CN111539599A - Coordination planning method for regional comprehensive energy system - Google Patents

Coordination planning method for regional comprehensive energy system Download PDF

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CN111539599A
CN111539599A CN202010264026.2A CN202010264026A CN111539599A CN 111539599 A CN111539599 A CN 111539599A CN 202010264026 A CN202010264026 A CN 202010264026A CN 111539599 A CN111539599 A CN 111539599A
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徐晨博
孙可
张利军
孙轶恺
邹波
王蕾
郑朝明
章浩
袁翔
范明霞
杨文涛
文福拴
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a coordination planning method for a regional comprehensive energy system. The technical scheme of the invention comprises the following steps: 1) adopting a steady-state comprehensive power flow analysis method suitable for the 'electricity-gas-heat' collaborative planning problem, namely an OMEF analysis method; 2) based on the OMEF analysis method in the step 1), a coordination planning model is adopted, and the model consists of an operation sub-model and an extension planning sub-model; the running sub-model is an OMEF model; 3) solving the coordination planning model in the step 2) by adopting an optimization algorithm based on Benders decomposition. The coordination planning model and the method of the invention can optimize the network topology and reasonably plan the coupling device; the invention can effectively realize stable operation and reasonable coordination planning of the regional comprehensive energy system and obviously improve the energy utilization efficiency of the terminal.

Description

Coordination planning method for regional comprehensive energy system
Technical Field
The invention belongs to the field of regional integrated energy systems, and relates to a regional integrated energy system coordination planning method considering an electric-gas-heat coupling relation.
Background
In recent years, with the gradual depletion of fossil energy, renewable energy power generation resources (mainly solar energy and wind energy) have attracted much attention. In order to promote the utilization of renewable energy and to improve the efficiency of terminal energy consumption, coupling devices, i.e., Cogeneration (CHP) plants, power-to-gas (P2G) plants and electric boilers, are widely used in the field of regional energy. As these devices consume/generate energy in different forms, interconnection of different energy carriers is facilitated. This phenomenon is particularly evident in the design and operation of regional integrated energy systems. Therefore, it is very important to fully research the coordination planning strategy of the regional comprehensive energy system to promote the energy optimization and improve the energy utilization efficiency.
At present, the cooperative operation problem related to multi-energy cooperation is researched, but the research on the cooperative planning problem is relatively coarse and shallow. The main points are as follows: 1) most researches only relate to the cooperative planning of two energy networks of 'electricity-gas' or 'electricity-heat', and can not cover three commonly used terminal energy utilization forms of 'electricity-gas-heat'; 2) due to the lack of accurate and efficient heat supply network analysis models and methods, the existing collaborative planning method cannot optimize the model and topological structure of the heat pipeline and only can solve the problem of location/volume fixing of the coupling device. Therefore, a coordinated planning method capable of optimizing a network topology and planning a coupling device is needed for an electricity-gas-heat integrated energy system.
In addition, as the researched regional comprehensive energy system relates to subsystems in three forms of electricity, gas and heat, the solving complexity of the planning model can be greatly improved, and therefore, an efficient optimization algorithm is needed, convenient, fast, accurate and modularized calculation is convenient to carry out, and the practicability of the method is improved.
Disclosure of Invention
In order to solve the problems, the invention provides a regional comprehensive energy system coordination planning method considering the electricity-gas-heat coupling relation so as to optimize a network topological structure and improve the energy utilization efficiency.
Therefore, the invention adopts the following technical scheme: the coordinated planning method of the regional comprehensive energy system comprises the following steps:
1) adopting a steady-state comprehensive power flow analysis method suitable for the 'electricity-gas-heat' collaborative planning problem, namely an OMEF analysis method;
2) based on the OMEF analysis method in the step 1), a coordination planning model is adopted, and the model consists of an operation sub-model and an extension planning sub-model; the running sub-model is an OMEF model;
3) solving the coordination planning model in the step 2) by adopting an optimization algorithm based on Benders decomposition.
The invention provides a steady-state relaxation model capable of comprehensively simulating electricity-gas-heat three-network power flow, which is called an optimal multi-energy flow (OMEF) method. The invention provides a coordinated planning method which can optimize an electricity-gas-heat three-network topological structure and plan a CHP unit, a P2G plant station and an electric boiler. The coordinated planning model provided by the invention belongs to the Mixed-Integer quadratic structured Program (MIQCP) problem, is difficult to directly solve, and provides a matched efficient optimization algorithm based on Benders decomposition.
Further, in step 1), the optimization objective of OMEF, i.e. minimization
Figure BDA0002439405110000021
For minimizing the operating costs of regional integrated energy systems:
Figure BDA0002439405110000022
in the formula (I), the compound is shown in the specification,
Figure BDA0002439405110000023
consists of three parts: 1) the cost of purchasing power, power loss and boiler operation and maintenance in the power distribution network; 2) the cost of purchasing natural gas and operating and maintaining the electric power-to-gas plant station in the gas distribution network; 3) heat loss of the heat supply network and operation and maintenance cost of the cogeneration unit;
wherein the content of the first and second substances,
Figure BDA0002439405110000024
represents the electric power purchased by node u during time period t; omegaP、ΩG、ΩLRepresenting a set of nodes in a distribution network, a distribution network and a heating network; Δ t represents the simulation time slot;
Figure BDA0002439405110000025
respectively representing market prices of electricity, natural gas and unit heat energy; thetaP、θG、θHRespectively representing the unit cost of operation and maintenance of an electric boiler, an electric-to-gas plant and a combined heat and power generation unit, converting the unit cost into unit electric power, unit: $ kW;
Figure BDA0002439405110000026
representing the output electric power of the cogeneration unit;
Figure BDA0002439405110000027
and
Figure BDA0002439405110000028
respectively representing the power consumptions of the electric heating boiler and the electric transfer gas plant station; delta Pu,tAnd Δ Φl,tRespectively representing the electric power loss and the thermal power loss when the power distribution network and the heat supply network transmit energy;
Figure BDA0002439405110000029
indicating that the user purchased natural gas from the heating network.
Further, the relaxation constraints of the distribution network are as follows:
Figure BDA0002439405110000031
Figure BDA0002439405110000032
in the formula, Pu,tOr Pv,tAnd
Figure BDA0002439405110000033
respectively, the injected active power and the purchased electric power, respectively, of which the magnitudes are limited to
Figure BDA0002439405110000034
And
Figure BDA0002439405110000035
Qu,tor Qv,tAnd
Figure BDA0002439405110000036
representing the injected reactive power and the purchased electric power at node u or v, respectively, for a time period t, the magnitudes of which are limitedIs prepared into
Figure BDA0002439405110000037
And
Figure BDA0002439405110000038
Ruand XuThe resistance and reactance of a feeder line with a power transmission tail end as a node u are respectively;
Figure BDA0002439405110000039
is the apparent power of the feeder with the power transmission end being node u;
Figure BDA00024394051100000310
and
Figure BDA00024394051100000311
local photovoltaic and wind-electricity output respectively; delta Pu,tRepresenting the electrical power when the power distribution network is transmitting energy; vBRepresenting a rated voltage of the distribution network; vu,tRepresenting the real-time voltage of each electrical node;Vand
Figure BDA00024394051100000312
respectively represent Vu,tUpper and lower limits of (d);
Figure BDA00024394051100000313
a topological incidence matrix representing the distribution network; omegaPRepresenting a set of nodes in a power distribution network;
Figure BDA00024394051100000314
and
Figure BDA00024394051100000315
representing the active and reactive loads of the node u at the moment t;
Figure BDA00024394051100000316
representing the output electric power of the cogeneration unit;
Figure BDA00024394051100000317
respectively showing the power consumptions of the electric boiler, the electric gas-transferring plant and the water pump.
