CN117452812A - Nonlinear model-based integrated energy system MPC control system and construction method thereof - Google Patents
Nonlinear model-based integrated energy system MPC control system and construction method thereof Download PDFInfo
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Abstract
The invention discloses a comprehensive energy system MPC control system based on a nonlinear model and a construction method thereof, and relates to the field of operation optimization and regulation of a power system. According to the invention, through building simulation modules of each device and pipe network, a transfer function model of each device and pipe network is obtained based on a system identification method, and a nonlinear model of the comprehensive energy system is built according to the integral structure of the system. The hierarchical optimization control structure based on the MPC strategy is characterized in that a prediction module of an MPC control system is constructed through a comprehensive energy system nonlinear model to form a supply-demand balance objective function, and a multi-objective optimization function based on weight distribution is established by combining an operation cost optimization objective function. And constructing a custom constraint set based on the connection mode and the structure of each device of the comprehensive energy system. And finally, solving by adopting an optimization algorithm. The invention fully considers the nonlinear characteristics of the comprehensive energy system, can effectively improve the rationality of the load distribution of the comprehensive energy system and improves the running economy of the whole system.
Description
Technical Field
The invention relates to the field of operation optimization and regulation of power systems, in particular to an integrated energy system MPC control system based on a nonlinear model.
Background
The increasingly severe problems of energy shortage, energy utilization, environmental pollution and the like lead the clean transformation of energy structures to be urgent. With the development of energy utilization technologies such as cogeneration, electric energy storage, heat storage/cold storage and the like, the conversion, distribution and storage links of different types of energy coupling processes are more complicated, and an integrated comprehensive energy system integrating energy production, supply and marketing is gradually formed. By means of concepts of multi-energy complementation, energy cascade utilization and the like, the comprehensive energy system can balance the 'conversion-distribution-storage' links of multiple energy sources, enlarge the regulation and control boundary of the power system, enhance the operation flexibility and provide a feasible path for promoting the optimization of the energy structure and the improvement of energy efficiency. The research fields of supply and demand uncertain prediction, optimal scheduling, system control and the like of a comprehensive energy system have attracted wide attention. The scheduling process of the comprehensive energy system involves the problems of uncertainty of output, multiple dynamic characteristics, time scale and coupling of multiple energy flows, and has great influence on actual running performance such as stability, robustness, economy and the like. The existing optimization scheduling method is concentrated in two parts of model research and algorithm research, and regarding the model research, scholars mainly pay attention to a basic model and a scheduling model considering energy flow difference, system flexibility and system randomness; algorithm researches are mainly divided into an analytic method and an artificial intelligence method. Because of the diversity of the integrated energy system, the electric power system faces the problem of coordinating the supply and utilization of different energy resources, and the whole operation of the system can be detected, managed and regulated by introducing control so as to maintain the balance and stability of the system and ensure the matching between the energy supply and the demand. At present, the coordination control of the comprehensive energy system is generally realized through means of control strategy optimization, intelligent control and the like, and how to accurately predict uncertain factors in the system and carry out adaptive adjustment in the control process becomes a current urgent problem to be solved.
The comprehensive energy system has the characteristics of multi-energy flow coupling, strong uncertainty and multi-time scale, and can describe the complex input-output relationship of the system by adopting a nonlinear model. The model predictive control (Model Predictive Control, MPC) can solve the problem of optimal control of the multivariate constraint by adjusting the output value by predictive model, rolling optimization, and feedback correction. Therefore, compared with the traditional optimal scheduling and control means, the prediction module of the MPC control system is constructed through the comprehensive energy system nonlinear model, and the closed loop, nonlinear and self-adaptive control effects can be realized, so that the operation uncertainty is reduced, and the economic benefit of the whole system is improved. Based on this, there is a need for an integrated energy system controller that can achieve optimal load distribution and real-time power balance in consideration of the nonlinear dynamic characteristics of the integrated energy system.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an integrated energy system MPC control system based on a nonlinear model. The system can fully consider the nonlinear characteristics of the comprehensive energy system, and improves the matching degree of the supply and the demand of the system and the running economy of the whole system.
