CN110610276B - Comprehensive energy system scheduling method and system containing generalized predictive control - Google Patents

Comprehensive energy system scheduling method and system containing generalized predictive control Download PDF

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CN110610276B
CN110610276B CN201910888813.1A CN201910888813A CN110610276B CN 110610276 B CN110610276 B CN 110610276B CN 201910888813 A CN201910888813 A CN 201910888813A CN 110610276 B CN110610276 B CN 110610276B
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朱刘柱
王绪利
马静
赵锋
胡斌
胡旭东
张德广
周远科
周帆
江桂芬
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State Grid Anhui Zhongxing Electric Power Design Institute Co ltd
Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd
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Abstract

The invention discloses a comprehensive energy system scheduling method and system with generalized predictive control, which are used for respectively modeling energy conversion equipment and energy storage equipment in a comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment; constructing an energy conversion equipment controller according to the generalized predictive control; determining an objective function, and solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment; setting scheduling time, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through an energy conversion equipment controller according to equipment constraint conditions and the capacity of the energy storage equipment so as to realize scheduling of energy; the method and the device realize accurate control of the comprehensive energy system and solve the problems of poor scheduling flexibility and poor accuracy of the traditional energy scheduling method.

Description

Comprehensive energy system scheduling method and system containing generalized predictive control
Technical Field
The invention relates to the technical field of energy scheduling, in particular to a comprehensive energy system scheduling method and system with generalized predictive control.
Background
With the continuous development of economic society, the urbanization process of China is continuously accelerated, at present, urban energy of China faces the problems of energy resource supply shortage, low comprehensive utilization efficiency and the like, a park is used as an important platform for urban economic structure adjustment and crossing development, and the economic, reliable and efficient operation of an energy system is of great significance.
The existing comprehensive energy system optimization scheduling model has certain disadvantages when in use, firstly, the differences among energy varieties can not be fully utilized in the current research, such as the differences of inherent characteristics of gas, heat and electricity, more redundancy can easily appear when energy supply equipment is built, further the utilization rate of partial equipment is low, the energy supply economy of the system is influenced, in addition, under the extreme condition of insufficient energy supply and the like, the differences of energy utilization reliability requirements among different energy varieties can not be fully utilized, and the integral energy supply reliability of the system is influenced.
At present, there is a need for an energy scheduling method to solve the problems of poor scheduling flexibility and poor scheduling accuracy of the energy scheduling method in the prior art.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a comprehensive energy system scheduling method and system with generalized predictive control, which can complete quick response to a comprehensive energy system scheduling signal, enable accurate conversion of electricity, gas and heat and realize accurate control of the comprehensive energy system.
The invention provides a comprehensive energy system scheduling method with generalized predictive control, which comprises the following steps:
respectively modeling energy conversion equipment and energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
constructing an energy conversion equipment controller according to the generalized predictive control;
determining an objective function, and solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment;
and setting scheduling time, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through an energy conversion equipment controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize scheduling of energy.
Further, the energy conversion apparatus includes a gas turbine and an electric boiler, including:
constructing an energy supply model of a gas turbine and an energy supply model of an electric boiler;
constructing a gas turbine controller and an electric boiler controller according to the generalized predictive control;
determining an objective function, and respectively solving an energy supply model of the gas turbine, an energy supply model of the electric boiler and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy supply model, a predicted output value of the electric boiler and the capacity of the energy storage equipment;
setting scheduling time, and respectively controlling the output of an energy supply model of the gas turbine and the output of an energy supply model of the electric boiler through the gas turbine controller and the electric boiler controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize the scheduling of energy.
Further, the objective function S is determined by the following formula:
min S＝minF(L)+min price；
min F(L)＝F(LE)+F(LH)+F(LG)；
wherein minF (L) is the minimum value of the energy shortage function of the integrated energy system, and minprice is the minimum value of the daily operation cost function of the integrated energy system.
Further, the calculation formula of the daily operation cost function of the integrated energy system is as follows:
min price＝min(prifuel+prigrid+primaintain)；
Figure GDA0002793031320000031
Figure GDA0002793031320000032
Figure GDA0002793031320000033
wherein prifuelAs a function of fuel cost, prigridPower cost function for grid interaction, primaintainFor the system operation maintenance cost function, fGHPiAs a function of the gas turbine consumption characteristic,
Figure GDA0002793031320000034
is the gas price per hour,
Figure GDA0002793031320000035
is the price of electricity for each hour,
Figure GDA0002793031320000036
is real-time electric power provided by an external network to an integrated energy system, pmdisCost of maintenance for unit power operation of distributed power generation equipment, pmCHPiOperating maintenance costs for a unit power of the gas turbine; p is a radical ofEBiThe unit power operation and maintenance cost of the electric boiler is saved; p is a radical ofmstorRefers to the operating and maintenance cost per unit power of the energy storage device,
Figure GDA0002793031320000037
is the charging power of the accumulator at the time t, Pi tRefers to the real-time power generation power of the ith gas turbine at the moment t,
Figure GDA0002793031320000038
the power consumption of the electric boiler at the moment tThe ratio of the total weight of the particles,
Figure GDA0002793031320000039
for the charging power of the energy storage device,
Figure GDA00027930313200000310
for discharging power of energy storage devices, nCHPNumber of gas turbines, ndisIs the number of storage batteries, nEBThe number of the electric boilers is.
Further, the equipment constraint conditions comprise energy conversion equipment constraint conditions and energy storage equipment constraint conditions, the energy conversion equipment constraint conditions comprise gas turbine constraint conditions and electric boiler constraint conditions, and the energy storage equipment constraint conditions comprise storage battery constraint conditions, gas storage equipment constraint conditions and heat storage equipment constraint conditions;
the gas turbine constraints are as follows:
Pi min≤Pi t≤Pi max，i∈nCHP；
the constraint conditions of the electric boiler are as follows:
Figure GDA00027930313200000311
the constraint conditions of the storage battery are as follows:
Figure GDA0002793031320000041
Figure GDA0002793031320000042
the constraint conditions of the gas storage device are as follows:
Figure GDA0002793031320000043
Figure GDA0002793031320000044
the heat storage equipment has the following constraint conditions:
Figure GDA0002793031320000045
Figure GDA0002793031320000046
wherein,
Figure GDA0002793031320000047
is the real-time power generation power, P, of the ith gas turbine at time ti minIs the minimum value of the generated power of the ith gas turbine, Pi maxIs the maximum value of the generated power of the ith gas turbine,
Figure GDA0002793031320000048
is the power consumption of the electric boiler,
Figure GDA0002793031320000049
is the minimum value of the power consumption of the electric boiler,
Figure GDA00027930313200000410
is the maximum value of the electric power consumption of the electric boiler, PchFor charging the accumulator, PdisIs the discharge power of the accumulator, PchminMinimum value of charging power, P, for accumulatorchmaxMaximum value of charging power, P, for accumulatordisminIs the minimum value of the discharge power of the accumulator, PdismaxIs the maximum value of the discharge power of the storage battery,
Figure GDA00027930313200000411
the energy storage capacity of the storage battery at the moment t,
Figure GDA00027930313200000412
is the minimum value of the energy storage of the storage battery,
Figure GDA00027930313200000413
is the maximum value of the energy storage of the storage battery,
Figure GDA00027930313200000414
the air charging quantity of the air storage tank,
Figure GDA00027930313200000415
is the air discharge quantity of the air storage tank,
Figure GDA00027930313200000416
is the minimum value of the air charging quantity of the air storage tank,
Figure GDA00027930313200000417
is the maximum value of the air charging quantity of the air storage tank,
Figure GDA00027930313200000418
is the minimum value of the air discharge quantity of the air storage tank,
Figure GDA00027930313200000419
is the maximum value of the air discharge amount of the air storage tank,
Figure GDA00027930313200000420
the amount of the gas stored in the gas storage tank at the moment t,
Figure GDA00027930313200000421
the minimum sum of the gas storage capacity of the gas storage tank
Figure GDA00027930313200000422
Is the maximum value of the air storage quantity of the air storage tank,
Figure GDA00027930313200000423
in order to charge the heat of the heat storage tank,
Figure GDA00027930313200000424
in order to release the heat of the heat storage tank,
Figure GDA00027930313200000425
the minimum amount of heat charged in the heat storage tank,
Figure GDA0002793031320000051
the maximum amount of heat charged in the heat storage tank,
Figure GDA0002793031320000052
is the minimum value of the heat release of the heat storage tank,
Figure GDA0002793031320000053
is the maximum value of the heat release quantity of the heat storage tank,
Figure GDA0002793031320000054
the amount of heat stored in the heat storage tank at time t,
Figure GDA0002793031320000055
is the minimum value of the heat storage quantity of the heat storage tank,
Figure GDA0002793031320000056
is the maximum value of the heat storage capacity of the heat storage tank, nCHPThe number of gas turbines.
