.

[0148]

[0149] In order to avoid the pipeline from storing or releasing natural gas for a long time, which causes the pipeline to operate at a higher or lower pressure level for a long time, it is necessary to set the maximum gas storage time and gas release time constraints. Assuming that the pipeline is in the gas release state at the initial moment, it starts to store gas at time t8 and turns to gas release at time t9, then:

[0150]

[0151]

[0152] Where: and They are the longest gas storage time and the longest gas release time of natural gas pipelines respectively.

[0153] The operational flexibility requirements analysis includes:

[0154] The operational flexibility constraint needs to meet the flexibility requirements of real-time scheduling in the hourly scheduling results, so it is necessary to consider the changes in net load at the sub-hourly scale in the hourly scale optimization scheduling. Therefore, when determining the limit of flexibility requirements, in addition to considering the sub-hourly scale changes in net load, the changes in net load values ​​in the time period should also be considered. Therefore, the operational flexibility requirements of the system can be expressed as:

[0155]

[0156] In the formula, and are the uplink and downlink flexibility demand limits for time period t, respectively; and are the uplink and downlink flexibility requirements of the system in time period t, which are determined by the sub-hourly changes in net load. This part is difficult to predict at the day before and can be set according to historical statistical laws; ΔT0 is the scheduling time interval; ΔT is the sub-hourly scale; P L,t is the net load value in time period t.

[0157] The IES supply operation flexibility capability analysis includes:

[0158] All the adjustment capabilities that can adjust the system output and load uncertainty can be regarded as the flexibility resources of the power system. The IES flexibility resources considered in this paper are: adjustable thermal power units; the heat network system provides flexibility through heat storage and release after taking into account the dynamic characteristics; the gas network system provides flexibility for the system through the storage and release of gas in the pipeline. Therefore, the up and down flexibility provided by IES at time t and for:

[0159]

[0160] In the formula, and are the upstream and downstream flexibility capacities (MW) supplied by the power system, heat network system, and gas network system in time period t, respectively.

[0161] The operational flexibility evaluation indicators include:

[0162] The flexibility judgment basis adopted in this embodiment is: within the ΔT hour time scale, the supply capacity of system operation flexibility should not be less than a certain limit value.

[0163]

[0164]

[0165] In order to more intuitively represent the operational flexibility of the power system, a flexibility margin index is introduced, whose value is the difference between the available flexibility resources and the flexibility demand.

[0166]

[0167] In the formula, They are the uplink and downlink flexibility margins (MW) at time t respectively.

[0168] In summary, equations (1)-(51) constitute the comprehensive energy optimization scheduling model in this embodiment that considers sub-hourly operation flexibility.

[0169] In step 2, considering the square of thermal power output in the IES model (P u.t ) 2 Formula (1), the square of the average flow rate in the pipeline Formula (38) and the square of the pipeline node pressure (P g,t ) 2Equation (38) has nonlinear expressions, and it is difficult to solve them directly, and the solution time is long. Therefore, this paper uses the quadratic constraint incremental linearization method to linearize them respectively; at the same time, considering that the constraint has an equation expression of the type X = min (Y, Z), this type of expression can be converted into two terms X ≤ Y and Y ≤ Z. Then the model is converted into a mixed integer linear model, which can be directly solved by calling the CPLEX solver on the GAMS platform. The specific linearization process is as follows Figure 6 As shown, the steps are as follows:

[0170] In the model of this embodiment, the nonlinear function of constraint equation (1) and equation (38) and the function f(x)=x 2 The specific linearization process for this type of function is as follows:

[0171] 1) According to the nonlinear characteristics of the model and the acceptable accuracy, select an appropriate number of linearization segments K-1.

[0172] 2) Obtain the discrete value x of each segment point k (k∈K).

[0173] 3) Obtaining process 2) Each discrete value x k The corresponding value of f(x).

[0174] 4) Introduce the continuous variable λ k and binary variable θ k , express f(x) according to steps (52)-(55).

