Multi-stage Planning Method and System for Integrated Energy System Considering Dynamic Characteristics of Gas Network

Abstract

The present invention belongs to the field of integrated energy system planning, and provides a multi-stage planning method and system for an integrated energy system considering the dynamic characteristics of the gas network, including deriving the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow and performing differential processing on it; dividing the entire planning period into N planning stages according to the growth of the load within the planning period; taking the minimum full-life cycle cost of the electricity-gas integrated energy system planning as the first optimization objective and the minimum total carbon dioxide emissions within the planning period as the second optimization objective; constructing a planning model with the power flow equations of the power and natural gas systems, node energy flow balance, node output limit, and coupling device output as constraint conditions; fuzzifying each optimization objective based on the fuzzy membership function, converting the multi-objective planning into a single-objective planning problem using the weighted satisfaction index method, and then solving it.

Description

Technical Field

[0001] The invention belongs to the technical field of integrated energy system planning, and particularly relates to a multi-stage planning method and system for an integrated energy system considering the dynamic characteristics of a gas network. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] Under the increasingly severe situation of resources and environment, the development of efficient and clean energy systems has become the core of the energy policies of various countries. With the gradual maturity and commercial application of the power-to-gas technology, the integrated electricity-gas energy system composed of gas turbines and power-to-gas units enables the two-way flow of energy, further strengthening the connection between the power system and the natural gas system. Reasonable modeling of the integrated electricity-gas energy system is the basis for the optimal planning of the integrated electricity-gas energy system. Since the operation of the power system and the operation of the natural gas system belong to two different time scales, the modeling of the integrated electricity-gas energy system must consider the influence of this factor.

[0004] In the medium- and long-term planning, the load of the integrated electricity-gas energy system is not constant. Therefore, the single-stage planning of configuring equipment at one time at the beginning of the planning period is likely to cause problems such as over-construction and equipment idleness in the initial stage of system operation, and equipment aging and capacity shortage in the later stage of operation, affecting the economic benefits of the planning. The multi-stage planning divides the planning period into multiple stages. As the load level of the system continuously increases, new equipment is invested at the beginning of each planning stage, which is beneficial to improving the economy of the planning.

[0005] In recent years, the "dual carbon" strategic goal proposed in China has accelerated the pace of reducing carbon emissions, promoted the adjustment of the energy structure, vigorously developed renewable energy, and strived to balance economic development and green transformation simultaneously. Therefore, in the planning of the integrated electricity-gas energy system, not only the lowest economic cost should be pursued, but also the environmental protection of the planning should be emphasized.

[0006] According to the inventor's understanding, at present, the following problems exist in the planning method of the integrated electricity-gas energy system:

[0007] (1) In the existing research on the optimal planning of the integrated electricity-gas energy system, the natural gas network model often adopts the steady-state power flow model, ignoring the pipeline storage in the natural gas system. In theory, fully considering the pipeline storage in the natural gas system in the planning can achieve better economy.

[0008] (2) At present, the planning of the integrated electricity-gas energy system is mainly single-stage planning, and less consideration is given to the load growth within the planning period. The research on the multi-stage planning method of the integrated electricity-gas energy system is insufficient.

[0009] (3) At present, although the optimization planning of the integrated electricity-gas energy system considers the role of power-to-gas units in consuming the excess wind power in the system, it lacks sufficient consideration for the environmental protection objectives of the planning. Summary of the Invention

[0010] To solve the above problems, the present invention proposes a multi-stage planning method and system for an integrated energy system considering the dynamic characteristics of the gas network. Through the collaborative optimization configuration of multi-stage gas turbines and power-to-gas units, the present invention consumes the excess wind power in the system, improves the economy of the planning, and takes into account the environmental protection of the planning.

[0011] According to some embodiments, the first solution of the present invention provides a multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network, adopting the following technical solutions:

[0012] The multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network includes:

[0013] Derive the dynamic power flow equation of the natural gas system according to the fluid dynamics model of gas flow, and perform discretization processing using the Wendroff difference method;

[0014] According to the growth of the load within the planning period, divide the entire planning period into N planning stages, and add the configuration of gas turbines and power-to-gas units at the beginning of each planning stage;

[0015] Take the minimum total life cycle cost of the integrated electricity-gas energy system planning as the first optimization goal, and take the minimum total carbon dioxide emissions within the planning period as the second optimization goal;

[0016] Construct a planning model with the Distflow power flow equation of the distribution network based on second-order cone relaxation, the discretized dynamic power flow equation of the natural gas system, the node gas flow balance equation of the natural gas system, the gas pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the wind power output limit as the constraint conditions;

[0017] Fuzzify each optimization goal based on the fuzzy membership function, and use the weighted satisfaction index method to transform the multi-objective planning into a single-objective planning problem, and then solve it.

