CN111952965B - CCHP system optimized operation method based on predictive control and interval planning - Google Patents
CCHP system optimized operation method based on predictive control and interval planning Download PDFInfo
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Abstract
The invention provides a CCHP system optimized operation method based on predictive control and interval planning, which comprises the steps of obtaining historical operation data of a renewable energy source combined cooling heating and power system; interval prediction is carried out on renewable energy sources and loads to obtain wind power, a predicted value of the cold, heat and power loads and an interval value; performing error prediction on renewable energy output and cooling, heating and power loads, and compensating a predicted value by using an error predicted value on the basis of prediction of a Gaussian process regression interval; comprehensively considering the economy, the energy utilization rate and the environmental benefits of the renewable energy source combined cooling heating and power system, constructing a target function containing interval numbers, and performing rolling optimization solution on the target function under the constraint condition to obtain a system equipment output value; the stability can be improved while the system economy, the energy utilization rate and the environmental benefit are improved.
Description
Technical Field
The disclosure belongs to the technical field of combined cooling heating and power systems, and relates to a CCHP system optimized operation method based on predictive control and interval planning.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The world is facing serious energy and environmental crisis, and China is a big energy consumption country, and the situation of energy conservation and emission reduction is more severe. Renewable energy represented by wind energy and solar energy in China is very rich and has great development potential, and the renewable energy is significant for realizing energy conservation and emission reduction, and the development of renewable energy is a key way for solving problems.
A Combined Cooling, heating and Power (CCHP) system of renewable energy introduces renewable energy on the basis of a CCHP system to realize efficient and gradient utilization of energy and improve the consumption rate of the renewable energy at the same time. The renewable energy CCHP system has numerous devices, complex working conditions, heterogeneous multi-energy flow coupling, strong uncertainty of renewable energy and load of cold, heat and electricity, and great challenge for realizing economic and efficient operation of the system.
To the inventor's knowledge, the renewable energy, load power prediction of the renewable energy CCHP system in the prior art generally gives only one deterministic prediction value. Performing CCHP system operation optimization based on the deterministic prediction value, thereby converting the system optimization containing uncertainty into deterministic optimization; in some patents, a feedback correction link is added while system rolling optimization is carried out, and system optimization scheduling is carried out based on prediction control, but under the condition that the fluctuation of uncertain variables in the system is large, the deviation from the actual variable is large. The existing research currently has the following problems:
(1) Although many results have been obtained in research on deterministic prediction, in practical situations, renewable energy power generation (such as photovoltaic and wind power) is affected by weather factors such as illumination, wind speed and the like, the output prediction has strong uncertainty, the load prediction also has uncertainty of prediction error, the probability of occurrence of the error is almost one hundred percent, and the error is usually subject to normal distribution. The optimal operation method based on deterministic prediction seriously depends on the prediction precision, and the prediction uncertainty of the system at each time point cannot be fully considered by a model prediction control method based on the deterministic prediction value.
(2) In order to describe the uncertain situation of the renewable energy CCHP system, if a random planning method is adopted, probability distribution characteristics of uncertain variables need to be obtained, and the problem is often difficult. The influence of system uncertainty on system optimization operation is not considered, so that the reliability of the system is reduced, and the feasibility is difficult to guarantee.
Disclosure of Invention
In order to solve the problems, the invention provides a CCHP system optimized operation method based on predictive control and interval planning, and the method can improve the system economy, the energy utilization rate and the environmental benefit and simultaneously improve the stability.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a CCHP system optimized operation method based on predictive control and interval planning comprises the following steps:
acquiring historical operation data of a renewable energy source combined cooling heating and power system;
interval prediction is carried out on renewable energy sources and loads to obtain wind power, a predicted value of the cold, heat and power loads and an interval value;
performing error prediction on renewable energy output and cooling, heating and power loads, and compensating a predicted value by using an error predicted value on the basis of prediction of a regression interval in a Gaussian process;
comprehensively considering the economy, the energy utilization rate and the environmental benefits of the renewable energy combined cooling heating and power system, constructing an objective function containing interval numbers, and performing rolling optimization solution on the objective function under the constraint condition to obtain a system equipment output value.
As an alternative embodiment, in the interval prediction process of renewable energy and load, a gaussian process regression interval prediction is adopted.