[0175]

[0176]

[0177]

[0178]

[0179] Where: K is the segment point set; λ k is a continuous variable ranging from 0 to 1, indicating the position in the segmented interval; θ k It is a binary 0-1 variable, which ensures that all segment intervals are filled from left to right.

[0180] In this implementation, the flow rate and node pressure of the natural gas pipeline in formula (38) are both quadratic terms. They are linearized according to the above incremental linearization method. First, formula (38) is rewritten as:

[0181]

[0182] Where: d gl,tand p g,t is a newly introduced linear variable to replace the square term and

[0183] According to the above incremental linearization process principle, the linearization representation is performed according to the following steps:

[0184]

[0185]

[0186]

[0187]

[0188] Where: K is the segment point set; λ gl,k is the position of the natural gas pipeline gl in the kth segment interval and is a continuous variable ranging from 0 to 1; θ gl,k It is a binary 0-1 variable, which ensures that the segmentation points can be divided from left to right within the entire segmentation interval.

[0189] Therefore, the nonlinear constraint (23) can be expressed by linear constraints (57)-(60) to express the square term of the thermal power unit output.

[0190] The IEEE 24-node power system, 20-node natural gas system and 16-node thermal system are used. The power grid system has 17 electric loads, 4 thermal power units, 2 CHP units, 2 gas turbine units and 1 wind turbine unit; the natural gas system has 8 gas loads, 6 gas wells and 2 compressors; the thermal system has 8 thermal loads and 14 thermal pipelines. The number, equipment parameters and operating costs of each subsystem of the simulation system are shown in Appendix B. The net load peak is 2440MW. According to the actual situation, the gas purchase cost is 0.08652$ / (Mm 3 ·h), the power failure load penalty coefficient is 100$ / (MW·h), and the wind abandonment penalty coefficient is 50$ / (MW·h). The dispatch cycle is 24h, the dispatch interval is 1h, and the sub-hour scale is 10min. The IES prediction curves of electric load, thermal load, gas load and wind power prediction values ​​are as follows Figure 7 The net load is the difference between the electric load and the wind power forecast value, as shown in Figure 8 shown.

[0191] In order to analyze the impact of IES network dynamic characteristics on the supply flexibility of the power system, this paper sets up the following four scenarios for comparative analysis.

[0192] Case 1, does not consider the dynamic characteristics of the network;

[0193] Case 2, only the dynamic characteristics of the gas grid are considered;

[0194] Case 3, only the dynamic characteristics of the heating network are considered;

[0195] Case 4, taking into account the dynamic characteristics of gas and heat networks at the same time.

[0196] In order to verify the necessity of taking into account the dynamic characteristics of the gas-heat network, four scenarios will be modeled and solved respectively; the comparison results are shown in Tables 1 and 2.

[0197] Table 1 Comparison of operating costs

[0198]

[0199] Table 1 lists the system operating costs under different scenarios. Comparing the different flexibility demands in Case 1, it can be seen that considering the changes in net load at the sub-hour scale will lead to an increase in operating costs. When the system flexibility demand increases to 120MW, the Case 1 system has limited power generation regulation capabilities and cannot cope with the strong variability of net load. In Case 2 and Case 3, the increase in operating costs is relatively small with the increase in net load at the sub-hour scale. This is because the flexibility resources provided by the dynamic characteristics of the network structure are utilized. When the dynamic characteristics of the heat network or gas network are taken into account alone, the pipeline requires a certain recovery time and cannot meet the higher operational flexibility requirements. In Case 4, the dynamic characteristics of the heat network and the gas network are taken into account at the same time. When providing flexibility, the gas-heat pipelines can cooperate and complement each other to cope with higher operational flexibility requirements.

[0200] Table 2 Operation status of thermal power units

[0201]

[0202] Table 2 lists the average number of online thermal power units under different scenarios. When the flexibility demand increases, the average number of online units in various scenarios will increase to follow the rapid changes in net load. However, when the network dynamic characteristics are taken into account, the number of online units will decrease due to the buffering effect of the hot gas network pipeline. It can be seen that taking into account the network dynamic characteristics can not only improve the system operation flexibility level, but also reduce the average number of online thermal power units. The higher the demand for operation flexibility, the more obvious the effect of IES network dynamics.

