CN114077934B - Comprehensive energy microgrid interconnection system and scheduling method thereof - Google Patents

Comprehensive energy microgrid interconnection system and scheduling method thereof Download PDF

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CN114077934B
CN114077934B CN202210058626.2A CN202210058626A CN114077934B CN 114077934 B CN114077934 B CN 114077934B CN 202210058626 A CN202210058626 A CN 202210058626A CN 114077934 B CN114077934 B CN 114077934B
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gas
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heat
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CN114077934A (en
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桑子夏
方仍存
雷何
杨东俊
詹智红
赵红生
郑旭
颜炯
侯婷婷
杨洁
王娅镭
陈竹
王琪鑫
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

A scheduling method of an integrated energy microgrid interconnection system comprises the following steps: s1, acquiring parameter information of the comprehensive energy microgrid interconnection system; s2, establishing an energy coupling equipment model, wherein the energy coupling equipment model comprises a combined heat and power system model, a gas boiler model, an electric gas conversion equipment model, an electric boiler model and an energy storage equipment model, and the combined heat and power system model comprises a gas turbine model and a waste heat boiler model; s3, establishing an energy router model; s4, establishing a unified steady-state power flow model of the electric power, thermal power and natural gas system; s5, establishing an optimized scheduling model of the comprehensive energy microgrid interconnection system; and S6, solving the optimized scheduling model of the comprehensive energy microgrid interconnection system to obtain an optimal scheduling strategy. The design improves the energy efficiency utilization level of the system, thereby improving the environmental benefit and the economic benefit of the comprehensive energy microgrid.

Description

Comprehensive energy microgrid interconnection system and scheduling method thereof
Technical Field
The invention relates to the technical field of energy, in particular to an integrated energy microgrid interconnection system and a scheduling method thereof, which are mainly suitable for improving the energy efficiency utilization level.
Background
With the development of energy crisis, distributed energy such as photovoltaic and wind power is widely concerned, and the distributed energy cannot be applied on a large scale due to the defects of decentralization, intermittence, volatility and the like. Traditionally, energy management systems such as electric energy, heat energy and natural gas are mutually independent, interaction does not exist among various energy sources, the comprehensive utilization rate of the energy sources is low, the condition of energy abandonment is easy to occur, and then the concept of an energy internet is provided. The energy internet is a novel energy utilization system for realizing safe, efficient and coordinated sharing by closely coupling energy and information, can flexibly and efficiently utilize various energy sources, relieves the energy crisis, and simultaneously conforms to the low-carbon, green and sustainable development concept. The energy router is used as important equipment in an energy internet, is a plug-in capable of controlling energy transmission and distribution and regulating and controlling energy, integrates the modern power electronic technology and the information communication technology, integrates a power electronic transformer, an energy converter, distributed energy, an energy storage device, a load, an information acquisition and transmission device and the like, and extends the characteristics of peer-to-peer opening, plug-and-play and open interconnection in the field of information networks.
With the diversification and distribution trend of energy demand, the interconnection of multiple energy systems becomes an important development direction of energy internet. In all aspects of energy production, transmission, storage, utilization and the like, the method of interconnection integration, cooperative scheduling and flexible configuration is required to be considered to analyze the whole energy system, so that energy interconnection is enhanced, and the necessary trend of development of a future energy system is formed by promoting the cooperative optimization and complementation of various energy sources. In the prior energy system, planning and design are mostly carried out independently, and the correlation among various energy systems is ignored; in the energy system considering energy coupling, the optimized operation and energy management aiming at a single comprehensive energy system are more realized, the interconnection and intercommunication of the comprehensive energy systems under multiple regions are not considered, and the characteristic mining on the energy Internet is insufficient.
In summary, currently, researches on the collaborative operation optimization of a plurality of comprehensive energy systems in the energy internet are few, and under the background of the rapid development of related researches on the energy internet, the energy management strategy of the comprehensive energy microgrid interconnection system has important research values and application prospects.
Disclosure of Invention
The invention aims to overcome the defects and problems of low energy efficiency utilization level in the prior art, and provides a comprehensive energy microgrid interconnection system with high energy efficiency utilization level and a scheduling method thereof.
In order to achieve the above purpose, the technical solution of the invention is as follows: a scheduling method of an integrated energy microgrid interconnection system comprises the following steps:
s1, acquiring parameter information of the comprehensive energy microgrid interconnection system;
s2, establishing an energy coupling equipment model, wherein the energy coupling equipment model comprises a combined heat and power system model, a gas boiler model, an electric gas conversion equipment model, an electric boiler model and an energy storage equipment model, and the combined heat and power system model comprises a gas turbine model and a waste heat boiler model;
s3, establishing an energy router model;
s4, establishing a unified steady-state power flow model of the electric power, thermal power and natural gas system;
s5, establishing an optimized scheduling model of the comprehensive energy microgrid interconnection system;
and S6, solving the optimized scheduling model of the comprehensive energy microgrid interconnection system to obtain an optimal scheduling strategy.
In step S1, the parameter information includes an energy coupling device parameter, an energy router parameter, a sub-energy system parameter inside the microgrid, interconnection topology information, economic cost information, carbon emission information, safe operation constraint information, and various microgrid load information.
In step S2, the gas turbine model is:
HWH，in＝PGTηr/ηc
ηcLNGVGT＝∑PGT
in the formula etacFor the efficiency of the gas turbinerFor gas turbine waste heat recovery efficiency, HWH，inFor recovering power, P, from exhaust gas waste heat of gas turbinesGTFor the generated power of a gas turbine, VGTAmount of natural gas consumed by gas turbine for operating time, LNGIs the heat value of natural gas;
the waste heat boiler model is as follows:
HWH，out＝HWH，inηWH
in the formula, HWH，outIs the output power of the waste heat boiler, HWH，inFor recovering power, eta, from exhaust gas waste heat of gas turbinesWHThe heat conversion efficiency of the waste heat boiler is obtained;
the gas boiler model is as follows:
Figure GDA0003556969440000021
in the formula, HGBIs the thermal power of the gas-fired boiler,
Figure GDA0003556969440000031
amount of gas consumed by the gas boiler during a time period of Δ t, ηGBThe heat efficiency of the gas boiler;
the electric gas conversion equipment model is as follows:
Figure GDA0003556969440000032
in the formula, VatNatural gas production, P, for electric gas-conversion plantsP2GIn order for the electric power conversion equipment to consume electric power,
Figure GDA0003556969440000033
for the working efficiency of the electric-to-gas equipment, phi is the energy conversion systemNumber, κHHVA high calorific value;
the electric boiler model is as follows:
HEH，out＝PEH，inηEH
in the formula, HEH，outFor the output heating power of an electric boiler, PEH，inIs the input electric power of the electric refrigerator, etaEHThe energy efficiency ratio of the electric refrigerator;
the energy storage equipment model is as follows:
Figure GDA0003556969440000034
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Δ t is the time interval from time period t to time period t +1, Pabs(t) power of energy storage in time t [. eta. ]absFor the energy storage efficiency of the energy storage device, Prelea(t) is the power of energy discharge in the period of t, u is the energy coefficient of energy dissipation loss or self-loss of the energy storage device to the environment, and etareleaThe discharging efficiency of the energy storage device is improved.