The specific technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for constructing an MPC control system of a comprehensive energy system based on a nonlinear model, which mainly includes integrating the nonlinear model, an optimization algorithm, real-time data acquisition, predictive control execution, etc., so as to realize system operation optimization and control, and specifically includes the following steps:
s1: setting up simulation modules of each operation device and a cold and hot pipe network in the comprehensive energy system, setting up a transfer function model of each simulation module through a system identification method based on input and output data, performing model accuracy test, and finally setting up a comprehensive energy system dynamic model considering nonlinear characteristics based on simulation identification results of each device according to the overall structure composition of the comprehensive energy system; the equipment comprises a gas turbine, a waste heat boiler, a gas boiler, an electric refrigerator and an absorption refrigerator;
s2: hierarchical optimization control structure based on MPC strategy, the following operations are carried out:
at the upper layer, establishing a cost objective function considering the running economy; in a lower MPC control layer, based on the comprehensive energy system dynamic model considering nonlinear characteristics in the step S1, setting the optimized output results of all the devices as set values, and establishing a supply-demand balance objective function;
then establishing a multi-objective optimization function based on weight distribution in a weighted summation mode;
s3: constructing a custom constraint set for the multi-objective optimization function in the step S2 based on the connection mode and the structural characteristics of each device in the comprehensive energy system, wherein the constraint form comprises soft constraint and hard constraint;
s4: according to the comprehensive energy system dynamic model considering the nonlinear characteristics in the step S1, constructing a comprehensive energy system MPC control system based on the nonlinear model; by setting sampling time Ts, predicting time domain p, controlling time domain m and supply and demand balance target weight w c And running cost target weight w y The expected operation optimization and control effect of the multi-objective optimization function after the constraint of the step S3 is realized, and the MPC control system calculation is realized by adopting an optimization algorithm.
The overall algorithm of the invention can carry out the structural design of the controller by means of a Nonlinear Model Predictive Control (NMPC) tool box in MATLAB.
Preferably, the comprehensive energy system comprises a gas turbine, a waste heat boiler, a gas boiler, an electric refrigerator, an absorption refrigerator, a heat supply and cooling pipe network, a natural gas pipe network and a power grid; the gas turbine generates electric energy by combusting natural gas supplied by a natural gas pipe network, and supplies power to a user, and when the power supply of the gas turbine is insufficient, the external power network supplies power to the user in a supplementing manner; the waste heat boiler is used for producing heat energy by recovering waste heat of flue gas generated by power generation of the gas turbine, and the heat energy is transmitted to a user side through a heat pipe network; the absorption refrigerator is used for recovering the flue gas waste heat generated by the gas turbine, and the generated cold energy is conveyed to a user side by a cold supply pipeline; the electric refrigerator is connected to the external electric network, and when the cooling of the absorption refrigerator is insufficient, the external electric network supplies power to the electric refrigerator to perform additional cooling so as to meet the cooling energy requirement of a user; the gas boiler is connected to the natural gas pipe network, and when the heat supply of the waste heat boiler is insufficient, the natural gas is combusted by the gas boiler to supplement heat supply so as to meet the heat energy requirement of a user; the heat supply and cold supply pipe networks are respectively provided with a cold water supply pipeline and a hot water return pipeline which run relatively independently, and the whole heat supply and cold supply pipe networks are divided into a primary network and a secondary network; the natural gas pipe network is connected with a gas turbine and a gas boiler, so that the requirements of power supply and heat load on a user side are met; the power grid is connected with the gas turbine and the electric refrigerator, and the electric load requirement of a user side is met.
In the invention, the primary network and the secondary network specifically refer to a primary pipe network and a secondary pipe network, wherein the primary pipe network refers to a heat source plant for central heat supply and a conveying pipeline from a valve at a home (interface) of each heat utilization unit; the secondary pipe network refers to the pipes between individual building units within each heat unit.
Preferably, in the step S2, the multi-objective optimization function based on weight allocation is determined by a supply-demand balance objective function, a cost objective function considering operation economy, an operation cost objective weight, and a supply-demand balance objective weight.