Further, the relaxation constraints of the gas distribution network are as follows:
Figure BDA00024394051100000318
Figure BDA00024394051100000319
in the formula: fk,tIs the natural gas flow rate of gas pipeline k at time t, and is represented by F when the sending end node of gas pipeline k is w and q, respectivelywq,tRepresents; gq,tOr Gw,tThe natural gas pressure of a gas node q or a node w at the moment t is represented;
Figure BDA00024394051100000320
and
Figure BDA00024394051100000321
respectively represent Fk,t、Gq,tOr Gw,tAnd
Figure BDA00024394051100000322
the upper limit of (a) is,Grepresents Gq,tOr Gw,tThe lower limit of (d); ψ is a positive number greater than 1000;wq,tis a binary variable in the direction of natural gas flow, when Gw,t>Gq,t，wq,t1, otherwise 0;
Figure BDA0002439405110000041
a topological correlation matrix representing a gas distribution network; omegaWRepresenting a collection of gas conduits in a gas distribution network ηP2GRepresenting the energy conversion efficiency of the electric-to-gas plant;
Figure BDA0002439405110000042
representing the natural gas load of the gas node q at time t;
Figure BDA0002439405110000043
and
Figure BDA0002439405110000044
respectively representing the power consumption and the natural gas-electricity conversion efficiency of the cogeneration unit; mkRepresenting the parameters of the pipeline k.
Further, the relaxation constraint of the heating network is as follows:
Figure BDA0002439405110000045
Figure BDA0002439405110000046
in the formula ξj,lRepresents the relationship between heat pipes j and l, ξ when heat pipe j is upstream of heat pipe l j,l1 when heat pipe j is downstream of heat pipe l ξj,lWhen heat pipes j and l are not directly connected, ξj,l＝0；Φl,tAnd pi,tRespectively representing the energy contained in hot water transferred in unit time by the hot pipeline and the water pressure of a hot node i;
Figure BDA0002439405110000047
and
Figure BDA0002439405110000048
are respectively a variable Dl,t、Φl,tAnd pi,tThe upper limit of (a) is,Φ lis phil,tThe lower limit of (d);pis pi,tThe lower limit of (d); delta phil,tRepresents the heat loss of the heat pipe l; Δ pl,tRepresents the water pressure drop of the heat pipe l; omegaLRepresents a collection of thermal conduits; omegaHRepresenting a set of hot nodes; phil,tIs the heat power injected by the heat pipe l at time t; dl,tRepresents the flow rate of hot water in the hot pipe l in unit time;
Figure BDA0002439405110000049
and
Figure BDA00024394051100000410
respectively representing the output electric power and the 'heat-electricity' conversion efficiency of the cogeneration unit ηEBAnd
Figure BDA00024394051100000411
respectively representing the electricity-heat conversion efficiency and the power consumption of the electric boiler;
Figure BDA00024394051100000412
indicating a topological correlation matrix of the heating network ηWPAnd
Figure BDA00024394051100000413
respectively representing the electric-hydraulic pressure conversion efficiency and the power consumption of the water pump.
Further, the ome model is expressed as:
Figure BDA00024394051100000414
in the formula:
Figure BDA00024394051100000415
and
Figure BDA00024394051100000416
respectively representing variables
Figure BDA00024394051100000417
And
Figure BDA00024394051100000418
the upper limit of (3).
Further, in step 2), the extension planning submodel is as follows:
Figure BDA0002439405110000051
Figure BDA0002439405110000052
Figure BDA0002439405110000053
Figure BDA0002439405110000054
Figure BDA0002439405110000055
Figure BDA0002439405110000056
Figure BDA0002439405110000057
in the formula: f. ofExpaPlanning cost f of distribution network in the tau planning stagePτAnd the planning cost f of the gas distribution networkGτAnd heating network planning cost fHτThe components of the composition are as follows,
Figure BDA0002439405110000058
gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively representing existing electric nodes, gas pipelines and heat pipeline sets;
Figure BDA0002439405110000059
and
Figure BDA00024394051100000510
respectively representing the cost coefficients of newly added feeder installation, the existing feeder expansion and the electric boiler construction;
Figure BDA00024394051100000511
and
Figure BDA00024394051100000512
respectively representing the cost coefficients of newly added gas pipeline installation, existing gas pipeline expansion and P2G plant station construction;
Figure BDA00024394051100000513
and
Figure BDA00024394051100000514
respectively representing the cost coefficients of the installation of the newly added heat pipeline, the expansion of the existing heat pipeline and the construction of the CHP unit;
Figure BDA00024394051100000515
and
Figure BDA00024394051100000516
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure BDA00024394051100000517
and
Figure BDA00024394051100000518
0-1 decision variables of the expanded feeder line, the expanded gas pipeline and the expanded heat pipeline respectively;
Figure BDA00024394051100000519
and
Figure BDA00024394051100000520
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、
Figure BDA00024394051100000521
And
Figure BDA00024394051100000522
respectively setting the newly built/expanded capacity, the feeder line parameters and the rated power of the electric boiler in the construction scheme a; fbAnd
Figure BDA00024394051100000523
respectively representing the newly built/expanded gas pipeline capacity and the rated power of a P2G plant station in the construction scheme b; dcAnd
Figure BDA00024394051100000524
respectively representing the capacity of the newly built/expanded heat pipeline and the rated power of the CHP unit in the construction scheme c; omegaEB、ΩP2GAnd ΩCHPRespectively integrating feasible planning schemes for constructing an electric heating boiler, a P2G plant station and a CHP unit; omegaPS、ΩGSAnd ΩHSAnd respectively representing feasible planning scheme sets of the power distribution network, the gas distribution network and the heat supply network.
Further, in step 2), the planning model M is coordinated1Is expressed as:
Figure BDA0002439405110000061
in the formula: dAAnd ΩTRespectively representing the number of days in a planning phase and the time set of daily operation; f. ofM1Represents M1The target of (1);
Figure BDA0002439405110000062
representing the operating cost of the regional integrated energy system for the tau planned horizontal year;
some variables in the OMEF model need to be re-described to reflect the impact of the binary investment decision variables x, y and z on equipment capacity and optimized operation:
Figure BDA0002439405110000063
Figure BDA0002439405110000064
Figure BDA0002439405110000065
Figure BDA0002439405110000066
Figure BDA0002439405110000067
Figure BDA0002439405110000068
in the formula:
Figure BDA0002439405110000069
and
Figure BDA00024394051100000610
the initial gas flow capacity of the gas pipeline k and the water mass flow of the heat pipe l at the stage tau are respectively represented; and the number of the first and second electrodes,
Figure BDA00024394051100000611
and
Figure BDA00024394051100000612
respectively representing the initial capacity and the safety threshold of a power distribution network line with a receiving end as a bus u; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively representing existing electric nodes, gas pipelines and heat pipeline sets;
Figure BDA00024394051100000613
and
Figure BDA00024394051100000614
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure BDA00024394051100000615
and
Figure BDA00024394051100000616
respectively a dilatation feeder line, an air pipeline and a heat pipeDecision variable 0-1 of lane;
Figure BDA00024394051100000617
Figure BDA00024394051100000618
and
Figure BDA00024394051100000619
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、FbAnd DcThe capacity of the newly built/expanded air pipeline in the construction scheme a, the capacity of the newly built/expanded air pipeline in the construction scheme b and the capacity of the newly built/expanded heat pipeline in the construction scheme c are respectively set;
Figure BDA0002439405110000071
is the apparent power of the feeder with the power transmission end being node u;
Figure BDA0002439405110000072
represents the upper limit of the natural gas flow of the gas pipeline k;
Figure BDA0002439405110000073
the upper limit of the flow of hot water in the hot pipeline l in unit time;
Figure BDA0002439405110000074
and
Figure BDA0002439405110000075
rated power of the electric heating boiler in the construction scheme a, rated power of a P2G plant station in the construction scheme b and rated power of a CHP unit in the construction scheme c are respectively set;
Figure BDA0002439405110000076
a topological correlation matrix representing a gas distribution network;
Figure BDA0002439405110000077
and
Figure BDA0002439405110000078
respectively representing the maximum power of the electric boiler, the P2G plant station and the CHP unit.