[0018] Further, the derivation of the dynamic power flow equation of the natural gas system according to the fluid dynamics model of gas flow and the discretization processing of the partial differential equation using the Wendroff difference method include:

[0019] Derive the dynamic power flow equation of the natural gas system according to the fluid dynamics model of gas flow;

[0020] Simplify the dynamic power flow method of the natural gas system to obtain a partial differential equation describing the dynamic characteristics of the gas network;

[0021] The partial differential equation is discretized using the Wendroff difference method to obtain the dynamic power flow equation of the natural gas system after difference.

[0022] Furthermore, the gas turbine and power-to-gas unit configuration takes the coupling nodes of the integrated electricity-gas energy system as the candidate planning locations for the gas turbine and power-to-gas units.

[0023] Furthermore, the life cycle cost includes all investment costs, operating costs, and maintenance costs incurred during the planning period, and the salvage value of the equipment at the end of the planning period should be subtracted; among them, all costs should be converted into the present value at the beginning of the planning period.

[0024] Furthermore, the fuzzy membership function is used to fuzzify each optimization objective, and the weighted satisfaction index method is used to transform the multi-objective planning into a single-objective planning problem, including:

[0025] Determine the corresponding membership functions based on the first optimization objective and the second optimization objective;

[0026] Fuzzify each optimization objective according to the fuzzy membership function, sum the satisfaction degrees of the first optimization objective and the second optimization objective weighted, construct an overall satisfaction objective function, and obtain a multi-objective weighted fuzzy planning model;

[0027] Realize the transformation from multi-objective planning to a single-objective planning problem.

[0028] Furthermore, the greater the membership degrees of the first optimization objective and the second optimization objective, the higher the satisfaction degree.

[0029] Furthermore, the dynamic power flow equation of the natural gas system is composed of a momentum equation, a mass balance equation, and a state equation.

[0030] According to some embodiments, the second solution of the present invention provides a multi-stage planning system for an integrated energy system considering the dynamic characteristics of the gas network, and adopts the following technical solutions:

[0031] A multi-stage planning system for an integrated energy system considering the dynamic characteristics of the gas network, including:

[0032] A dynamic power flow equation construction module of the gas network, configured to derive the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow, and perform discretization processing using the Wendroff difference method;

[0033] A planning period division module, configured to divide the entire planning period into N planning stages according to the load growth situation during the planning period, and add gas turbine and power-to-gas unit configurations at the beginning of each planning stage;

[0034] A multi-optimization objective construction module, configured to take the minimum life-cycle cost of the integrated electricity-gas energy system planning as the first optimization objective and the minimum total carbon dioxide emissions during the planning period as the second optimization objective;

[0035] A multi-objective planning model construction module, configured to construct a planning model with the distribution network Distflow power flow equation based on second-order cone relaxation, the differentialized dynamic power flow equation of the natural gas system, the node gas flow balance equation of the natural gas system, the air pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the wind power output limit as constraint conditions;

[0036] A multi-objective transformation and solution module, configured to fuzzify each optimization objective based on the fuzzy membership function, transform the multi-objective planning into a single-objective planning problem using the weighted satisfaction index method, and then solve it.

[0037] According to some embodiments, the third aspect of the present invention provides a computer-readable storage medium.

[0038] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps in the multi-stage planning method of the integrated energy system considering the dynamic characteristics of the gas network as described in the first aspect above.

[0039] According to some embodiments, the fourth aspect of the present invention provides a computer device.

[0040] A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-stage planning method of the integrated energy system considering the dynamic characteristics of the gas network as described in the first aspect above.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. The present invention is for the integrated electricity-gas energy system planning considering the dynamic characteristics of the gas network, performs refined modeling on the natural gas system, and can utilize the pipeline storage during the dynamic process of natural gas, thereby being able to buffer the fluctuations of natural gas load, effectively reduce the gas purchase cost, and improve the economy of the planning; comprehensively considering the economic and environmental protection objectives of the planning, through the collaborative optimization configuration of multi-stage gas turbines and power-to-gas units, absorb the excess wind power in the system, improve the energy utilization efficiency, reduce the carbon dioxide emissions, and obtain a planning result that takes into account both economy and environmental protection.

[0043] 2. The present invention is a multi-stage planning for an electric-gas integrated energy system. Compared with single-stage planning, this planning method avoids investing in equipment all at once at the beginning of the planning period, thus effectively avoiding problems such as over-investment and equipment idleness in the early stage of planning, and equipment capacity shortage and decline in energy supply quality in the later stage of planning, improving the economy of the planning.

[0044] 3. The present invention comprehensively considers the economic and environmental protection objectives of the planning, conducts multi-objective optimization planning for the electric-gas integrated energy system, and can minimize the carbon dioxide emissions of the system to the greatest extent on the premise of ensuring the economy of the planning, improving the environmental protection of the planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0046] Figure 1 is a flowchart of a multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network according to an embodiment of the present invention;

[0047] Figure 2 is a structure diagram of an electric-gas integrated energy system according to an embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of multi-stage planning according to an embodiment of the present invention;

[0049] FIG. 4(a) is a membership function corresponding to planning objective 1 according to an embodiment of the present invention;

[0050] FIG. 4(b) is a membership function corresponding to planning objective 2 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The present invention will be further described below in conjunction with the drawings and embodiments.