As an alternative implementation mode, in the specific process of constructing the objective function containing the number of the sections, various indexes in the objective function are weighted, and on the basis of meeting the cooling, heating and power loads of users and the operation constraints of all devices of the system, the best comprehensive index is expected to be obtained by performing section multi-objective optimization on the renewable energy source combined cooling, heating and power system.
As an alternative embodiment, considering the economics of the renewable energy cogeneration system includes considering a cost saving rate equal to: the difference value of the economic cost of the sub-supply system and the economic cost of the system is compared with the ratio of the economic cost of the sub-supply system;
the economic cost of the sub-supply system comprises the electricity and gas cost of the sub-supply system;
the economic cost of the system comprises the operation and maintenance cost and the fuel cost of the renewable energy source combined cooling heating and power system.
As an alternative embodiment, the energy utilization rate of the renewable energy cogeneration system is considered, in particular the primary energy saving rate of the system is considered, which is equal to: the difference value of the energy cost of the sub-supply system and the energy cost of the system, and the ratio of the energy cost of the sub-supply system.
As an alternative embodiment, the environmental benefit of the renewable energy cogeneration system is considered, in particular the carbon emission reduction rate of the system is considered, which is equal to the ratio of the difference between the environmental cost of the sub-supply system and the environmental cost of the system to the environmental cost of the sub-supply system.
As an alternative embodiment, the constraint conditions include a cooling, heating and power energy balance constraint and an operation constraint of each device.
As an alternative embodiment, a weight coefficient is introduced, the objective function is converted into a single objective form, the objective function values of each decision variable are compared, and finally the objective function value with the minimum midpoint value and the minimum interval width is found, so that the optimal decision variable is found.
As an alternative embodiment, the optimization method is adopted for solving, the interval objective function is converted into a deterministic objective function, and the constraint is processed by a penalty function so as to complete the system optimization solution with the interval constraint.
A CCHP system optimized operation system based on prediction control and interval planning comprises:
the source load prediction module is configured to perform interval prediction on renewable energy sources and loads according to historical operating data of the renewable energy source combined cooling heating and power system to obtain wind power, a predicted value of the cooling heating and power load and an interval value;
the feedback correction module is configured to carry out error prediction on the renewable energy output and the cooling, heating and power loads and compensate a predicted value by using an error predicted value on the basis of prediction of a Gaussian process regression interval;
and the rolling optimization module is configured to comprehensively consider the economy, the energy utilization rate and the environmental benefit of the renewable energy combined cooling heating and power system, construct an objective function containing intervals, and perform rolling optimization solution on the objective function under the constraint condition to obtain a system equipment output value.
A computer readable storage medium, wherein a plurality of instructions are stored, said instructions are adapted to be loaded by a processor of a terminal device and execute said CCHP system optimized operation method based on prediction control and interval planning.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the CCHP system optimized operation method based on the prediction control and the interval planning.
Compared with the prior art, this disclosed beneficial effect does:
the method adopts a Gaussian process regression interval prediction method to predict renewable energy and load in an ultra-short period, can predict and obtain the predicted values and the interval values of wind power, cooling, heating and power loads, can quantify the fluctuation of source and load values caused by uncertain factors, and under a certain confidence coefficient, the actual output of the renewable energy and the actual cooling, heating and power loads fall in an interval formed by the upper limit and the lower limit of the interval predicted values. As an extension of point prediction, the Gaussian process regression interval prediction can effectively describe uncertainty on one hand by giving out a possible fluctuation range of source load power under the condition of meeting a given confidence probability; on the other hand, a decision maker can flexibly select the possible fluctuation range under different confidence probabilities and further determine the optimized operation mode of the system.
According to the method, uncertainty is considered in rolling optimization, and the uncertainty variable can be represented by the interval number only by knowing the possible fluctuation range of the uncertainty variable. The method is simple and effective. And the optimization link utilizes the feedback information, and can correct the scheduling instruction in time to form closed loop optimization, so that the system has good robustness to interference.
The method is provided with error prediction and real-time adjustment links, performs error prediction according to historical data, corrects the predicted value in real time, and reduces errors output by the system. In order to reduce the influence of errors on the optimized operation of the system, the renewable energy and load predicted values are corrected correspondingly according to the predicted values of the errors based on the real-time adjustment method of error prediction.