Further, in step 3), the coordination planning model M is matched1Equivalent decomposition into a main problem M2And a sub-question M3And a virtual sub-problem M4：
Figure BDA0002439405110000079
Figure BDA00024394051100000710
In the formula: f. ofM2、fM3And fM4Respectively represent M2、M3And M4The target of (1); b iscut1 (m)And Bcut2 (h)Feasible Benders cut set of m-th update and infeasible Benders cut set of h-th update, β is a non-negative variable, fExpaRepresents the total expansion cost; gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; dAAnd ΩTRepresenting the number of days and the time set of daily runs, respectively, within a planning phase.
Further, in step 3), a virtual sub-question M is adopted4By introducing a non-negative virtual power supply
Figure BDA00024394051100000711
Virtual electrical load
Figure BDA00024394051100000712
Virtual air source
Figure BDA00024394051100000713
Virtual air load
Figure BDA00024394051100000714
Virtual heat source
Figure BDA00024394051100000715
And virtual heat load
Figure BDA00024394051100000716
The problem of low Benders decomposition efficiency is solved; m4Simply expressed as:
Figure BDA00024394051100000717
Figure BDA00024394051100000718
in the formula:
Figure BDA00024394051100000719
represents the virtual cost of the phase τ at time t;
Figure BDA00024394051100000720
respectively representing market prices of electricity, natural gas and unit heat energy;
linear Benders cut set Bcut1 (m)And Bcut2 (h)Generated according to the following formula:
Figure BDA0002439405110000081
Figure BDA0002439405110000082
in the formula: sigma1And σ2Respectively represent sub-problems M3And M4A vector of dual multipliers of (1); x, y and z are respectively 0-1 decision variable matrixes of the power distribution system, the gas distribution system and the local heat supply system in the planning process;
the dual multiplier is dynamically adjusted according to the following formula:
Figure BDA0002439405110000083
Λg×p＝exp(-|μIg×1Θ1×p-[S1×g1,F1×g2,D1×g3]TI1×p|) (30)
Figure BDA0002439405110000084
Figure BDA0002439405110000085
in the formula, theta, S, F and D respectively represent a maximum capacity matrix, a transformer capacity matrix, a designed natural gas flow matrix and a designed mass flow matrix of all time periods, the sizes of the matrices are p, g1, g2 and g3 respectively, a function max () is used for selecting the maximum value, a function exp () represents an exponential function with a natural constant e as the base, mu is a coefficient, Λ is an intermediate variable, and a normalization matrix of the intermediate variable is
Figure BDA0002439405110000086
Λ<1：g1>A sub-matrix representing Λ, taken from row 1 to row g1, I and Y being the identity matrix and the lower triangular matrix, respectively, g-g 1+ g2+ g 3;
Figure BDA0002439405110000087
and
Figure BDA0002439405110000088
respectively representing the injected active and reactive power of the bus u of the planning stage tau;
Figure BDA0002439405110000089
natural gas flow of the gas pipeline k for the planning stage tau;
Figure BDA00024394051100000810
is the hot water flow of the hot pipe l at the planning stage τ;
Figure BDA00024394051100000811
representing the safety threshold of bus u.
The invention has the following beneficial effects: the coordination planning model and the method of the invention can optimize the network topology and reasonably plan the coupling device. The invention adopts an improved high-efficiency algorithm based on Benders decomposition, and improves the solving efficiency of the MIQCP problem. The invention can effectively realize stable operation and reasonable coordination planning of the regional comprehensive energy system and obviously improve the energy utilization efficiency of the terminal.
Drawings
FIG. 1 is a diagram of a lumped parameter steady state model of a heat pipe in accordance with an embodiment of the present invention;
FIG. 2 is an initial topology of a regional energy complex in an embodiment of the present invention;
FIG. 3 is an expanded topology of a regional integrated energy system in an embodiment of the present invention;
FIG. 4 is a graph of the daily energy input/output of the devices in the initial regional integrated energy system in accordance with an embodiment of the present invention;
FIG. 5 is a graph illustrating the iterative convergence in the improved Benders algorithm in accordance with an embodiment of the present invention;
fig. 6 is a diagram comparing different heating network models in an embodiment of the present invention.
Detailed Description
For better understanding of the objects, technical solutions and technical effects of the present invention, the present invention will be further described with reference to the accompanying drawings.
The invention provides a regional comprehensive energy system coordination planning method considering an 'electricity-gas-heat' coupling relation, and the implementation process comprises the following detailed steps.
Step 1: due to the lack of an efficient heat supply network analysis method, the existing method related to the electricity-gas-heat three-network collaborative power flow analysis cannot be directly applied to the collaborative planning problem considering network topology optimization and heat pipe type selection. Because the existing method can not fully consider the parameters of the length, the inner diameter, the material and the like of the heat pipeline when the temperature and the pressure loss of the pipeline are calculated. Therefore, firstly, it is necessary to propose a steady-state comprehensive power flow analysis method, i.e. an OMEF method, which is suitable for the studied 'electricity-gas-heat' collaborative planning problem.
(1) Optimization objectives for OMEF
The OMEF aims to minimize the operating cost of regional integrated energy systems:
Figure BDA0002439405110000091
in the formula:
Figure BDA0002439405110000092
consists of three parts: 1) purchasing power in the power distribution network, power loss and the cost of boiler operation and maintenance; 2) the cost of purchasing natural gas and operating and maintaining a P2G plant station in a gas distribution network; 3) heat loss of the heat supply network and the operation and maintenance cost of the CHP unit.
Wherein the content of the first and second substances,
Figure BDA0002439405110000101
represents the electric power purchased by node u during time period t; omegaP、ΩG、ΩLRepresenting a set of nodes in a distribution network, a distribution network and a heating network; Δ t represents the simulation time slot;
Figure BDA0002439405110000102
respectively representing market prices of electricity, natural gas and unit heat energy; thetaP、θG、θHRespectively representing the unit cost of operation and maintenance of an electric boiler, an electric-to-gas plant and a combined heat and power generation unit, converting the unit cost into unit electric power, unit: $ kW;
Figure BDA0002439405110000103
representing the output electric power of the cogeneration unit;
Figure BDA0002439405110000104
and
Figure BDA0002439405110000105
respectively representing the power consumptions of the electric heating boiler and the electric transfer gas plant station; delta Pu,tAnd Δ Φl,tRespectively representing the electric power sum of the distribution network and the heat supply network during energy transmissionLoss of thermal power;
Figure BDA0002439405110000106
indicating that the user purchased natural gas from the heating network.