Preferably, the supply and demand balance objective function is to dynamically track the set value by adopting an MPC controller, and the tracking degree J y (Z k ) Determined by formula (1):
wherein Z is k Is an input variable sequence; k is the current scheduling time; p is the prediction time domain; n is n y The number of output variables; r is (r) j (k+i|k) is the value of the jth reference output at time k, predicted at time k+i, typically predicted user demand; y is j (k+i|k) is a value of the jth system output at the time k predicted at the time k+i;the weight coefficient at the k+i time is output as the j-th output, and 1 is taken as a default.
Preferably, the cost objective function J is designed to take into account operational economics c (Z k ) Determined by formula (2):
wherein COST is as follows gas 、COST ele 、COST om The unit is the total cost of natural gas purchasing, the total cost of power grid purchasing and the total cost of equipment operation and maintenance; ts is a time interval, i.e. sampling time, in s; ρ gas For natural gas density, 0.7174kg/m is generally taken 3 ；C gas The price of the natural gas is per cubic meter, and the unit is yuan/m 3 ；gas gt_i 、gas gb_i The gas flow is input to a gas turbine and the gas flow is input to a gas boiler at the ith moment in the predicted time domain, and the unit is kg/s; ele is ec_i 、ele grid_i 、ele gt_i 、heat b_i 、heat gb_i 、cold ec_i 、cold ac_i The method comprises the steps of respectively inputting electric power to an electric refrigerator, supplying power to a user from a power grid, supplying power to a gas turbine, supplying power to a waste heat boiler, supplying power to the gas boiler, refrigerating power to the electric refrigerator and refrigerating power to the absorption refrigerator in a prediction time domain, wherein the unit is kW; r is R ele_i In order to predict the power grid electricity price at the ith moment in the time domain, the unit is yuan/kWh; r is R gt 、R gb 、R ec 、R ac 、R b The operation and maintenance costs of the gas turbine, the gas boiler, the electric refrigerator, the absorption refrigerator and the waste heat boiler are respectively shown in the unit of yuan/kWh.
Preferably, the weight-based multi-objective optimization function J (Z k ) Determined by formula (3):
wherein w is y Weighting an operation cost target; w (w) c The target weights are balanced for supply and demand.
In a second aspect, the present invention provides a nonlinear model-based integrated energy system MPC control system obtained by using the construction method of any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
the invention can describe the complex running state of the comprehensive energy system more accurately by establishing the nonlinear model of the comprehensive energy system, on the basis, a layered optimization control structure based on the MPC strategy is used for constructing an objective function comprehensively considering running economy and supply and demand balance, and the power adjustment of supplying power, heating power and cooling power is realized from the aspects of running optimization and rolling control. Compared with the traditional comprehensive energy system scheduling and control method, the method can fully consider the nonlinear characteristics of each device and pipe network of the comprehensive energy system in the variable working condition operation process, has the performance of optimizing scheduling and real-time control, better realizes reasonable distribution of electric, thermal and cold loads, optimizes feedback closed loops, reduces operation uncertainty, improves the energy efficiency of the comprehensive energy system and reduces the operation cost of the comprehensive energy system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the basic structure of an integrated energy system;
FIG. 2 is a simulation model of a gas turbine constructed based on APROS software;
FIG. 3 is a graph showing step response test results for different load points of a gas turbine;
FIG. 4 is a graph of model identification test results for different load points of a gas turbine, from left to right, for low, medium, and high load operating point identification test results for the gas turbine in sequence;
FIG. 5 is a graph showing the results of model test of a gas turbine at different load points, from left to right, for the low, medium, and high load operating point models of the gas turbine in sequence;
FIG. 6 is a gas turbine LPV model accuracy test result;
FIG. 7 depicts a simulation program of an MPC control system of the integrated energy system based on a nonlinear model;
fig. 8 is a simulation model of the integrated energy system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
In the prior art, an electric power system often adopts an operation control mode of 'electricity-based heating' or 'electricity-based heating', but the traditional operation control cannot be effectively applied to a comprehensive energy system, and the operation economy and other performances of the whole system cannot be ensured. In addition, the operation optimization and control technology of the comprehensive energy system is based on a specific working condition point at present, and complex dynamic characteristics of the comprehensive energy system are not considered. Therefore, the integrated energy system MPC control system based on the nonlinear model can fully consider the nonlinear characteristic of the operation process of the integrated energy system, realize the improvement of the operation economy and ensure the matching degree between the energy supply of the system and the requirements of users.