The method and the device convert the uncertainty problem of the system into the deterministic optimization problem based on the interval planning theory, utilize the converted optimization model to perform rolling optimization solution on the output interval of the system equipment, and adjust the scheduling instruction in time according to the fluctuation change of the renewable energy and the cooling, heating and power load requirements, thereby ensuring the real-time performance of optimization and improving the comprehensive performance index of the system.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic diagram of a renewable energy combined cooling heating and power system;
fig. 2 is a schematic flow chart of a system optimization control process based on model predictive control and interval planning.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
A CCHP system optimized operation method based on predictive control and interval planning is disclosed. The system optimization scheduling based on the predictive control framework can improve the system economy, the energy utilization rate and the environmental benefit and simultaneously improve the stability. As shown in fig. 1, the renewable energy CCHP system includes an internal combustion generator set, an absorption chiller, a wind power generation system, an electric chiller, and a gas boiler. As shown in fig. 2, the operation method of the present embodiment includes: source load prediction, rolling optimization and feedback correction.
Source load prediction: in the actual operation of the system, the fluctuation interval of the source load prediction uncertain variable is more feasible to obtain. The embodiment provides a prediction method of a regression interval of a Gaussian process, and the prediction method can predict and obtain the predicted values and interval values of wind power, cold, heat and electricity loads for renewable energy sources and loads in an ultra-short period.
And (3) rolling optimization: in the physical sense of the system, errors necessarily exist in renewable energy and load prediction, and the influence of uncertain factors should be fully considered in the optimization operation. Uncertainty is considered in the rolling optimization, and the random planning and the fuzzy planning are selected to have certain limitations. The processing method based on the interval number does not need to know the probability distribution function of the source load power prediction deviation, only needs to know the source load power value interval under a certain confidence probability, and adopts the interval number to carry out the uncertainty modeling of the source load. When the interval planning modeling is applied, only the possible fluctuation range of the uncertain variable needs to be known, and the uncertain variable is represented by the number of intervals.
And (3) feedback correction: due to uncertainty of renewable energy and cooling, heating and power loads of the system, the predicted values and the measured values of the renewable energy and the cooling, heating and power loads cannot be completely the same, and errors are inevitable. Therefore, the error prediction is carried out on the renewable energy output and the cooling, heating and power loads, and the predicted value is compensated by using the error predicted value on the basis of the source load ultra-short-term prediction, so that the system scheduling error can be further reduced. According to the embodiment, error prediction and real-time adjustment links are designed, error prediction is carried out according to historical data, a predicted value is corrected in real time, and errors output by a system are reduced. In order to reduce the influence of errors on the optimized operation of the system, the renewable energy and load predicted values are correspondingly corrected according to the predicted values of the errors based on the real-time adjustment method of error prediction. Therefore, the rolling optimization is not only based on the model, but also utilizes the feedback information, and can correct the scheduling instruction in time to form closed loop optimization, so that the system has good robustness to interference.
The optimized operation method comprises the following steps:
renewable energy and load interval prediction: and predicting the Gaussian process regression interval of the wind power load, the cold and the heat power load. The Gaussian process is a common stochastic process, f (X) 1 )、f(X 2 )、…、f(X n ) Is a random variable and follows a gaussian distribution, and the statistical characteristics of the gaussian process consist of a mean function m (x) and a covariance function k (x, x').
f(X)～GP(m(x),k(x,x'))
For the observed value Y, a regression model that takes noise into account is as follows:
Y＝f(X)+ξ
in the formula, f (X) follows Gaussian distribution, gaussian white noise is set as xi, the mean value is 0, and the variance is
Namely, it is
The observed value Y also follows a gaussian distribution. Thus a joint prior distribution of the observations Y is obtained.
Wherein K (x, x) = (K) ij ) Is a positive definite covariance matrix; kernel function k which passes its element k ij To represent x i And x j The correlation between them; k (x) * ,x)＝K(x,x * ) T Is test set x * And a covariance matrix between training sets x; k (x) * ,x * ) Is the covariance matrix of the test set; i is n Is an n-dimensional identity matrix. Square exponential kernels, linear kernels and polynomial kernels are all very common kernel functions.