(2) Relaxation constraints for power distribution networks
The Distflow is a mature power distribution network power flow analysis method, and combines variables in a feeder line and variables of power transmission end nodes of the feeder line by fully utilizing radial running characteristics so as to simplify a traditional alternating current power flow model. Note that the power loss Δ Pu,tApproximated by the relaxed form shown in equation (3) to ensure convexity of the power flow constraint. The relaxation constraint is as follows:
Figure BDA0002439405110000107
Figure BDA0002439405110000108
in the formula, Pu,tOr Pv,tAnd
Figure BDA0002439405110000109
respectively, the injected active power and the purchased electric power, respectively, of which the magnitudes are limited to
Figure BDA00024394051100001010
And
Figure BDA00024394051100001011
Qu,tor Qv,tAnd
Figure BDA00024394051100001012
representing the injected reactive power and the purchased electric power at node u or v, respectively, for a time period t, the magnitudes of which are limited to
Figure BDA00024394051100001013
And
Figure BDA00024394051100001014
Ruand XuThe resistance and reactance of a feeder line with a power transmission tail end as a node u are respectively;
Figure BDA00024394051100001015
is the apparent power of the feeder with the power transmission end being node u;
Figure BDA00024394051100001016
and
Figure BDA00024394051100001017
local photovoltaic and wind-electricity output respectively; delta Pu,tRepresenting the electrical power when the power distribution network is transmitting energy; vBRepresenting a rated voltage of the distribution network; vu,tRepresenting the real-time voltage of each electrical node;Vand
Figure BDA0002439405110000111
respectively represent Vu,tUpper and lower limits of (d);
Figure BDA0002439405110000112
a topological incidence matrix representing the distribution network; omegaPRepresenting a set of nodes in a power distribution network;
Figure BDA0002439405110000113
and
Figure BDA0002439405110000114
representing the active and reactive loads of the node u at the moment t;
Figure BDA0002439405110000115
representing the output electric power of the cogeneration unit;
Figure BDA0002439405110000116
respectively showing the power consumptions of the electric boiler, the electric gas-transferring plant and the water pump.
(3) Relaxation constraint of gas distribution network
The existing GDS models mainly include a dynamic model and a steady-state model, where a relaxed transient model is used to ensure that the proposed optimization model is a convex optimization problem that is easy to solve for the optimal value.
Figure BDA0002439405110000117
Figure BDA0002439405110000118
In the formula: fk,tIs the natural gas flow rate of gas pipeline k at time t, and is represented by F when the sending end node of gas pipeline k is w and q, respectivelywq,tRepresents; gq,tOr Gw,tThe natural gas pressure of a gas node q or a node w at the moment t is represented;
Figure BDA0002439405110000119
Figure BDA00024394051100001110
and
Figure BDA00024394051100001111
respectively represent Fk,t、Gq,tOr Gw,tAnd
Figure BDA00024394051100001112
the upper limit of (a) is,Grepresents Gq,tOr Gw,tThe lower limit of (d); ψ is a positive number greater than 1000;wq,tis a binary variable in the direction of natural gas flow, when Gw,t>Gq,t，wq,t1, otherwise 0;
Figure BDA00024394051100001113
a topological correlation matrix representing a gas distribution network; omegaWRepresenting a collection of gas conduits in a gas distribution network ηP2GRepresenting the energy conversion efficiency of the electric-to-gas plant;
Figure BDA00024394051100001114
gas node q representing time tThe natural gas load of (a);
Figure BDA00024394051100001115
and
Figure BDA00024394051100001116
respectively representing the power consumption and the natural gas-electricity conversion efficiency of the cogeneration unit; mkRepresenting the parameters of the pipeline k.
(4) Slack restraint for heating network
In the invention, the state variables of the heat supply network, namely the heat energy and the hot water pressure, are uniformly simulated in the mixed hot water pressure field. Compared with the existing methods (such as hydrothermal models simulating thermodynamic and hydraulic submodels respectively), the proposed thermal model is more reasonable in considering the physical coupling relationship of state variables. Referring to lumped parameter models of transmission lines, the present invention proposes a steady state model for heat pipe analysis, as shown in FIG. 1.
According to fig. 1, for any heat pipe l, the lumped piezoresistive (Z) and thermal conductance (Y) can be calculated as follows:
Figure BDA0002439405110000121
z0＝8ΥDl,t 2/(π2dl 5ρΦi,t) (7)
y0＝κ(Φj,t/c-Dl,tTt 0)/(pj,tLl) (8)
further, the formulas "sinh (-) and" cosh (-) in equation (6) can be expanded by taylor series, and the high-order nonlinear terms are ignored, so that the simplified expressions of Z and Y can be obtained:
Z＝8ΥDl,t 2Ll/(π2dl 5ρΦi,t)；Y＝κ(Φj,t/c-Dl,tTt 0)/pj,t(9)
heat loss (Δ Φ) of Heat pipe l based on the simplified τ -type steady-state model in FIG. 1l,t) And pressure drop (Δ p)l,t) Respectively establishing as follows:
Figure BDA0002439405110000122
Δpl,t＝ZΦi,t＝8γDl,t 2Ll/(π2dl 5ρ) (11)
in the formula:
Figure BDA0002439405110000123
is the thermal conductivity of the heat sink; phil,t＝Φi,tIs the heat power injected by the heat pipe l at time t; kappa, gamma, LlAnd dlRespectively representing the heat transfer coefficient, the surface roughness coefficient, the length and the inner diameter of the heat supply pipeline; c and rho are the specific heat capacity and density of the hot water respectively;
Figure BDA0002439405110000124
is the external ambient temperature at time t;
Figure BDA0002439405110000125
this indicates the thermal load power that the heating pipeline needs to supply at time t.
More specifically, the relaxation constraint of the heating network comprises:
Figure BDA0002439405110000126
Figure BDA0002439405110000127
in the formula ξj,lRepresents the relationship between heat pipes j and l, ξ when heat pipe j is upstream of heat pipe lj,l1 when heat pipe j is downstream of heat pipe l ξj,lWhen heat pipes j and l are not directly connected, ξj,l＝0；Φl,tAnd pi,tRespectively representing the energy contained in hot water transferred in unit time by the hot pipeline and the water pressure of a hot node i;
Figure BDA0002439405110000131
and
Figure BDA0002439405110000132
are respectively a variable Dl,t、Φl,tAnd pi,tThe upper limit of (a) is,Φ lis phil,tThe lower limit of (d);pis pi,tThe lower limit of (d); delta phil,tRepresents the heat loss of the heat pipe l; Δ pl,tRepresents the water pressure drop of the heat pipe l; omegaLRepresents a collection of thermal conduits; omegaHRepresenting a set of hot nodes; phil,tIs the heat power injected by the heat pipe l at time t; dl,tRepresents the flow rate of hot water in the hot pipe l in unit time;
Figure BDA0002439405110000133
and
Figure BDA0002439405110000134
respectively representing the output electric power and the 'heat-electricity' conversion efficiency of the cogeneration unit ηEBAnd
Figure BDA0002439405110000135
respectively representing the electricity-heat conversion efficiency and the power consumption of the electric boiler;
Figure BDA0002439405110000136
indicating a topological correlation matrix of the heating network ηWPAnd
Figure BDA0002439405110000137
respectively representing the electric-hydraulic pressure conversion efficiency and the power consumption of the water pump.
(5) OMEF integral model
In general, the proposed OMEF model can be expressed as:
Figure BDA0002439405110000138
in the formula:
Figure BDA0002439405110000139
and
Figure BDA00024394051100001310
respectively representing variables
Figure BDA00024394051100001311
And
Figure BDA00024394051100001312
the upper limit of (3).
Step 2: and (4) further providing a coordination planning model based on the OMEF analysis method provided in the step 1. The model is composed of two parts, namely an operation sub-model (namely an OMEF model) and an extended planning sub-model.
