CN116151565A

CN116151565A - Urban intelligent energy system construction method and collaborative planning method based on multi-energy flow and multi-level

Info

Publication number: CN116151565A
Application number: CN202310128916.4A
Authority: CN
Inventors: 李骥; 陆海; 冯晓梅; 乔镖; 薛汇宇; 阳春; 吴志; 张广秋
Original assignee: Southeast University; Electric Power Research Institute of Yunnan Power Grid Co Ltd; China Academy of Building Research CABR
Current assignee: Southeast University; Electric Power Research Institute of Yunnan Power Grid Co Ltd; China Academy of Building Research CABR
Priority date: 2023-02-03
Filing date: 2023-02-03
Publication date: 2023-05-23

Abstract

A city intelligent energy system construction method and a collaborative planning method based on multiple energy flows and multiple levels relate to the field of city intelligent energy planning. The method solves the problems that a complete and optimized planning and design method and a professional tool are needed to support the early planning and design of the intelligent energy system. The method comprises the following steps: constructing an energy system carrying a cold source, a heat source, an air source and electric power; assigning values to the equipment of the energy system according to the equipment dynamic load characteristics of the energy system to obtain a multi-energy flow module; performing multi-level energy network division according to an energy network city-region-land parcel multi-level energy network division principle, and establishing a multi-level multi-energy flow intelligent energy network inter-level coupling model; constructing a multi-level solving model according to the coupling model among the levels of the multi-level multi-energy flow intelligent energy network; and determining an optimized path of the multi-energy flow energy network according to the multi-level solving model, and completing the construction of the multi-energy flow multi-level city intelligent energy system. The intelligent energy source system is applied to the field of intelligent energy sources.

Description

Urban intelligent energy system construction method and collaborative planning method based on multi-energy flow and multi-level

Technical Field

The invention relates to the field of urban intelligent energy planning, in particular to a method for constructing an urban intelligent energy system based on multiple energy flows and multiple levels.

Background

The intelligent energy system organically integrates links such as electricity, heat, cold, gas and the like in a certain area, such as production, transmission, conversion, storage, consumption and the like, so that complementary coordination of energy system planning and operation is realized, various load demands are met, the comprehensive benefit of the system is improved, the absorption rate of renewable energy sources is improved, and the dependence on fossil energy sources is reduced. Compared with the traditional discrete energy system, the intelligent energy system can effectively meet the multi-type energy requirements of users, and the coupling cost of the system is reduced. With the rapid development of the intelligent energy industry, a complete and optimized planning and design method and a professional tool for supporting the early planning and design of the intelligent energy system are needed.

At present, in the construction process of a support theory system of energy system planning design, domestic and foreign scholars develop universal theoretical research of various energy forms, realize the research of coordination configuration planning problem of various types of energy, and develop some planning design software development, but most focus on research in a certain technical field or are used for academic research, and have great difference with the important field of current intelligent energy development and the requirement of required professional auxiliary support tools.

Disclosure of Invention

The invention solves the problems of a complete and optimized planning and design method and a professional tool for supporting the early planning and design of the intelligent energy system.

The invention discloses a method for constructing an urban intelligent energy system based on multiple energy flows and multiple levels, which comprises the following steps:

constructing an energy system carrying a cold source, a heat source, an air source and electric power;

assigning values to relevant parameters and dynamic curves of equipment of an energy system according to the equipment dynamic load characteristics of the energy system to obtain a multi-energy flow module;

performing multi-level energy network division according to an energy network city-region-land parcel multi-level energy network division principle, and establishing a multi-level multi-energy flow intelligent energy network inter-level coupling model according to a multi-energy flow module;

constructing a multi-level solving model according to the multi-level multi-energy flow intelligent energy network inter-level coupling model;

and determining an optimized path of the multi-energy flow energy network according to the multi-level solving model, and completing the construction of the multi-energy flow multi-level city intelligent energy system.

Further, there is provided a preferable mode of constructing an energy system carrying a cold source, a heat source, a gas source, and electric power, comprising:

obtaining rated electrical efficiency eta of internal combustion engine equipment _r,pgu,e And rated integrated efficiency eta _r,pgu,o ：

Wherein E is _r,pgu Is the fuel gas consumption;

acquiring condensation temperature and evaporation temperature of a heat pump unit;

obtaining the energy consumption of a solar heat supply model:

wherein eta is the efficiency of the solar heat collector, a ₀ 、a ₁ 、a ₂ As a thermal efficiency parameter, it can be obtained by standard test, ΔT is the difference between the collector inlet fluid temperature and the outdoor ambient temperature, I _T Is the total radiation incident on the solar collector;

acquiring energy consumption of a water chilling unit;

and according to the rated electric efficiency and the rated comprehensive efficiency of the internal combustion engine equipment, the condensation temperature and the evaporation temperature of the heat pump unit, the energy consumption of the solar heat supply model and the energy consumption of the water chilling unit are obtained, and the energy system carrying the cold source, the heat source, the air source and the electric power is obtained.

Further, there is provided a preferred manner, the multi-level energy network division according to the energy network city-region-plot multi-level energy network division principle includes:

the energy network city-region-land parcel multi-level energy network division principle is that the first level is a city level, the second level is a region level, and the third level is a land parcel level;

the urban energy network plans 220kV and above large-scale high-voltage power transmission, 35kV, 66kV or 110kV high-voltage power distribution, 1.6-4.0 MPa high-voltage fuel gas, 0.4-1.6 MPa secondary high-voltage fuel gas, a large-scale heat supply network with the water supply temperature of a primary heat supply network of 110-150 ℃ and an urban energy station;

The regional energy network plans 10kV or 20kV medium-voltage distribution, medium-voltage fuel gas of 0.01-0.4 MPa, a heat supply or cold network system with the water supply temperature of the secondary heat supply network of 90-110 ℃, a regional energy station and renewable energy sources;

the land level energy network is divided into a 380V or 220V low-voltage power system, a low-voltage fuel gas with the pressure less than 0.01MPa and a distributed heat or cold supply system.