[0052] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the 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, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] Without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0055] Embodiment 1

[0056] As Figure 1 shown, this embodiment provides a multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network. This embodiment takes the application of this method to a server as an example. It can be understood that this method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, web servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not limit this. In this embodiment, the method includes the following steps:

[0057] Derive the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow, and perform discretization processing using the Wendroff difference method;

[0058] According to the growth of the load during the planning period, divide the entire planning period into N planning stages, and add the configuration of gas turbines and power-to-gas units at the beginning of each planning stage;

[0059] Take the minimum total life cycle cost of the power-gas integrated energy system planning as the first optimization goal, and take the minimum total carbon dioxide emissions during the planning period as the second optimization goal;

[0060] Construct a planning model with the Distflow power flow equation of the distribution network based on second-order cone relaxation, the node gas flow balance equation of the natural gas system, the discretized dynamic power flow equation of the natural gas system, the air pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the wind power output limit as constraints;

[0061] Fuzzify each optimization goal based on the fuzzy membership function, use the weighted satisfaction index method to transform the multi-objective planning into a single-objective planning problem, and then solve it.

[0062] As Figure 1 shown, the multi-stage planning and solution method for the power-gas integrated energy system considering the dynamic characteristics of the gas network provided in this embodiment includes the following steps:

[0063] (1) Taking the flow velocity, pressure, and density of natural gas as state variables, the dynamic power flow equations of the natural gas system are derived based on the hydrodynamic model of gas flow, and the partial differential equations are further discretized using the Wendroff difference method.

[0064] (2) According to the growth of the load within the planning period, the entire planning period is divided into N planning stages, and the location and capacity of gas turbines and power-to-gas units are determined at the beginning of each planning stage.

[0065] (3) Taking the minimum total life-cycle cost of the integrated electricity-gas energy system planning as optimization objective 1 and the minimum total carbon dioxide emissions within the planning period as optimization objective 2.

[0066] (4) A planning model is constructed with the Distflow power flow model of the distribution network based on second-order cone relaxation, the node gas flow balance equation of the natural gas system, the pressure continuity equation, the energy conversion equations and output limits of gas turbines and power-to-gas units, and the output limit of wind power as constraint conditions.

[0067] (5) Each optimization objective is fuzzified based on the fuzzy membership function, and then the weighted satisfaction index method is used to achieve the transformation from multi-objectives to a single objective. The transformed single-objective planning model is solved by calling the Cplex solver through Matlab.

[0068] The dynamic power flow equations of the natural gas system in the aforementioned step (1) include the momentum equation, the mass balance equation, and the state equation.

[0069] 1) The momentum equation describing the momentum transfer of natural gas:

[0070]

[0071] In the formula, x and t represent the spatial distance and time respectively; ρ and ρ α represent the gas densities at the horizontal plane and at an angle α with it respectively, with the unit of kg / m 3 ; p represents the gas pressure, with the unit of Pa; ω represents the gas flow velocity, with the unit of m / s; g represents the acceleration due to gravity, with the unit of m / s 2 ; d represents the pipeline diameter, with the unit of m; λ represents the pipeline friction coefficient.

[0072] The physical meanings of the terms in the momentum equation are as follows: the first term represents the acceleration effect of natural gas flow; the second term represents the convection effect of natural gas; the third term represents the hydrostatic effect of natural gas; the fourth term represents the influence of the horizontal height of the pipeline on momentum; the fifth term represents the component of the second-order partial stress tensor. In actual analysis, it is usually assumed that the natural gas pipeline is at the same horizontal position, i.e., α = 0, and at the same time, the convection effect that only exists when the gas flow velocity is close to the speed of sound is ignored. Then the momentum equation can be simplified as:

[0073]

[0074] 2) The material balance equation describing the mass conservation of natural gas in the pipeline:

[0075]

[0076] 3) The state equation describing the relationship between the pressure and density of natural gas:

[0077] p = c 2 ρ (4)

[0078] where c represents the speed of sound, with the unit of m / s.

[0079] To linearize the model, the average gas velocity of natural gas is used to approximately replace a factor of the quadratic term of the gas velocity in the third term of the momentum equation. And define the mass flow rate M = ρωA, and substitute it into the momentum equation and the material balance equation.

[0080] Finally, a set of simplified partial differential equations describing the dynamic characteristics of the gas network is obtained:

[0081]

[0082] where M is the mass flow rate of natural gas in the pipeline, with the unit of kg / s; A is the cross-sectional area of the pipeline, with the unit of m 2 .