The formula of the square index kernel is as follows, p 1 Is an adjustable parameter.
The posterior distribution of predicted values y is as follows:
thus, the Gaussian process regresses the predicted point values
And the interval prediction result corresponding to 95% confidence is
The probability density function for the ith predictor is as follows:
and (3) rolling optimization based on an interval model: and establishing a mathematical model for the optimized operation of the CCHP system, wherein the mathematical model further comprises an objective function containing interval number, an equipment full-working-condition model and a constraint condition containing interval number.
According to the embodiment, each index in the objective function is weighted, and on the basis of meeting the cooling, heating and power loads of users and the operation constraints of each device of the system, the best comprehensive index is obtained by performing interval multi-objective optimization on the CCHP system. The wind power generation power has uncertainty, and the uncertainty in the objective function can be described by using the interval number representation method, so that the method is more in line with the actual operation condition of the system and has higher application value. The objective function for system optimization is as follows.
Wherein, the interval number of the wind power generation capacity
The operation and maintenance cost in the economic cost CSR is the interval number. As follows:
the relevant parameters in the objective function are as follows. The sub-supply system is used as a reference system of the renewable energy source CCHP system designed by the embodiment, and the economic cost is as follows:
Eco sp ＝W grid_sp ·B grid +W b_sp ·B gas
in the formula, eco sp The economic cost (rah) of the separate supply system; w grid_sp The method is characterized in that the electric quantity (kWh) is purchased by a power distribution system power grid; b is grid The unit price ([ gamma/kWh) of electricity purchased from the power grid; w b_sp Is gas of a separate supply systemBoiler gas consumption (m) 3 )；B gas Is the unit price ([ gamma ]/m) of natural gas 3 )。
The cost of the renewable energy CCHP system includes depreciation cost of investment, fuel cost, operational maintenance cost, wherein the depreciation cost of investment is negligible. The system fuel costs are as follows:
C F ＝W grid ·B grid +(V PGU +V b )·B gas
in the formula, C F Is the system fuel cost (<); w grid Is system power purchase (kWh); v PGU Is the PGU gas quantity (m) 3 )；V b Is the gas quantity (m) of the gas boiler 3 )。
The system operation and maintenance costs are as follows:
C R ＝W PGU ·R PGU +W ab ·R ab +W ec ·R ec +W b ·R b +W wt ·R wt
in the formula, C R The running and maintenance cost (this) of the CCHP system of the renewable energy source; w is a group of PGU Is the power generation capacity (kWh) of the internal combustion generator set; r PGU The unit price (/ kWh) of PGU operation maintenance; w ab Is absorption chiller energy consumption (kWh); r is ab Is the unit price (Rhr) of the absorption refrigerator for maintenance; w ec Is the electrical refrigerator energy consumption (kWh); r ec The unit price (/ kWh) for maintaining the operation of the electric refrigerator; w b Is gas boiler energy consumption (kWh); r b Is the unit price of operation and maintenance of the gas boiler (Rrah/kWh); w is a group of wt Is wind power generation (kWh); r wt Is the unit price (/ kWh) of the operation and maintenance of the wind power generation system.
The system economic cost is as follows:
Eco＝C R +C F
in the formula, eco is the system economic cost ([ case ]).
The economic indexes of the renewable energy CCHP system, cost Saving Rate CSR (Cost Saving Rate, CSR) are as follows:
the energy cost of the sub-supply system is as follows:
Ene sp ＝W grid_sp +F b_sp
in the formula, F b_sp Is the gas consumption (kWh) of the distribution system.
The energy cost of the renewable energy CCHP system is as follows:
Ene＝W grid /η ge /η tr +F PGU +W b
where Ene is the system energy cost (kWh); f PGU Is the PGU gas consumption (kWh).