In step S3, the energy router model is:
Figure GDA0003556969440000035
wherein I is an input matrix of the energy router, T is a transformation and transmission matrix of the energy router, O is an output matrix of the energy router, and O is an output matrix of the energy routernAnd InOutput and input of the nth energy source, respectively, off-diagonal element TijDiagonal element T as a conversion factor between energy i and energy jiiThe distribution and loss coefficients of the same energy source i;
fii＝aiicii
Tij＝aijbij
in the formula, aijAnd aiiThe distribution coefficient, the sum of the distribution coefficients of the same energy sources is 1,
Figure GDA0003556969440000041
bijcoupling the conversion efficiency of the equipment for energy; c. CiiThe transmission loss coefficient between the same energy sources.
In step S4, the unified steady-state power flow model of the power, thermal and natural gas system includes:
the thermodynamic system model is as follows:
Figure GDA0003556969440000042
Figure GDA0003556969440000043
in the formula, Hi(t) is the heat source supply heat power or heat load demand heat power at node i, κ is the hot water specific heat capacity, mi(t) is the mass of water flowing out of the heat source or into the heat load node i,
Figure GDA0003556969440000044
is the output heat power of the waste heat boiler,
Figure GDA0003556969440000045
is the output thermal power of the gas-fired boiler,
Figure GDA0003556969440000046
is used for outputting the thermal power of the electric boiler,
Figure GDA0003556969440000047
is the thermal power of the thermal energy port of the energy router,
Figure GDA0003556969440000048
in order to be the thermal load power,
Figure GDA0003556969440000049
is the temperature at node i at which hot water flows out of the heat source or into the heat load,
Figure GDA00035569694400000410
the temperature of hot water flowing into a heat source or flowing out of a heat load at a node i is shown;
the loss of thermal energy in the thermal conduit transmission is described by the drop in temperature of the water stream in the conduit:
Figure GDA00035569694400000411
in the formula (I), the compound is shown in the specification,
Figure GDA00035569694400000412
respectively the ambient temperature, head end temperature, tail end temperature, delta, of the pipe ijij、Lij、mijThe heat transfer coefficient, the length and the mass flow of the pipeline ij are respectively;
at the junction of the pipelines, the hot water meets the law of conservation of energy, and the inflow and outflow relations at the junction of the nodes are as follows:
Figure GDA00035569694400000413
Figure GDA00035569694400000414
Figure GDA00035569694400000415
in the formula (I), the compound is shown in the specification,
Figure GDA00035569694400000416
respectively the temperature and the mass flow of the hot water flowing into the tail end of the pipeline at the node i,
Figure GDA00035569694400000417
respectively the temperature and the mass flow of hot water at the initial end of the pipeline at the outflow node i;
the natural gas system model is as follows:
Figure GDA0003556969440000051
in the formula, f, sigma, v and pi are respectively natural gas flow, pipeline characteristic parameters, natural gas flow direction variables and pressure intensity; subscripts i, j and ij are respectively a pipeline head end node, a pipeline tail end node and a pipeline;
the compressor model is as follows:
Figure GDA0003556969440000052
in the formula, Pcom(t) is the pressure generated by the compressor; o, tau and Z are respectively a compressor characteristic parameter, a compressor consumed flow and a compressor factor related parameter; subscript com is the compressor; omega, theta and rho are parameters of the consumption characteristic curve of the compressor;
nodes in the natural gas system satisfy the law of conservation of flow, namely:
Figure GDA0003556969440000053
in the formula, Vin(t) as the gas purchase quantity, Ω (i) as the set of equipment connected to node i of the distribution network, δ (i) as the set of branch end nodes with i as the head end node in the distribution network, θ (i) as the set of branch head end nodes with i as the end node in the distribution network, V (i)ni(t) amount of natural gas flowing into end node i, Vir(t) Natural gas flow into head end node i, VLD(t) is a predicted value of the gas load q in the period t,
Figure GDA0003556969440000054
respectively the natural gas consumption of the gas turbine, the natural gas consumption of the gas boiler and the natural gas generation amount of the electric gas conversion equipment in the t period;
the power system model is as follows:
Figure GDA0003556969440000061
in the formula, Pij，t、Qij，t、Iij，tThe active power, the reactive power and the current of a branch ij from a node i to a node j are respectively; pji，tAnd Qji，tRespectively the active power and the reactive power of a branch ji from a node j to a node i; r isijAnd xijThe resistance and reactance of branch ij are respectively; u shapei，tAnd Uj，tThe voltages at node i and node j, respectively;
Figure GDA0003556969440000062
and
Figure GDA0003556969440000063
respectively the active power and the reactive power of the power supporting equipment in the microgrid;
Figure GDA0003556969440000064
and
Figure GDA0003556969440000065
respectively the active power and the reactive power of the internal load of the microgrid;
Figure GDA0003556969440000066
and
Figure GDA0003556969440000067
respectively generating active power and reactive power for the combined heat and power system;
Figure GDA0003556969440000068
and
Figure GDA0003556969440000069
respectively the active power and the reactive power consumed by the electric gas conversion equipment;
Figure GDA00035569694400000610
and
Figure GDA00035569694400000611
respectively the active power and the reactive power consumed by the electric boiler; j: i → j indicates that the starting point of the branch is i and the ending point is j.
In step S5, the objective function of the integrated energy microgrid interconnection system optimization scheduling model is to minimize the operating cost of the system, as follows:
C＝Cgas+Ce+Cheat+Cm+Closs+Cwp
in the formula, CgasTo purchase gas cost, CeTo purchase electricity cost, CheatFor cost of heat supply, CmFor the operating maintenance costs of the energy coupling apparatus, ClossFor energy network loss cost, CwpPunishing cost for wind and light abandonment;
cost of gas purchase CgasComprises the following steps:
Figure GDA00035569694400000612
in the formula, RgasFor the price of natural gas, Vk(t) the purchase gas quantity of the kth natural gas plant;
cost of electricity purchase CeComprises the following steps:
Figure GDA0003556969440000071
in the formula, ReTo the electricity price, Pk(t) the purchased power of the kth type of power equipment;
cost of heat supply CheatComprises the following steps:
Figure GDA0003556969440000072
in the formula, RheatFor the price of heat supply, Hk(t) thermal power requirement of kth thermal plant;
energy coupling equipment operation maintenance cost CmComprises the following steps:
Figure GDA0003556969440000073
in the formula, RkFor the kth equipment maintenance cost coefficient, Kk(t) power of kth energy coupling device;
energy network loss cost ClossComprises the following steps:
Figure GDA0003556969440000074
in the formula, Vloss(t) network loss of Natural gas, Ploss(t) network loss of electric power, Hloss(t) network loss of thermal energy;
wind and light abandoning punishment cost CwpComprises the following steps:
Figure GDA0003556969440000075
in the formula (I), the compound is shown in the specification,
Figure GDA0003556969440000076
in order to discard the wind power,
Figure GDA0003556969440000077
to discard the optical power.