Further, there is provided a preferred mode, the establishing a multi-level multi-energy flow intelligent energy network level-to-level coupling model according to the multi-energy flow module includes:

the system comprises an urban external energy network acquisition module, an urban energy network scheduling module, an regional energy network scheduling module and a land parcel energy network module;

the urban external energy network acquisition module is used for acquiring energy information of a high-voltage power line, a long-distance gas pipeline and a thermal long-distance gas pipeline outside the city, and transmitting the energy information to the urban energy network scheduling module through an energy station positioned at the edge of the city;

the urban energy network scheduling module is used for receiving the energy information and scheduling energy among energy stations and areas;

the regional energy network scheduling module is used for consuming energy mainly comprising natural gas from cities and producing the consumed energy into other energy, the produced energy and part of energy from the urban energy network scheduling module are output from energy stations to the regional energy network scheduling module, and the regional energy network scheduling module transmits the energy to the land-level energy network module;

The land parcel level energy network module is used for receiving the energy transmitted by the regional level energy network scheduling module and distributing the energy from the region to each building in the land parcel.

Further, there is provided a preferred mode, constructed according to the multi-level multi-energy flow intelligent energy network inter-level coupling model, including:

the multi-level solution model comprises a double-layer planning solution model, wherein the double-layer planning solution model comprises an upper-layer planning solution model and a lower-layer planning solution model;

the upper planning solving model is used for determining the arrangement position of the regional pipe network according to the condition that the energy loss of each pipe network is minimum;

the lower planning solving model is used for determining the optimized path of the multi-energy flow energy network according to the condition that the total cost is minimum in the whole life cycle of the energy network.

Based on the same inventive concept, the invention also provides a cooperative planning method of the intelligent energy system based on the total life cycle cost, the cooperative planning method is realized based on the construction of the intelligent energy system, and the method comprises the following steps:

constructing a full life cycle cost model based on the intelligent energy system;

calculating and analyzing initial investment and operation maintenance cost according to the full life cycle cost model;

And obtaining the minimum full life cycle cost of the intelligent energy system according to a mixed optimization algorithm combining the particle swarm algorithm and the Hooke-Schiff algorithm.

Further, there is provided a preferred mode of calculating and analyzing initial investment and operation maintenance costs according to the full life cycle cost model, including:

wherein, LCC is the life cycle cost, IC is the scheme initial investment cost, OC is the scheme operation and maintenance cost, RC is the fixed cost net residual value; x is the discount rate.

Further, there is provided a preferred mode, wherein the minimum life cycle cost of the intelligent energy system is obtained according to a mixed optimization algorithm of a particle swarm algorithm and a Hooke-Schiff algorithm, and the mixed optimization algorithm of the particle swarm algorithm and the Hooke-Schiff algorithm comprises:

optimizing the intelligent energy system by adopting a particle swarm algorithm to obtain a full life cycle cost continuous variable optimization result of the intelligent energy system;

taking the continuous variable optimization result as an initial value;

optimizing an initial value by adopting a Hooke's algorithm, and fixing a discrete variable as an optimal value of a particle swarm optimization result;

and (3) carrying out data iteration by adopting a particle swarm algorithm to obtain the minimum life cycle cost of the intelligent energy system.

Based on the same inventive concept, the invention also provides a computer readable storage medium for storing a computer program, wherein the computer program executes the method for constructing the urban intelligent energy system based on the multi-energy-flow multi-level.

Based on the same inventive concept, the invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a multi-energy-flow multi-level-based city intelligent energy system construction method according to any one of the above.

The invention has the advantages that:

The invention discloses a method for constructing an urban intelligent energy system based on multiple energy flows and multiple levels, which is based on the overall requirement of intelligent energy planning and design, comprehensively considers the multiple energy flows such as cold, hot, electricity and the like, establishes the intelligent energy system covering multiple levels of cities, areas and plots, considers the dynamic load characteristics of each device, assigns related parameters and dynamic curves of each device, and accurately reflects the dynamic characteristics of each device; through system construction, data transmission and energy conversion among all devices and modules are realized, and meanwhile, a control strategy is added in a system model, so that the intelligent energy system can be flexibly and dynamically scheduled.

According to the intelligent energy system collaborative planning method based on the full life cycle cost, various optimization algorithm characteristics are compared and analyzed, and aiming at the problems that the traditional single optimization mathematical algorithm is difficult to converge, the optimization result is easy to fall into local optimum, the local searching capability is insufficient or the intelligent energy system collaborative planning method is sensitive to a value and the like when being applied to optimization solution, the hybrid algorithm which is high in solving precision and high in convergence speed is built for the energy system collaborative planning method, the full life cycle cost is minimum as an optimization target, and the developed hybrid optimization algorithm is utilized to realize the optimal configuration of the cold-hot electricity multi-energy coupling intelligent energy system.

The intelligent energy source system is applied to the field of intelligent energy sources.

Drawings

FIG. 1 is a flowchart of a method for constructing a multi-energy-flow multi-level based urban intelligent energy system according to an embodiment;

FIG. 2 shows the power generation efficiency of a gas internal combustion engine at different load rates according to the first embodiment;

FIG. 3 is a graph showing water heat dissipation ratio of cylinder liners of a gas internal combustion engine at different load rates according to the first embodiment;

FIG. 4 shows the heat dissipation ratio of the flue gas of the gas internal combustion engine at different load factors according to the first embodiment;

Fig. 5 is a diagram showing a configuration of a power system according to a fourth embodiment;

FIG. 6 is a diagram of a heat grid structure according to a fourth embodiment;

fig. 7 is a diagram of a node air network structure according to a fourth embodiment, wherein 1, 2, 3, 4, 5, 6, 7 and 8 are all nodes;

FIG. 8 is a flow chart of the hybrid optimization algorithm calculation in accordance with the eighth embodiment;

fig. 9 is a schematic diagram of an energy consumption simulation computing platform of a TRNSYS energy station integrated energy system according to an eleventh embodiment;

FIG. 10 is a schematic diagram of an architecture of an information flow of a simulation platform according to an eleventh embodiment;

FIG. 11 is a schematic view of a project function setting interface according to an eleventh embodiment;

FIG. 12 is a schematic diagram of an intelligent energy system optimization configuration interface according to an eleventh embodiment;

fig. 13 is a schematic diagram of an area-land parcel level multi-energy network optimization interface according to an eleventh embodiment;

fig. 14 is a schematic diagram of a land level network node interface according to an eleventh embodiment;

FIG. 15 is a schematic diagram of a software simulation result display interface according to an eleventh embodiment;

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments.