Further, in the building an energy conversion device controller according to the generalized predictive control, it includes:
adopting a minimum variance control method to construct an output prediction model of the gas turbine and the electric boiler;
modifying the output prediction model by adopting rolling optimization to obtain an optimized prediction value;
judging whether the optimized predicted value is in the equipment constraint condition;
if so, respectively scheduling the prediction outputs of an energy supply model of the gas turbine and an energy supply model of the electric boiler according to the optimized predicted values;
and if not, respectively scheduling the predicted outputs of the energy supply model of the gas turbine and the energy supply model of the electric boiler through the critical value of the equipment constraint condition.
Further, the energy storage device comprises a storage battery, an air storage tank and a heat storage tank, and the gas turbine comprises a rotating speed control module, a temperature control module, a fuel supply module and a compressor-turbine module;
(1) the battery is modeled by the following equation:
Figure GDA0002793031320000057
wherein S isoc(t) is the residual capacity of the battery at time t, Soc(t0) For the accumulator at t0The remaining capacity at that moment; delta is the self-discharge rate of the storage battery, and the unit is%/h; Δ t is t0The time span to t; pchFor charging the accumulator, PdisIs the discharge power of the storage battery; etachFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the battery.
(2) The gas storage tank is modeled by the following formula:
Figure GDA0002793031320000058
wherein, VGSThe effective gas storage volume of the gas storage tank; vCThe geometric volume of the gas storage tank; p is a radical ofhighIs the absolute pressure, p, at the highest operating conditionlowAbsolute pressure under the lowest working condition; p is a radical of0Representing engineering standard pressure;
(3) the thermal storage tank is modeled by the following formula:
Figure GDA0002793031320000061
wherein Q isHS(t) represents the heat storage amount of the heat storage tank at time t; mu.sLossRepresenting the heat dissipation loss rate of the heat storage tank; qHS(t0) Denotes the initial t0Storage of time heat storage tankHeat quantity,. DELTA.t, is t0The time span to t is the time span,
Figure GDA0002793031320000062
represents t0The heat charging quantity of the heat storage tank is increased until t moment;
Figure GDA0002793031320000063
representing the heat charging efficiency of the heat storage tank;
Figure GDA0002793031320000064
represents t0The heat release amount of the heat storage tank is up to t time;
Figure GDA0002793031320000065
representing the heat release efficiency of the heat storage tank;
(4) the rotating speed control module carries out modeling through the following steps:
input reference rotational speed WrefAnd the actual rotor speed omega of the gas turbine power generation system;
using a lead-lag transfer function pair WrefAnd omega are controlled and output through a minimum value selector;
(5) the temperature control module is modeled by:
obtaining an exhaust temperature TxWill TxTo rated exhaust temperature TrefThe comparison is carried out in such a way that,
when T isxHigher than TrefThen, the output is controlled to a minimum value selector through a proportional integral regulator PI until TxLess than TrefStopping the PI from working;
(6) the fueling module is modeled by the following equation:
Figure GDA0002793031320000066
wherein f is3As a transfer function, Kv、KfTo gain, TvFor valve opening, TfTo implement the time constant of the structure, cIs a constant.
(7) The compressor-turbine module is modeled by the following equation:
f1＝Tref-700(1-Wf1)+500(1-ω)；
f2＝1.3(Wf2-0.23)+0.5(1-ω)；
wherein f is1As a function of the turbine exhaust temperature of the gas turbine, f2As a function of the turbine torque output of the gas turbine, TrefIs the nominal exhaust temperature; wf1、Wf2To signal fuel flow, ω is the actual turbine speed.
Further, the integrated energy system also comprises a network transmission pipeline, wherein the network transmission pipeline comprises a natural gas pipeline and a heat power pipeline;
the natural gas pipeline is modeled by the following formula:
Figure GDA0002793031320000071
wherein M isdRepresenting the mass flow of natural gas; q. q.sd,0Representing the volumetric flow rate of natural gas at 101.325kPa, 273.15K; psRepresenting the absolute pressure rating of the natural gas at the beginning of the pipeline; zsRepresenting the compression factor of the natural gas at the beginning of the pipeline; cBRepresenting a potential energy factor function of the natural gas; peAn absolute pressure rating indicative of the natural gas at the end of the pipeline; zeA compression factor representing natural gas at the end of the pipeline; zaveRepresents the average compression factor of the natural gas; d represents the inner diameter of the pipe; λ represents the coefficient of on-way resistance of the pipeline; l represents a natural gas pipeline length; r represents a natural gas constant; t represents the natural gas temperature; rho0Represents the density of natural gas at 101.325kPa, 273.15K;
the thermal pipeline is modeled by the following formula:
Figure GDA0002793031320000072
wherein, P1The pressure at the beginning of the heat distribution pipeline, P2Is the pressure at the end of the thermal conduit,
Figure GDA0002793031320000073
is the average flow velocity of the thermal conduit,
Figure GDA0002793031320000074
is the average specific volume of the thermal conduit, g is the acceleration of gravity, D1Is the inner diameter of the heat distribution pipe, D0Is the external diameter of the heat distribution pipeline, lambda is the on-way resistance coefficient of the heat distribution pipeline, Sigma xi is the local resistance coefficient, H1Height of the initial end of the heat distribution pipe, H2Height of end of thermal conduit, tinIs the initial temperature of the heat distribution pipeline, toutIs the temperature at the end of the thermal conduit, QLossFor heat loss of the pipe, GLIs the flow of the heat distribution pipe, cpIs the specific constant pressure heat capacity of hot water, K is the equivalent length coefficient of the heat loss element, L represents the length of the heat distribution pipeline, R is the heat resistance of the heat distribution pipeline, t is the average temperature of the medium in the heat distribution pipelineaIs ambient temperature.