In the step S5, the constraint conditions of the integrated energy microgrid interconnection system optimized scheduling model include energy coupling device operation constraints, integrated energy microgrid system operation constraints and power balance constraints;
(1) energy coupling device operational constraints
The power constraints of the energy coupling device are:
Pk.min≤Pk(t)≤Pk.max
where k is a kth-class energy coupling device, Pk(t) Power of kth class energy coupling device during t time period, Pk.minIs the lower power limit, Pk.maxIs the upper power limit;
for energy storage devices, the capacity constraint is:
Sstor.min≤Sstor(t)≤Sstor.max
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Sstor.minTo the lower capacity limit of the energy storage device, Sstor.maxIs the upper limit of the capacity of the energy storage equipment;
(2) operation constraint of comprehensive energy micro-grid interconnection system
The operating constraints of the thermodynamic system are:
Figure GDA0003556969440000081
in the formula (I), the compound is shown in the specification,
Figure GDA0003556969440000082
and
Figure GDA0003556969440000083
the lower limit and the upper limit of the temperature of the hot water for supplying water to the node are respectively,
Figure GDA0003556969440000084
and
Figure GDA0003556969440000085
respectively is the lower limit and the upper limit of the temperature of the node return water hot water,m pand
Figure GDA0003556969440000086
respectively is the lower limit and the upper limit of the mass flow of the thermal power pipeline;
the operating constraints of a natural gas system are:
Figure GDA0003556969440000087
in the formula (I), the compound is shown in the specification,π iand
Figure GDA0003556969440000088
respectively the lower limit and the upper limit of the node pressure, ijfand
Figure GDA0003556969440000089
respectively the lower limit and the upper limit of the natural gas flow of the pipeline,R comand
Figure GDA00035569694400000810
the lower limit and the upper limit of the compression ratio of the compressor are respectively;
the operating constraints of the power system are:
Figure GDA00035569694400000811
Figure GDA00035569694400000812
in the formula (I), the compound is shown in the specification,U iand
Figure GDA00035569694400000813
respectively the lower and upper voltage limits of node i,
Figure GDA00035569694400000814
the upper current value limit for branch ij.
In step S6, an improved quantum-behaved particle swarm optimization is used to solve the optimized scheduling model of the integrated energy microgrid interconnection system, and the solving step is:
(1) inputting initial data;
(2) initializing a particle population according to the probability amplitude of the qubit;
(3) solving the electricity, heat and gas comprehensive power flow in each microgrid, judging whether the optimized scheduling has a solution or not, and if not, setting a fitness function value to be infinite; otherwise, storing the solution;
(4) calculating a fitness function value;
(5) checking whether the iteration times reach an upper limit, and if so, outputting an optimal scheduling strategy; otherwise, updating the particles and returning to the step (3).
In step S6, the quantum-behaved particle swarm optimization is improved as follows:
(1) particle encoding
The improved quantum particle swarm algorithm adopts the probability amplitude of the quantum bit as the current position code of the particle, and the formula is as follows:
Figure GDA0003556969440000091
in the formula, SmIs the mth particle position; n is a solution space dimension; cos (theta)mn) And sin (theta)mn) Respectively a cosine position and a sine position corresponding to the nth dimension of the mth particle;
converting two unit space positions of particles into solution space sine positions of optimization problem
Figure GDA0003556969440000092
And cosine position
Figure GDA0003556969440000093
The conversion formula is as follows:
Figure GDA0003556969440000094
in the formula, ajAnd bjMaximum and minimum values of j-th position of quantum;
(2) particle location update
Updating the preferred position with the quantum behavioral position update equation, and then returning to θmValues to form updated sine and cosine positions of the mth particle to form a current position code of the mth particle of the new generation; the position update equation is as follows:
Pm(t)＝λ·Xmb(t)+(1-λ)Xg(t)
Figure GDA0003556969440000095
Xm(t+1)＝Pm(t)±α|mbest(t)-Xm(t)|·ln(1/u)
Figure GDA0003556969440000101
where m is the current particle number, t is the t-th iteration, XmbAnd XgRespectively the individual optimal position and the global optimal position of the population of the particle, wherein lambda and u are both [0, 1%]The random number, N is the size of the population, mbest is the average value of the optimal positions of all particle individuals in the population, and alpha is a contraction-expansion factor.
The comprehensive energy microgrid interconnection system comprises a comprehensive energy microgrid and an energy router, wherein the comprehensive energy microgrid comprises an electric power network, a heat network, a natural gas network, a combined heat and power system, an electric heating boiler, a gas boiler and electric gas conversion equipment, the electric power network is connected with the heat network through the electric heating boiler, the electric power network is connected with the natural gas network through the electric gas conversion equipment, the electric power network is connected with the natural gas network and the heat network through the combined heat and power system, the natural gas network is connected with the heat network through the gas boiler, the combined heat and power system comprises a gas turbine and a waste heat boiler, the comprehensive energy microgrids are mutually connected through energy, the energy router comprises an electric energy port, a heat energy port, a gas port, an electric energy conversion power module, an energy conversion power module and a control center, and the control center is respectively connected with the electric energy conversion power module, the energy router, The energy conversion power module is connected.
Compared with the prior art, the invention has the beneficial effects that:
in the comprehensive energy microgrid interconnection system and the scheduling method thereof, the power, heat and natural gas system is cooperatively scheduled by strengthening the coupling complementary relationship between different energy flow forms such as electricity, heat and gas in the microgrid and between the microgrid, so that the multi-energy coordination complementary benefit potential can be exerted, the economy, low carbon and flexibility of the interconnection system are improved, and the capability of resource optimization configuration is improved; the interconnection of the electric and thermal gas systems provides more flexibility for operation scheduling, and distributed optimization realizes coordinated optimization scheduling considering all areas, balance low carbon and economic targets. Therefore, the invention improves the energy efficiency utilization level of the system, thereby improving the environmental benefit and the economic benefit of the comprehensive energy microgrid.
Drawings
Fig. 1 is a flowchart of a scheduling method of the integrated energy microgrid interconnection system according to the present invention.
Fig. 2 is a schematic structural diagram of the integrated energy microgrid interconnection system.
FIG. 3 is a schematic diagram of an energy router according to the present invention.
Fig. 4 is a schematic diagram of the electrical/thermal/gas energy conversion of the present invention.
Fig. 5 is a schematic diagram of a solving process of the optimization scheduling model of the integrated energy microgrid interconnection system in the invention.
Detailed Description
The present invention will be described in further detail with reference to the following description and embodiments in conjunction with the accompanying drawings.