[0083] Using the Wendroff difference method, the simplified partial differential equations are discretized in time and space to obtain linear algebraic equations that can be directly used in subsequent optimization problems. The specific difference format of the Wendroff difference method is:

[0084]

[0085] where Δt represents the time step and Δx represents the space step.

[0086] Using this difference format, the pipeline is divided into several segments, each with a length of Δx, and the momentum equation and the material balance equation are applied to analyze it. The variable space step Δx is adopted, and the observation points are set at both ends i, j of the pipeline. Therefore, i + 1 in the Wendroff difference format is replaced by the other end j of the pipeline, and at the same time Δx is replaced by the pipeline length L. Then, for each pipeline segment ij, applying the Wendroff difference format to the simplified partial differential equation set, the differentialized dynamic power flow equations of the natural gas system are obtained as follows:

[0087]

[0088] Wherein, M i,t and M j,t are the mass flow rates of natural gas at the head and end of pipeline ij at time t, respectively; p i,t and p j,t are the gas pressures at the head and end of pipeline ij at time t, respectively; ρ i,t and ρ j,t are the gas densities at the head and end of pipeline ij at time t, respectively; L ij , A ij , d ij are the length, cross-sectional area, and pipeline diameter of natural gas pipeline ij, respectively; is the average flow velocity of natural gas in pipeline ij; λ is the friction coefficient of the natural gas pipeline.

[0089] The schematic diagram of the multi-stage planning of the electric-gas integrated energy system in the aforementioned step (2) is as shown in Figure 3 . S i represents the i-th planning stage, and the expressions of the new capacity configuration matrices Q i and Q gt,i of the gas turbine and the power-to-gas unit in S p2g,i are as follows:

[0090]

[0091]

[0092] Wherein, and represent the new configuration capacities of the gas turbine and the power-to-gas unit at the m-th candidate location in S i respectively; M represents the total number of candidate locations for the planning of the gas turbine and the power-to-gas unit.

[0093] The upper and lower limits of the new capacity configuration of the gas turbine and the power-to-gas unit at the m-th candidate location in S i are as follows:

[0094]

[0095]

[0096] Wherein, and represent the upper limit values of the new capacities of the gas turbine and the power-to-gas unit at each candidate location, respectively; and are the 0-1 integer variables indicating whether the gas turbine and the power-to-gas unit are configured at the m-th candidate location in S i respectively.

[0097] The cumulative capacity configuration matrix W of the gas turbine and the power-to-gas unit in S i ​gt,i and W p2g,i The expressions and calculation formulas are as follows:

[0098]

[0099]

[0100]

[0101]

[0102] In the formula, and respectively represent the cumulative installed capacity of the gas turbine and the power-to-gas unit at the m-th candidate location in S i The cumulative installed capacity of the gas turbine and the power-to-gas unit at the m-th candidate location in S

[0103] The calculation formula for the total life cycle cost of Target 1 in the aforementioned step (3) is as follows:

[0104]

[0105] In the formula, N is the number of stages divided in the planning period; n is the total number of years in the planning period; S i represents the i-th planning stage, i = 1, 2,..., N; is the present value factor of the starting year of S i R k is the present value factor of the k-th year; R n is the present value factor at the end of the planning period; is the equipment investment cost of S i ; and are the system operation cost and equipment maintenance cost in the k-th year of the planning period respectively; F RV is the equipment salvage value at the end of the planning period.

[0106] The present value factor converts the costs generated in each year into the present value at the beginning of the planning period. The calculation formula for the present value factor is:

[0107] R k =(1 + r) -k

[0108] In the formula, k represents the number of years from the time when the cost occurs to the beginning of the planning period; r represents the discount rate.

[0109] 1) The calculation formula for the equipment investment cost of S i is as follows:

[0110]

[0111] In the formula, are the unit capacity investment costs of the gas turbine and the power-to-gas unit, respectively; Q gt,i and Q p2g,i respectively represent the capacity matrices of the gas turbine and the power-to-gas unit newly configured at each coupling node in the electric-gas integrated energy system in the i-th planning stage.

[0112] 2) The calculation formula for the system operation cost in the k-th year is:

[0113]

[0114] In the formula, c grid (t) is the electricity price at time t; c gas is the natural gas price; P grid (t) represents the purchased electric power; M gas (t) represents the purchased gas mass flow rate.

[0115] 3) The calculation formula for the equipment maintenance cost in the k-th year is:

[0116]

[0117] In the formula, and are the unit output maintenance costs of the gas turbine and the power-to-gas unit, respectively; P gt (t) and P p2g (t) are the active power generated by the gas turbine and the active power consumed by the power-to-gas unit at time t, respectively.

[0118] 4) The straight-line method of depreciation is used to calculate the depreciation of equipment, and the residual value of each piece of equipment configured after cumulative depreciation at the end of the planning period is calculated.