Embodiment one, this embodiment will be described with reference to fig. 1, 2, 3 and 4. The embodiment of the method for constructing the urban intelligent energy system based on the multi-energy flow and multi-level comprises the following steps:

In practical application, the assigning values to the relevant parameters and the dynamic curves of the devices of the energy system according to the dynamic load characteristics of the devices of the energy system to obtain a multi-energy flow module includes:

when the cold-hot electric equipment is actually operated, most of the primary partial load operation conditions are needed to be researched on the dynamic characteristics of the equipment in a variable working condition mode. The partial load characteristic models and curves of different equipment are obtained by researching the partial load operation characteristics of the different equipment including an internal combustion engine, a ground source heat pump, a water chilling unit, a water pump, a solar heat collector and the like, and the partial load characteristic changes of the different equipment caused by the boundary parameter changes are accurately depicted. The description will be given by taking an internal combustion engine as an example:

Generally, the energy efficiency of the CCHP subsystem under partial working condition operation is reduced, and the degree of the reduction is related to the load factor, and the power generation efficiency, the cylinder liner water heat dissipation ratio and the flue gas heat dissipation ratio of the gas internal combustion engine are all changed along with the change of the load factor. The research refers to ASHRAE standard, and the partial load thermal efficiency and the partial load power generation efficiency rule of the gas internal combustion engine are integrated in the gas internal combustion engine performance analysis module in the simulation calculation platform, wherein the specific formulas are as follows, and the performance curves are shown in the following figures 2, 3 and 4:

In a second embodiment, the present embodiment is a further limitation of the method for constructing a multi-energy-flow multi-level-based urban intelligent energy system according to the first embodiment, wherein the construction of the energy system carrying the cold source, the heat source, the air source and the electric power includes:

Wherein E is _r,pgu Is the fuel gas consumption;

obtaining the energy consumption of a solar heat supply model:

acquiring energy consumption of a water chilling unit;

Specifically, the variable working condition performance of the heat pump unit mainly comprises:

parameters such as refrigeration (heat) quantity, COP and power consumption of the heat pump unit under different load side and source side temperatures and flow rates;

Parameters such as refrigerating (heating) capacity, COP and power consumption of the heat pump unit under different load rates.

The main factors influencing the performance of the ground source heat pump unit include the condensing temperature and the evaporating temperature. Based on the performance parameters of the ground source heat pump unit under the rated working condition, the time-by-time refrigerating (heating) capacity and time-by-time power of the unit which is operated under the rated working condition and is full of the load can be determined by adopting a difference value or intelligent learning method, and the data such as the time-by-time water outlet temperature, the time-by-time energy efficiency ratio, the time-by-time absorbed (discharged) heat of the source side and the like of the evaporator and the condenser which are operated under the rated working condition of the heat pump unit are deduced and calculated, wherein the heating mode formula is as follows:

the refrigeration mode formula is as follows:

solar heat supply model energy consumption:

/>

wherein Qu is useful and heat of the heat collector, A is the area of the heat collector, I _T F for total radiation incident on the solar collector _R For collector heat rejection coefficient based on collector inlet temperature, U _L Is the total heat loss coefficient of the unit area of the heat collector, ti is the inlet fluid temperature of the heat collector, T _a Is the outdoor ambient temperature.

The above can be written as:

the performance parameters of the water chilling unit are determined by the characteristics of the water chilling unit and the operation conditions of the evaporation and condensation sides. The evaporation side is determined by load demand, and the condensation operation condition is determined according to factors such as cooling water pump flow, cooling tower performance and outdoor meteorological parameters. The following is a mathematical model for calculating the energy consumption of the water chiller:

COP _nom ＝COP _rated *COP _ratio

Capacity＝Capacity _rated *Capacity _ratio

Q _load ＝m _chw *Cp _chw (T _chw,in -T _chw,set )

Q _rejected ＝Q _load +P

Wherein, COP _nom Is the nominal COP; COP of _rated Is rated COP; COP of _ratio Is the nominal COP relative to the nominal COP ratio; capacity is the nominal refrigeration Capacity; capacity _rated Is rated refrigerating capacity; capacity _ratio Is the ratio of the nominal refrigerating capacity to the rated refrigerating capacity; q (Q) _load Is a building load; m is m _chw The flow rate of the refrigerating water is the flow rate of the main machine; cp _chw Specific heat capacity for chilled water; t (T) _chw,in Freezing the water inlet temperature for the host; t (T) _chw,set Setting a value for the temperature of a chilled water outlet of a host; PLR is load factor; p is the actual energy consumption of the cooler; FFLP is the compressor motor load factor of the refrigerator; COP is the running COP of the chiller; q (Q) _rejected Heat discharged outwards for the chiller; t (T) _cw,out Cooling water outlet temperature for the host; t (T) _chw,in Cooling water inlet temperature for the host; m is m _cw Is the flow of cooling water; cp _cw Is the specific heat capacity of the cooling water.

The third embodiment is further defined by the method for constructing a multi-energy-flow multi-level-based urban intelligent energy system according to the first embodiment, wherein the multi-level energy network division is performed according to an energy network city-region-plot multi-level energy network division principle, and the method comprises the following steps:

Specifically, the first level is city level, the research object is a district or county with more centralized population distribution, and the research object is composed of a plurality of areas. The urban energy network has the functions of receiving, transmitting and distributing energy, and is planned to take large-scale high-voltage transmission (220 kV and above), high-voltage distribution (35 kV, 66kV and 110 kV), high-pressure gas (1.6-4.0 MPa), secondary high-pressure gas (0.4-1.6 MPa), large-scale heat supply network (door station and primary heat supply network water supply temperature of 110-150 ℃), large-scale energy station and other systems as research objects, and energy conversion is not considered. City level energy utilization characteristic consideration factors are consistent with regional levels (functional areas or parks), and different regional level energy utilization factors are accumulated to form the city level. Total amount prediction and regional level checking can be used.