As a preferred embodiment of the present invention, as can be seen from fig. 1, the main apparatus of the integrated energy system mentioned in this example includes: gas turbine, exhaust-heat boiler, gas boiler, electric refrigerator, absorption refrigerator, heat supply and cold supply pipe network, natural gas pipe network and electric wire netting. The user load demands are in the form of electric, thermal and cold loads, and the electric loads mainly come from lighting and electrical equipment; the heat load mainly comes from heating air and hot water; the cold load is mainly from cold air and cold water.
The connection and structure of each device will be described in detail.
The natural gas pipe network is sequentially connected with the gas turbine and the gas boiler, the exhaust outlet of the gas turbine is sequentially connected with the inlet of the absorption refrigerator and the inlet of the waste heat boiler, and the power grid is connected with the inlet of the electricity utilization end of the electric refrigerator. The natural gas pipe network transmits natural gas to the gas turbine to burn into gas, the gas expands in the turbine to do work to generate electric energy to supply power to users, and according to the requirements of the users, the exhaust gas from the outlet of the gas turbine can enter the absorption refrigerator and the waste heat boiler to generate cold energy/heat energy to supply heat/cool to the users through the pipe network. When the power supply of the gas turbine is insufficient, the power grid can directly transmit electric energy to a user, and the user is supplied with power in a supplementing mode. When the cooling of the absorption refrigerator is insufficient, the power grid can transmit electric energy to the electric refrigerator, the electric energy drives the refrigeration cycle in the electric refrigerator, and the cooling is supplemented for a user through a pipeline.
As a preferred implementation mode of the invention, the comprehensive energy system builds simulation modules of each device and the pipe network based on APROS simulation software, the building form of the simulation modules is shown in figure 2, and a nonlinear model of the whole system is built on the basis of the simulation modules.
As a preferred implementation mode of the invention, in order to realize the optimal distribution and real-time control of the load, the invention provides a non-linear model-based MPC controller for a comprehensive energy system, which fully considers the complex working condition of the comprehensive energy system, realizes the matching between the system supply and the user demand through rolling optimization, a prediction model and feedback correction, and achieves the effect of the economic operation of the comprehensive energy system, and the construction method comprises the following steps:
s1: input and output data of each device at different working condition points are collected and acquired through a designed excitation test, a dynamic transmission model of each device and a pipe network at different working condition points is built by a system identification method, a Linear Parameter-variable (LPV) model of each device and the pipe network under the global working condition is built based on a Linear interpolation method, and a dynamic nonlinear model of the comprehensive energy system is built according to a system composition structure.
Taking gas turbine equipment modeling as an example, a gas turbine model is constructed based on APROS dynamic simulation software, wherein input variables and output variables are respectively the opening degree of a gas valve and the power supply quantity of the gas turbine, and the gas turbine model is shown in figure 3. First, step response tests (the variation of the opening of the gas valve of the input signal is 0.2 in the test process) are carried out on the gas turbine near the low, medium and high load points, the test results are shown in fig. 4, and the power generated by the gas turbine corresponding to the low, medium and high loads in the figures is 5200kW, 5600kW and 5900kW respectively. From the step results, it can be seen that the gas turbine variable operating mode process exhibits certain nonlinear characteristics. And constructing an LPV model of the gas turbine by taking the generated energy of the gas turbine as a scheduling variable. Firstly, near low, medium and high load working points, an identification test is designed according to the principle shown in the following formula, corresponding excitation input signals and output signals acquired on an APROS simulation system are shown in fig. 5, and GBN signals are adopted as the excitation input.
On three working condition points, the transfer function model is obtained by identification to be G gt_l 、G gt_m 、G gt_h The model accuracy test results are shown in fig. 6, as shown in the following equation.