[0119] The annual depreciation cost C Dep,j of the j-th piece of equipment during its service life is:

[0120] C Dep,j = C inv,j (1 - δ j ) / T j (20)

[0121] C inv,j = c inv,j * Q j (21)

[0122] In the formula, δ j is the net salvage value rate of the j-th piece of equipment; T j is the service life of the j-th piece of equipment; (1 - δ j ) / T j is the annual depreciation rate of the j-th piece of equipment during its service life; C inv,j is the investment cost of the j-th piece of equipment; cinv,j is the investment cost per unit capacity of the j-th device; Q j is the configured capacity of the j-th device.

[0123] The total salvage value of the equipment at the end of the planning period is:

[0124]

[0125] In the formula, J x is the total number of equipment owned at the end of the planning period; Y j is the total number of years the j-th device has been in operation from configuration to the end of the planning period.

[0126] The calculation formula for the total carbon dioxide emissions during the planning period of Target 2 in the aforementioned step (3) is as follows:

[0127]

[0128] In the formula, N is the number of stages into which the planning period is divided; y i is the number of years the i-th planning stage lasts; is the power purchase from the grid at time t; is the gas purchase power at time t; α e is the carbon dioxide emission coefficient for grid power purchase; α gas is the carbon dioxide emission coefficient for gas purchase from the gas source.

[0129] The Distflow power flow equation of the distribution network based on second-order cone relaxation in the aforementioned step (4) is as follows:

[0130]

[0131] In the formula, Ω(i) is the set of branches with node i as the end node in the distribution network; Ψ(i) is the set of branches with node i as the head node in the distribution network; P i,t and Q i,t are the active and reactive powers of node i at time t, respectively; P ji,t and Q ji,t are the active and reactive powers at the head end of branch ji, respectively; U i,t is the node voltage; I ji,t is the branch current; r ji and x ji are the branch resistance and reactance, respectively.

[0132] The power purchase constraint from the superior grid, node voltage constraint, and line capacity constraint of the distribution network are as follows:

[0133]

[0134] In the formula, P grid (t) represents the power purchase from the grid at time t; Uimin and U imax are the lower and upper limits of the voltage of node i respectively; P ji,min and P ji,max are the lower and upper limits of the active power of the line respectively; Q ji,min and Q ji,max are the lower and upper limits of the reactive power of the line respectively.

[0135] The gas flow balance equation, gas pressure continuity equation, gas state equation, node gas pressure limit, pipeline head and end flow limit, and gas source gas purchase volume limit of the natural gas system are as follows:

[0136]

[0137] In the formula, is the output gas flow of the gas source at node i at time t; is the gas consumption of the gas turbine unit at node i of the natural gas system at time t; is the output gas flow of the power-to-gas unit at node i of the natural gas system at time t; is the gas load at node i at time t; and are the end gas flow and pressure of the pipeline with node i as the end node at time t respectively; and are the head gas flow and pressure of the pipeline with node i as the head node at time t respectively; Ω(i) is the set of pipelines with node i as the end node in the gas network; Ψ(i) is the set of pipelines with node i as the head node in the gas network; p i,t and ρ i,t are the gas pressure and gas density at node i at time t respectively; c is the speed of sound; p imin and p imax are the lower and upper limits of the gas pressure of node i respectively; and are the lower and upper limits of the head gas flow of the pipeline respectively; and are the lower and upper limits of the end gas flow of the pipeline respectively.

[0138] The energy conversion constraint and output limit constraint of the gas turbine unit are as follows:

[0139]

[0140] In the formula, is the active power generated by the gas turbine unit at the power system node n at time t; η gt is the conversion efficiency of the gas turbine; is the cumulative installed capacity of the gas turbine unit at the power system node n.

[0141] The energy conversion constraints and output limit constraints of the power-to-gas unit are as follows:

[0142]

[0143] Wherein, is the active power consumed by the power-to-gas unit at power system node k at time t; η p2g is the energy conversion efficiency of the power-to-gas unit. is the cumulative installed capacity of the power-to-gas unit at power system node k.

[0144] The absorbed power of the wind farm at each moment is not greater than its predicted power. The output limit of the wind farm is as follows:

[0145]

[0146] Wherein, P wfc (t) is the absorbed power of the wind farm at time t; is the maximum predicted power of the wind farm at time t.

[0147] As shown in Figures 4(a) and 4(b), the membership functions corresponding to optimization objective 1 and optimization objective 2 in the foregoing step (5) are:

[0148]

[0149]

[0150] Wherein: μ(F g ) and μ(F c ) are the membership functions of the minimum total life cycle cost objective and the minimum total carbon dioxide emissions within the planning period respectively; F g,min and F c,min are the optimal solutions of each single-objective planning, representing the minimum total life cycle cost of the system in theory and the minimum total carbon dioxide emissions within the planning period respectively; β g and β c are the elastic satisfaction degrees; β g F g,min and β c F c,min represent the allowable increase in the total life cycle cost and the allowable increase in the total carbon dioxide emissions within the planning period by the decision maker respectively.