The Energy index of the renewable Energy source CCHP system, primary Energy Saving Rate PESR (Primary Energy Saving Rate, PESR) is as follows:
the environmental cost of the distribution system is as follows:
Env sp ＝E grid_sp ·k 1 +F b_sp ·k 2
in the formula, k 1 The carbon emission coefficient (kg/kWh) is the electricity purchasing coefficient; k is a radical of formula 2 Is carbon emission coefficient (kg/m) of natural gas 3 )。
The environmental cost of the renewable energy CCHP system is as follows:
Env＝E grid ·k 1 +(F PGU +F b )·k 2
the energy index of the renewable energy CCHP system, carbon Emission Reduction Rate CERR (Carbon Emission Reduction Rate, CERR), is as follows:
the comprehensive evaluation index provided by the embodiment takes the characteristics of the three into consideration so as to realize the best comprehensive performance of the system, as follows:
I＝ω 1 CSR+ω 2 PESR+ω 3 CERR
in the formula, omega 1 ，ω 2 ，ω 3 The weight coefficients are the weight coefficients of three evaluation indexes of the renewable energy CCHP system.
And establishing an objective function containing interval numbers. The system optimization problem is an objective function containing the number of intervals, so that the objective function containing uncertainty needs to be converted into a corresponding deterministic objective function based on an interval planning theory.
The system constraint conditions comprise cold-heat-electricity energy balance constraint and each equipment operation constraint. The electric power balance constraint of the renewable energy source CCHP system at the time t is expressed as follows:
in the formula, E PGU (t) is the power generated by the internal combustion generator set; e grid (t) purchasing electric power from the grid;
is the number of wind power intervals; e ec (t) is the power consumed by the electric refrigerator;
is the electrical load of the user.
At time t, the cold load balancing constraint of the renewable energy CCHP system may be expressed as:
in the formula, C ab (t)，C ec (t)，
Respectively showing the power of the absorption refrigerator, the power of the electric refrigerator and the cold load of a user.
The thermal energy balance constraint of the renewable energy CCHP system at time t can be expressed as:
in the formula, H hr (t)，H b (t)，H ab (t)，
The thermal power of PGU waste heat recovery, the thermal power generated by the gas boiler, the thermal power input by the absorption refrigerator and the hot water load of the user are respectively at the moment t.
The operation power constraint and the climbing constraint of the gas internal combustion generator are as follows:
P PGU,min ≤E PGU (t)≤P PGU,rc
in the formula (I), the compound is shown in the specification,
respectively representing the maximum power-up per unit time and the maximum power-down per unit time.
The upper and lower limits of operation of each device are constrained as follows:
0≤C ab (t)≤P ab_rc
0≤C ec (t)≤P ec_rc
0≤H b (t)≤P b_rc
and introducing a weight coefficient mu E [0,1], converting an objective function of the model into a single objective form shown in the specification, comparing the objective function values of all decision variables, and finally finding the objective function value with the minimum midpoint value and the minimum interval width so as to find the optimal decision variable. The objective function is obtained by the interval programming theory:
the system objective function contains the number of intervals of the power generation amount of the wind power generation system, and considering that the operation and maintenance cost of the wind power generation system is low, the lower limit value of the objective function corresponds to the upper limit value of the wind power generation system, and correspondingly, the upper limit value of the objective function corresponds to the lower limit value of the wind power generation system. Due to the existence of system equipment model nonlinearity, the optimization is a nonlinear optimization problem, and a problem optimal solution solved by an optimization method is needed. The following can be obtained:
from the above, the interval objective function can be converted into a deterministic objective function. For the cold and heat energy balance equality constraint containing the interval number, the following can be obtained:
and representing the satisfaction degree of the constraint conditions by using the interval probability lambda, and establishing an interval planning and scheduling model of the renewable energy CCHP system. The restriction on the cold and heat balance can be obtained,
the following can be obtained:
for the constraint of electric power balance, the interval planning theory can obtain:
in the algorithmic solution, the constraints are processed with penalty functions to complete the system optimization solution with interband constraints. The objective function and the constraint comprise uncertainty, and the uncertainty is multi-objective optimization represented by the interval, so that a scheduling worker can adjust the weight coefficient and the value of the interval possibility parameter according to the actual condition to obtain a scheduling result giving consideration to energy benefits, environmental benefits, economic benefits and system reliability.