The second level is regional level, and the study object is a functional area or park and is composed of a plurality of plots with specific functions. The regional energy network is used for receiving urban energy and energy produced by regional energy stations, distributing different forms of energy to each land according to land requirements, and planning to take medium-voltage distribution (10 kV and 20 kV), medium-voltage fuel gas (0.01-0.4 MPa), a heat supply/cold network system (door station and secondary heat supply network water supply temperature of 90-110 ℃), a regional energy station, renewable energy and other systems as research objects.

The three-level is a land level, the research object is a building cluster with specific attributes, and a plurality of single buildings with different functions are contained in the range. The land parcel energy network receives energy from the regional energy network and distributes the energy to each building, mainly focuses on the utilization of the energy, and the planning takes a low-pressure electricity utilization system (380/220V), low-pressure gas (< 0.01 MPa), a distributed heat supply or cold supply system and the like as a research object.

Embodiment four, this embodiment will be described with reference to fig. 5, 6 and 7. The present embodiment is further defined by the method for constructing a multi-energy-flow multi-level-based urban intelligent energy system according to the first or third embodiment, wherein the establishing a multi-level multi-energy-flow intelligent energy network inter-level coupling model according to the multi-energy-flow module includes:

Specifically, a high-voltage power line, a long-distance gas pipeline and a thermal long-distance gas pipeline from outside a city enter a city grade energy network through an energy hub positioned at the edge of the city, and are transmitted by the city grade energy network and enter an area through the energy hub between the city and the land block; the urban energy network is distributed along regional boundaries or regional main roads, and is communicated with all energy stations nearby, so that energy scheduling among the energy stations and among the regions is facilitated, interconnection and intercommunication of networks among the layers and among the energy stations are realized, and the reliability of energy supply is ensured; the regional large-scale energy station consumes part of energy (mainly natural gas) from a city to realize energy production, the generated energy and part of energy from a city energy network are output from the energy station, the energy is conveyed to a land block load center covered by each energy station through the regional energy network, the energy enters the land block through an energy hub of the load center, and if the energy produced by the energy station cannot be consumed in the covered area, the energy can also be output to the city energy network for use by the land block covered by other energy stations or other areas; the parcel level energy network ultimately distributes energy from an area to individual buildings in the parcel. The energy is transmitted between the networks of all levels and simultaneously realizes upgrading and degradation.

The level of each energy network varies as follows:

electric power network: and a transformer is arranged at a boundary node of the area, the high-voltage power (220 kV) of the urban level is reduced to 110kV, and the transformer penetrates through the area through a bus and is distributed to each energy station. The transformer at the energy station is used for carrying out secondary voltage reduction to 10kV on the electric energy, the electric energy is transmitted to each land through the regional power transmission network, and the electric energy is distributed in the land after being received by the land-level substation.

Taking a topology structure diagram of a certain power system as an example, as shown in fig. 5:

the power flow calculation of the power system selects the Newton-Laportson method, which is roughly divided into the following steps:

(1) Forming a node admittance matrix according to the network structure and parameters of the power system;

(2) Setting the initial voltage value of each node

(plural form) or->

(polar form);

(3) Substituting the initial value into the power equation to obtain the unbalance

(4) Calculating each element in the Jacobian matrix by using the initial voltage value;

(5) Solving a correction equation to calculate the unbalance of the voltage

Or->

(6) Superposing the node voltage initial value and the voltage unbalance to obtain a new voltage initial value;

(7) And judging whether the obtained unbalance amount is converged in a preset range, if not, performing the next iteration, and if so, calculating the power distribution in each line and the injection power of the balance node.

Heating/cooling network: the primary heating power network is annularly distributed along the main road and is connected with each energy station to serve as standby peak regulation; the heat energy generated by the energy station is conveyed to the land block through the secondary heating pipeline and then distributed in the land block through the tertiary heating pipeline network.

The heat supply network model structure is shown in fig. 6, and the heat energy transmission needs to rely on the flow of working medium (hot water), and parameters related to the heat supply network include pressure, flow, temperature and the like. There are two ways of regulating the heating system: quality regulation and quantity regulation; quality regulation means to keep the flow and pressure of the hot water flow constant and change the temperature so as to regulate heat transfer; the quantity adjustment means to maintain the temperature of the hot water unchanged and adjust the flow rate of the water supply to thereby adjust the heat transfer. The embodiment mainly adopts a mass regulation mode and mainly considers the steady state of a thermodynamic steady state model.

The steady-state thermal model of the pipeline is as follows without considering the internal heat transfer phenomenon of the working medium:

wherein m is the hot water flow of the pipeline; c (C) _w 4.168 kJ/(kg. Deg.C) is taken as the specific heat capacity of water; t, T _e Respectively representing the pipeline temperature and the environment temperature; x is the length of the pipeline; r is R _p Is the thermal resistance per unit length of the pipeline.

Integration from inlet to outlet is obtained:

wherein T is _i 、T _o The inlet average temperature and the outlet average temperature of the pipe are indicated, respectively.

The heat energy carried by the working medium can be represented by the following formula:

Q＝C _w m _p T/λ

wherein m is _p Is the flow rate; t is the temperature of working medium, and the unit is DEG C; λ is a unit conversion coefficient, and the value is 3600.

The relation between the node injection heat energy and the temperature can be obtained according to the water supply temperature and the water return temperature:

Φ＝Q _s -Q _r

wherein phi is the node injection heat energy; t (T) _s And T _r Carrying heat energy for water supply and return water respectively.

The available pipeline energy loss expression:

gas network: the structure of a three-level gas network is adopted, the first-level gas network distributes and conveys secondary high-pressure gas along the peripheral roads of the area, the secondary high-pressure gas is distributed to each energy station through a gas throttle, and after each energy station receives the gas, the gas utilization of energy utilization equipment in the energy station is met on one hand, and medium-pressure gas is conveyed to each load center through the gas throttle on the other hand; and finally, the middle-pressure fuel gas is depressurized into low-pressure fuel gas again by the load center, and is conveyed inside the land parcels through a three-level fuel gas network.