An integrated energy system dispatching system with generalized predictive control, comprising: the system comprises a classification modeling module, a controller construction module, an optimization solving module and a scheduling module;
the classification modeling module is used for respectively modeling the energy conversion equipment and the energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
the controller construction module is used for constructing an energy conversion equipment controller according to the generalized predictive control;
the optimization solving module is used for determining an objective function, solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function, and obtaining a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment;
the scheduling module is used for setting scheduling duration, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through the energy conversion equipment controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize scheduling of energy.
Further, the computer readable storage medium has stored thereon a number of get classification programs for being invoked by the processor and performing the steps of:
respectively modeling energy conversion equipment and energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
constructing an energy conversion equipment controller according to the generalized predictive control;
determining an objective function, and solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment;
and setting scheduling time, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through an energy conversion equipment controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize scheduling of energy.
The comprehensive energy system scheduling method and system with generalized predictive control provided by the invention have the advantages that: according to the comprehensive energy system scheduling method and system with generalized predictive control, a gas turbine controller controls the predictive output of an energy supply model of a gas turbine, an electric heating boiler controller controls the predictive output of the energy supply model of an electric heating boiler, when the energy supply and demand in the comprehensive energy system are unbalanced, the comprehensive energy system makes an optimal energy mediation scheme through a comprehensive energy network structure according to energy difference information, so that the rapid response of a comprehensive energy system scheduling signal is completed, the accurate conversion of electricity, gas and heat is realized, and the accurate control of the comprehensive energy system is realized; meanwhile, the comprehensive energy system carries out classified modeling, so that the calculated amount of the model is reduced, and the accuracy of the model is improved; the optimal prediction expression is obtained by deducing a series of formulas by the gas turbine controller and the electric boiler controller, and the formulas are corrected in real time by rolling optimization, so that the accurate prediction of the output of the next moment after the current moment of the equipment is further improved.
Drawings
FIG. 1 is a schematic structural diagram of a generalized predictive control-based scheduling method for an integrated energy system according to the present invention;
FIG. 2 is a structural framework diagram of the integrated energy system;
FIG. 3 is a modeling schematic of a speed control module;
FIG. 4 is a modeling schematic of a temperature control module;
FIG. 5 is a modeling schematic of a fueling module;
FIG. 6 is a modeling schematic of a compressor-turbine module;
fig. 7 is a detailed step flowchart of step S4;
FIG. 8 is a schematic flow diagram of an integrated energy system dispatch system with generalized predictive control;
the system comprises a classification modeling module 100, a controller construction module 200, an optimization solving module 300 and a scheduling module 400.
Detailed Description
The present invention is described in detail below with reference to specific embodiments, and in the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Referring to fig. 1 to 7, the scheduling method of an integrated energy system with generalized predictive control according to the present invention includes steps S1 to S4:
s1: respectively modeling energy conversion equipment and energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
the energy conversion equipment comprises a gas turbine and an electric boiler, wherein the gas turbine converts gas source into electricity and heat to supply energy to an electric load and a thermal load, and the electric boiler converts the electricity into the heat to supply energy to the thermal load. The energy supply model of the energy conversion apparatus thus constructed includes an energy supply model of a gas turbine and an energy supply model of an electric boiler.
S2: constructing an energy conversion equipment controller according to the generalized predictive control;
the energy conversion equipment controller comprises a gas turbine controller and an electric boiler controller.
S3: determining an objective function, and solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment;
through steps S1 and S2, the energy supply model of the gas turbine, the energy supply model of the electric boiler, and the energy supply model of the energy storage device are solved according to the objective function, and the predicted output value of the energy supply model, the predicted output value of the electric boiler, and the capacity of the energy storage device are obtained.
Further, the comprehensive energy system performs scheduling control on the whole system by taking energy shortage and daily operation cost as the lowest targets, and determines an objective function S through the following formula:
min S＝minF(L)+min price；
wherein minF (L) is the minimum value of the energy shortage function of the integrated energy system, and minprice is the minimum value of the daily operation cost function of the integrated energy system.
The calculation formula of the energy shortage function of the integrated energy system is as follows:
min F(L)＝F(LE)+F(LH)+F(LG)；
wherein, F (L)E) As a function of the power shortage of the integrated energy system, F (L)H) Is the heat deficiency function of the integrated energy system, F (L)G) Is the gas shortage function of the integrated energy system;
the calculation formula of the daily operation cost function of the comprehensive energy system is as follows:
min price＝min(prifuel+prigrid+primaintain)；
Figure GDA0002793031320000111
Figure GDA0002793031320000112
Figure GDA0002793031320000113
wherein prifuelAs a function of fuel cost, prigridPower cost function for grid interaction, primaintainFor the system operation maintenance cost function, fGHPiAs a function of the consumption characteristic of the gas turbine, PiFor the generated power of the ith gas turbine,
Figure GDA0002793031320000114
is the gas price per hour,
Figure GDA0002793031320000115
is the price of electricity for each hour,
Figure GDA0002793031320000116
is real-time electric power, P, provided by an external network to the integrated energy systemmdisCost of maintenance for unit power operation of distributed power generation equipment, PmCHPiFor the maintenance cost per power operation of the gas turbine, PEBiThe unit power operation and maintenance cost of the electric boiler is saved; pmstorRefers to the operating and maintenance cost per unit power of the energy storage device,
Figure GDA0002793031320000117
is the charging power of the accumulator at the time t, Pi tRefers to the real-time power generation power of the ith gas turbine at the moment t,
Figure GDA0002793031320000118
the unit power operation and maintenance cost of the electric boiler at the moment t,
Figure GDA0002793031320000119
for charging energy of an energy storage device, the energy storage device comprises a storage battery, a storage battery and a heat storage tank,
Figure GDA00027930313200001110
for discharging power of energy storage devices, nCHPNumber of gas turbines, ndisIs the number of storage batteries, nEBThe number of the electric boilers is.
S4: and setting scheduling time, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through an energy conversion equipment controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize scheduling of energy.