And establishing a global interpolation LPV model through a model structure shown in the following formula to obtain a gas turbine model under variable working conditions:
ELE gt ＝α gt_l (ω gt )G gt_l V gt +α gt_m (ω gt )G gt_m V gt +α gt_h (ω gt )G gt_h V gt
wherein V is gt For input variables, i.e. gas valve opening v gt A corresponding laplace transform; ELE (electronic toll Collection) gt For outputting variable, i.e. generating power ele of combustion engine gt Is a laplace transform of (a); alpha gt_l (ω gt )、α gt_m (ω gt ) And alpha gt_h (ω gt ) The weight coefficient of each working condition point model is used; omega gt For the schedule variable, i.e. the gas turbine power generation.
A linear piecewise function is used to build a linear interpolation LPV model of the gas turbine, the weighting coefficients of which are shown in the following formula. And comparing the input and output data of the gas turbine model built by the APROS near a certain working point with the LPV model of the gas turbine to check the accuracy of the LPV model.
The rest equipment and the pipe network adopt the same method to construct the dynamic nonlinear model.
S2: based on a hierarchical optimization control structure of an MPC strategy, establishing a cost objective function considering operation economy at an upper layer; and at the lower MPC control layer, taking the optimized output result of each device as a set value, and establishing a supply-demand balance objective function. And establishing a multi-objective optimization function based on weight distribution in a weighted summation mode.
The supply-demand balance objective function adopts an MPC controller to dynamically track a set value, and the tracking degree is determined by the following formula:
wherein Z is k Is an input variable sequence; k is the current scheduling time; p is the prediction time domain; n is n y The number of output variables; r is (r) j (k+i|k) is the value of the jth reference output at time k, predicted at time k+i, typically predicted user demand; y is j (k+i|k) is a value of the jth system output at the time k predicted at the time k+i;the j-th output takes 1 by default at the weight coefficient at the k+i-th moment.
The cost objective function considering the operational economy is determined by the following equation:
wherein COST is as follows gas 、COST ele 、COST om The total cost of natural gas purchasing, the total cost of power grid purchasing and the total cost of equipment operation and maintenance are respectively; ts is a time interval (sampling time), s; ρ gas For natural gas density, 0.7174kg/m is generally taken 3 ；C gas Price per cubic meter of natural gas, yuan/m 3 ；gas gt_i 、gas gb_i The gas flow is input to a gas turbine and the gas flow is input to a gas boiler at the ith moment in the predicted time domain, and kg/s; ele is ec_i 、ele grid_i 、ele gt_i 、heat b_i 、heat gb_i 、cold ec_i 、cold ac_i The method comprises the steps of respectively inputting electric power to an electric refrigerator, supplying power to a user from a power grid, supplying power to a gas turbine, supplying power to a waste heat boiler, supplying power to the gas boiler, supplying power to the electric refrigerator, and supplying power to an absorption refrigerator in a prediction time domain, wherein the electric power is the electric refrigerator; r is R ele_i In order to predict the power grid electricity price at the ith moment in the time domain, the unit/kWh; r is R gt 、R gb 、R ec 、R ac 、R b The operation and maintenance costs of the gas turbine, the gas boiler, the electric refrigerator, the absorption refrigerator and the waste heat boiler are respectively,meta/kWh.
The weight-based multi-objective optimization function is determined by the following equation:
wherein w is y Weighting an operation cost target; w (w) c The target weights are balanced for supply and demand.
S3: based on the characteristics of all devices of the comprehensive energy system, a self-defined constraint set is constructed, and the constraint form comprises soft constraint and hard constraint.
In the embodiment, in order to realize the operation optimization and the efficient control of the comprehensive energy system, a constraint set is established for the input and output data, the input increment and the waste heat distribution of each device. Wherein, each device obtains input and output constraint as:
z j,min (i)≤z j (k+i-1|k)≤z j,max (i)i＝1,2,...,p,j＝1,2,...,n z
wherein n is z Inputting the number of variables; n is n y Outputting the number of variables; z j,min (i)、z j,max (i) The lower bound and the upper bound of the jth input variable at the ith moment are respectively; y is j,min (i)、y j,max (i) The lower bound and the upper bound of the jth output variable at the ith moment are respectively; epsilon k Is a relaxation variable for softening the constraint form;to soften the optimized weight coefficient of the output constraint, which is generally a non-negative value, a larger coefficient indicates a looser constraint condition, and a coefficient of 0 indicates that the constraint is in a hard constraint form and violation is not allowed.