[0151] By weighted summing the satisfaction degrees of objective 1 and objective 2, an overall satisfaction objective function is constructed, thereby realizing the transformation from multiple objectives to a single objective. The established multi-objective weighted fuzzy programming model is as follows:

[0152]

[0153] where μ is the overall satisfaction degree; and are the satisfaction degrees of the minimum full - life - cycle cost target and the minimum total carbon dioxide emissions target within the planning period respectively; a 1 and a 2 are the weight coefficients of Target 1 and Target 2 respectively, which are set according to the different requirements of decision - makers for economy and environmental protection; H(x) represents all the equality constraints in the model, including the differential - form natural - gas system dynamic power - flow equation (7), the distribution - network Distflow power - flow equation (24) based on second - order cone relaxation, and the equality constraints in (26) - (28); G(x) represents all the inequality constraints in (25) - (29).

[0154] The finally constructed optimization problem (32) is a single - objective linear programming problem. The optimization problem is constructed through Matlab, and the Cplex solver is called for solution.

[0155] This embodiment discloses a multi - stage planning method for an electric - gas integrated energy system considering the dynamic characteristics of the gas network. The dynamic power - flow equation of the natural - gas system is derived according to the hydrodynamic model of gas flow, and the partial differential equation is further discretized by the Wendroff differentiation method. The natural - gas dynamic model can take into account the "pipe storage", thereby reducing the gas purchase cost and system operation cost and improving the economy of the planning. Considering the load growth within the planning period, the planning period is divided into multiple planning stages, and the site selection and capacity determination of newly added gas turbines and power - to - gas units are carried out at the beginning of each planning stage. The multi - stage planning method for the electric - gas integrated energy system avoids investing in equipment all at once at the beginning of the planning period, thus avoiding problems such as over - construction and equipment idleness in the initial stage of system operation, and equipment aging and capacity shortage in the later stage of operation, and can improve the economic benefits of the planning. This planning method comprehensively considers the minimum full - life - cycle cost target and the minimum total carbon dioxide emissions target within the planning period. Based on the fuzzy membership function, the multi - objective is weighted and fuzzified, and the multi - objective planning is transformed into a single - objective planning with the maximum comprehensive satisfaction degree as the goal, effectively taking into account the economy and environmental protection of the planning. Therefore, the multi - stage planning method for the electric - gas integrated energy system considering the dynamic characteristics of the gas network proposed by the present invention can achieve better economy and take into account the environmental protection of the planning, reaching the comprehensive optimum of economy and environmental protection.

[0156] A simulation calculation is carried out using an electric - gas integrated energy system composed of an IEEE33 - node distribution network and a 7 - node natural - gas network. The structure of the electric - gas integrated energy system is as Figure 2 shown. Gas turbines and power - to - gas units are configured at the coupling nodes, and wind farms WF1 and WF2 with capacities of 4MW and 5MW are connected to the power grid at nodes 8 and 27 respectively.

[0157] Parameters related to Planning Goals 1 and 2: Investment cost per unit capacity of gas turbines is 7×10 6 yuan / MW, and the investment cost per unit capacity of the power-to-gas unit is 8×10 6 yuan / MW; The service lives of both the gas turbine and the power-to-gas unit are 25 years; The net residual value rate δ of the gas turbine and the power-to-gas unit is taken as 6%; The maintenance cost of the gas turbine and the power-to-gas unit is 0.05 yuan / kW; The natural gas price coefficient is 0.4 yuan / kg; The gas turbine efficiency parameter η gt is 1.8 MW·s / kg, and the power-to-gas unit efficiency parameter η p2g is 0.11 kg / (s·MW); The CO 2 emission factor α e of grid power purchase is 2 968 g / kWh; The CO gas emission factor α 1 of gas network gas purchase is 2 220 g / kWh; The weight coefficients a g and a c of Goals 1 and 2 each take 0.5; The elastic satisfaction degrees β g and β c of Goals 1 and 2 each take 0.5.

[0158] The 8-year planning period is divided into two stages. S 1 lasts for 3 years, and S 2 lasts for 5 years. The electrical load and gas load in Stage 2 are both twice those in Stage 1.

[0159] Table 1 presents a comparison of the multi-stage planning results of the electricity-gas integrated energy system considering the Weymouth steady-state model and the dynamic model of the gas network. As can be seen from Table 1, the life-cycle cost of the plan considering the dynamic model of the gas network is lower. The first reason is that the dynamic model of the gas network can take into account the natural gas stored in the pipeline, thereby reducing the gas purchase cost; The second reason is that when considering the dynamic model of the gas network, larger-capacity electrical coupling equipment is configured, reducing the system operation cost, thereby improving the economy of the plan.