The equipment constraint conditions comprise energy conversion equipment constraint conditions and energy storage equipment constraint conditions, the energy conversion equipment constraint conditions comprise gas turbine constraint conditions and electric boiler constraint conditions, and the energy storage equipment constraint conditions comprise storage battery constraint conditions, gas storage equipment constraint conditions and heat storage equipment constraint conditions;
the gas turbine constraints are as follows:
Pi min≤Pi t≤Pi max，i∈nCHP
the constraint conditions of the electric boiler are as follows:
Figure GDA0002793031320000121
the constraint conditions of the storage battery are as follows:
Figure GDA0002793031320000122
Figure GDA0002793031320000123
the constraint conditions of the gas storage device are as follows:
Figure GDA0002793031320000124
Figure GDA0002793031320000125
the heat storage equipment has the following constraint conditions:
Figure GDA0002793031320000126
Figure GDA0002793031320000127
wherein,
Figure GDA0002793031320000131
is the real-time power generation power, P, of the ith gas turbine at time ti minIs the minimum value of the generated power of the ith gas turbine, Pi maxIs the maximum value of the generated power of the ith gas turbine,
Figure GDA0002793031320000132
is the power consumption of the electric boiler,
Figure GDA0002793031320000133
is the minimum value of the power consumption of the electric boiler,
Figure GDA0002793031320000134
is the maximum value of the electric power consumption of the electric boiler, PchFor charging the accumulator, PdisIs the discharge power of the accumulator, PchminMinimum value of charging power, P, for accumulatorchmaxMaximum value of charging power, P, for accumulatordisminIs the minimum value of the discharge power of the accumulator, PdismaxTo storeThe maximum value of the discharge power of the battery,
Figure GDA0002793031320000135
the energy storage capacity of the storage battery at the moment t,
Figure GDA0002793031320000136
is the minimum value of the energy storage of the storage battery,
Figure GDA0002793031320000137
is the maximum value of the energy storage of the storage battery,
Figure GDA0002793031320000138
the air charging quantity of the air storage tank,
Figure GDA0002793031320000139
is the air discharge quantity of the air storage tank,
Figure GDA00027930313200001310
is the minimum value of the air charging quantity of the air storage tank,
Figure GDA00027930313200001311
is the maximum value of the air charging quantity of the air storage tank,
Figure GDA00027930313200001312
is the minimum value of the air discharge quantity of the air storage tank,
Figure GDA00027930313200001313
is the maximum value of the air discharge quantity of the air storage tank,
Figure GDA00027930313200001314
the amount of the gas stored in the gas storage tank at the moment t,
Figure GDA00027930313200001315
the minimum sum of the gas storage capacity of the gas storage tank
Figure GDA00027930313200001316
Is the maximum value of the air storage quantity of the air storage tank,
Figure GDA00027930313200001317
in order to charge the heat of the heat storage tank,
Figure GDA00027930313200001318
in order to release the heat of the heat storage tank,
Figure GDA00027930313200001319
is the minimum value of the heat charging amount of the heat storage tank,
Figure GDA00027930313200001320
the maximum amount of heat charged in the heat storage tank,
Figure GDA00027930313200001321
is the minimum value of the heat release of the heat storage tank,
Figure GDA00027930313200001322
is the maximum value of the heat release quantity of the heat storage tank,
Figure GDA00027930313200001323
the amount of heat stored in the heat storage tank at time t,
Figure GDA00027930313200001324
is the minimum value of the heat storage quantity of the heat storage tank,
Figure GDA00027930313200001325
is the maximum value of the heat storage capacity of the heat storage tank, nCHPThe number of gas turbines.
According to the steps S1 to S4, the energy conversion equipment controller controls the output of the energy supply model of the energy conversion equipment, completes the quick response to the dispatching signal of the comprehensive energy system, enables the accurate conversion of electricity, gas and heat, and realizes the accurate control of the comprehensive energy system. That is, the gas turbine controller controls the predicted output of the energy supply model of the gas turbine, and the electric boiler controller controls the predicted output of the energy supply model of the electric boiler. Meanwhile, the comprehensive energy system carries out classified modeling, so that the calculated amount of the model is reduced, and the accuracy of the model is improved.
Further, in the present embodiment, the energy conversion apparatus described in step S1 includes a gas turbine and an electric boiler, the energy storage apparatus includes a storage battery, an air storage tank, and a heat storage tank, and the gas turbine includes a rotation speed control module, a temperature control module, a fuel supply module, and a compressor-turbine module; the modeling may be performed by the following steps or equations (1) to (7):
(1) the battery is modeled by the following equation:
Figure GDA0002793031320000141
wherein S isoc(t) is the residual capacity of the battery at time t, Soc(t0) For the accumulator at t0The remaining capacity at that moment; delta is the self-discharge rate of the storage battery, and the unit is%/h; Δ t is t0The time span to t; pchFor charging the accumulator, PdisIs the discharge power of the storage battery; etachFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the battery.
(2) The gas storage tank is modeled by the following formula:
Figure GDA0002793031320000142
wherein, VGSThe effective gas storage volume of the gas storage tank; vCThe geometric volume of the gas storage tank; p is a radical ofhighIs the absolute pressure, p, at the highest operating conditionlowAbsolute pressure under the lowest working condition; p is a radical of0Representing engineering standard pressure;
(3) the thermal storage tank is modeled by the following formula:
Figure GDA0002793031320000143
wherein Q isHS(t) represents the heat storage amount of the heat storage tank at time t;μLossrepresenting the heat dissipation loss rate of the heat storage tank; qHS(t0) Denotes the initial t0The heat storage quantity of the heat storage tank at the moment, delta t is t0The time span to t is the time span,
Figure GDA0002793031320000144
represents t0The amount of heat charged in the heat storage tank up to time t,
Figure GDA0002793031320000145
indicating the heat charging efficiency of the thermal storage tank,
Figure GDA0002793031320000146
represents t0The heat release amount of the heat storage tank until the time t,
Figure GDA0002793031320000147
representing the heat release efficiency of the heat storage tank;
(4) the speed control module is modeled by the following steps, as shown in FIG. 3:
s101: input reference rotational speed WrefAnd the actual rotating speed omega of the rotor of the power generation system of the gas turbine;
input is a reference rotating speed WrefDeviation from the actual rotor speed ω of the gas turbine power generation system;
s102: using a lead-lag transfer function pair WrefAnd omega are controlled and output through a minimum value selector; g is the controller gain, T1And T2Respectively, lead-lag time constant, formula
Figure GDA0002793031320000151
Is an expression after the laplace transform.
In actual operation, the speed control module plays a major role in partial load, and changes the output fuel reference value by adjusting the deviation between the reference speed and the actual speed, so as to achieve the purpose of adjusting the load.
(5) The temperature control module is modeled by the following steps, as shown in fig. 4:
s111: obtaining an exhaust temperature TxWill TxTo rated exhaust temperature TrefComparing;
the temperature control reflects the temperature of the turbine inlet of the gas turbine by controlling the fuel flow, and the temperature of the gas at the turbine inlet is difficult to measure and control due to severe change of the temperature of the gas at the turbine inlet; the temperature control ensures that the inlet temperature is within a safe range by controlling the exhaust temperature. The temperature regulation module is a proportional-integral regulator (PI) whose input signal is the exhaust temperature T measured by a thermocouplex；
S112: when T isxHigher than TrefThen, the output is controlled to a minimum value selector through a proportional integral regulator PI until TxLess than Tref；
As long as TxAnd TrefIf there is a deviation between them, the temperature controller will continue to integrate and lower the fuel reference value until TxBelow TrefUntil now. During normal operation, the gas turbine is controlled by changing the fuel quantity to prevent the turbine inlet temperature from exceeding the maximum design value Tmax。
(6) The fueling module is modeled by the following equation:
Figure GDA0002793031320000152
wherein f is3As a transfer function, Kv、KfTo gain, TvFor valve opening, TfTo implement the time constant of the structure, c is a constant, f3Is obtained by the time domain laplacian transform, and s corresponds to t in the time domain laplacian transform and represents a variable in space.
The process of the fueling module, as shown in fig. 5, speed control, temperature control, and acceleration control generate fuel references F1, F2, and F3, respectively, which are acted upon by the Min selector (Min module) and the high and low limit modules to generate a minimum fuel reference command to the fueling module. Since the fuel pressure and the rotation speed of the fuel pump are both in direct proportion to the rotor rotation speed, the limited valuesThe actual sub-rotational speed ω is multiplied to obtain an actual fuel quantity signal. The fuel system of the gas turbine consists of a valve and an actuator, the fuel flowing out of the fuel system has certain inertia with the action time of the actuator and the valve, and the transfer function f3 is obtained by the formula. In FIG. 5, where WfGain K for fuel flow2＝1-K1，K1To ensure the minimum value of the continuous proceeding of the combustion process of the combustion chamber. K1The selection must be such that the rotor can reach the rated speed in the absence of load, typically 0.15-0.3, preferably 0.23.