The natural gas pipe network model adopts an 8-node model, and the structure diagram is shown in fig. 7:

in the figure, the 8 nodes are connected with a gas source, the 2, 3, 4, 6 and 7 nodes are connected with a natural gas load, and the 1 and 5 nodes are arranged in a natural gas pipe network. Modeling the natural gas pipe network is needed after the model topology structure is determined. The method adopts a natural gas steady-state isothermal model, and common solutions for the steady-state isothermal model include a node method, a ring network method and a ring energy method. For natural gas networks, it is first necessary to describe the network structure with a matrix, where a matrix a describing the "node-pipe" relationship and a matrix B describing the "loop-pipe" relationship are constructed. The relation between analogy and power line and natural gas flow of natural gas pipe network also satisfies kirchhoff law, and each element a in matrix A is determined according to kirchhoff first law _ij ：

a _ij ＝n _i ×p _j

n _i ×p _j =0 means that node i is not directly connected to pipe j; n is n _i ×p _j -1 represents natural gas flowing from node i to conduit j; n is n _i ×p _j =1 means that natural gas flows from pipeline j to node i.

Determining each element B in the matrix B according to the kirchhoff second law _ij ：

b _ij ＝p _i ×l _j

p _i ×l _i =0 means that pipe i is not present in loop j; p is p _i ×l _i -1 means that conduit i is present in loop j and natural gas flows counter-clockwise along the loop; p is p _i ×l _j =1 means that conduit i is present in loop j and natural gas flows clockwise along the loop, assuming clockwise direction is positive.

The orthogonal relation between the matrix A and the matrix B can be obtained according to the logic relation:

A×B ^T =0 in the natural gas network there is the following quantitative relationship of the node number i, pipeline number j and loop number k:

j＝i+k-1

from the sum of the natural gas flows flowing into the node and the sum of the natural gas flows flowing out of the node (the first law of kirchhoff of the natural gas network), the flow equation of the node can be obtained:

A _(i-1)×j V _j×1 ＝q _(i-1)×1

in which A _(i-1)×j To remove the matrix A, V behind the row of reference nodes _j×1 A row vector consisting of the flow for each pipe.

The loop pressure drop equation is derived from the pipeline pressure drop sum of any closed loop being 0 (natural gas pipeline network kirchhoff second law):

B _k×j Δp _j×1 ＝0

in the formula Δp _j×1 Is a row vector consisting of the pressure drops of the individual pipes.

The pipe diameter is known, the unknown quantity to be solved in each pipe is pipe pressure drop and pipe flow, and the relation between the pipe pressure drop deltap and the pipe flow V is given in the town gas design specification (2002) edition:

Δp＝G|V|V

Wherein G represents a pipeline flow resistance coefficient, and the expression of the pipeline flow resistance coefficient G for a medium-high pressure pipe network (pipeline pressure: 0.01 MPa-4 MPa) is as follows:

wherein lambda represents the hydraulic friction coefficient of the pipeline and is related to the Reynolds number Re and the natural gas relative density delta; d represents the inner diameter of the pipeline, and the unit is mm; ρ is the natural gas density in kg/m ³ The method comprises the steps of carrying out a first treatment on the surface of the T represents the temperature of the pipeline, and the unit is K; t (T) _n The temperature in the standard state is 273.15K; l represents the length of the pipeline, and the unit is m; z is a gas compression factor, which is 1 in this embodiment.

The pressure drop of the pipeline in the medium-high pressure pipe network is no longer the subtraction of the pressure values of the nodes at the two ends of the pipeline, and the expression is as follows:

wherein Δp _ab Representing the pressure drop of the pipe ab, p _a And p _b The pressures at node a and node b at the ends of the pipe are shown, respectively.

The fifth embodiment is further defined by the method for constructing a multi-energy-flow multi-level-based urban intelligent energy system according to the first embodiment, wherein the constructing includes:

In practical application, the lower planning solution model aims at the minimum total cost in the whole life cycle of the energy network, and relates to initial investment installation, operation maintenance cost and pipe network residual value of the pipe network, so that the optimal path of the multi-energy flow energy network is determined. And an improved particle swarm algorithm is adopted to optimize the distribution of the multi-level energy network.

Aiming at the characteristics of the energy network, the method is further perfected on the basis of a particle swarm algorithm with stronger searching capability, and the problem that the algorithm is easy to fall into a local optimal solution in the later period of algorithm iteration is avoided. The improvement strategy is as follows:

(1) Evolutionary thought strategy

Constructing an evolution function by using fitness values of particles and populations, and enabling the optimizing range of the objective function solution to move towards the global optimal direction, wherein the evolution rule is as follows:

wherein, the liquid crystal display device comprises a liquid crystal display device,

respectively representing particle evolution degree, population evolution degree and evolution rate; />

Indicating the fitness value of the ith particle at generation t; />

Representing worst and best fit values among all particles at t generation; / >

Indicating the fitness of the population history optimal particles at the t-generation.

(2) Parameter adjustment strategy

The inertia weight and the learning factor are used as 2 basic adjustment operators in the particle swarm algorithm, and play an important role in the aspects of algorithm convergence, convergence speed and the like. The basic particle swarm algorithm sets the 2 parameters to be fixed values, so that the problem of premature or locally optimal sinking in the later stage of iteration easily occurs in the iteration process. By integrating the evolution strategy into the 2 parameters, the numerical value of the evolution strategy can be dynamically adjusted in each iteration process, so that the particle swarm can be quickly searched in the global scope in the early stage of iteration, and the global optimal solution can be determined by accurate search in the later stage.

The adjustment formula is:

wherein omega _init 、ω _end Representing initial and final values of inertial weights; c _1max 、c _2max Representing the maximum and minimum values of the learning factor.

An embodiment six, an intelligent energy system collaborative planning method based on full life cycle cost according to the embodiment, where the collaborative planning method is implemented based on the intelligent energy system constructed according to the embodiment one, and the method includes:

The method is based on an intelligent energy system, and a full life cycle cost model is built and used as a dynamic optimization target for collaborative planning and design of the energy system; secondly, analyzing the characteristics of various optimization algorithms, and developing a hybrid algorithm with high solving precision and high convergence speed for the collaborative planning design of the intelligent energy system; and finally, with the minimum cost of the whole life cycle as an optimization target, utilizing a hybrid optimization algorithm to realize the optimal configuration of the cold-hot electric multi-energy coupling intelligent energy system.