[0203] In order to verify the ability of the transmission delay characteristics of the heat network pipeline to provide flexibility, the present invention conducts comparative analysis through the results of the heat output of the CHP unit in Case 1 and Case 3 (as shown in Figure 9 (a)) and the temperature change of the heat network system (as shown in Figure 9 (b)). As shown in Figure 9 (a), the heat output of the CHP unit in Case 3 is decoupled from the heat load. In the 9-22h period, the CHP heat output is higher than the heat load, and part of the heat energy is stored in the heat network pipeline. At this time, the output of the CHP unit increases to cope with the rapid increase in net load. In addition, this part of the heat energy passively stored in the heat network pipeline is reserved for the next stage of heat release in the heat network. As shown in Figure 9 (b), in the 1-8h and 23-24h periods, the heat output of the CHP is less than the heat load, and the heat shortage is provided by the heat energy previously stored in the heat network pipeline. At this time, the output of the CHP unit is reduced, which can cope with the sudden drop in load and provide more space for wind power. Therefore, the heat network pipeline can play the role of passive energy storage to cope with changes in the net load of the power system, reduce the number of starts and stops of thermal power units, assist thermal power units in peak load shaving and valley filling, and provide flexibility on a certain time scale within a certain period of time.

[0204] In order to verify the potential of the gas network storage characteristics to provide flexibility, this paper compares and analyzes the changes in the pressure of the gas network system (as shown in Figure 10 (a)) and the changes in the storage capacity of the natural gas pipeline (as shown in Figure 10 (b)). As shown in Figure 10 (a), after taking into account the dynamic characteristics of the gas network, the inlet and outlet flows of the natural gas pipeline no longer need to be consistent. For example, at 2-6h and 19-23h, the pipeline inlet flow is greater than the outlet flow. Due to the compressibility of natural gas, this is equivalent to inflating the natural gas pipeline, and the available gas volume of the gas turbine is reduced to cope with the rapid decrease in net load and prepare for the release of the pipeline storage in the next stage; for example, at 7-14h, the pipeline inlet flow is less than the outlet flow. At this time, the gas and electricity loads are both high, and the natural gas pipeline releases the pipeline storage. At this time, the gas turbine has sufficient available gas volume to cope with the rapid increase in net load. As shown in Figure 10(a), compared with Case 1, the change in gas source output in Case 2 is more moderate. It can be seen that after taking into account the dynamic characteristics of the natural gas pipeline, the storage and release of gas in the pipeline reduces the impact on the gas source and relieves the pressure of gas source output during the peak hours of gas and electricity. In summary, the characteristics of the dynamic pipeline storage of natural gas pipelines can buffer the fluctuation of gas load and provide sufficient adjustment margin for gas turbines when the net load fluctuates greatly. Therefore, after taking into account the dynamic characteristics of the gas network, the output of the gas turbine can be optimized, thereby improving the operating flexibility of the system at a certain time scale.

[0205] The influence of IES network dynamic characteristics on power system flexibility is further studied. For Case 4 under the 120MW flexibility limit constraint, the available upstream flexibility capacity supplied by IES in each period is calculated. The results are shown in Figure 11(a). The broken line in the figure is the flexibility demand in each period under the 120MW limit constraint. The upstream flexibility demand in each period is related to the change of net load in each period, so the flexibility demand in different periods is different. As can be seen from Figure 11(a), the flexibility of the gas network system and the heat network system is time-sharing. The main reason is that the natural gas pipeline has a maximum gas storage and release time limit, and the heat network has a maximum heat storage and release time limit. The coordination of the heat network and the gas network can ensure that sufficient flexibility can be provided throughout the dispatch cycle. In Figure 11(a), the upstream flexibility supplied by IES is above the flexibility demand, indicating that the upstream flexibility of the power system is sufficient. Similarly, the situation of downstream flexibility can be analyzed, as shown in Figure 11(b).