(1) Extension planning submodel
Total extended cost, denoted fExpaPlanning the phase by the τ th
Figure BDA00024394051100001313
Distribution network, distribution network and heating network (f)Pτ，fGτ，fHτ) The planning cost of (c). In addition to the extended planning of the network topology, the localization and dimensioning of the coupling devices is also taken into account in the proposed model. Furthermore, discrete candidate extensions are considered in the planning model, since key parameters such as rated capacity and power cannot be continuously varied. The extended optimization model is as follows:
Figure BDA00024394051100001314
Figure BDA00024394051100001315
Figure BDA0002439405110000141
Figure BDA0002439405110000142
Figure BDA0002439405110000143
Figure BDA0002439405110000144
Figure BDA0002439405110000145
equations (16) - (18) calculate the extended costs of the distribution, distribution and heating networks, respectively. Each cost includes three components, namely the construction of new equipment, the expansion of existing equipment, and the construction of associated complex. Constraints (19) ensure that no duplication of construction occurs and (20) limit the number of expansions of existing nodes, gas pipes and heat pipes within a planning phase. The constraint (21) indicates that no duplication of construction of the candidate coupling device is allowed.
In the formula: f. ofExpaPlanning cost f of distribution network in the tau planning stagePτAnd the planning cost f of the gas distribution networkGτAnd heating network planning cost fHτThe components of the composition are as follows,
Figure BDA0002439405110000146
gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively representing existing electric nodes, gas pipelines and heat pipeline sets;
Figure BDA0002439405110000147
and
Figure BDA0002439405110000148
respectively representing the cost coefficients of newly added feeder installation, the existing feeder expansion and the electric boiler construction;
Figure BDA0002439405110000149
and
Figure BDA00024394051100001410
respectively representing the cost coefficients of newly added gas pipeline installation, existing gas pipeline expansion and P2G plant station construction;
Figure BDA00024394051100001411
and
Figure BDA00024394051100001412
respectively representing the cost coefficients of the installation of the newly added heat pipeline, the expansion of the existing heat pipeline and the construction of the CHP unit;
Figure BDA00024394051100001413
and
Figure BDA00024394051100001414
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure BDA00024394051100001415
and
Figure BDA00024394051100001416
0-1 decision variables of the expanded feeder line, the expanded gas pipeline and the expanded heat pipeline respectively;
Figure BDA00024394051100001417
and
Figure BDA00024394051100001418
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、
Figure BDA00024394051100001419
And
Figure BDA00024394051100001420
respectively setting the newly built/expanded capacity, the feeder line parameters and the rated power of the electric boiler in the construction scheme a; fbAnd
Figure BDA00024394051100001421
respectively representing the newly built/expanded gas pipeline capacity and the rated power of a P2G plant station in the construction scheme b; dcAnd
Figure BDA00024394051100001422
respectively representing the capacity of the newly built/expanded heat pipeline and the rated power of the CHP unit in the construction scheme c; omegaEB、ΩP2GAndΩ CHPrespectively integrating feasible planning schemes for constructing an electric heating boiler, a P2G plant station and a CHP unit;Ω PS、Ω GSandΩ HSand respectively representing feasible planning scheme sets of the power distribution network, the gas distribution network and the heat supply network.
(2) Collaborative planning model
Coordinating a planning model (M) based on the proposed run and extended planning submodels1) Is expressed as:
Figure BDA0002439405110000151
in the formula: dAAnd ΩTRespectively representing the number of days in a planning phase and the time set of daily operation; f. ofM1Represents M1The target of (1);
Figure BDA0002439405110000152
represents the operating cost of the regional integrated energy system for the τ -th planned horizontal year.
Note that some variables in the ome model need to be re-described to reflect the impact of the binary investment decision variables x, y and z on plant capacity and optimization operation. Equations (23) to (28) represent variables, respectively
Figure BDA0002439405110000153
And
Figure BDA0002439405110000154
Figure BDA0002439405110000155
a change in (c).
Figure BDA0002439405110000156
Figure BDA0002439405110000157
Figure BDA0002439405110000158
Figure BDA0002439405110000159
Figure BDA00024394051100001510
Figure BDA00024394051100001511
In the formula:
Figure BDA00024394051100001512
and
Figure BDA00024394051100001513
the initial gas flow capacity of the gas pipeline k and the water mass flow of the heat pipe l at the stage tau are respectively represented; and the number of the first and second electrodes,
Figure BDA00024394051100001514
and
Figure BDA00024394051100001515
respectively representing the initial capacity and the safety threshold of a power distribution network line with a receiving end as a bus u; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively indicate to haveThe electrical node, the gas pipeline and the heat pipeline are integrated;
Figure BDA00024394051100001516
and
Figure BDA00024394051100001517
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure BDA0002439405110000161
and
Figure BDA0002439405110000162
0-1 decision variables of the expanded feeder line, the expanded gas pipeline and the expanded heat pipeline respectively;
Figure BDA0002439405110000163
Figure BDA0002439405110000164
and
Figure BDA0002439405110000165
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、FbAnd DcThe capacity of the newly built/expanded air pipeline in the construction scheme a, the capacity of the newly built/expanded air pipeline in the construction scheme b and the capacity of the newly built/expanded heat pipeline in the construction scheme c are respectively set;
Figure BDA0002439405110000166
is the apparent power of the feeder with the power transmission end being node u;
Figure BDA0002439405110000167
represents the upper limit of the natural gas flow of the gas pipeline k;
Figure BDA0002439405110000168
the upper limit of the flow of hot water in the hot pipeline l in unit time;
Figure BDA0002439405110000169
and
Figure BDA00024394051100001610
rated power of the electric heating boiler in the construction scheme a, rated power of a P2G plant station in the construction scheme b and rated power of a CHP unit in the construction scheme c are respectively set;
Figure BDA00024394051100001611
a topological correlation matrix representing a gas distribution network;
Figure BDA00024394051100001612
and
Figure BDA00024394051100001613
respectively representing the maximum power of the electric boiler, the P2G plant station and the CHP unit.
And step 3: the collaborative planning model provided in the step 2 belongs to the MIQCP problem, and is difficult to be solved directly and efficiently by using the existing commercial solver, so that an improved Benders decomposition algorithm capable of dynamically adjusting dual multipliers is provided.
Original question M presented1Can be equivalently decomposed into a main problem (M)2) And a sub-problem (M)3)：
Figure BDA00024394051100001614
Figure BDA00024394051100001615
In the formula: f. ofM2、fM3And fM4Respectively represent M2、M3And M4The target of (1); b iscut1 (m)And Bcut2 (h)Feasible Benders cut set of m-th update and infeasible Benders cut set of h-th update, β is a non-negative variable, fExpaRepresents the total expansion cost; gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; dAAnd ΩTEach representing one of the planning phasesDays and time of day runs.
The Benders decomposition proceeds in an iterative manner, in the M-th iteration, M2Will first be optimized to obtain a binary variable representing the investment decision process. M3According to M2The optimization result of (2) is planned to reduce the operation cost to the maximum extent. However, because of M2And M3Are optimized individually, interconnected/coordinated only by binary variables x, y, z, so M2May result in M3Not feasible and thus may reduce the efficiency of Benders' decomposition. In the present invention, a virtual sub-problem M is presented4By introducing a non-negative virtual power supply
Figure BDA0002439405110000171
Virtual electrical load
Figure BDA0002439405110000172
Virtual air source
Figure BDA0002439405110000173
Virtual air load
Figure BDA0002439405110000174
Virtual heat source
Figure BDA0002439405110000175
And virtual heat load
Figure BDA0002439405110000176
The problem of low Benders decomposition efficiency is solved. M4Can be described as:
Figure BDA0002439405110000177
Figure BDA0002439405110000178
in the formula:
Figure BDA0002439405110000179
represents the virtual cost of the phase τ at time t;
Figure BDA00024394051100001710
respectively, represent market prices for electricity, natural gas, and unit heat energy.