An seventh embodiment is a further limitation of the intelligent energy system collaborative planning method based on full life cycle cost according to the sixth embodiment, wherein the calculating and analyzing initial investment and operation maintenance costs according to the full life cycle cost model includes:

Embodiment eight will be described with reference to fig. 8. The present embodiment is further defined by the method for collaborative planning of an intelligent energy system based on full life cycle cost according to the sixth embodiment, wherein the minimum full life cycle cost of the intelligent energy system is obtained according to a hybrid optimization algorithm combined by a particle swarm algorithm and a hook-and-fast algorithm, and the hybrid optimization algorithm combined by the particle swarm algorithm and the hook-and-fast algorithm includes:

taking the continuous variable optimization result as an initial value;

The method is used for comparing and analyzing characteristics of various optimization algorithms, and is difficult to consider local searching capacity and efficient global optimizing performance, a hybrid algorithm with high solving precision and high convergence speed is built for collaborative planning design of an energy system, and the hybrid algorithm is a hybrid optimization algorithm combined by a particle swarm algorithm and a Hooke-Schiff algorithm.

The basic process of solving the hybrid algorithm is as follows: firstly, carrying out primary optimization by adopting a particle swarm algorithm, wherein the optimization process is carried out within a specific threshold grid range; and secondly, taking a continuous variable optimization result of the particle swarm algorithm as an initial value, adopting the Hooke's algorithm to perform re-optimization, fixing a discrete variable as an optimal value of the particle swarm optimization result, and finally, using the particle swarm algorithm to perform iteration, wherein the specific solving process is shown in fig. 6. The hybrid algorithm fuses the capabilities of the global and local optimization optimizing algorithms.

In the seventh embodiment, the present embodiment optimizes the configuration of the intelligent energy system by using the hybrid optimization algorithm developed with the minimum cost of the whole life cycle. And after the Genopt adopts a hybrid optimization algorithm to analyze data, capacity configuration of different devices is changed according to the variable change range set in the program, then the simulation model is started again, and the changed variables are input into the simulation model to complete simulation. And the method is to reciprocate until the minimum full life cycle cost is reached, and the method is the optimal intelligent energy system configuration taking the minimum full life cycle cost as the optimization target.

The computer readable storage medium according to the ninth embodiment is used for storing a computer program, and the computer program executes the method for constructing the urban intelligent energy system based on the multiple energy streams and multiple levels according to any one of the first to fourth embodiments.

The computer device according to the tenth embodiment includes a memory and a processor, the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the urban intelligent energy system construction method based on the multi-energy-flow multi-hierarchy according to any one of the first to fifth embodiments.

Embodiment eleven, referring to fig. 9 and 10, this embodiment is a specific example provided for embodiment one, and is also used for explaining embodiment two to embodiment five:

according to the urban intelligent energy system based on the multi-energy flow and multi-level, data transmission and energy flow conversion among all equipment and all modules are achieved, and meanwhile, a control strategy is added in a system model, so that the intelligent energy system can be flexibly and dynamically scheduled. In general, the types of external input parameters of the energy system model comprise meteorological parameters, user cold and hot electrical loads, equipment dynamic performance curves, system equipment design and model selection parameters and the like, and the external input parameters become boundary conditions of system simulation calculation. After the system model obtains the cold and hot electricity demands of the user, relevant load data are distributed to different subsystems according to control logic, after each subsystem receives a control signal, dynamic simulation calculation is realized by combining the performance parameters of equipment, finally, the system dynamic simulation is realized, and relevant calculation results are output, the TRNSYS energy station comprehensive energy system energy consumption simulation calculation platform schematic diagram is shown in fig. 9, and the simulation platform information flow architecture schematic diagram is shown in fig. 10.

Based on the first embodiment, the present embodiment develops a multi-energy-flow multi-level city intelligent energy system planning and designing software, which includes the following steps:

the first step: firstly, according to software requirements, defining a software architecture, dividing the software architecture into a data layer, a service layer and a presentation layer, and determining main constituent factors of each layer. The data layer comprises city-level, regional-level and land-level multi-level energy network data information, including meteorological data, load data, source side equipment data, network system data, energy price, equipment price and the like, and forms subsequent iterative optimization of the database supporting software; the service layer is a core part of software and supports the realization of intelligent energy system planning design, takes a source network double-side model as a support, invokes a full life cycle cost algorithm, a hybrid optimization algorithm and an improved particle swarm network optimization algorithm, realizes the optimal design of a multi-level energy system, and comprises the functions of capacity configuration of each device at the source side, multi-level energy network route optimization and the like. The display layer displays the planning effect under the planning scene in a form of a graph or a data table.

And a second step of: taking the first step as a core to form a logic flow for developing the intelligent energy system multi-level planning design software. The software predicts the cold-hot electric load under the design working condition of the area and time by time all the year through the information of the project location, the energy consumption side land mass property, the building area and the like, and takes the information as the requirement input parameter for the optimal design of the follow-up intelligent energy system; based on the supply and demand matching idea, selecting a system optimization target, and based on a constructed intelligent energy system simulation model, invoking an optimization algorithm to respectively realize capacity optimization configuration of cold and hot electrical equipment of a source side system and multi-level path optimization of a network side cold and hot electrical multi-energy flow network; then based on the optimization result, the software can realize the dynamic simulation of the whole intelligent energy system time by time all the year; and finally, displaying the planning and design result.

And a third step of: and developing the planning and designing software of the multi-energy-flow multi-level city intelligent energy system to realize the main function of the software.

The software interface developed is shown in fig. 11-15.

Taking triple supply, a ground source heat pump, a conventional cold and heat source and an energy storage system as an example.

The project is located in Hebei province, the functions of the building group are office and hospital, and the total building area is about 47.6 ten thousand m ² The comprehensive utilization rate of the system energy is improved to the maximum extent, the running cost is saved, and the pollutant emission is reduced.