[0206] The flexibility margins of Case 2, Case 3 and Case 4 under the flexibility demand of 120MW are calculated respectively. As shown in Figure 11(c) and Figure 11(d), the values ​​of Case 4 are all greater than or equal to zero, while Case 2 and Case 3 have moments when they are less than zero. As can be seen from Figure 11(c) and Figure 11(d), under high flexibility demand, considering the dynamic characteristics of the gas network or the dynamic characteristics of the heat network alone may result in insufficient flexibility at certain moments. Comparing Figure 11(c) and Figure 11(d), it can also be found that insufficient upward flexibility and insufficient downward flexibility do not appear at the same time, and there is no definite coupling relationship between the two. It can be concluded that: on the basis of considering the operating flexibility of conventional units, considering the dynamic characteristics of the gas network alone or the dynamic characteristics of the heat network alone, due to the limitations of gas storage and release and thermal energy storage and release, the flexibility capacity provided is relatively small; the coordinated scheduling of the dynamic characteristics of the gas network and the heat network at the same time can reduce the limitations of thermal energy and natural gas by adjusting the pipeline to store and release heat and gas at different times, thereby providing more flexibility.

[0207] In summary, after taking into account the dynamic characteristics of the network, the CHP thermal output no longer needs to completely follow the changes in the thermal load, and the import and export flows of the natural gas pipeline no longer need to remain consistent. The characteristics of the thermal energy storage and release of the heat network pipeline and the storage and release of gas in the gas network can be used to cope with the rapid changes in net load and the demand for a higher level of flexibility. In addition, the comprehensive coordinated scheduling of the gas-heat network can effectively prevent the problem of gas-heat storage and release energy not being able to be put into use for a long time, thereby providing more flexibility for the system.

[0208] In order to analyze the impact of flexible resources on the optimization results of the IES system, Figures 12(a) and 12(b) respectively list the power outage load and wind power output in different scenarios. As shown in Figure 12(a), compared with the power outage load in the four scenarios, the first three scenarios did not fully tap the operational flexibility potential of the actual system, resulting in insufficient flexibility adjustment capacity of the power system for the net load and a large power outage load. In Case 4, the flexibility capacity provided by the dynamic characteristics of the gas-heat network is fully considered, so that the power system has sufficient flexibility capacity to cope with the fluctuation of the net load. In addition, there is a certain amount of power outage load in Case 2 and Case 3, while Case 4 achieves zero power outage load, indicating that the dynamic characteristics of the heat network and the gas network are interrelated, further expanding the space for collaborative optimization and providing more flexibility resources for the power system. As shown in Figure 12(b), the acceptance space of Case 4 is larger than that of the other three scenarios, indicating that taking into account the dynamic characteristics of the IES network can improve the system's acceptance capacity for new energy while ensuring the stable operation of the system.

[0209] Embodiment 2

[0210] This embodiment provides a comprehensive energy optimization scheduling system based on the dynamic characteristics of the electrical and thermal network, including:

[0211] The optimization dispatch model construction module is configured as follows: taking the minimum total operating cost of IES within the day-ahead dispatch period as the objective function, and taking the power system constraints, heat network system constraints, gas network system constraints and operation flexibility constraints as constraints, the systems considering the dynamic characteristics of the electrical and heat networks are modeled separately, and a comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility is jointly constructed;

[0212] The operation scheduling scheme acquisition module is configured as follows: taking the output power of the coupled units as the coupling variable, based on the comprehensive energy optimization scheduling model taking into account the sub-hourly operation flexibility, a centralized solution method is used for optimization processing to obtain the optimal unit combination operation scheduling scheme.

[0213] Embodiment 3

[0214] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the above-mentioned comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network are implemented.

[0215] Embodiment 4

[0216] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network as described above are implemented.