(7) The compressor-turbine module is modeled by the following equation:
the compressor-turbine system is the core part of the gas turbine, which enables the conversion of energy, which is essentially a linear non-dynamic system (except for the rotor time constant). As shown in FIG. 6, the single shaft gas turbine output torque Tmtur and the exhaust temperature TxAs outputs, fuel flow and turbine as inputs, with the output being linearly related to the speed of the input, the compressor-turbine module is modeled by the following equation:
f1＝Tref-700(1-Wf1)+500(1-ω)；
f2＝1.3(Wf2-0.23)+0.5(1-ω)；
wherein f is1As a function of the turbine exhaust temperature of the gas turbine, f2As a function of the turbine torque output of the gas turbine, TrefThe rated exhaust temperature can be 950 ℃ or other fixed values; wf1、Wf2To signal fuel flow, ω is the actual turbine speed.
It should be clear that the torque output function is substantially accurate at 100% load, that in other cases there is less than 5% error, and that the exhaust temperature equation is effective around the temperature reference, so its effect can be ignored.
Further, at step S2: the building of the energy conversion device controller according to the generalized predictive control includes S21 to S23:
s21: an output prediction model of the energy conversion equipment controller is constructed by adopting a minimum variance control method;
the output prediction model of the energy conversion equipment controller comprises an output prediction model of a gas turbine and an output prediction model of an electric boiler;
in generalized predictive control, a controlled autoregressive integrated moving average model (CARIMA) used in least square error control is used to describe the subject suffering random disturbances, as follows:
Figure GDA0002793031320000171
wherein,
Figure GDA0002793031320000172
Figure GDA0002793031320000173
Figure GDA0002793031320000174
q-1is a backward shift operator; y (k) q-1＝y(k-1)；Δ＝1-q-1Is a difference operator; ξ (k) is an independent random noise sequence.
Introducing a Diphantine equation and taking C (q)-1) 1, the optimal predicted output for time k to the future time k + j is:
Figure GDA0002793031320000175
Figure GDA0002793031320000176
where y1(k + j) is determined by past control inputs and outputs
Figure GDA0002793031320000177
Determined by present and future control inputs.
The optimal prediction expression is thus obtained as:
y(k+j)＝y*(k+j|k)+E(q-1)ξ(k+j)；
where y (k + j) is the actual output at the future time k + j; y is*(k + j | k) optimizing the predicted output at a future time k + j, Ej(q-1)＝ej,0+ej,1q-1+…+ej,j-1q-(j-1)，EjFrom A (q)-1) And the prediction length j is uniquely determined; ξ (k + j) is the noise at time k + j in the future.
y (k + j) may be represented as yM(k + j), where M represents the M step output that can be predicted. Since the generalized predictive control adopts rolling optimization, the predictive control at each moment only actually acts on the next optimization index, so that y is used in the multi-step prediction of y (k + j) every time1(k+j)，y1(k + j) represents the next prediction output.
S22: correcting an output prediction model of the energy conversion equipment controller by adopting rolling optimization to obtain an optimized prediction value;
the rolling optimization adopts a quadratic performance index weighted by an output error and a control increment to correct an output prediction model to obtain:
Δu＝(GTG+λI)-1GT(yr-y1)；
wherein Δ u ═ Δ u (k), Δ u (k +1), … Δ u (k + N)u-1)]T(ii) a Δ u represents the current control action.
Figure GDA0002793031320000181
yr＝[yr(k+N1),yr(k+N1+1),…yr(k+N2)]T；
y1＝[y1(k+N1),y1(k+N1+1),…y1(k+N2)]T；
λ is a control increment weighting coefficient;
then the current integrated energy system inputs u (k) are:
u(k)＝u(k-1)+[1,0,…,0](GTG+λI)-1GT(yr-y1)；
then through online identification and feedback correction pair, the following results are obtained:
Figure GDA0002793031320000182
estimating a model parameter theta value by using a recursive least square method (RLS) with forgetting factors, finally obtaining y (k) after online identification, substituting the y (k) after online identification into a formula
Figure GDA0002793031320000191
And then, obtaining corrected u (k) through a series of deductions, controlling the input of the current comprehensive energy system through the corrected u (k), and ensuring the accuracy of the output of the prediction result through online feedback correction so as to realize accurate scheduling of the energy.
S23: and controlling the predicted output of the energy supply model of the gas turbine and the energy supply model of the electric boiler through the optimized predicted value:
obtaining a controller model of the gas turbine and the electric boiler through a generalized predictive control algorithm according to mathematical models of the gas turbine and the electric boiler; the controller model is obtained through derivation of formulas of generalized predictive control on the basis of a mathematical model, and the optimal predictive expression is realized through modeling, and is corrected in real time through rolling optimization, so that accurate output prediction of output of the equipment at the next moment after the current moment is further improved.
Applying a pseudo-random signal to a gas turbine rotating speed open loop system under a rated working condition by an energy supply model of a gas turbine in energy conversion equipment in a Matlab/Simulink environment, and accumulating input and output data. Based on an augmented least square method with forgetting factors, A and B in the CARIMA are obtained through parameter identification. And obtaining the optimal prediction expression according to the steps S21 to S23, and further obtaining the optimized control quantity and the expected output of the gas turbine.
And applying a pseudo-random signal to an electric heating boiler rotating speed open loop system under a rated working condition by an energy supply model of the electric heating boiler in the energy conversion equipment in a Matlab/Simulink environment, and accumulating input and output data. Based on an augmented least square method with forgetting factors, A and B in the CARIMA are obtained through parameter identification. And obtaining the optimal prediction expression according to the steps S21 to S23, and further obtaining the optimized control quantity and the expected output of the electric boiler. The pseudo-random signal may be a white noise signal or an M-sequence signal, etc.
As shown in fig. 7, in step S4: in the scheduling of the energy source by the energy conversion plant controller correcting the predicted output value of the energy source supply model controlling the energy conversion plant, since the energy conversion plant includes a gas turbine and an electric boiler and the energy source supply model of the energy conversion plant includes an energy source supply model of the gas turbine and an energy source supply model of the electric boiler, the scheduling process of the energy source includes S41 to S49:
s41: acquiring energy supply and load demand data in the comprehensive energy system at the current moment;
s42: judging whether the energy supply meets the load demand data;
if yes, go to step S43;
if not, go to step S44;
s43: storing the redundant energy and entering the step S49;
s44: respectively obtaining a predicted output value of an energy supply model of the gas turbine and a predicted output value of an energy supply model of the electric boiler through a gas turbine controller and an electric boiler controller; the predicted output value can be the power to be adjusted of the element, and can also be other output parameters to be adjusted of the equipment;
s45: judging whether the predicted output value of the ith element exceeds the corresponding equipment constraint condition or not; e.g., whether the ith element is power off-limit;
if yes, go to step S46;
if not, go to step S47;
s46: the ith element is operated at the critical value of the constraint condition of the equipment, and the other elements are adjusted and operated at the predicted output value, and the step S48 is entered;
s47: adjusting operation of each element according to the predicted output value, and proceeding to step S48;
s48: the gas turbine controller and the electric boiler controller respectively control the output of the gas turbine and the output of the electric boiler according to the scheduling signal, and the step S49 is entered;
s49: judging whether the scheduling time length is reached;
if yes, finishing scheduling;
if not, the process proceeds to update the time domain, and proceeds to step S41.