Referring to fig. 1, a scheduling method of an integrated energy microgrid interconnection system includes the following steps:
s1, acquiring parameter information of the comprehensive energy microgrid interconnection system;
the parameter information comprises energy coupling equipment parameters, energy router parameters, energy subsystem parameters in the micro-grid, interconnection topological structure information, economic cost information, carbon emission information, safe operation constraint information and various micro-grid load information;
s2, establishing an energy coupling equipment model, wherein the energy coupling equipment model comprises a combined heat and power system model, a gas boiler model, an electric gas conversion equipment model, an electric boiler model and an energy storage equipment model, and the combined heat and power system model comprises a gas turbine model and a waste heat boiler model;
the gas turbine model is as follows:
HWH，in＝PGTηr/ηc
ηcLNGVGT＝∑PGT
in the formula etacFor the efficiency of the gas turbinerFor gas turbine waste heat recovery efficiency, HWH，inFor recovering power, P, from exhaust gas waste heat of gas turbinesGTFor the generated power of a gas turbine, VGTThe amount of natural gas consumed by the gas turbine for runtime; l isNGFor the heat value of natural gas, generally 9.7kW multiplied by h/m is taken3；
The exhaust-heat boiler collects the exhaust heat generated by the gas turbine, the output power is related to the efficiency of the exhaust-heat boiler, and the exhaust-heat boiler model is as follows:
HWH，out＝HWH，inηWH
in the formula, HWH，outIs the output power of the waste heat boiler, HWH，inFor recovering power, eta, from exhaust gas waste heat of gas turbinesWHThe heat conversion efficiency of the waste heat boiler is obtained;
the heat quantity generated by the gas boiler is related to the boiler efficiency and the fuel quantity, and the gas boiler model is as follows:
Figure GDA0003556969440000111
in the formula, HGBIs the thermal power of the gas-fired boiler,
Figure GDA0003556969440000112
amount of gas consumed by the gas boiler during a time period of Δ t, ηGBThe heat efficiency of the gas boiler;
the electric gas conversion equipment is regarded as a gas source in a natural gas network and regarded as a load in an electric power system, and the electric gas conversion equipment model is as follows:
Figure GDA0003556969440000113
in the formula, VatNatural gas production, P, for electric gas-conversion plantsP2GConsuming electricity for electric-to-gas equipmentThe amount of the compound (A) is,
Figure GDA0003556969440000114
the working efficiency of the electric gas conversion equipment is improved; phi is an energy conversion coefficient, and is usually equal to 3.4 MBtu/MWh; kappaHHVIs high calorific value, and its value is kHHV＝1.026MBtu/kcf；
The heating power provided by the electric boiler is related to the input electric power and the energy efficiency ratio, and the electric boiler model is as follows:
HEH，out＝PEH，inηEH
in the formula, HEH，outFor the output heating power of an electric boiler, PEH，inIs the input electric power of the electric refrigerator, etaEHThe energy efficiency ratio of the electric refrigerator;
the energy storage device comprises energy storage devices of various energy sources, including heat storage, electricity storage, gas storage devices and the like, and the energy storage device model is as follows:
Figure GDA0003556969440000121
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Δ t is the time interval from time period t to time period t +1, Pabs(t) power of energy storage in time t [. eta. ]absFor the energy storage efficiency of the energy storage device, Prelea(t) is the power of energy discharge in the period of t, u is the energy coefficient of energy dissipation loss or self-loss of the energy storage device to the environment, and etareleaThe discharging efficiency of the energy storage equipment is obtained; the energy storage device cannot store and release energy simultaneously within a certain time period;
s3, establishing an energy router model, wherein the energy router model comprises a transmission and conversion model of various energies;
as shown in fig. 3, the energy router uses a matrix to describe the energy flow characteristics in the energy router, and connects the input, conversion, and output of multiple energy sources together, so as to more intuitively embody energy interaction and coupling, and the energy router model is as follows:
Figure GDA0003556969440000122
wherein I is an input matrix of the energy router, T is a transformation and transmission matrix of the energy router, O is an output matrix of the energy router, and O is an output matrix of the energy routernAnd InThe output and the input of the nth energy source are respectively; off diagonal element TijThe energy conversion coefficient is the conversion coefficient between the energy i and the energy j, the conversion coefficient mainly comprises an energy distribution coefficient and the efficiency of an energy element, wherein the distribution coefficient is that the input energy is distributed to different energy conversion devices in proportion; diagonal element TiiThe distribution and loss coefficients of the same energy source i;
Tij＝aiicii
Tij＝aijbij
in the formula, aijAnd aiiThe distribution coefficient, the sum of the distribution coefficients of the same energy sources is 1,
Figure GDA0003556969440000131
bijcoupling the conversion efficiency of the equipment for energy; c. CiiThe transmission loss coefficient between the same energy sources;
s4, establishing a unified steady-state power flow model of the electric power, thermal power and natural gas system;
the unified steady-state power flow model of the electric power, thermal power and natural gas system comprises the following steps:
the thermodynamic system generally comprises a heat source, a heat supply network and a heat load, the heat energy transmitted or consumed by the thermodynamic system is determined by the flow rate and the temperature of water, and the thermodynamic system model is as follows:
Figure GDA0003556969440000132
Figure GDA0003556969440000133
in the formula, Hi(t) supply of Heat Source at node iThermal power or thermal load demand thermal power, kappa is the specific heat capacity of hot water, mi(t) is the mass of water flowing out of the heat source or into the heat load node i,
Figure GDA0003556969440000134
is the output heat power of the waste heat boiler,
Figure GDA0003556969440000135
is the output thermal power of the gas-fired boiler,
Figure GDA0003556969440000136
is used for outputting the thermal power of the electric boiler,
Figure GDA0003556969440000137
is the thermal power of the thermal energy port of the energy router,
Figure GDA0003556969440000138
in order to be the thermal load power,
Figure GDA0003556969440000139
is the temperature at node i at which hot water flows out of the heat source or into the heat load,
Figure GDA00035569694400001310
the temperature of hot water flowing into a heat source or flowing out of a heat load at a node i is shown;
the loss of thermal energy in the thermal conduit transmission is described by the drop in temperature of the water stream in the conduit:
Figure GDA00035569694400001311
in the formula (I), the compound is shown in the specification,
Figure GDA00035569694400001312
respectively the ambient temperature, head end temperature, tail end temperature, delta, of the pipe ijij、Lij、mijThe heat transfer coefficient, the length and the mass flow of the pipeline ij are respectively;
at the junction of the pipelines, the hot water meets the law of conservation of energy, and the inflow and outflow relations at the junction of the nodes are as follows:
Figure GDA00035569694400001313
Figure GDA00035569694400001314
Figure GDA00035569694400001315
in the formula, because the quality and the temperature of water flow of different pipelines flowing into the node i are different, the temperature of water flow flowing out of the node i is the same,
Figure GDA0003556969440000141
respectively the temperature and the mass flow of the hot water flowing into the tail end of the pipeline at the node i,
Figure GDA0003556969440000142
respectively the temperature and the mass flow of hot water at the initial end of the pipeline at the outflow node i;
the natural gas system mainly comprises a natural gas pipeline, a pressurizing station, a gas load, a regulating valve and the like; can realize the control to the natural gas line gas flow effectively through adjusting air-vent valve etc. and the natural gas line gas flow is closely related with the pressure and the pipeline transmission condition of pipeline both sides node, and the natural gas system model is:
Figure GDA0003556969440000143
in the formula, f, sigma, v and pi are respectively natural gas flow, pipeline characteristic parameters, natural gas flow direction variables and pressure intensity; subscripts i, j and ij are respectively a pipeline head end node, a pipeline tail end node and a pipeline; when pii≤πjWhen, vij-1; otherwise, vij＝+1；