[0217] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0218] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0220] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A comprehensive energy optimization scheduling method based on the dynamic characteristics of electrical and thermal networks, characterized in that: The steps include: Taking the minimum total operating cost of IES within the day-ahead dispatch period as the objective function, and taking the constraints of power system, heat network system, gas network system and operation flexibility as constraints, the relationship between the characteristics of the power grid and the dynamic characteristics of the gas and heat networks is analyzed, and a comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility is jointly constructed; The expression of the objective function is: In the formula, Ω tu is the set of thermal power units, Ω well ,Ω ele ,Ω gas ,Ω heat and Ω wt I respectively represent the set of gas source, power network node, natural gas network node, thermal network node and wind turbine; u,t is the startup cost of the thermal power unit; is the output gas flow of gas source w at time t; C well is the gas source output cost coefficient; and are the electrical power, gas flow and thermal power that are not met by the node respectively; C nsp , C nsg and C nsh are the penalty coefficients for unsatisfied electric power, air flow, and thermal power, respectively; and are the predicted power and actual output of wind turbine p at time t respectively; is the wind abandonment penalty coefficient; a u , b u , and c u is the operating cost coefficient of the thermal power unit; Taking the output power of coupled units as coupling variables, based on the comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility, the centralized solution method is used for optimization processing to obtain the optimal unit combination operation dispatch plan; The operation flexibility constraints include the supply flexibility constraints of thermal power units, the flexibility constraints provided by thermal dynamic characteristics, the flexibility constraints provided by gas dynamic characteristics, and the supply and demand balance constraints of operation flexibility; The flexibility constraints provided by the thermal dynamic characteristics are determined by a dynamic water system model, a dynamic thermal system model, and a heat network system supply flexibility model; the flexibility constraints provided by the gas dynamic characteristics are determined by a natural gas network dynamic characteristics model and a gas network supply flexibility model; the operational flexibility supply and demand balance constraints are determined by operational flexibility demand analysis, IES supply operational flexibility capacity analysis, and operational flexibility evaluation indicators; The power system constraints include: active power balance constraint (2), upper and lower limit constraints of thermal power unit output power (3), upper and lower limit constraints of wind power unit output power (4), unit ramp rate constraint (5), node power balance and line flow constraint and phase angle constraint (6), and unit minimum start and stop time constraint (7); The expression is: In the formula, is the unsatisfied electrical power of the node, is the predicted power of wind turbine p at time t, is the load power of node i; P ij,t is the line power connected to node i; and They are the upper and lower limits of the unit’s output respectively; are the upward and downward climbing rates of the unit respectively; θ ij,t is the phase angle difference between nodes i and j; x ij and P ijmax are the reactance and upper limit of power flow of branch ij respectively; and They are the minimum operating time and minimum shutdown time of thermal power units respectively; are the operating and shutdown time of the thermal power units, δ u,t is a 0-1 variable representing the start and stop status of the thermal power unit. are the upward and downward climbing rates of the unit respectively; Ω tu ,Ω chp and Ω gu They are respectively a collection of thermal power units, CHP units and gas turbine units; The gas network system constraints include: node flow balance constraints (8), gas source output constraints (9), and booster station constraints (10), specifically: p l,t ≤β com p g,t (10) In the formula, is the natural gas load flow at node g; η gt and η chp are the conversion efficiency of gas turbine and CHP unit, β com is the compression factor of the compressor; and are the gas flow rates at the pipeline inlet and outlet at time t, respectively, H GV It is the high calorific value of natural gas; The heat network constraints include: CHP unit constraints (11)-(12); water supply pipeline temperature constraints (13); heat exchange station constraints (14)-(15); heat network temperature constraints (16)-(18); Specifically: P h,t =k chp H h,t (11) In the formula, m h , and are the hot water flow rate, supply water temperature and return water temperature at the outlet of the CHP unit respectively; and are the upper and lower limits of the water supply temperature at the outlet of the CHP unit; H hes,t is the thermal power required by the heat exchange station; m hes , and are the hot water flow, supply water temperature and return water temperature at the inlet of the heat exchange station respectively; and They are the upper and lower limits of the return water temperature at the inlet of the heat exchange station respectively; and They represent the pipeline with node h as the end node and the start node respectively; m hl is the water flow in pipe hl; and are the inlet temperature and outlet temperature of the water supply pipe hl respectively; and are the inlet temperature and outlet temperature of the return pipe hl respectively; and are the temperatures at the mixing points of the supply pipe and return pipe