Linear Benders cut set Bcut1 (m)And Bcut2 (m)Generated according to the following formula:
Figure BDA00024394051100001711
Figure BDA00024394051100001712
in the formula: sigma1And σ2Respectively represent sub-problems M3And M4A vector of dual multipliers of (1); and x, y and z are 0-1 decision variable matrixes of the power distribution system, the gas distribution system and the local heat supply system in the planning process respectively.
The dual multiplier is dynamically adjusted according to the following formula:
Figure BDA00024394051100001713
Λg×p＝exp(-|μIg×1Θ1×p-[S1×g1,F1×g2,D1×g3]TI1×p|) (36)
Figure BDA00024394051100001714
Figure BDA00024394051100001715
in the formula: Θ, S, F and D represent the maximum capacity matrix, the transformer capacity matrix, the designed natural gas flow matrix and the designed mass flow matrix of all periods, respectively, with dimensions p, g1, g2 and g 3; function max () for selectingChoosing the maximum value, the function exp () representing an exponential function with the natural constant e as the base, mu being the coefficient, Λ being the intermediate variable whose normalization matrix is
Figure BDA0002439405110000181
Λ<1：g1>A sub-matrix representing Λ, taken from row 1 to row g1, I and Y being the identity matrix and the lower triangular matrix, respectively, g-g 1+ g2+ g 3;
Figure BDA0002439405110000182
and
Figure BDA0002439405110000183
respectively representing the injected active and reactive power of the bus u of the planning stage tau;
Figure BDA0002439405110000184
natural gas flow of the gas pipeline k for the planning stage tau;
Figure BDA0002439405110000185
is the hot water flow of the hot pipe l at the planning stage τ;
Figure BDA0002439405110000186
representing the safety threshold of bus u.
And 4, step 4: the effectiveness of the proposed "electro-pneumatic-thermal" co-planning method (i.e. the method of the present invention) is illustrated by example simulations, with an initial network topology as shown in fig. 2.
(1) Effectiveness of OMEF method and coordinated planning model
The optimized planning topology of the "electro-gas-thermal" regional integrated energy system in different planning phases is shown in fig. 3, where the phases of installation of branch/gas/heat pipes are marked on the line or in their vicinity.
The ome model is a MIQCP problem and will therefore be optimized using the well-established commercial solver, CPLEX. The simulation time ranges from 0:00 to 24:00, and the time slot Δ t is set to 1 h. The average computation time was 1.42s when run on a desktop computer with a 1.90GHz processor (A8-4500M) and 8GB memory.
The optimal power output daily curves for several devices from three different energy sources of electricity/gas/heat are shown in fig. 4. In fig. 4(b), the P2G plant converts more electrical energy to natural gas for the purpose of consuming renewable energy. At this point, the CHP unit takes on more electrical and thermal load to keep the natural gas flow in the grid lines balanced, as shown in (a) and (c), respectively. Therefore, when renewable energy is excessive, it is helpful to reduce the energy purchased by the regional integrated energy system and save the cost. Fig. 4(c) shows that when the CHP unit is unable to meet all the thermal loads, it is still necessary to operate at 2: 00-23: an electric boiler is used between 00.
In addition, by contrast, when there is not all coupling facilities, the energy purchase cost will increase from $ 3049.84 to $ 4418.39 per day. Among these, the absence of CHP plants, P2G plants and electric boilers will result in an overall cost increase of 16.69%, 73.98% and 9.33%, respectively.
Using the proposed model M1And the improved Benders algorithm obtains an optimal planning scheme. As shown in fig. 5, the deviation of the main/sub-problem gradually decreases with the iterative process. The convergence procedure in the 2 nd planning phase is described as an example. As shown in fig. 5, initially, the deviation of the main/sub problem remains unchanged. This is because the model M is now3Is not resolvable. As the iteration progresses, M3It becomes increasingly feasible with the help of linear Benders cut-sets. Thereafter, the deviation begins to gradually decrease until a convergence criterion is met, i.e.,
Figure BDA0002439405110000191
the lowest cost for each planning phase in fig. 5 is shown separately in table 1. Table 1 shows the costs of planning (E) and operating (O), i.e. the distribution network cost, the heating network cost, the boiler cost, the P2G plant costs and the CHP unit costs, from the 6 itemized costs, respectively. To cope with the increase in energy load, more energy needs to be transported through feeders/pipes, which means that more coupling facilities and larger bus/pipe capacity are urgently needed. Thus, during the entire planning period, existingThe capacity of both the electrical node and the heat/gas pipe is increasing. Furthermore, increasing load levels will result in more energy procurement and greater energy loss (Δ Ρ)u,tAnd Δ Φl,t) This can be reflected by progressively increasing operating costs. According to table 1, the extension of the distribution network and the installation of the CHP units constitute the main body of the total construction investment. For example, the construction costs of stage 1 and stage 3 are high because the CHP units are newly added in these two stages.
TABLE 1 lowest cost for each planning phase
Figure BDA0002439405110000192
(2) Accuracy and validity of steady-state model of heating network
The main contribution of the proposed OMEF model is in the establishment of the heating network steady-state model. The accuracy and effectiveness of such a model is further analyzed.
Based on the initial heating network in fig. 2, a differential equation model/transient model, a model based on Kirchhoff method and a steady state model were compared. FIG. 6 shows in detail the heating network state variable, i.e. unit heat energy Φ, of 13:00 (i.e. 13 th operational period)l,tAnd water pressure pi,t。
TABLE 2 comparison between different heating network analysis models
Model (model) Accuracy of measurement Time of unit [ s ]] Total time consumption [ s ]] Total cost × 104$]
Differential equation/transient 100％ 7.06 66131.02 102.91
Kirchhoff-based method 64.53％ 1.18 10312.02 143.77
Steady state 85.06％ 1.53 13098.33 116.15
The calculated time and performance of the different heating network analysis models are compared in table 2. The comparison in table 2 shows that the model proposed by the present invention is most effective in analysing a heating network, because: 1) compared with differential equation/transient state, the method provided by the invention has less calculation complexity, but can keep higher calculation precision; 2) compared with a method based on Kirchoff, the steady-state model provided by the invention can accurately calculate the energy and pressure loss (namely delta phi)l,tAnd Δ pl,t) In order to obtain better accuracy. In the case of the optimized 5-stage coordinated planning problem, the operation state of the heating network is simulated for 9000 times, and the small difference in unit time consumption is continuously enlarged along with the iteration. Therefore, compared to differential equation/transient models and models based on Kirchhoff methods, the steady-state model proposed by the present invention not only takes less time, but also maintains higher accuracy.

Claims (10)

1. The method for coordinately planning the regional integrated energy system is characterized by comprising the following steps of:
1) adopting a steady-state comprehensive power flow analysis method suitable for the 'electricity-gas-heat' collaborative planning problem, namely an OMEF analysis method;
2) based on the OMEF analysis method in the step 1), a coordination planning model is adopted, and the model consists of an operation sub-model and an extension planning sub-model; the running sub-model is an OMEF model;
3) solving the coordination planning model in the step 2) by adopting an optimization algorithm based on Benders decomposition.