The temperature of the water supply and return in summer is set to be 5/12 ℃ and the temperature of the water supply and return in winter is set to be 50/40 ℃. Through load calculation, the total cooling load of the area is 41364.4kW, and the total heating load is 24609.2kW. I.e. a specific area cooling load of 86.9W/m ² The method comprises the steps of carrying out a first treatment on the surface of the The heat load per unit area was 51.7W/m ² Cumulative cooling load per unit area of whole year97.86kWh/m ² The cumulative heat load per unit area of the whole year is 53.60kWh/m ² 。

Initial capacity configuration conditions

The initial capacity of the system is determined according to conventional engineering data, the triple supply system takes project basic electric load and certain starting hours as a principle, 10% of project design electric load is taken as rated power generation, 60% of smaller value in project cold and hot design load is taken as rated capacity, 60% of accumulated heat load is taken as total energy storage according to typical design day, and the specific initial configuration conditions are as follows:

(1) Triple co-generation system

According to the principle of electric heating, the basic electricity consumption of the energy station is predicted according to regional load information, and 10% of typical design electric load is used as a type selection basis, so that an internal combustion engine with rated electricity generation capacity of 1600kW is selected. Under the condition, the internal combustion engine is started as long as the unit load rate reaches 50% during peak, peak and average power price, the unit adopts a variable frequency operation mode, the waste heat of the internal combustion engine can meet a certain heating air conditioning load, and the rest part is supplied by a ground source heat pump system and an auxiliary cold and heat source.

After the internal combustion engine is selected, a single-effect and double-effect composite absorber unit is selected, parameters of a smoke hot water type lithium bromide unit are determined according to the type of the internal combustion engine, rated refrigerating capacity of the lithium bromide unit is 2208kW, and rated heating capacity of the lithium bromide unit is 1840kW.

The corresponding conveying equipment is selected according to the selected capacity parameters of the internal combustion engine and the lithium bromide unit, and corresponding equipment parameters of the medium temperature, the cylinder liner water pump, the lithium bromide cooling tower and the lithium bromide load side circulating pump can be obtained, as shown in table 1.

Table 1 parameter table for auxiliary equipment of combined cooling heating and power system

(2) Ground source heat pump system

After the equipment capacity and the type selection of the combined heat and power and gas supply system are determined, the main load requirement of the area is met by the ground source heat pump system according to the principle of preferentially utilizing renewable energy sources. The capacity of the ground source heat pump unit is determined according to 60% of the smaller value in the regional design heat load and the cold load, 3 ground source heat pump units are determined, the rated refrigerating capacity of each unit is 4346kW, the rated heating capacity is 4389kW, the refrigerating COP of the unit design working condition is 5.9, and the heating COP is 4.8.

At present, two types of buried pipes of the underground buried pipe heat exchanger of the ground source heat pump are mainly used, namely vertical buried pipes and horizontal buried pipes. The two buried pipe types have the characteristics and the application environment of the two buried pipe types, and the mode is mainly selected according to the size of a field, the type of local rock and soil and the excavation cost. Because the horizontal pipe has shallower burial depth, the performance of the buried pipe heat exchanger is inferior to that of a vertical buried pipe, and the occupied space is large in construction, and the shallow buried horizontal pipe is greatly influenced by the ground temperature, the horizontal pipe is suitable for the condition of single-season use (such as European use only for winter heating and domestic hot water supply), and the horizontal pipe heat exchanger has few users for winter and summer cold and hot combined supply systems. Moreover, for the vertical pipe burying system, the vertical pipe burying system adopted in China has the advantages that: the space is saved, and the heat exchange performance is good, so the vertical pipe burying system is adopted in the embodiment. Among the various vertical borehole heat exchangers, the single U-tube type is currently the most widely used. Therefore, the energy station of the embodiment adopts a vertical single U-shaped pipe underground heat exchanger. Meanwhile, in order to maintain the hydraulic balance among the loops, the same-program system is adopted.

The heat exchange amount of the underground heat exchanger in summer and the underground heat exchanger in winter can be respectively determined according to the following calculation formula:

Q _{summer with air conditioner} ＝Q _c ×(1+1/COP _c )

Q _Winter ＝Q _H ×(1-1/COP _H )

Wherein Q is _c The refrigerating capacity of the heat pump unit is kW; q (Q) _H The heat pump unit heats heat, and the unit is kW; COP of _c The refrigerating performance coefficient of the heat pump unit; COP of _H Is the heating performance coefficient of the heat pump unit.

The length of the underground heat exchanger is related to the geology, the geothermal parameters and the water temperature entering the heat pump unit. When specific data is lacking, the double-U heat exchange holes can be determined according to domestic practical engineering experience, the heat exchange amount per meter of pipe length is 35-55W, and according to Beijing practical engineering, the heat exchange amount q per unit of pipe length is 45W/m in winter and 65W/m in summer, and the required length L of the underground heat exchanger is 235169 meters.

When the drilling is designed, the drilling is carried out according to the burial depth of 120 meters, the total drilling number is 1960, and the pitch of the drilling holes is calculated according to 5 meters.

After the capacity of the ground source heat pump unit is determined, the ground source side of the ground source heat pump unit exchanges heat according to the temperature difference of 5 ℃ by combining with the actual water supply temperature requirement of the unit, and the load side supplies water according to the temperature difference of 7 ℃, so that the refrigerating and heating performance has certain loss under the working condition. From the above, the model selection parameters of the ground source side water pump and the ground source heat pump load side water pump of the ground source heat pump are shown in table 2.

Table 2 selection parameters of ground source side water pump and ground source heat pump load side water pump

(2) Ground source heat pump system

After the equipment capacity and the type selection of the combined heat and power and gas supply system are determined, the main load requirement of the area is met by the ground source heat pump system under the principle of preferentially utilizing renewable energy sources. The capacity of the ground source heat pump unit is determined according to 60% of the smaller value of the regional design heat load and the cold load, 3 ground source heat pump units are determined, the rated refrigerating capacity of each unit is 4346kW, and the rated heating capacity is 4389kW.