nodes, respectively; The supply flexibility constraint of the thermal power unit is (20): In the formula, and are the rising and falling climbing rates of thermal power units respectively; P u.t is the active power output of the thermal power unit at time t; and are the upper and lower limits of active power output of thermal power units respectively; δ u,t is a 0-1 variable representing the start and stop status of the thermal power unit; Ω tu is the set of thermal power units, ΔT is the sub-hourly scale; The dynamic hydraulic system model is as follows: without considering the temperature loss, the historical pipeline inlet temperature is used to estimate the pipeline end temperature; ρ, A in the longitudinal section of the heat network pipeline kl and L kl are respectively represented by the density of hot water, the cross-sectional area and the length of the pipe. The water in the pipe is divided into blocks of equal mass. The shaded part represents the mass of hot water injected into the pipe over a continuous period of time. m t Δt represents the amount of water entering the hot water pipe from t to t+Δt; at the outlet of the pipe, the water volume in the outlet area overlaps due to the transmission delay; is the amount of water flowing out of the pipe at time t, including t-γ kl,t and t-φ kl,t The amount of water flowing out of the pipe; Among them, t-γ kl,t is the time when the part of hot water that flows out of the pipe at time t flows into the pipe, t-φ kl,t It is the moment when the part of hot water that flows out of the pipe at time t-1 flows into the pipe; where γ kl,t and φ kl,t The expression is: Where: kl is the hot water pipe number; m kl,b is the working medium flow rate of the hot water pipe kl at time b; n is the retention time of the water flowing out of the pipe at time t in the heating network pipe; m is the retention time of the water flowing out of the pipe at time t+1 in the heating network pipe; Therefore, the outlet temperature of the pipe is T t out′ It can be expressed as t-φ kl,t Moment and t-γ kl,t The linear weighting of the three, time and the pipe inlet temperature between these two times, the relationship between the outlet temperature and the inlet temperature of the pipe can be expressed as: In the formula, In the formula, and is the outlet temperature of the water supply pipe and the return pipe without considering the temperature loss; and is the inlet temperature of the water supply pipe and the return pipe; S kl,t From t-φ kl,t The mass of hot water flowing into the pipe from time +1 to time t; R kl,t From t-γ kl,t The mass of hot water flowing into the pipe from time to time t; The dynamic thermal system model is: Taking into account the heat loss of hot water during the transmission process, the pipe outlet temperature should be corrected using the Sukhov temperature drop formula: In the formula, and are the outlet temperatures of the water supply pipe and the return pipe after accounting for temperature loss; T t am is the ambient temperature; J kl,t is the temperature drop coefficient; kl and c are the thermal conductivity of the hot water pipe and the specific heat capacity of water, respectively; In the heat network system supply flexibility model: The transmission delay and heat loss in the pipeline will produce temperature difference, which will passively store or release energy, which can be used to adjust the output of CHP units and improve the flexibility of the power system. The operation state of the heat network pipeline is divided into two states: heat storage and heat release, and meets the following requirements: c hot,t +d hot,t ≤1 (29) Where: c hot,t and d hot,t are binary 0-1 variables, representing the heat storage and heat release status of the heat network pipeline; When the heat output of the heat network system is balanced with the heat load, the heat network pipeline increases the output of the CHP unit by storing heat, thereby providing upward flexibility; it reduces the output of the CHP unit by releasing heat, thereby providing downward flexibility; based on the principle of maximum satisfaction of the heat load, the flexibility capacity provided by the heat network pipeline at the sub-hour time scale cannot exceed the maximum heat storage and release capacity of the pipeline during this time period; therefore: Where: Indicates the heat release capacity of the heat network pipeline, which must meet Indicates the heat storage capacity of the heat network pipeline, which must meet k chp is the thermoelectric conversion ratio; On a sub-hourly time scale, the maximum heat release power and maximum heat storage power available for the heat network pipeline are expressed as: Where: H chp,t and H hload,t They are the heat output of the CHP unit and the heat load demand of the heating network; Considering the capacity and ramp rate limitations of the CHP units, the flexibility of the heat supply network system is expressed as: In the formula, and are the upward and downward climbing rates of the CHP unit respectively; P h,t is the power output of the CHP unit at time t; and They are the upper and lower limits of the power output of the CHP unit respectively; Ω chp is a collection of CHP units; Under actual working conditions, the temperature of the heating network pipeline needs to be maintained within the upper and lower limits; Where: T kl,t represents the pipe temperature at time t, and Respectively represent the upper and lower limits of the pipeline temperature; In order to avoid the pipeline from storing or releasing heat for a long time, which causes the pipeline temperature to continue to run at a high or low level, it is necessary to set the maximum heat storage time and heat release time constraints; the pipeline is in the heat release state at the initial moment, starts to store heat at time t3, and turns to heat release at time t4, then: Where: and They are the longest heat release time