In addition, to avoid the influence of excessive input data fluctuation on the system operation, input increment constraint is introduced to limit the input increment constraint, which can be expressed as follows:
Δz j,min (i)≤Δz j (k+i-1|k)≤Δz j,max (i)i＝1,2,...,p,j＝1,2,...,n z
wherein Δz j,min (i)、Δz j,max (i) Respectively the minimum increment and the maximum increment of the jth input variable at the ith moment; Δz j (k+i-1|k) is the increment of the jth input variable predicted at time k at time i.
Further, since the residual energy utilized by the absorption refrigerator and the waste heat boiler comes from the exhaust gas waste heat of the gas turbine, there is a waste heat distribution method in the following formula, which is expressed as follows:
q ex_ac +q ex_b ≤q ex
wherein q ex_ac 、q ex_b 、q ex The method comprises the steps of absorbing the residual heat of the flue gas input by a refrigerator, the residual heat of the flue gas input by a waste heat boiler and the total amount of the residual heat of the flue gas discharged by a gas turbine, and the kW.
S4: constructing a non-linear model-based integrated energy system MPC controller by setting sampling time Ts, predicting time domain p, controlling time domain m and balancing supply and demand target weight w c And running cost target weight w y To achieve the desired operational optimization and control effects, and to achieve MPC controller calculations via optimization algorithms.
The MPC controller of the comprehensive energy system based on the nonlinear model invokes a nonlinear tool box in MATLAB to carry out controller design, and firstly, a nonlinear model module, a multi-objective optimization function module and a custom constraint set module based on the comprehensive energy system are constructed; secondly, the optimization calculation is realized by means of a self-contained 'fmincon' nonlinear optimization solver of MATLAB. Since the system power up process takes 2-3 hours to reach substantially steady state, the sampling time is empirically set to 15 minutes. The MPC control system of the comprehensive energy system based on the nonlinear model, which is built on the Simulink simulation platform, is shown in FIG. 7. The simulation model of the comprehensive energy system is shown in fig. 8.
The above embodiment is only a preferred embodiment of the present invention, but it is not intended to limit the present invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, all the technical schemes obtained by adopting the equivalent substitution or equivalent transformation are within the protection scope of the invention.
Claims (7)
1. The construction method of the MPC control system of the comprehensive energy system based on the nonlinear model is characterized by comprising the following steps:
s1: setting up simulation modules of each operation device and a cold and hot pipe network in the comprehensive energy system, setting up a transfer function model of each simulation module through a system identification method based on input and output data, performing model accuracy test, and finally setting up a comprehensive energy system dynamic model considering nonlinear characteristics based on simulation identification results of each device according to the overall structure composition of the comprehensive energy system; the equipment comprises a gas turbine, a waste heat boiler, a gas boiler, an electric refrigerator and an absorption refrigerator;
s2: hierarchical optimization control structure based on MPC strategy, the following operations are carried out:
at the upper layer, establishing a cost objective function considering the running economy; in a lower MPC control layer, based on the comprehensive energy system dynamic model considering nonlinear characteristics in the step S1, setting the optimized output results of all the devices as set values, and establishing a supply-demand balance objective function;
then establishing a multi-objective optimization function based on weight distribution in a weighted summation mode;
s3: constructing a custom constraint set for the multi-objective optimization function in the step S2 based on the connection mode and the structural characteristics of each device in the comprehensive energy system, wherein the constraint form comprises soft constraint and hard constraint;
s4: according to the comprehensive energy system dynamic model considering the nonlinear characteristics in the step S1, constructing a comprehensive energy system MPC control system based on the nonlinear model; by setting sampling time Ts, predicting time domain p, controlling time domain m and supply and demand balance target weight w c And running cost target weight w y To realize multi-objective optimization after constraint in step S3And (3) carrying out operation optimization and control effects expected by the functions, and adopting an optimization algorithm to realize calculation of the MPC control system.