[0160] Table 2 presents a comparison of the single-stage planning and multi-stage planning results of the electricity-gas integrated energy system considering the dynamic characteristics of the gas network. As can be seen from Table 2, the life-cycle cost of the multi-stage planning is lower than that of the single-stage planning. The first reason is that the total investment cost of the equipment in the multi-stage planning is lower; The second reason is that the total residual value of the equipment at the end of the planning period in the multi-stage planning is higher; Therefore, the economy of the multi-stage planning of the electricity-gas integrated energy system is better.

[0161] Table 3 presents the comparison of the single-objective planning and multi-objective planning results of the integrated electricity-gas energy system considering the dynamic characteristics of the gas network. It can be seen from Table 3 that comprehensively considering the full life cycle cost objective and the carbon dioxide emission objective can effectively balance the economy and environmental protection of the planning, obtain the compromise solution with the maximum satisfaction degree of each sub-objective and the comprehensive satisfaction degree, and achieve the comprehensive optimization of the economy and environmental protection of the planning. Table 4 presents the values of the satisfaction degrees of each sub-objective and the comprehensive satisfaction degree.

[0162] Table 1 Comparison of multi-stage planning results considering steady-state model and dynamic model of gas network

[0163]

[0164]

[0165] Table 2 Comparison of single-stage planning and multi-stage planning results

[0166] Cost (tens of millions of yuan) Single-stage planning Multi-stage planning Total equipment investment cost 3.9364 3.4069 Total operation and maintenance cost during the planning period 30.178 30.417 Total equipment salvage value 1.6060 1.9776 Life cycle cost 32.509 31.846

[0167] Table 3 Comparison of single-objective planning and multi-objective planning results

[0168] Planning objective Life cycle cost / tens of millions of yuan Total carbon dioxide emissions / kg Minimum life cycle cost 31.846 <![CDATA[1.4245×10 8 > Minimum total carbon dioxide emissions 92.386 <![CDATA[1.2672×10 8 > Maximum comprehensive satisfaction 32.161 <![CDATA[1.3551×10 8 >

[0169] Table 4 Values of satisfaction degrees of each sub-objective and comprehensive satisfaction degree

[0170]

[0171] Embodiment 2

[0172] This embodiment provides a multi-stage planning system for an integrated energy system considering the dynamic characteristics of the gas network, including:

[0173] A gas network dynamic power flow equation construction module, configured to derive the natural gas system dynamic power flow equation according to the hydrodynamic model of gas flow, and perform discretization processing using the Wendroff difference method;

[0174] A planning period division module, configured to divide the entire planning period into N planning stages according to the load growth situation within the planning period, and add gas turbine and power-to-gas unit configurations at the beginning of each planning stage;

[0175] A multi-optimization objective construction module, configured to take the minimum full life cycle cost of the integrated electricity-gas energy system planning as the first optimization objective, and take the minimum total carbon dioxide emissions within the planning period as the second optimization objective;

[0176] The multi-objective programming model construction module is configured to construct a planning model with the Distflow power flow equation of the distribution network based on second-order cone relaxation, the differentialized dynamic power flow equation of the natural gas system, the node gas flow balance equation of the natural gas system, the air pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the wind power output limit as constraint conditions;

[0177] The multi-objective transformation and solution module is configured to fuzzify each optimization objective based on the fuzzy membership function, transform the multi-objective programming into a single-objective programming problem using the weighted satisfaction index method, and then solve it.

[0178] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the first embodiment above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0179] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] The proposed system can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the above module division is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0181] Embodiment III

[0182] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in the multi-stage planning method of the integrated energy system considering the dynamic characteristics of the gas network as described in the first embodiment above.

[0183] Embodiment IV

[0184] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-stage planning method of the integrated energy system considering the dynamic characteristics of the gas network as described in the first embodiment above.

[0185] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program code.

[0186] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0187] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0189] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0190] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts on the basis of the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. Multi-stage planning method for integrated energy system considering dynamic characteristics of gas network, Characterized in that, It includes: Derive the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow, and use the Wendroff difference method for discretization processing; According to the growth of load within the planning period, divide the entire planning period into N planning stages, and add the configuration of gas turbines and power-to-gas units at the beginning of each planning stage; Taking the minimum total life cycle cost of the electricity-gas integrated energy system planning as the first optimization goal, specifically: Where N is the number of stages into which the planning period is divided; n is the total number of years in the planning period; S i represents the i-th planning stage, where i = 1, 2, …, N; is the present value factor of S i at the starting year; R k is the present value factor at the k-th year; R n is the present value factor at the end of the planning period; is the equipment investment cost of S i ; and are the system operation cost and equipment maintenance cost respectively in the k-th year of the planning period; F RV is the equipment salvage value at the end of the planning period; Taking the minimum total carbon dioxide emissions within the planning period as the second optimization goal, specifically: where N is the number of stages divided in the planning period; y i is the number of years that the planning stage i lasts; is the electricity purchase power at time t; is the gas purchase power at time t; α e is the carbon dioxide emission factor for grid electricity purchase; α gas is the carbon dioxide emission factor for gas purchase from the gas source; Construct a planning model with the Distflow power flow equation of the distribution network based on second-order cone relaxation, the discretized dynamic power flow equation of the natural gas system, the node gas flow balance equation of the natural gas system, the air pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the output limit of wind power as constraints; Fuzzify each optimization goal based on the fuzzy membership function, use the weighted satisfaction index method to transform the multi-objective planning into a single-objective planning problem, and then solve it.