and the longest heat storage time of the heat network pipeline; The dynamic characteristics of the natural gas network include: natural gas has a large inertia time constant and compressibility during the transmission process, and the natural gas pipeline has a pipe storage capacity, so there is a flow difference between the beginning and the end of the pipeline; the pipe storage capacity constraint expression is formula (36) and (37); the pipeline flow constraint expression is formula (38) and (39): Where M gl,t is the storage capacity of pipeline gl at time t; R is the gas constant; T gas is the natural gas temperature; Z is the natural gas compression factor; ρ0 is the natural gas density under standard conditions; F gl is the friction coefficient of the pipeline gl; and They are the upper and lower limits of the flow rate of pipeline gl respectively; In order to ensure that there is still room for adjustment in the next scheduling cycle, the pipe storage capacity after one cycle is restored to the initial value, leaving an adjustment margin for the next scheduling cycle, which is expressed as formula (40); In the formula, Ω gline It is a collection of gas network pipelines; Initial storage for pipelines; The gas grid supply flexibility model includes: the time delay characteristics of natural gas transmission process make the natural gas pipeline have pipeline storage capacity, the storage and release of pipeline storage can be used to adjust the output of gas turbine units, thereby improving the operation flexibility of the power system; the operation state of the natural gas pipeline is divided into two states: gas storage and gas release, and must meet the following requirements: c gas,t +d gas,t ≤1 (41) Where: c gas,t and d gas,t are binary 0-1 variables, representing the storage and release states of the natural gas pipeline. To meet the gas load demand to the maximum extent, the flexibility provided by the natural gas pipeline on the sub-hourly time scale cannot exceed the maximum storage and release capacity of the pipeline within the sub-hourly time period; thus: Where: and are the maximum gas storage power and the maximum gas release power, which must be satisfied and On a sub-hourly time scale, the maximum gas release power and maximum gas storage power available in a natural gas pipeline can be expressed as: Where: H GV It is the high calorific value of natural gas; The flexibility provided by the natural gas system is also limited by the capacity and ramp rate of the gas turbines of the coupled units. Therefore, the flexibility of the natural gas system supply is: Where: and are the upward and downward climbing rates of the gas turbine respectively; P n,t is the output of gas turbine n at time t; and are the upper and lower limits of gas turbine output respectively; Ω gt A collection of gas turbine units; In actual operation, the pipeline pressure should be maintained within the upper and lower limits; In order to avoid the pipeline from storing or releasing natural gas for a long time, which causes the pipeline to run at a higher or lower pressure level for a long time, it is necessary to set the maximum gas storage time and gas release time constraints; the pipeline is in the gas release state at the initial moment, starts to store gas at time t8, and turns to gas release at time t9, then: Where: and They are the longest gas storage time and the longest gas release time of natural gas pipelines; The operational flexibility requirements analysis includes: The operational flexibility constraint needs to meet the flexibility requirements of real-time scheduling in the scheduling results of the hourly stage. Therefore, it is necessary to consider the changes in net load at the sub-hourly scale in the optimal scheduling at the hourly scale. Therefore, when determining the limit of the flexibility requirement, in addition to considering the sub-hourly scale changes in net load, the changes in the net load value in the time period should also be considered. Therefore, the operational flexibility requirement of the system can be expressed as: In the formula, and are the uplink and downlink flexibility demand limits for time period t, respectively; and are the uplink and downlink flexibility requirements of the system in time period t, which are determined by the sub-hourly changes in net load. This part is difficult to predict at the day before and can be set according to historical statistical laws; ΔT0 is the scheduling time interval; ΔT is the sub-hourly scale; P L,t is the net load value in time period t; The IES supply operation flexibility capability analysis includes: All the regulatory capabilities that can adjust the system output and load uncertainty are considered as the flexibility resources of the power system. The IES flexibility resources include: adjustable thermal power units; the heat network system provides flexibility through heat storage and release after taking into account the dynamic characteristics; the gas network system provides flexibility for the system through the storage and release of gas in the pipeline; therefore, the up and down flexibility provided by IES at time t and for: In the formula, and are the upstream and downstream flexibility capacities supplied by the power system, heat network system, and gas network system in time period t, respectively; The operational flexibility evaluation indicators include: The flexibility judgment basis adopted is: within the ΔT hourly time scale, the supply capacity of system operation flexibility should not be less than a certain limit value; that is, In order to more intuitively represent the operational flexibility of the power system, the flexibility margin index is introduced, and its value is the difference between the available flexibility resources and the flexibility demand; In the formula, They are the uplink and downlink flexibility margins at time t respectively.