And (3) feedback correction: the error prediction is carried out on the renewable energy output and the cooling, heating and power loads, and the error prediction value is used for compensating the prediction value on the basis of the ultra-short-term source load prediction, so that the system scheduling error can be further reduced. In order to reduce the influence of errors on the optimized operation of the system, the real-time adjustment method based on error prediction is provided, and the renewable energy source and load predicted values are corrected correspondingly according to the predicted values of the errors. Therefore, the rolling optimization is not only based on the model, but also utilizes the feedback information, and can correct the scheduling instruction in time to form closed loop optimization, so that the system has good robustness to interference.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (8)
1. A CCHP system optimized operation method based on predictive control and interval planning is characterized in that: the method comprises the following steps:
acquiring historical operation data of a renewable energy source combined cooling heating and power system;
interval prediction is carried out on renewable energy sources and loads to obtain wind power, a predicted value of the cold, heat and power loads and an interval value; quantifying fluctuation of source and load values caused by uncertainty factors, and enabling the actual output of the renewable energy sources and the actual cooling, heating and power load to fall within an interval formed by upper and lower limit predicted values of the interval under a certain confidence coefficient;
performing error prediction on renewable energy output and cooling, heating and power loads, and compensating a predicted value by using an error predicted value on the basis of prediction of a Gaussian process regression interval;
comprehensively considering the economy, the energy utilization rate and the environmental benefits of the renewable energy combined cooling heating and power system, constructing a target function containing interval numbers, and performing rolling optimization solution on the target function under the constraint condition to obtain a system equipment output value; the objective function f for system optimization is as follows:
where i is the number of predictors, M is the total number of predictors, ω 1 ，ω 2 ，ω 3 The weight coefficients are economic evaluation indexes, energy evaluation indexes and environment evaluation indexes in the renewable energy source combined cooling heating and power system respectively; CSR represents economic cost, PESR i Indicating the first energy saving rate, CERR, of the ith prediction value i A carbon emission reduction rate representing the ith predicted value;
interval number of wind power generation capacity
The operation and maintenance cost in the economic cost CSR is the interval number, and is shown as follows:
wherein, W PGU Is the generated energy of the internal combustion generator set; r PGU The operating maintenance unit price of the PGU of the internal combustion generator set is as follows; w ab Is the absorption chiller energy consumption; r is ab Is the unit price of operation and maintenance of the absorption refrigerator; w ec Is the energy consumption of the electric refrigerator;
is the number of the intervals of the wind power generation amount,
the operation and maintenance cost is the interval number, eco sp Is the economic cost of the separate supply system, R ec Is the unit price of the operation and maintenance of the electric refrigerator; w b Is the energy consumption of the gas boiler; r b Is the unit price of operation and maintenance of the gas boiler; w wt Is the wind power generation amount; r wt The method is the unit price of operation and maintenance of the wind power generation system; c F Is the system fuel cost; c R The operation and maintenance cost of the CCHP system is the renewable energy source; eco is the system economic cost;
introducing a weight coefficient, converting the interior of the objective function into a single objective form, comparing the objective function value of each decision variable, and finally finding out the objective function value with the minimum midpoint value and the minimum interval width so as to find out the optimal decision variable; converting the interval target function into a deterministic target function, and establishing an interval planning and scheduling model of the CCHP system according to the satisfaction degree of the interval possibility degree representation constraint condition; and processing the constraint by using a penalty function to complete the optimization solution of the interval planning scheduling model of the CCHP system with the interval constraint, so as to realize the optimized operation of the CCHP system.
2. The CCHP system optimal operation method based on predictive control and interval planning as claimed in claim 1, wherein: and in the interval prediction process of the renewable energy and the load, gaussian process regression region prediction is adopted.
3. The CCHP system optimal operation method based on predictive control and interval planning as claimed in claim 1, wherein: in the specific process of constructing the objective function containing the interval number, various indexes in the objective function are weighted, and on the basis of meeting the cooling, heating and power loads of users and the operation constraints of all devices of the system, the renewable energy source combined cooling, heating and power system is subjected to interval multi-objective optimization to obtain the best comprehensive index.