2. The regional integrated energy system coordination planning method according to claim 1, wherein in step 1), the optimization objective of OMEF is minimization
Figure FDA0002439405100000011
For minimizing the operating costs of regional integrated energy systems:
Figure FDA0002439405100000012
in the formula (f)t OMEFConsists of three parts: 1) the cost of purchasing power, power loss and boiler operation and maintenance in the power distribution network; 2) the cost of purchasing natural gas and operating and maintaining the electric power-to-gas plant station in the gas distribution network; 3) heat loss of the heat supply network and operation and maintenance cost of the cogeneration unit;
wherein the content of the first and second substances,
Figure FDA0002439405100000013
represents the electric power purchased by node u during time period t; omegaP、ΩG、ΩLRepresenting a set of nodes in a distribution network, a distribution network and a heating network; Δ t represents the simulation time slot;
Figure FDA0002439405100000014
respectively representing electricity, gas and unitsMarket price of heat energy; thetaP、θG、θHRespectively representing the unit cost of operation and maintenance of an electric boiler, an electric-to-gas plant and a combined heat and power generation unit, converting the unit cost into unit electric power, unit: $ kW;
Figure FDA0002439405100000015
representing the output electric power of the cogeneration unit;
Figure FDA0002439405100000016
and
Figure FDA0002439405100000017
respectively representing the power consumptions of the electric heating boiler and the electric transfer gas plant station; delta Pu,tAnd Δ Φl,tRespectively representing the electric power loss and the thermal power loss when the power distribution network and the heat supply network transmit energy;
Figure FDA0002439405100000018
indicating that the user purchased natural gas from the heating network.
3. The method according to claim 2, wherein the relaxation constraints of the distribution grid are as follows:
Figure FDA0002439405100000019
Figure FDA0002439405100000021
in the formula, Pu,tOr Pv,tAnd
Figure FDA0002439405100000022
respectively, the injected active power and the purchased electric power, respectively, of which the magnitudes are limited to
Figure FDA0002439405100000023
And
Figure FDA0002439405100000024
Qu,tor Qv,tAnd
Figure FDA0002439405100000025
representing the injected reactive power and the purchased electric power at node u or v, respectively, for a time period t, the magnitudes of which are limited to
Figure FDA0002439405100000026
And
Figure FDA0002439405100000027
Ruand XuThe resistance and reactance of a feeder line with a power transmission tail end as a node u are respectively;
Figure FDA0002439405100000028
is the apparent power of the feeder with the power transmission end being node u;
Figure FDA0002439405100000029
and
Figure FDA00024394051000000210
local photovoltaic and wind-electricity output respectively; delta Pu,tRepresenting the electrical power when the power distribution network is transmitting energy; vBRepresenting a rated voltage of the distribution network; vu,tRepresenting the real-time voltage of each electrical node; v and
Figure FDA00024394051000000211
respectively represent Vu,tUpper and lower limits of (d);
Figure FDA00024394051000000212
a topological incidence matrix representing the distribution network; omegaPRepresenting a set of nodes in a power distribution network;
Figure FDA00024394051000000213
and
Figure FDA00024394051000000214
representing the active and reactive loads of the node u at the moment t;
Figure FDA00024394051000000215
representing the output electric power of the cogeneration unit;
Figure FDA00024394051000000216
respectively showing the power consumptions of the electric boiler, the electric gas-transferring plant and the water pump.
4. The method according to claim 3, wherein the relaxation constraint of the distribution network is as follows:
Figure FDA00024394051000000217
Figure FDA00024394051000000218
in the formula: fk,tIs the natural gas flow rate of gas pipeline k at time t, and is represented by F when the sending end node of gas pipeline k is w and q, respectivelywq,tRepresents; gq,tOr Gw,tThe natural gas pressure of a gas node q or a node w at the moment t is represented;
Figure FDA00024394051000000219
Figure FDA00024394051000000220
and
Figure FDA00024394051000000221
respectively represent Fk,t、Gq,tOr Gw,tAnd
Figure FDA00024394051000000222
g represents Gq,tOr Gw,tThe lower limit of (d); ψ is a positive number greater than 1000;wq,tis a binary variable in the direction of natural gas flow, when Gw,t>Gq,t，wq,t1, otherwise 0;
Figure FDA00024394051000000223
a topological correlation matrix representing a gas distribution network; omegaWRepresenting a collection of gas conduits in a gas distribution network ηP2GRepresenting the energy conversion efficiency of the electric-to-gas plant;
Figure FDA0002439405100000031
representing the natural gas load of the gas node q at time t;
Figure FDA0002439405100000032
and
Figure FDA0002439405100000033
respectively representing the power consumption and the natural gas-electricity conversion efficiency of the cogeneration unit; mkRepresenting the parameters of the pipeline k.
5. The method according to claim 4, wherein the relaxation constraint of the heating network is as follows:
Figure FDA0002439405100000034
Figure FDA0002439405100000035
in the formula ξj,lRepresents the relationship between heat pipes j and l, ξ when heat pipe j is upstream of heat pipe lj,l1 when heat pipe j is downstream of heat pipe l ξj,lWhen heat pipes j and l are not directly connected, ξj,l＝0；Φl,tAnd pi,tRespectively representing the energy contained in hot water transferred in unit time by the hot pipeline and the water pressure of a hot node i;
Figure FDA0002439405100000036
and
Figure FDA0002439405100000037
are respectively a variable Dl,t、Φl,tAnd pi,tUpper limit of (b), philIs phil,tThe lower limit of (d);pis pi,tThe lower limit of (d); delta phil,tRepresents the heat loss of the heat pipe l; Δ pl,tRepresents the water pressure drop of the heat pipe l; omegaLRepresents a collection of thermal conduits; omegaHRepresenting a set of hot nodes; phil,tIs the heat power injected by the heat pipe l at time t; dl,tRepresents the flow rate of hot water in the hot pipe l in unit time;
Figure FDA0002439405100000038
and
Figure FDA0002439405100000039
respectively representing the output electric power and the 'heat-electricity' conversion efficiency of the cogeneration unit ηEBAnd
Figure FDA00024394051000000310
respectively representing the electricity-heat conversion efficiency and the power consumption of the electric boiler;
Figure FDA00024394051000000311
indicating a topological correlation matrix of the heating network ηWPAnd
Figure FDA00024394051000000312
respectively representing the electric-hydraulic pressure conversion efficiency and the power consumption of the water pump.
6. The regional integrated energy system coordination planning method of claim 5, wherein said OMEF model is expressed as:
Figure FDA00024394051000000313
in the formula:
Figure FDA00024394051000000314
and
Figure FDA00024394051000000315
respectively representing variables
Figure FDA00024394051000000316
And
Figure FDA00024394051000000317
the upper limit of (3).