2. The comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network according to claim 1 is characterized in that: The objective function includes: the operating cost and startup cost of conventional units, gas source output cost, power loss load penalty cost, gas loss load penalty cost, heat loss load penalty cost and wind abandonment penalty cost.

3. The comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network according to claim 1 is characterized in that: When the centralized solution method is used for optimization, the incremental piecewise linearization method is used to deal with the non-convexity of the natural gas grid and the power grid models, and the models are converted into mixed integer linear programming problems.

4. The integrated energy optimization dispatching system based on the dynamic characteristics of the electrical and thermal network adopts the integrated energy optimization dispatching method based on the dynamic characteristics of the electrical and thermal network as claimed in claim 1, characterized in that: include: The optimization dispatch model construction module is configured as follows: taking the minimum total operating cost of IES within the day-ahead dispatch period as the objective function, and taking the power system constraints, heat network system constraints, gas network system constraints and operation flexibility constraints as constraints, the systems considering the dynamic characteristics of the electrical and heat networks are modeled separately, and a comprehensive energy optimization dispatch model taking into account the sub-hourly operation flexibility is jointly constructed; The operation scheduling scheme acquisition module is configured as follows: taking the output power of the coupled units as the coupling variable, based on the comprehensive energy optimization scheduling model taking into account the sub-hourly operation flexibility, a centralized solution method is used for optimization processing to obtain the optimal unit combination operation scheduling scheme; The thermal dynamic characteristics provide flexibility constraints that are determined by a dynamic water system model, a dynamic thermal system model, and a heat network system supply flexibility model; The flexibility constraints provided by the gas dynamic characteristics are determined by a natural gas network dynamic characteristics model and a gas network supply flexibility model; The operational flexibility supply and demand balance constraint is determined through operational flexibility demand analysis, IES supply operational flexibility capability analysis, and operational flexibility evaluation indicators.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network as described in any one of claims 1 to 3 are implemented.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the comprehensive energy optimization scheduling method based on the dynamic characteristics of the electrical and thermal network as described in any one of claims 1-3 are implemented.

Images