The pressurization station comprises gas turbine, engine and compressor, offsets the pressure that consumes in the transportation process through the natural gas pressurization in to the pipeline, and gas turbine draws the natural gas from the filling station and provides required electric energy for compressor work, and the compressor model is:
Figure GDA0003556969440000144
in the formula, Pcom(t) is the pressure generated by the compressor; o, tau and Z are respectively a compressor characteristic parameter, a compressor consumed flow and a compressor factor related parameter; subscript com is the compressor; omega, theta and rho are parameters of the consumption characteristic curve of the compressor;
nodes in the natural gas system satisfy the law of conservation of flow, namely:
Figure GDA0003556969440000145
in the formula, Vin(t) as the gas purchase quantity, Ω (i) as the set of equipment connected to node i of the distribution network, δ (i) as the set of branch end nodes with i as the head end node in the distribution network, θ (i) as the set of branch head end nodes with i as the end node in the distribution network, V (i)ni(t) amount of natural gas flowing into end node i, Vir(t) Natural gas flow into head end node i, VLD(t) is a predicted value of the gas load q in the period t,
Figure GDA0003556969440000151
respectively the natural gas consumption of the gas turbine, the natural gas consumption of the gas boiler and the natural gas generation amount of the electric gas conversion equipment in the t period;
the electric power system adopts a DistFlow power flow model of an alternating-current power distribution network, and the electric power system model is as follows:
Figure GDA0003556969440000152
in the formula, Pij，t、Qij，t、Iij，tThe active power, the reactive power and the current of a branch ij from a node i to a node j are respectively; pji，tAnd Qji，tRespectively the active power and the reactive power of a branch ji from a node j to a node i; r isijAnd xijThe resistance and reactance of branch ij are respectively; u shapei，tAnd Uj，tThe voltages at node i and node j, respectively;
Figure GDA0003556969440000153
and
Figure GDA0003556969440000154
respectively the active power and the reactive power of the power supporting equipment in the microgrid;
Figure GDA0003556969440000155
and
Figure GDA0003556969440000156
respectively the active power and the reactive power of the internal load of the microgrid;
Figure GDA0003556969440000157
and
Figure GDA0003556969440000158
respectively generating active power and reactive power for the combined heat and power system;
Figure GDA0003556969440000159
and
Figure GDA00035569694400001510
respectively the active power and the reactive power consumed by the electric gas conversion equipment;
Figure GDA00035569694400001511
and
Figure GDA00035569694400001512
are respectively electric heatActive power and reactive power consumed by the boiler; j: i → j indicates that the starting point of the branch is i, the end point is j, and the reference direction of the power is from the starting point i to the end point j;
s5, establishing an optimized scheduling model of the comprehensive energy microgrid interconnection system;
the objective function of the optimization scheduling model of the comprehensive energy microgrid interconnection system is the operation cost of the minimized system, and the following formula is adopted:
C＝Cgas+Ce+Cheat+Cm+Closs+Cwp
in the formula, CgasTo purchase gas cost, CeTo purchase electricity cost, CheatFor cost of heat supply, CmFor the operating maintenance costs of the energy coupling apparatus, ClossFor energy network loss cost, CwpPunishing cost for wind and light abandonment;
cost of gas purchase CgasComprises the following steps:
Figure GDA0003556969440000161
in the formula, RgasFor the price of natural gas, Vk(t) the purchase gas quantity of the kth natural gas plant;
cost of electricity purchase CeComprises the following steps:
Figure GDA0003556969440000162
in the formula, ReTo the electricity price, Pk(t) the purchased power of the kth type of power equipment;
cost of heat supply CheatComprises the following steps:
Figure GDA0003556969440000163
in the formula, RheatFor the price of heat supply, Hk(t) thermal power requirement of kth thermal plant;
operating and maintaining cost of energy coupling equipmentCmComprises the following steps:
Figure GDA0003556969440000164
in the formula, RkMaintaining cost coefficients for the kth equipment; kk(t) the power of the kth energy coupling equipment, which comprises electric gas conversion equipment, a combined heat and power system, a gas boiler, an electric boiler and an energy router;
energy network loss cost ClossComprises the following steps:
Figure GDA0003556969440000165
in the formula, Vloss(t) network loss of Natural gas, Ploss(t) network loss of electric power, Hloss(t) network loss of thermal energy;
wind and light abandoning punishment cost CwpComprises the following steps:
Figure GDA0003556969440000166
in the formula (I), the compound is shown in the specification,
Figure GDA0003556969440000167
in order to discard the wind power,
Figure GDA0003556969440000168
the optical power is abandoned;
the constraint conditions of the optimization scheduling model of the comprehensive energy microgrid interconnection system comprise energy coupling equipment operation constraint, comprehensive energy microgrid system operation constraint and power balance constraint;
(1) energy coupling device operational constraints
The energy coupling equipment comprises a gas turbine, a waste heat boiler, a gas boiler, electric gas conversion equipment, an electric heating boiler and energy storage equipment, and the operation constraint of the energy coupling equipment mainly comprises the following power constraint of the energy coupling equipment:
Pk.min≤Pk(t)≤Pk.max
where k is a kth-class energy coupling device, Pk(t) Power of kth class energy coupling device during t time period, Pk.minIs the lower power limit, Pk.maxIs the upper power limit;
for energy storage devices, the capacity constraint is:
Sstor.min≤Sstor(t)≤Sstor.max
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Sstor.minTo the lower capacity limit of the energy storage device, Sstor.maxIs the upper limit of the capacity of the energy storage equipment;
(2) operation constraint of comprehensive energy micro-grid interconnection system
The operating constraints of the thermodynamic system are:
Figure GDA0003556969440000171
in the formula (I), the compound is shown in the specification,
Figure GDA0003556969440000172
and
Figure GDA0003556969440000173
the lower limit and the upper limit of the temperature of the hot water for supplying water to the node are respectively,
Figure GDA0003556969440000174
and
Figure GDA0003556969440000175
respectively is the lower limit and the upper limit of the temperature of the node return water hot water,m pand
Figure GDA0003556969440000176
respectively is the lower limit and the upper limit of the mass flow of the thermal power pipeline;
the operating constraints of a natural gas system are:
Figure GDA0003556969440000177
in the formula (I), the compound is shown in the specification,π iand
Figure GDA0003556969440000178
respectively the lower limit and the upper limit of the node pressure, ijfand
Figure GDA0003556969440000179
respectively the lower limit and the upper limit of the natural gas flow of the pipeline,R comand
Figure GDA00035569694400001710
the lower limit and the upper limit of the compression ratio of the compressor are respectively;
the operating constraints of the power system are:
Figure GDA0003556969440000181
Figure GDA0003556969440000182
in the formula (I), the compound is shown in the specification,U iand
Figure GDA0003556969440000183
respectively the lower and upper voltage limits of node i,
Figure GDA0003556969440000184
is the upper current value limit of branch ij;
s6, solving the optimized scheduling model of the comprehensive energy microgrid interconnection system to obtain the output of the energy coupling equipment and the power of each port of the energy router, so as to obtain an optimal scheduling strategy;
referring to fig. 5, the optimized scheduling model of the integrated energy microgrid interconnection system is solved by using an improved quantum particle swarm algorithm, and the solving steps are as follows:
(1) inputting initial data; the method comprises the steps of calculating the power price, the gas price and the heat price, the network structures of a power network, a heating power network and a natural gas network, original parameters and operation constraints, and photovoltaic and wind power day-ahead predicted output data in the micro-grid;
(2) initializing a particle population according to the probability amplitude of the qubit; the system comprises electric gas conversion equipment, an electric boiler, a combined heat and power system, a gas boiler and power of each port of an energy router;
(3) solving the electricity, heat and gas comprehensive power flow in each microgrid, judging whether the optimized scheduling has a solution or not, and if not, setting a fitness function value to be infinite; otherwise, storing the solution;
(4) calculating a fitness function value;
(5) checking whether the iteration times reach an upper limit, and if so, outputting an optimal scheduling strategy; otherwise, updating the particles and returning to the step (3).