7. The regional integrated energy system coordination planning method according to claim 6, wherein in the step 2), the extension planning submodel is as follows:
Figure FDA0002439405100000041
Figure FDA0002439405100000042
Figure FDA0002439405100000043
Figure FDA0002439405100000044
Figure FDA0002439405100000045
Figure FDA0002439405100000046
Figure FDA0002439405100000047
in the formula: f. ofExpaPlanning cost f of distribution network in the tau planning stagePτAnd the planning cost f of the gas distribution networkG τAnd heating network planning cost fHτThe components of the composition are as follows,
Figure FDA0002439405100000048
gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively representing existing electric nodes, gas pipelines and heat pipeline sets;
Figure FDA0002439405100000049
and
Figure FDA00024394051000000410
respectively representing the cost coefficients of newly added feeder installation, the existing feeder expansion and the electric boiler construction;
Figure FDA00024394051000000411
and
Figure FDA00024394051000000412
respectively representing the cost coefficients of newly added gas pipeline installation, existing gas pipeline expansion and P2G plant station construction;
Figure FDA00024394051000000413
and
Figure FDA00024394051000000414
respectively representing the cost coefficients of the installation of the newly added heat pipeline, the expansion of the existing heat pipeline and the construction of the CHP unit;
Figure FDA00024394051000000415
and
Figure FDA00024394051000000416
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure FDA00024394051000000417
and
Figure FDA00024394051000000418
0-1 decision variables of the expanded feeder line, the expanded gas pipeline and the expanded heat pipeline respectively;
Figure FDA00024394051000000419
and
Figure FDA00024394051000000420
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、
Figure FDA00024394051000000421
And
Figure FDA00024394051000000422
respectively setting the newly built/expanded capacity, the feeder line parameters and the rated power of the electric boiler in the construction scheme a; fbAnd
Figure FDA00024394051000000423
respectively representing the newly built/expanded gas pipeline capacity and the rated power of a P2G plant station in the construction scheme b; dcAnd
Figure FDA00024394051000000424
respectively representing the capacity of the newly built/expanded heat pipeline and the rated power of the CHP unit in the construction scheme c; omegaEB、ΩP2GAnd ΩCHPRespectively integrating feasible planning schemes for constructing an electric heating boiler, a P2G plant station and a CHP unit; omegaPS、ΩGSAnd ΩHSAnd respectively representing feasible planning scheme sets of the power distribution network, the gas distribution network and the heat supply network.
8. The regional integrated energy system coordination planning method according to claim 7, wherein in step 2), the coordination planning model M is adopted1Is expressed as:
Figure FDA0002439405100000051
in the formula: dAAnd ΩTRespectively representing the number of days in a planning phase and the time set of daily operation; f. ofM1Represents M1The target of (1); f. oft OMEFτRepresenting the operating cost of the regional integrated energy system for the tau planned horizontal year;
some variables in the OMEF model need to be re-described to reflect the impact of the binary investment decision variables x, y and z on equipment capacity and optimized operation:
Figure FDA0002439405100000052
Figure FDA0002439405100000053
Figure FDA0002439405100000054
Figure FDA0002439405100000055
Figure FDA0002439405100000056
Figure FDA0002439405100000057
in the formula:
Figure FDA0002439405100000058
and
Figure FDA0002439405100000059
the initial gas flow capacity of the gas pipeline k and the water mass flow of the heat pipe l at the stage tau are respectively represented; and the number of the first and second electrodes,
Figure FDA00024394051000000510
and
Figure FDA00024394051000000511
respectively representing the initial capacity and the safety threshold of a power distribution network line with a receiving end as a bus u; omegaNPτ、ΩNWτAnd ΩNLτRespectively representing candidate sets of electrical nodes, gas pipelines and heat pipelines; omegaPτ、ΩWτAnd ΩLτRespectively representing existing electric nodes, gas pipelines and heat pipeline sets;
Figure FDA00024394051000000512
and
Figure FDA00024394051000000513
respectively 0-1 decision variables of the newly added feeder line, the newly added gas pipeline and the newly added heat pipeline;
Figure FDA00024394051000000514
and
Figure FDA00024394051000000515
respectively a dilatation feeder line, an air pipeline and a heat pipeDecision variable 0-1 of lane;
Figure FDA00024394051000000516
Figure FDA0002439405100000061
and
Figure FDA0002439405100000062
respectively setting 0-1 decision variables for building an electric boiler, a P2G plant station and a CHP unit; sa、FbAnd DcThe capacity of the newly built/expanded air pipeline in the construction scheme a, the capacity of the newly built/expanded air pipeline in the construction scheme b and the capacity of the newly built/expanded heat pipeline in the construction scheme c are respectively set;
Figure FDA0002439405100000063
is the apparent power of the feeder with the power transmission end being node u;
Figure FDA0002439405100000064
represents the upper limit of the natural gas flow of the gas pipeline k;
Figure FDA0002439405100000065
the upper limit of the flow of hot water in the hot pipeline l in unit time;
Figure FDA0002439405100000066
and
Figure FDA0002439405100000067
rated power of the electric heating boiler in the construction scheme a, rated power of a P2G plant station in the construction scheme b and rated power of a CHP unit in the construction scheme c are respectively set;
Figure FDA0002439405100000068
a topological correlation matrix representing a gas distribution network;
Figure FDA0002439405100000069
and
Figure FDA00024394051000000610
respectively representing the maximum power of the electric boiler, the P2G plant station and the CHP unit.
9. The regional integrated energy system coordination planning method according to claim 8, wherein in step 3), the coordination planning model M is applied1Equivalent decomposition into a main problem M2And a sub-question M3And a virtual sub-problem M4：
Figure FDA00024394051000000611
Figure FDA00024394051000000612
In the formula: f. ofM2、fM3And fM4Respectively represent M2、M3And M4The target of (1); b iscut1 (m)And Bcut2 (h)Feasible Benders cut set of m-th update and infeasible Benders cut set of h-th update, β is a non-negative variable, fExpaRepresents the total expansion cost; gamma represents the depreciation rate; and τ denote the set of planning phases and the labeled variables, respectively; dAAnd ΩTRepresenting the number of days and the time set of daily runs, respectively, within a planning phase.
10. The regional integrated energy system coordination planning method according to claim 9, wherein in step 3), a virtual subproblem M is adopted4By introducing a non-negative virtual power supply
Figure FDA00024394051000000613
Virtual electrical load
Figure FDA00024394051000000614
Virtual air source
Figure FDA00024394051000000615
Virtual air load
Figure FDA00024394051000000616
Virtual heat source
Figure FDA00024394051000000617
And virtual heat load
Figure FDA00024394051000000618
To solve the problem of low decomposition efficiency of Benders, M4Simply expressed as:
Figure FDA00024394051000000619
Figure FDA0002439405100000071
in the formula:
Figure FDA0002439405100000072
represents the virtual cost of the phase τ at time t;
Figure FDA0002439405100000073
respectively representing market prices of electricity, natural gas and unit heat energy;
linear Benders cut set Bcut1 (m)And Bcut2 (m)Generated according to the following formula:
Figure FDA0002439405100000074
Figure FDA0002439405100000075
in the formula: sigma1And σ2Respectively represent sub-problems M3And M4A vector of dual multipliers of (1); x, y and z are respectively 0-1 decision variable matrixes of the power distribution system, the gas distribution system and the local heat supply system in the planning process;
the dual multiplier is dynamically adjusted according to the following formula:
Figure FDA0002439405100000076
Λg×p＝exp(-|μIg×1Θ1×p-[S1×g1,F1×g2,D1×g3]TI1×p|) (30)
Figure FDA0002439405100000077
Figure FDA0002439405100000078
in the formula, theta, S, F and D respectively represent a maximum capacity matrix, a transformer capacity matrix, a designed natural gas flow matrix and a designed mass flow matrix of all time periods, the sizes of the matrices are p, g1, g2 and g3 respectively, a function max () is used for selecting the maximum value, a function exp () represents an exponential function with a natural constant e as the base, mu is a coefficient, Λ is an intermediate variable, and a normalization matrix of the intermediate variable is
Figure FDA0002439405100000079
Λ<1：g1>A sub-matrix representing Λ, taken from row 1 to row g1, I and Y being the identity matrix and the lower triangular matrix, respectively, g-g 1+ g2+ g 3;
Figure FDA00024394051000000710
and
Figure FDA00024394051000000711
the injection of the bus u representing the planning phase τ respectivelyPower and reactive power;
Figure FDA00024394051000000712
natural gas flow of the gas pipeline k for the planning stage tau;
Figure FDA00024394051000000713
is the hot water flow of the hot pipe l at the planning stage τ;
Figure FDA00024394051000000714
representing the safety threshold of bus u.
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