Through steps S41 to S49, the internal components of the integrated energy system respond to the scheduling signal in time, and when the energy supply and demand are unbalanced in the integrated energy system, the integrated energy system makes an optimal energy mediation scheme through the integrated energy network structure according to the energy difference information. Specifically, when certain energy is insufficient, the comprehensive energy system compensates the difference through energy conversion according to the collected internal information if redundant heterogeneous energy exists and the stability of the system is not affected after conversion, otherwise, the rest part is supplied with energy through a gas turbine and an electric heating boiler.
As an embodiment, in the scheduling of the integrated energy system, the loss in the energy transportation process affects the scheduling accuracy, so in this embodiment, the loss in the energy transportation process is considered through modeling, and the integrated energy system further includes a network transmission pipeline, where the network transmission pipeline includes a natural gas pipeline and a heat power pipeline;
the natural gas pipeline is modeled by the following formula:
Figure GDA0002793031320000211
wherein M isdRepresenting the mass flow of natural gas;qd,0representing the volumetric flow rate of natural gas at 101.325kPa (pressure), 273.15K (temperature); psRepresenting the absolute pressure rating of the natural gas at the beginning of the pipeline; zsRepresenting the compression factor of the natural gas at the beginning of the pipeline; cBRepresenting a potential energy factor function of the natural gas; peAn absolute pressure rating indicative of the natural gas at the end of the pipeline; zeA compression factor representing natural gas at the end of the pipeline; zaveRepresents the average compression factor of natural gas, is ZsAnd ZeAverage value of (d); d represents the inner diameter of the pipe; λ represents the coefficient of on-way resistance of the pipeline; l represents a natural gas pipeline length; r represents a natural gas constant; t represents the natural gas temperature; rho0Represents the density of natural gas at 101.325kPa, 273.15K;
the natural gas pipeline is used for transmitting natural gas, the purpose of establishing a natural gas pipeline model is to simulate the actual natural gas transmission process, the loss of the natural gas in the transmission process is considered, so that the dispatching of the comprehensive energy system is more accurate, and the natural gas pipeline is used for transmitting the natural gas to energy conversion equipment and other users needing gas.
The thermal pipeline is modeled by the following formula:
Figure GDA0002793031320000221
wherein, P1The pressure at the beginning of the heat distribution pipeline, P2Is the pressure at the end of the thermal conduit,
Figure GDA0002793031320000222
is the average flow velocity of the thermal conduit,
Figure GDA0002793031320000223
is the average specific volume of the thermal conduit, g is the acceleration of gravity, D1Is the inner diameter of the heat distribution pipe, D0Is the external diameter of the heat distribution pipeline, lambda is the on-way resistance coefficient of the heat distribution pipeline, L represents the length of the heat distribution pipeline, Sigma xi is the local resistance coefficient, H1Is the beginning of the heat distribution pipelineHeight, H2Height of end of thermal conduit, tinIs the initial temperature of the heat distribution pipeline, toutIs the temperature at the end of the thermal conduit, QLossFor heat loss of the pipe, GLIs the flow of the heat distribution pipe, cpIs the specific constant pressure heat capacity of hot water, K is the equivalent length coefficient of the heat loss element, R is the heat resistance of the heat distribution pipeline, t is the average temperature of the medium in the heat distribution pipelineaIs ambient temperature.
The heat distribution pipeline has similar action with the natural gas pipeline, and the heat energy loss in the transmission process is considered, so that the comprehensive energy system is more accurately scheduled; since the transfer of heat energy is generally provided by a change in water temperature, water is also required to be transported to various locations where it is needed through pipes. The heat pipeline only provides heat energy to one side of a user through temperature change of flowing water, and does not provide the heat energy to the energy conversion equipment.
As shown in fig. 8, an integrated energy system dispatching system with generalized predictive control includes: the system comprises a classification modeling module 100, a controller construction module 200, an optimization solving module 300 and a scheduling module 400;
the classification modeling module 100 is configured to divide the integrated energy system into a plurality of single energy subsystems according to energy sources, and respectively construct an energy supply model of the single energy subsystems;
the controller building module 200 is used for building a gas turbine controller and an electric boiler controller according to generalized predictive control;
the optimization solving module 300 is used for setting scheduling duration, determining an objective function and constructing an optimization scheduling model of the comprehensive energy system;
the scheduling module 400 is configured to control the outputs of the gas turbine and the electric boiler respectively according to the equipment constraint conditions and the predicted output result of the optimized scheduling model of the integrated energy system, so as to implement energy scheduling.
A computer readable storage medium having stored thereon a number of get classification programs for being invoked by a processor and performing the steps of:
dividing the comprehensive energy system into a plurality of single energy subsystems according to energy sources, and respectively constructing energy supply models of the single energy subsystems, wherein the energy supply models of the single energy subsystems comprise an energy supply model of a gas turbine and an energy supply model of an electric boiler;
constructing a gas turbine controller and an electric boiler controller according to generalized predictive control, wherein the gas turbine controller is used for controlling the predictive output of an energy supply model of the gas turbine, and the electric boiler controller is used for controlling the predictive output of the energy supply model of the electric boiler;
setting scheduling duration, determining a target function, and constructing an optimized scheduling model of the comprehensive energy system;
and respectively controlling the output of the gas turbine and the output of the electric boiler by the gas turbine controller and the electric boiler controller according to the equipment constraint condition and the predicted output result of the optimized scheduling model of the comprehensive energy system so as to realize the scheduling of energy.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (7)

1. A comprehensive energy system scheduling method containing generalized predictive control is characterized by comprising the following steps:
respectively modeling energy conversion equipment and energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
constructing an energy conversion equipment controller according to the generalized predictive control;
determining an objective function, and solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function to obtain a predicted output value of the energy conversion equipment and the real-time capacity of the energy storage equipment;
setting scheduling time, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through an energy conversion equipment controller according to equipment constraint conditions and the capacity of the energy storage equipment so as to realize scheduling of energy;
the energy conversion equipment comprises a gas turbine and an electric boiler, the energy storage equipment comprises a storage battery, an air storage tank and a heat storage tank, and the gas turbine comprises a rotating speed control module, a temperature control module, a fuel supply module and a compressor-turbine module;
(1) the battery is modeled by the following equation:
Figure FDA0002793031310000011
wherein S isoc(t) is the residual capacity of the battery at time t, Soc(t0) For the accumulator at t0The remaining capacity at that moment; delta is the self-discharge rate of the storage battery, and the unit is%/h; Δ t is t0The time span to t; pchFor charging the accumulator, PdisIs the discharge power of the storage battery; etachFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the storage battery;
(2) the gas storage tank is modeled by the following formula:
Figure FDA0002793031310000012