The quantum particle swarm algorithm is improved as follows:
(1) particle encoding
The improved quantum particle swarm algorithm adopts the probability amplitude of the quantum bit as the current position code of the particle, and the formula is as follows:
Figure GDA0003556969440000185
in the formula, SmIs the mth particle position; n is a solution space dimension; cos (theta)mn) And sin (theta)mn) Respectively corresponding to the nth dimension of the mth particle, and corresponding to quantum state |0>And |1>The probability amplitude of (c); the current positions of the particles are coded in such a way, so that one particle can simultaneously represent two states, and the convergence rate of the algorithm can be accelerated and the search accuracy of the algorithm can be improved corresponding to the positions of two solution spaces;
converting two unit space positions of particles into solution space sine positions of optimization problem
Figure GDA0003556969440000186
And cosine position
Figure GDA0003556969440000187
The conversion formula is as follows:
Figure GDA0003556969440000191
in the formula, ajAnd bjMaximum and minimum values of the j-th position (j-th variable for optimization problem) of the quantum, respectively;
(2) particle location update
Updating the preferred position with the quantum behavioral position update equation, and then returning to θmValues to form updated sine and cosine positions of the mth particle to form a current position code of the mth particle of the new generation; the position update equation is as follows:
Pm(t)＝λ·Xmb(t)+(1-λ)Xg(t)
Figure GDA0003556969440000192
Xm(t+1)＝Pm(t)±α|mbest(t)-Xm(t)|·ln(1/u)
Figure GDA0003556969440000193
where m is the current particle number, t is the t-th iteration, XmbAnd XgRespectively the individual optimal position and the global optimal position of the population of the particle, wherein lambda and u are both [0, 1%]The random number is N, the size of the population is N, and mbest is the average value of the optimal positions of all particle individuals in the population; α is a contraction-expansion factor, which generally decreases linearly.
Referring to fig. 2, an integrated energy microgrid interconnection system comprises an integrated energy microgrid and an energy router, wherein the integrated energy microgrid comprises a power network, a thermal network, a natural gas network, a cogeneration system, an electric heating boiler, a gas boiler, an electric-to-gas device, electric-to-steam gas and other multi-energy loads, coupling relations among the devices are shown in fig. 4, the power network is connected with the thermal network through the electric heating boiler, the power network is connected with the natural gas network through the electric-to-gas device, the power network is connected with the natural gas network and the thermal network through the cogeneration system, the natural gas network is connected with the thermal network through the gas boiler, the cogeneration system comprises energy conversion devices such as a gas turbine and a waste heat boiler, and the integrated energy microgrids are connected with each other through the energy router; referring to fig. 3, the energy router includes an electric energy port, a thermal energy port, a gas port, an electric energy conversion power module, an energy conversion power module, and a management and control center, and the management and control center is connected to the electric energy conversion power module and the energy conversion power module, respectively. The energy router can acquire external information such as system parameters including load data, network structures and the like, has the functions of energy optimization, energy management, risk assessment, path optimization, perception protection, electrical measurement and the like, is applied to the energy internet, and can improve the conversion, transmission and utilization efficiency of energy.
The method comprises the steps of obtaining an equipment parameter aggregate, energy network structure parameters and multi-energy load data acquired in preset time duration of each energy equipment in any scheduling period of the comprehensive energy microgrid; and constructing an energy device dynamic efficiency model, an energy router energy transmission and conversion model and a multi-energy flow network steady-state power flow model, and further constructing an environment-friendly economic system operation model to obtain an optimal scheduling scheme.
The invention constructs a collaborative optimization mathematical model of a regional distributed energy Internet topological structure, equipment configuration and operation strategy, thereby improving the comprehensive energy utilization efficiency of the system. The method comprises the steps of optimizing and scheduling in the microgrid and optimizing and scheduling among the microgrids; the optimized scheduling for the interior of the microgrid comprises the output of the energy coupling equipment, so that the energy optimization for the interior of the microgrid can be ensured; the dispatching among the micro-grids comprises that the power, heat and natural gas systems of different micro-grids realize energy conversion and transmission through the energy router, so that energy complementation of different areas is realized, the operation cost of the whole interconnection system is further reduced, and the economic benefit and the environmental benefit are improved.