2. The method for constructing an MPC control system of a comprehensive energy system based on a nonlinear model according to claim 1, wherein the comprehensive energy system comprises a gas turbine, a waste heat boiler, a gas boiler, an electric refrigerator, an absorption refrigerator, a heat supply and cooling pipe network, a natural gas pipe network and a power grid; the gas turbine generates electric energy by combusting natural gas supplied by a natural gas pipe network, and supplies power to a user, and when the power supply of the gas turbine is insufficient, the external power network supplies power to the user in a supplementing manner; the waste heat boiler is used for producing heat energy by recovering waste heat of flue gas generated by power generation of the gas turbine, and the heat energy is transmitted to a user side through a heat pipe network; the absorption refrigerator is used for recovering the flue gas waste heat generated by the gas turbine, and the generated cold energy is conveyed to a user side by a cold supply pipeline; the electric refrigerator is connected to the external electric network, and when the cooling of the absorption refrigerator is insufficient, the external electric network supplies power to the electric refrigerator to perform additional cooling so as to meet the cooling energy requirement of a user; the gas boiler is connected to the natural gas pipe network, and when the heat supply of the waste heat boiler is insufficient, the natural gas is combusted by the gas boiler to supplement heat supply so as to meet the heat energy requirement of a user; the heat supply and cold supply pipe networks are respectively provided with a cold water supply pipeline and a hot water return pipeline which run relatively independently, and the whole heat supply and cold supply pipe networks are divided into a primary network and a secondary network; the natural gas pipe network is connected with a gas turbine and a gas boiler, so that the requirements of power supply and heat load on a user side are met; the power grid is connected with the gas turbine and the electric refrigerator, and the electric load requirement of a user side is met.
3. The method for constructing an MPC control system of an integrated energy system based on a nonlinear model according to claim 1, wherein in the step S2, the multi-objective optimization function based on weight distribution is determined by a supply-demand balance objective function, a cost objective function considering running economy, a running cost objective weight, and a supply-demand balance objective weight.
4. A nonlinear model based in claim 3The construction method of the MPC control system of the comprehensive energy system is characterized in that the supply and demand balance objective function adopts an MPC controller to dynamically track a set value, and the tracking degree J y (Z k ) Determined by formula (1):
wherein Z is k Is an input variable sequence; k is the current scheduling time; p is the prediction time domain; n is n y The number of output variables; r is (r) j (k+i|k) is the value of the jth reference output predicting the k+i time at the k time; y is j (k+i|k) is a value of the jth system output at the time k predicted at the time k+i;the weight coefficient at the k+i time is output for the j-th.
5. The method for constructing an MPC control system of a non-linear model based integrated energy system as claimed in claim 4, wherein said cost objective function J is based on operational economics c (Z k ) Determined by formula (2):
wherein COST is as follows gas 、COST ele 、COST om The unit is the total cost of natural gas purchasing, the total cost of power grid purchasing and the total cost of equipment operation and maintenance; ts is a time interval, i.e. sampling time, in s; ρ gas Is natural gas density; c (C) gas The price of the natural gas is per cubic meter, and the unit is yuan/m 3 ；gas gt_i 、gas gb_i The gas flow is input to a gas turbine and the gas flow is input to a gas boiler at the ith moment in the predicted time domain, and the unit is kg/s; ele is ec_i 、ele grid_i 、ele gt_i 、heat b_i 、heat gb_i 、cold ec_i 、cold ac_i The method comprises the steps of respectively inputting electric power to an electric refrigerator, supplying power to a user from a power grid, supplying power to a gas turbine, supplying power to a waste heat boiler, supplying power to the gas boiler, refrigerating power to the electric refrigerator and refrigerating power to the absorption refrigerator in a prediction time domain, wherein the unit is kW; r is R ele_i In order to predict the power grid electricity price at the ith moment in the time domain, the unit is yuan/kWh; r is R gt 、R gb 、R ec 、R ac 、R b The operation and maintenance costs of the gas turbine, the gas boiler, the electric refrigerator, the absorption refrigerator and the waste heat boiler are respectively shown in the unit of yuan/kWh.
6. The method for constructing an MPC control system of a comprehensive energy system based on a nonlinear model according to claim 5, wherein the weight-based multi-objective optimization function J (Z k ) Determined by formula (3):
wherein w is y Weighting an operation cost target; w (w) c The supply and demand balance target weights.
7. An integrated energy system MPC control system based on a nonlinear model obtained by the construction method of any one of claims 1 to 6.
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