wherein, VGSIs a gas storage tankThe effective gas storage volume; vCThe geometric volume of the gas storage tank; p is a radical ofhighIs the absolute pressure, p, at the highest operating conditionlowAbsolute pressure under the lowest working condition; p is a radical of0Representing engineering standard pressure;
(3) the thermal storage tank is modeled by the following formula:
Figure FDA0002793031310000021
wherein Q isHS(t) represents the heat storage amount of the heat storage tank at time t; mu.sLossRepresenting the heat dissipation loss rate of the heat storage tank; qHS(t0) Denotes the initial t0The heat storage quantity of the heat storage tank at the moment, delta t is t0The time span to t is the time span,
Figure FDA0002793031310000022
represents t0The amount of heat charged in the heat storage tank up to time t,
Figure FDA0002793031310000023
indicating the heat charging efficiency of the thermal storage tank,
Figure FDA0002793031310000024
represents t0The heat release amount of the heat storage tank until the time t,
Figure FDA0002793031310000025
representing the heat release efficiency of the heat storage tank;
(4) the rotating speed control module carries out modeling through the following steps:
input reference rotational speed WrefAnd the actual rotor speed omega of the gas turbine power generation system;
using a lead-lag transfer function pair WrefAnd omega are controlled and output through a minimum value selector;
(5) the temperature control module is modeled by:
obtaining an exhaust temperature TxWill TxTo rated exhaust temperature TrefComparing;
when T isxHigher than TrefThen, the output is controlled to a minimum value selector through a proportional integral regulator PI until TxLess than TrefStopping the PI from working;
(6) the fueling module is modeled by the following equation:
Figure FDA0002793031310000026
wherein f is3As a transfer function, Kv、KfTo gain, TvFor valve opening, TfC is a constant, s is a variable in space;
(7) the compressor-turbine module is modeled by the following equation:
f1＝Tref-700(1-Wf1)+500(1-ω)
f2＝1.3(Wf2-0.23)+0.5(1-ω)
wherein f is1As a function of the turbine exhaust temperature of the gas turbine, f2As a function of the turbine torque output of the gas turbine, TrefIs the nominal exhaust temperature; wf1、Wf2As a fuel flow signal, ω is the turbine actual speed; wherein, in the constructing the energy conversion device controller according to the generalized predictive control, it includes:
an output prediction model of the energy conversion equipment is constructed by adopting a minimum variance control method;
modifying the output prediction model by adopting rolling optimization to obtain an optimized prediction value;
judging whether the optimized predicted value is in the equipment constraint condition;
if so, scheduling the prediction output of the energy supply model of the energy conversion equipment according to the optimized predicted value;
and if not, scheduling the prediction output of the energy supply model of the energy conversion equipment through the critical value of the equipment constraint condition.
2. The method according to claim 1, wherein the objective function S is determined by the following formula:
min S＝minF(L)+min price
wherein minF (L) is the minimum value of the energy shortage function of the integrated energy system, and minprice is the minimum value of the daily operation cost function of the integrated energy system.
3. The method for scheduling the comprehensive energy system with the generalized predictive control as claimed in claim 2, wherein the calculation formula of the daily operation cost function of the comprehensive energy system is as follows:
min price＝min(prifuel+prigrid+primaintain)
Figure FDA0002793031310000031
Figure FDA0002793031310000032
Figure FDA0002793031310000033
wherein prifuelAs a function of fuel cost, prigridPower cost function for grid interaction, primaintainFor the system operation maintenance cost function, fGHPiAs a function of the gas turbine consumption characteristic,
Figure FDA0002793031310000041
is the gas price per hour,
Figure FDA0002793031310000042
is the price of electricity for each hour,
Figure FDA0002793031310000043
is real-time electric power provided by an external network to an integrated energy system, pmdisCost of maintenance for unit power operation of distributed power generation equipment, pmCHPiOperating maintenance costs for a unit power of the gas turbine; p is a radical ofEBiFor the unit power operating maintenance cost, p, of the electric boilermstorRefers to the operating and maintenance cost per unit power of the energy storage device,
Figure FDA0002793031310000044
is the charging power of the accumulator at the time t, Pi tRefers to the real-time power generation power of the ith gas turbine at the moment t,
Figure FDA0002793031310000045
is the power consumption of the electric boiler at the moment t,
Figure FDA0002793031310000046
for charging energy of an energy storage device, the energy storage device comprises a storage battery, a storage battery and a heat storage tank,
Figure FDA0002793031310000047
for discharging power of energy storage devices, nCHPNumber of gas turbines, ndisIs the number of storage batteries, nEBThe number of the electric boilers is.
4. The method of claim 1, wherein the plant constraints comprise energy conversion plant constraints and energy storage plant constraints, the energy conversion plant constraints comprise gas turbine constraints and electric boiler constraints, and the energy storage plant constraints comprise battery constraints, gas storage plant constraints, and heat storage plant constraints;
the gas turbine constraints are as follows:
Pi min≤Pi t≤Pi max，i∈nCHP
the constraint conditions of the electric boiler are as follows:
Figure FDA0002793031310000048
the constraint conditions of the storage battery are as follows:
Figure FDA0002793031310000049
Figure FDA00027930313100000410
the constraint conditions of the gas storage device are as follows:
Figure FDA0002793031310000051
Figure FDA0002793031310000052
the heat storage equipment has the following constraint conditions:
Figure FDA0002793031310000053
Figure FDA0002793031310000054
wherein,
Figure FDA0002793031310000055
is the real-time power generation power, P, of the ith gas turbine at time ti minIs the minimum value of the generated power of the ith gas turbine, Pi maxIs the maximum value of the generated power of the ith gas turbine,
Figure FDA0002793031310000056
is the power consumption of the electric boiler,
Figure FDA0002793031310000057
is the minimum value of the power consumption of the electric boiler,
Figure FDA0002793031310000058
is the maximum value of the electric power consumption of the electric boiler, PchFor charging the accumulator, PdisIs the discharge power of the accumulator, PchminMinimum value of charging power, P, for accumulatorchmaxMaximum value of charging power, P, for accumulatordisminIs the minimum value of the discharge power of the accumulator, PdismaxIs the maximum value of the discharge power of the storage battery,
Figure FDA0002793031310000059
the energy storage capacity of the storage battery at the moment t,
Figure FDA00027930313100000510
is the minimum value of the energy storage of the storage battery,
Figure FDA00027930313100000511
is the maximum value of the energy storage of the storage battery,
Figure FDA00027930313100000512
the air charging quantity of the air storage tank,
Figure FDA00027930313100000513
is the air discharge quantity of the air storage tank,
Figure FDA00027930313100000514
is the minimum value of the air charging quantity of the air storage tank,
Figure FDA00027930313100000515
is the maximum value of the air charging quantity of the air storage tank,
Figure FDA00027930313100000516
is the minimum value of the air discharge quantity of the air storage tank,
Figure FDA00027930313100000517
is the maximum value of the air discharge quantity of the air storage tank,
Figure FDA00027930313100000518
the amount of the gas stored in the gas storage tank at the moment t,
Figure FDA00027930313100000519
the minimum sum of the gas storage capacity of the gas storage tank
Figure FDA00027930313100000520
Is the maximum value of the air storage quantity of the air storage tank,
Figure FDA00027930313100000521
in order to charge the heat of the heat storage tank,
Figure FDA00027930313100000522
in order to release the heat of the heat storage tank,
Figure FDA00027930313100000523
is the minimum value of the heat charging amount of the heat storage tank,
Figure FDA00027930313100000524
the maximum amount of heat charged in the heat storage tank,
Figure FDA00027930313100000525
is the minimum value of the heat release of the heat storage tank,
Figure FDA00027930313100000526
is the maximum value of the heat release quantity of the heat storage tank,
Figure FDA00027930313100000527
the amount of heat stored in the heat storage tank at time t,
Figure FDA00027930313100000528
is the minimum value of the heat storage quantity of the heat storage tank,
Figure FDA00027930313100000529
is the maximum value of the heat storage capacity of the heat storage tank, nCHPThe number of gas turbines.