Claims (5)

1. A scheduling method of an integrated energy microgrid interconnection system is characterized by comprising the following steps:
the system comprises an integrated energy microgrid and an energy router, wherein the integrated energy microgrid comprises an electric power network, a thermal power network, a natural gas network, a combined heat and power system, an electric heating boiler, a gas boiler and an electric gas conversion device, the electric power network is connected with the thermal power network through the electric heating boiler, the electric power network is connected with the natural gas network through the electric gas conversion device, the electric power network is connected with the natural gas network and the thermal power network through the combined heat and power system, the natural gas network is connected with the thermal power network through the gas boiler, the combined heat and power system comprises a gas turbine and a waste heat boiler, the integrated energy microgrids are mutually connected through the energy router, each energy comprises an electric energy port, a heat energy port, a gas port, an electric energy conversion power module, an energy conversion power module and a control center, and the control center is respectively connected with the electric energy conversion power module, the energy router, The energy conversion power module is connected;
the scheduling method comprises the following steps:
s1, acquiring parameter information of the comprehensive energy microgrid interconnection system;
s2, establishing an energy coupling equipment model, wherein the energy coupling equipment model comprises a combined heat and power system model, a gas boiler model, an electric gas conversion equipment model, an electric boiler model and an energy storage equipment model, and the combined heat and power system model comprises a gas turbine model and a waste heat boiler model;
s3, establishing an energy router model;
the energy router model is:
O＝TI
Figure FDA0003556969430000011
wherein I is the input matrix of the energy router, and T is the transformation and transmission matrix of the energy routerO is the output matrix of the energy router, OnAnd InOutput and input of the nth energy source, respectively, off-diagonal element TijDiagonal element T as a conversion factor between energy i and energy jiiThe distribution and loss coefficients of the same energy source i;
Tii＝aiicii
Tij＝aijbij
in the formula, aijAnd ciiThe distribution coefficient, the sum of the distribution coefficients of the same energy sources is 1,
Figure FDA0003556969430000021
bijcoupling the conversion efficiency of the equipment for energy; c. CiiThe transmission loss coefficient between the same energy sources;
s4, establishing a unified steady-state power flow model of the electric power, thermal power and natural gas system;
the unified steady-state power flow model of the electric power, thermal power and natural gas system comprises the following steps:
the thermodynamic system model is as follows:
Hi(t)＝κ·mi(t)·(Ti g(t)-Ti r(t))
Figure FDA0003556969430000022
in the formula, Hi(t) is the heat source supply heat power or heat load demand heat power at node i, κ is the hot water specific heat capacity, mi(t) is the mass of water flowing out of the heat source or into the heat load node i,
Figure FDA0003556969430000023
is the output heat power of the waste heat boiler,
Figure FDA0003556969430000024
is the output thermal power of the gas-fired boiler,
Figure FDA0003556969430000025
is used for outputting the thermal power of the electric boiler,
Figure FDA0003556969430000026
is the thermal power of the thermal energy port of the energy router,
Figure FDA0003556969430000027
for thermal load power, Ti g(T) is the temperature at which hot water at node i flows out of the heat source or into the heat load, Ti r(t) is the temperature at node i when hot water flows into the heat source or out of the heat load;
the loss of thermal energy in the thermal conduit transmission is described by the drop in temperature of the water stream in the conduit:
Figure FDA0003556969430000028
in the formula (I), the compound is shown in the specification,
Figure FDA0003556969430000029
respectively the ambient temperature, head end temperature, tail end temperature, delta, of the pipe ijij、Lij、mijThe heat transfer coefficient, the length and the mass flow of the pipeline ij are respectively;
at the junction of the pipelines, the hot water meets the law of conservation of energy, and the inflow and outflow relations at the junction of the nodes are as follows:
Figure FDA00035569694300000210
Figure FDA00035569694300000211
Figure FDA00035569694300000212
in the formula (I), the compound is shown in the specification,
Figure FDA00035569694300000213
respectively the temperature and the mass flow of the hot water flowing into the tail end of the pipeline at the node i,
Figure FDA00035569694300000214
respectively the temperature and the mass flow of hot water at the initial end of the pipeline at the outflow node i;
the natural gas system model is as follows:
Figure FDA0003556969430000031
in the formula, f, sigma, v and pi are respectively natural gas flow, pipeline characteristic parameters, natural gas flow direction variables and pressure intensity; subscripts i, j and ij are respectively a pipeline head end node, a pipeline tail end node and a pipeline;
the compressor model is as follows:
Figure FDA0003556969430000032
in the formula, Pcom(t) is the pressure generated by the compressor; o, tau and Z are respectively a compressor characteristic parameter, a compressor consumed flow and a compressor factor related parameter; subscript com is the compressor; omega, theta and rho are parameters of the consumption characteristic curve of the compressor;
nodes in the natural gas system satisfy the law of conservation of flow, namely:
Figure FDA0003556969430000033
in the formula, Vin(t) is the gas purchase quantity, Ω (i) is the set of equipment connected to node i of the distribution network, δ (i) is the end of the branch with i as the head-end node in the distribution networkA node set, theta (i) is a branch head node set taking i as an end node in the gas distribution network, Vni(t) amount of natural gas flowing into end node i, Vir(t) Natural gas flow into head end node i, VLD(t) is a predicted value of the gas load q in the period t,
Figure FDA0003556969430000034
respectively the natural gas consumption of the gas turbine, the natural gas consumption of the gas boiler and the natural gas generation amount of the electric gas conversion equipment in the t period;
the power system model is as follows:
Figure FDA0003556969430000041
in the formula, Pij，t、Qij，t、Iij，tThe active power, the reactive power and the current of a branch ij from a node i to a node j are respectively; pji，tAnd Qji，tRespectively the active power and the reactive power of a branch ji from a node j to a node i; r isijAnd xijThe resistance and reactance of branch ij are respectively; u shapei，tAnd Uj，tThe voltages at node i and node j, respectively;
Figure FDA0003556969430000042
and
Figure FDA0003556969430000043
respectively the active power and the reactive power of the power supporting equipment in the microgrid;
Figure FDA0003556969430000044
and
Figure FDA0003556969430000045
respectively the active power and the reactive power of the internal load of the microgrid;
Figure FDA0003556969430000046
and
Figure FDA0003556969430000047
respectively generating active power and reactive power for the combined heat and power system;
Figure FDA0003556969430000048
and
Figure FDA0003556969430000049
respectively the active power and the reactive power consumed by the electric gas conversion equipment;
Figure FDA00035569694300000410
and
Figure FDA00035569694300000411
respectively the active power and the reactive power consumed by the electric boiler; j: i → j indicates that the starting point of the branch is i and the end point is j;
s5, establishing an optimized scheduling model of the comprehensive energy microgrid interconnection system;
the objective function of the optimization scheduling model of the comprehensive energy microgrid interconnection system is the operation cost of the minimized system, and the following formula is adopted:
C＝Cgas+Ce+Cheat+Cm+Closs+Cwp
in the formula, CgasTo purchase gas cost, CeTo purchase electricity cost, CheatFor cost of heat supply, CmFor the operating maintenance costs of the energy coupling apparatus, ClossFor energy network loss cost, CwpPunishing cost for wind and light abandonment;
cost of gas purchase CgasComprises the following steps:
Figure FDA00035569694300000412
in the formula, RgasFor the price of natural gas, Vk(t) isThe gas purchasing quantity of k natural gas equipment;
cost of electricity purchase CeComprises the following steps:
Figure FDA0003556969430000051
in the formula, ReTo the electricity price, Pk(t) the purchased power of the kth type of power equipment;
cost of heat supply CheatComprises the following steps:
Figure FDA0003556969430000052