4. The CCHP system optimized operation method based on predictive control and interval planning as claimed in claim 1, wherein: considering the economics of the renewable energy combined cooling heating and power system includes considering a cost saving rate equal to: the difference value of the economic cost of the sub-supply system and the economic cost of the system is compared with the ratio of the economic cost of the sub-supply system;
the economic cost of the sub-supply system comprises the cost of electricity and gas of the sub-supply system;
the system economic cost comprises the operation maintenance cost and the fuel cost of the renewable energy source combined cooling heating and power system;
or/and considering the energy utilization rate of the renewable energy combined cooling heating and power system, specifically considering the primary energy saving rate of the system, wherein the primary energy saving rate is equal to: the ratio of the difference between the energy cost of the sub-supply system and the energy cost of the system to the energy cost of the sub-supply system;
or/and considering the environmental benefit of the renewable energy combined cooling heating and power system, specifically considering the carbon emission reduction rate of the system, wherein the carbon emission reduction rate is equal to the ratio of the difference between the environmental cost of the separate supply system and the environmental cost of the system to the environmental cost of the separate supply system.
5. The CCHP system optimal operation method based on predictive control and interval planning as claimed in claim 1, wherein: the constraint conditions comprise a cold-heat-electricity energy balance constraint and an operation constraint of each device.
6. A CCHP system optimized operation system based on predictive control and interval planning is characterized in that: the method comprises the following steps:
the source load prediction module is configured to perform interval prediction on renewable energy sources and loads according to historical operating data of the renewable energy source combined cooling heating and power system to obtain wind power, a predicted value of the cooling heating and power load and an interval value; quantifying the fluctuation of source and load values caused by uncertain factors, and enabling the actual output of the renewable energy and the actual cooling, heating and power loads to fall in an interval formed by the upper limit and the lower limit of an interval predicted value under a certain confidence coefficient;
the feedback correction module is configured to carry out error prediction on the renewable energy output and the cooling, heating and power loads and compensate a predicted value by using an error predicted value on the basis of prediction of a Gaussian process regression interval;
the rolling optimization module is configured to comprehensively consider the economy, the energy utilization rate and the environmental benefit of the renewable energy combined cooling heating and power system, construct an objective function containing intervals, and perform rolling optimization solution on the objective function under the constraint condition to obtain a system equipment output value; the objective function f for system optimization is as follows:
where i is the number of predicted values, M is the total number of predicted values, ω 1 ，ω 2 ，ω 3 The weight coefficients of an economic evaluation index, an energy evaluation index and an environmental evaluation index in the renewable energy combined cooling heating and power system are respectively; CSR denotes economic cost, PESR i Representing the Primary energy saving Rate, CERR, of the ith predictive value i A carbon emission reduction rate representing the ith predicted value;
the interval number of the wind power generation capacity
The operation and maintenance cost in the economic cost CSR is the interval number, and is shown as follows:
wherein, W PGU Is the generated energy of the internal combustion generator set; r is PGU The PGU operation maintenance unit price of the internal combustion generator set; w ab Is the absorption chiller energy consumption; r ab Is the unit price of operation and maintenance of the absorption refrigerator; w ec Is the energy consumption of the electric refrigerator;
is the interval number of the wind power generation amount,
is that the operation and maintenance cost is the number of intervals, eco sp Is the economic cost of the separate supply system, R ec Is the unit price of operation and maintenance of the electric refrigerator; w b Is the energy consumption of the gas boiler; r b Is the unit price of operation and maintenance of the gas boiler; w is a group of wt Is the wind power generation amount; r wt The unit price of the operation and maintenance of the wind power generation system; c F Is the system fuel cost; c R The operation and maintenance cost of the CCHP system is the renewable energy source; eco is the system economic cost;
introducing a weight coefficient, converting the interior of the objective function into a single objective form, comparing the objective function value of each decision variable, and finally finding out the objective function value with the minimum midpoint value and the minimum interval width so as to find out the optimal decision variable; converting the interval target function into a deterministic target function, and establishing an interval planning and scheduling model of the CCHP system according to the satisfaction degree of the interval possibility degree representation constraint condition; and processing the constraint by using a penalty function to complete the optimization solution of the interval planning scheduling model of the CCHP system with the interval constraint, so as to realize the optimized operation of the CCHP system.
7. A computer-readable storage medium, comprising: a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the CCHP system optimized operation method based on the prediction control and interval planning in any one of claims 1-5.
8. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the CCHP system optimized operation method based on the prediction control and interval planning in any one of claims 1-5.
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