5. The integrated energy system dispatching method with generalized predictive control of claim 1, wherein the integrated energy system further comprises a network transmission pipeline, the network transmission pipeline comprising a natural gas pipeline and a thermal pipeline;
the natural gas pipeline is modeled by the following formula:
Figure FDA0002793031310000061
wherein M isdRepresenting the mass flow of natural gas; q. q.sd,0Representing the volumetric flow rate of natural gas at 101.325kPa, 273.15K; psRepresenting the absolute pressure rating of the natural gas at the beginning of the pipeline; zsRepresenting the compression factor of the natural gas at the beginning of the pipeline; cBRepresenting a potential energy factor function of the natural gas; peAn absolute pressure rating indicative of the natural gas at the end of the pipeline; zeA compression factor representing natural gas at the end of the pipeline; zaveRepresents the average compression factor of the natural gas; d represents the inner diameter of the pipe; λ represents the coefficient of on-way resistance of the pipeline; l represents a natural gas pipeline length; r represents natural gasA gas constant; t represents the natural gas temperature; rho0Represents the density of natural gas at 101.325kPa, 273.15K;
the thermal pipeline is modeled by the following formula:
Figure FDA0002793031310000062
wherein, P1The pressure at the beginning of the heat distribution pipeline, P2Is the pressure at the end of the thermal conduit,
Figure FDA0002793031310000063
is the average flow velocity of the thermal conduit,
Figure FDA0002793031310000064
is the average specific volume of the thermal conduit, g is the acceleration of gravity, D1Is the inner diameter of the heat distribution pipe, D0Is the external diameter of the heat distribution pipeline, lambda is the on-way resistance coefficient of the heat distribution pipeline, Sigma xi is the local resistance coefficient, H1Height of the initial end of the heat distribution pipe, H2Height of end of thermal conduit, tinIs the initial temperature of the heat distribution pipeline, toutIs the temperature at the end of the thermal conduit, QLossFor heat loss of the pipe, GLIs the flow of the heat distribution pipe, cpIs the specific constant pressure heat capacity of hot water, K is the equivalent length coefficient of the heat loss element, L represents the length of the heat distribution pipeline, R is the heat resistance of the heat distribution pipeline, t is the average temperature of the medium in the heat distribution pipelineaIs ambient temperature.
6. An integrated energy system dispatching system with generalized predictive control, comprising: the system comprises a classification modeling module (100), a controller building module (200), an optimization solving module (300) and a scheduling module (400);
the classification modeling module (100) is used for respectively modeling energy conversion equipment and energy storage equipment in the comprehensive energy system to obtain an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment;
the controller construction module (200) is used for constructing the energy conversion equipment controller according to the generalized predictive control;
the optimization solving module (300) is used for determining an objective function, solving an energy supply model of the energy conversion equipment and an energy supply model of the energy storage equipment according to the objective function, and obtaining a predicted output value of the energy conversion equipment and the capacity of the energy storage equipment;
the scheduling module (400) is used for setting scheduling duration, and modifying and controlling a predicted output value of an energy supply model of the energy conversion equipment through the energy conversion equipment controller according to the equipment constraint condition and the capacity of the energy storage equipment so as to realize scheduling of energy;
wherein the classification modeling module (100) is specifically configured to:
the energy conversion equipment comprises a gas turbine and an electric boiler, the energy storage equipment comprises a storage battery, an air storage tank and a heat storage tank, and the gas turbine comprises a rotating speed control module, a temperature control module, a fuel supply module and a compressor-turbine module;
(1) the battery is modeled by the following equation:
Figure FDA0002793031310000071
wherein S isoc(t) is the residual capacity of the battery at time t, Soc(t0) For the accumulator at t0The remaining capacity at that moment; delta is the self-discharge rate of the storage battery, and the unit is%/h; Δ t is t0The time span to t; pchFor charging the accumulator, PdisIs the discharge power of the storage battery; etachFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the storage battery;
(2) the gas storage tank is modeled by the following formula:
Figure FDA0002793031310000081
wherein, VGSThe effective gas storage volume of the gas storage tank; vCThe geometric volume of the gas storage tank; p is a radical ofhighIs the absolute pressure, p, at the highest operating conditionlowAbsolute pressure under the lowest working condition; p is a radical of0Representing engineering standard pressure;
(3) the thermal storage tank is modeled by the following formula:
Figure FDA0002793031310000082
wherein Q isHS(t) represents the heat storage amount of the heat storage tank at time t; mu.sLossRepresenting the heat dissipation loss rate of the heat storage tank; qHS(t0) Denotes the initial t0The heat storage quantity of the heat storage tank at the moment, delta t is t0The time span to t is the time span,
Figure FDA0002793031310000083
represents t0The amount of heat charged in the heat storage tank up to time t,
Figure FDA0002793031310000084
indicating the heat charging efficiency of the thermal storage tank,
Figure FDA0002793031310000085
represents t0The heat release amount of the heat storage tank until the time t,
Figure FDA0002793031310000086
representing the heat release efficiency of the heat storage tank;
(4) the rotating speed control module carries out modeling through the following steps:
input reference rotational speed WrefAnd the actual rotor speed omega of the gas turbine power generation system;
using a lead-lag transfer function pair WrefAnd omega are controlled and output through a minimum value selector;
(5) the temperature control module is modeled by:
obtaining exhaust gasesTemperature TxWill TxTo rated exhaust temperature TrefComparing;
when T isxHigher than TrefThen, the output is controlled to a minimum value selector through a proportional integral regulator PI until TxLess than TrefStopping the PI from working;
(6) the fueling module is modeled by the following equation:
Figure FDA0002793031310000087
where f3 is the transfer function, Kv、KfTo gain, TvFor valve opening, TfC is a constant, s is a variable in space;
(7) the compressor-turbine module is modeled by the following equation:
f1＝Tref-700(1-Wf1)+500(1-ω)
f2＝1.3(Wf2-0.23)+0.5(1-ω)
where f1 is a function of turbine exhaust temperature of the gas turbine, f2As a function of the turbine torque output of the gas turbine, TrefIs the nominal exhaust temperature; wf1、Wf2As a fuel flow signal, ω is the turbine actual speed;
wherein the controller building block (200) is specifically configured to:
an output prediction model of the energy conversion equipment is constructed by adopting a minimum variance control method;
modifying the output prediction model by adopting rolling optimization to obtain an optimized prediction value;
judging whether the optimized predicted value is in the equipment constraint condition;
if so, scheduling the prediction output of the energy supply model of the energy conversion equipment according to the optimized predicted value;
and if not, scheduling the prediction output of the energy supply model of the energy conversion equipment through the critical value of the equipment constraint condition.
7. A computer readable storage medium storing a plurality of acquisition classification programs, the acquisition classification programs being used for being called by a processor and executing the method for scheduling an integrated energy system with generalized predictive control according to claim 1.
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