in the formula, RheatFor the price of heat supply, Hk(t) thermal power requirement of kth thermal plant;
energy coupling equipment operation maintenance cost CmComprises the following steps:
Figure FDA0003556969430000053
in the formula, RkFor the kth equipment maintenance cost coefficient, Kk(t) power of kth energy coupling device;
energy network loss cost ClossComprises the following steps:
Figure FDA0003556969430000054
in the formula, Vloss(t) network loss of Natural gas, Ploss(t) network loss of electric power, Hloss(t) network loss of thermal energy;
wind and light abandoning punishment cost CwpComprises the following steps:
Figure FDA0003556969430000055
in the formula (I), the compound is shown in the specification,
Figure FDA0003556969430000056
in order to discard the wind power,
Figure FDA0003556969430000057
the optical power is abandoned;
the constraint conditions of the optimization scheduling model of the comprehensive energy microgrid interconnection system comprise energy coupling equipment operation constraint, comprehensive energy microgrid system operation constraint and power balance constraint;
(1) energy coupling device operational constraints
The power constraints of the energy coupling device are:
Pk，min≤Pk(t)≤Pk，max
where k is a kth-class energy coupling device, Pk(t) Power of kth class energy coupling device during t time period, Pk，minIs the lower power limit, Pk，maxIs the upper power limit;
for energy storage devices, the capacity constraint is:
Sstor.min≤Sstor(t)≤Sstor.max
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Sstor.minTo the lower capacity limit of the energy storage device, Sstor.maxIs the upper limit of the capacity of the energy storage equipment;
(2) operation constraint of comprehensive energy micro-grid interconnection system
The operating constraints of the thermodynamic system are:
Figure FDA0003556969430000061
in the formula (I), the compound is shown in the specification,T i gand
Figure FDA0003556969430000062
the lower limit and the upper limit of the temperature of the hot water for supplying water to the node are respectively,T i rand
Figure FDA0003556969430000063
respectively is the lower limit and the upper limit of the temperature of the node return water hot water,m pand
Figure FDA0003556969430000064
respectively is the lower limit and the upper limit of the mass flow of the thermal power pipeline;
the operating constraints of a natural gas system are:
Figure FDA0003556969430000065
in the formula (I), the compound is shown in the specification,π iand
Figure FDA0003556969430000066
respectively the lower limit and the upper limit of the node pressure, ijfand
Figure FDA0003556969430000067
respectively the lower limit and the upper limit of the natural gas flow of the pipeline,R comand
Figure FDA0003556969430000068
the lower limit and the upper limit of the compression ratio of the compressor are respectively;
the operating constraints of the power system are:
Figure FDA0003556969430000069
Figure FDA00035569694300000610
in the formula (I), the compound is shown in the specification,U iand
Figure FDA00035569694300000611
respectively the lower and upper voltage limits of node i,
Figure FDA00035569694300000612
is the upper current value limit of branch ij;
and S6, solving the optimized scheduling model of the comprehensive energy microgrid interconnection system to obtain an optimal scheduling strategy.
2. The scheduling method of the integrated energy microgrid interconnection system according to claim 1, characterized in that: in step S1, the parameter information includes an energy coupling device parameter, an energy router parameter, a sub-energy system parameter inside the microgrid, interconnection topology information, economic cost information, carbon emission information, safe operation constraint information, and various microgrid load information.
3. The scheduling method of the integrated energy microgrid interconnection system according to claim 1, characterized in that:
in step S2, the gas turbine model is:
HWH，in＝PGTηr/ηc
ηcLNGVGT＝∑PGT
in the formula etacFor the efficiency of the gas turbinerFor gas turbine waste heat recovery efficiency, HWH，inFor recovering power, P, from exhaust gas waste heat of gas turbinesGTFor the generated power of a gas turbine, VGTAmount of natural gas consumed by gas turbine for operating time, LNGIs the heat value of natural gas;
the waste heat boiler model is as follows:
HWH，out＝HWH，inηWH
in the formula, HWH，outIs the output power of the waste heat boiler, HWH，inFor recovering power, eta, from exhaust gas waste heat of gas turbinesWHThe heat conversion efficiency of the waste heat boiler is obtained;
the gas boiler model is as follows:
Figure FDA0003556969430000071
in the formula, HGBIs the thermal power of the gas-fired boiler,
Figure FDA0003556969430000072
amount of gas consumed by the gas boiler during a time period of Δ t, ηGBThe heat efficiency of the gas boiler;
the electric gas conversion equipment model is as follows:
Figure FDA0003556969430000073
in the formula, VatNatural gas production, P, for electric gas-conversion plantsP2GIn order for the electric power conversion equipment to consume electric power,
Figure FDA0003556969430000074
for the working efficiency of the electric gas-converting equipment, phi is the energy conversion coefficient, kappaHHVA high calorific value;
the electric boiler model is as follows:
HEH，out＝PEH，inηEH
in the formula, HEH，outFor the output heating power of an electric boiler, PEH，inIs the input electric power of the electric refrigerator, etaEHThe energy efficiency ratio of the electric refrigerator;
the energy storage equipment model is as follows:
Figure FDA0003556969430000081
in the formula, Sstor(t) is the energy stored by the energy storage device during time period t, Δ t is the time interval from time period t to time period t +1, Pabs(t) power of energy storage in time t [. eta. ]absFor the energy storage efficiency of the energy storage device, Prelea(t) is the power of energy discharge in the period of t, u is the energy coefficient of energy dissipation loss or self-loss of the energy storage device to the environment, and etareleaThe discharging efficiency of the energy storage device is improved.
4. The scheduling method of the integrated energy microgrid interconnection system according to claim 1, characterized in that:
in step S6, an improved quantum-behaved particle swarm optimization is used to solve the optimized scheduling model of the integrated energy microgrid interconnection system, and the solving step is:
(1) inputting initial data;
(2) initializing a particle population according to the probability amplitude of the qubit;
(3) solving the electricity, heat and gas comprehensive power flow in each microgrid, judging whether the optimized scheduling has a solution or not, and if not, setting a fitness function value to be infinite; otherwise, storing the solution;
(4) calculating a fitness function value;
(5) checking whether the iteration times reach an upper limit, and if so, outputting an optimal scheduling strategy; otherwise, updating the particles and returning to the step (3).
5. The scheduling method of the integrated energy microgrid interconnection system according to claim 4, characterized in that:
in step S6, the quantum-behaved particle swarm optimization is improved as follows:
(1) particle encoding
The improved quantum particle swarm algorithm adopts the probability amplitude of the quantum bit as the current position code of the particle, and the formula is as follows:
Figure FDA0003556969430000082
in the formula, SmIs the mth particle position; n is a solution space dimension; cos (theta)mn) And sin (theta)mn) Respectively a cosine position and a sine position corresponding to the nth dimension of the mth particle;
converting two unit space positions of particles intoSolution space sine position of optimization problem
Figure FDA0003556969430000083
And cosine position
Figure FDA0003556969430000084
The conversion formula is as follows:
Figure FDA0003556969430000091
in the formula, ajAnd bjMaximum and minimum values of j-th position of quantum;
(2) particle location update
Updating the preferred position with the quantum behavioral position update equation, and then returning to θmValues to form updated sine and cosine positions of the mth particle to form a current position code of the mth particle of the new generation; the position update equation is as follows:
Pm(t)＝λ·Xmb(t)+(1-λ)Xg(t)
Figure FDA0003556969430000092
Xm(t+1)＝Pm(t)±α|mbest(t)-Xm(t)|·ln(1/u)
Figure FDA0003556969430000093
where m is the current particle number, t is the t-th iteration, XmbAnd XgRespectively the individual optimal position and the global optimal position of the population of the particle, wherein lambda and u are both [0, 1%]The random number, N is the size of the population, mbest is the average value of the optimal positions of all particle individuals in the population, and alpha is a contraction-expansion factor.
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