2. The multi-stage planning method for integrated energy system considering dynamic characteristics of gas network according to claim 1, Characterized in that, The derivation of the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow and the use of the Wendroff difference method for discretization processing include: Derive the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow; Simplify the dynamic power flow method of the natural gas system to obtain a partial differential equation describing the dynamic characteristics of the gas network; Use the Wendroff difference method to discretize the partial differential equation to obtain the discretized dynamic power flow equation of the natural gas system.

3. The multi-stage planning method for integrated energy system considering dynamic characteristics of gas network according to claim 1, Characterized in that, The configuration of the gas turbine and the power-to-gas unit is to use the coupling nodes of the electricity-gas integrated energy system as the candidate planning locations for the gas turbine and the power-to-gas unit.

4. The multi-stage planning method for integrated energy system considering dynamic characteristics of gas network according to claim 1, Characterized in that, The total life cycle cost includes all investment costs, operation costs and maintenance costs incurred during the planning period, and the equipment salvage value at the end of the planning period should be subtracted; among them, all costs should be converted into the present value at the beginning of the planning period.

5. The multi-stage planning method for integrated energy system considering dynamic characteristics of gas network according to claim 1, Characterized in that, The fuzzification of each optimization goal based on the fuzzy membership function and the transformation of the multi-objective planning into a single-objective planning problem using the weighted satisfaction index method include: Determine the corresponding membership functions based on the first optimization goal and the second optimization goal; Fuzzify each optimization goal according to the fuzzy membership function, sum the satisfaction degrees of the first optimization goal and the second optimization goal weighted, construct an overall satisfaction objective function, and obtain a multi-objective weighted fuzzy planning model; Realize the transformation from multi-objective planning to single-objective planning problem.

6. The multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network according to claim 5, characterized in that, the greater the membership degrees of the first optimization objective and the second optimization objective, the higher the satisfaction degree.

7. The multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network according to claim 1, characterized in that, the dynamic power flow equation of the natural gas system is composed of a momentum equation, a mass balance equation, and a state equation.

8. A multi-stage planning system for an integrated energy system considering the dynamic characteristics of the gas network, characterized in that, it includes: a dynamic power flow equation construction module of the gas network, configured to derive the dynamic power flow equation of the natural gas system according to the hydrodynamic model of gas flow, and perform discretization processing using the Wendroff difference method; a planning period division module, configured to divide the entire planning period into N planning stages according to the growth of the load within the planning period, and add the configuration of gas turbines and power-to-gas units at the beginning of each planning stage; a multi-optimization objective construction module, configured to take the minimum life-cycle cost of the electricity-gas integrated energy system planning as the first optimization objective, specifically: Where, N is the number of stages divided in the planning period; n is the total number of years in the planning period; S i represents the i-th planning stage, where i = 1, 2, …, N; is the present value factor of the starting year of S i ; R k is the present value factor of the k-th year; R n is the present value factor at the end of the planning period; is the equipment investment cost of S i ; and are the system operation cost and equipment maintenance cost in the k-th year of the planning period respectively; F RV is the equipment salvage value at the end of the planning period; take the minimum total carbon dioxide emissions within the planning period as the second optimization objective, specifically: Where N is the number of stages divided in the planning period; y i is the number of years that the planning stage i lasts; is the electricity purchase power at time t; is the gas purchase power at time t; α e is the carbon dioxide emission factor for grid electricity purchase; α gas is the carbon dioxide emission factor for gas purchase from the gas source; a multi-objective planning model construction module, configured to construct a planning model with the Distflow power flow equation of the distribution network based on second-order cone relaxation, the discretized dynamic power flow equation of the natural gas system, the node gas flow balance equation of the natural gas system, the gas pressure continuity equation, the energy conversion equation and output limit of gas turbines and power-to-gas units, and the output limit of wind power as constraints; a multi-objective transformation and solution module, configured to fuzzify each optimization objective based on the fuzzy membership function, transform the multi-objective planning into a single-objective planning problem using the weighted satisfaction index method, and then solve it.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, it implements the steps in the multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network according to any one of claims 1-7.

10. A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps in the multi-stage planning method for an integrated energy system considering the dynamic characteristics of the gas network according to any one of claims 1-7.

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