(3) Energy storage system

According to project typical daily load information conditions, the accumulated heat load demand of the summer typical day is 308440kWh. In the embodiment, a partial load chilled water storage system is adopted, and if energy storage equipment is arranged according to 60% daily accumulated heat demand, daily design energy storage capacity can reach 185064kWh. The volume of the energy storage water tank is 15915m according to the energy storage temperature difference of 10 DEG C ³ 。

(4) Auxiliary cold and heat source system

Different cold and heat source forms have different driving energy grades, functions, efficiency, operation cost and initial investment. For the combined supply system, the auxiliary system bears relatively more peak-shaving cold and heat sources under the condition that the waste heat bears most of the basic load. Therefore, compared with the conventional cold and heat source form selection, the economy of auxiliary cold and heat sources of the combined supply system is more important.

In addition, the auxiliary cold and heat source systems need to be well matched with the power generation system and adapt to the temperature requirement of the water supply and return so as to improve the performance of the whole system.

The present embodiment adopts electric refrigeration and gas boilers for peak shaving.

The electric refrigeration consumes high-grade electric energy and has higher COP. The cold accumulation technology can be combined, the peak-to-valley electricity price is utilized to adjust the electric load, and the flexibility of the power generation system is improved to a certain extent. The gas boiler has lower cost, convenient operation control and small construction and installation engineering quantity.

In the comprehensive energy system of the energy station mainly comprising the triple supply system and the ground source heat pump system, the electric refrigeration and gas boiler bears the peak regulation function of the redundant cold and hot loads, and the equipment capacity is determined according to the redundant load of the area.

Q boiler heat = Q total heat-Q combined heat-Q heat pump heat-Q energy storage = 7613kW

Q boiler cold = Q total cold-Q combined cold-Q heat pump cold-Q energy storage = 21863kW

And simultaneously, the electric refrigerating chilled water pump, the electric refrigerating cooling water pump and the electric refrigerating cooling tower can be selected.

Table 3 refrigeration equipment selection parameters

(4) Load side transmission and distribution two-stage water pump

The energy station triple co-generation system, the ground source heat pump system and the auxiliary cold and heat source system are all provided with load side primary circulating water pumps for overcoming the resistance of all equipment in the energy station, and load side transmission and distribution secondary water pumps are used for overcoming the resistance of an outdoor transmission and distribution pipe network between the energy station and a user. The calculation of the energy supply radius of the energy station is to study the energy consumption change of the load side transmission and distribution secondary water pump, the energy loss of pipe network transmission and the heat loss of the water pump after the change of the transmission distance.

Thus, the system initial capacity configuration results are shown in the following table:

optimizing capacity configuration calculation:

when the lowest LCC is used as a capacity configuration optimization target, the system calculates and analyzes initial investment and operation maintenance cost.

Wherein, LCC is life cycle cost, IC is scheme initial investment cost, OC is scheme operation and maintenance cost, RC is fixed cost net residual value; x is the discount rate.

Wherein the basic parameters of the economic analysis in the calculation tool are shown in tables 4, 5 and 6.

TABLE 4 initial investment calculation of base parameters

TABLE 5 Main System Equipment Performance parameters

TABLE 6 economic basis parameters

Regional power prices are divided into peak, flat, valley electricity. The price and time are as follows:

spike electricity (18:00-20:00) price of electricity: 1.0636 yuan;

peak electricity (8:00-10:00, 21:00-22:00) price of electricity: 0.9352 yuan;

flat electricity (7:00, 11:00-17:00) price: 0.6786 yuan;

valley electricity (23:00-7:00) price of electricity: 0.3578 yuan.

The gas price is 3.45 yuan/Nm ³ 。

When the LCC of the whole life cycle of the system is the lowest as the optimization target, the capacity occupation of the triple co-generation system and the ground source heat pump system is smaller under the condition of comprehensively considering comprehensive influence factors such as initial investment, operation cost and the like due to the higher initial investment of the triple co-generation system and the heat pump system, and specific data are shown in the table 7.

TABLE 7 System Capacity matching with LCC as optimization target

The technical solution provided by the present invention is described in further detail above with reference to the accompanying drawings, which is to highlight the advantages and benefits, not to limit the present invention, and any modification, combination of embodiments, improvement and equivalent substitution etc. within the scope of the spirit principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A city intelligent energy system construction method based on multi-energy flow and multi-level is characterized by comprising the following steps:

2. The method for constructing the urban intelligent energy system based on the multi-energy flow and multi-level according to claim 1, wherein the method for constructing the energy system carrying the cold source, the heat source, the air source and the electric power comprises the following steps:

Wherein E is _r,pgu Is the fuel gas consumption;

obtaining the energy consumption of a solar heat supply model:

acquiring energy consumption of a water chilling unit;

3. The method for constructing the urban intelligent energy system based on the multi-energy-flow and multi-level according to claim 1, wherein the multi-level energy network division according to the energy network urban-regional-land parcel multi-level energy network division principle comprises the following steps:

4. A method for constructing a multi-energy-flow multi-level based urban intelligent energy system according to claim 1 or 3, wherein said establishing a multi-level multi-energy-flow intelligent energy network inter-level coupling model according to a multi-energy-flow module comprises:

5. The method for constructing the urban intelligent energy system based on the multi-energy-flow and multi-level according to claim 1, wherein the constructing according to the coupling model between the multi-level and multi-energy-flow intelligent energy network levels comprises the following steps:

6. A cooperative planning method for an intelligent energy system based on full life cycle cost, wherein the cooperative planning method is implemented based on the construction of the intelligent energy system according to claim 1, and the method comprises:

7. The intelligent energy system collaborative planning method according to claim 6, wherein the computing and analyzing initial investment and operational maintenance costs according to the full life cycle cost model comprises:

8. The intelligent energy system collaborative planning method based on full life cycle cost according to claim 6, wherein the hybrid optimization algorithm combined by the particle swarm algorithm and the Hooke-jeff algorithm obtains the minimum full life cycle cost of the intelligent energy system, and the hybrid optimization algorithm combined by the particle swarm algorithm and the Hooke-jeff algorithm comprises:

taking the continuous variable optimization result as an initial value;

9. A computer readable storage medium for storing a computer program for executing a multi-energy flow multi-level based urban intelligent energy system construction method according to any one of claims 1-5.

10. A computer device, characterized by: comprising a memory and a processor, the memory having stored therein a computer program, which when executed by the processor performs a multi-energy flow multi-level based urban smart energy system construction method according to any one of claims 1-5.