CN111463836B - Comprehensive energy system optimal scheduling method - Google Patents
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Abstract
The invention discloses an optimized dispatching method of a comprehensive energy system, which comprises the following steps: acquiring predicted values of daily cold, hot, electric loads and photovoltaic output; generating an error scene set S according to the predicted error probability distribution of cold, heat, electric load and photovoltaic output; reducing the error scene set to obtain a typical error scene; overlapping the typical error scene with the predicted values of the cold, hot, electric loads and the photovoltaic output to obtain a typical scene of the cold, hot and electric loads and the photovoltaic output; constructing an equipment mathematical model of the comprehensive energy system; constructing an optimized dispatching model of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system; inputting the typical scene into an optimized scheduling model of the comprehensive energy system, and solving by adopting an NSGA-II multi-objective algorithm to obtain a pareto optimal solution set; and selecting an optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
Description
Technical Field
The invention belongs to the technical field of energy utilization methods, and relates to an optimized scheduling method of a comprehensive energy system.
Background
With the increasing severity of energy crisis and global warming, research for improving the utilization efficiency of traditional energy and clean energy has gained widespread attention from domestic and foreign students. The comprehensive energy system takes combined supply equipment (a gas unit and an absorption refrigerator) as a core, comprises a plurality of distributed units (power generation, load, energy storage and the like), and has multiple energy forms of cold, heat, electricity and the like. The comprehensive energy system is established on the basis of energy cascade utilization, utilizes primary energy to drive a generator to generate power, and then recovers waste heat through various waste heat utilization devices. The energy utilization rate is improved, and the energy-saving environment-friendly energy-saving system has lower energy cost, higher safety and better environment friendliness. In addition, the comprehensive energy system can be combined with wind power and photovoltaic for the uncertainty and intermittence of clean renewable energy sources such as wind power and photovoltaic, and effective support is provided for development and utilization of distributed renewable energy sources.
FIG. 1 is a diagram of a combined energy system architecture model and energy flow. In the figure, the gas unit takes natural gas as fuel to provide power for users, and simultaneously, heat carried by high-temperature flue gas and cylinder sleeve water generated by the gas unit can be transmitted to an absorption refrigerator and a heat exchange device to meet the cold and heat load demands of the users, and the proportion of the waste heat of the gas unit for refrigeration and heating is determined by the waste heat distribution ratio. In addition, photovoltaic and electric energy storage also participate in the supply of electric energy, if the electric energy provided by the gas turbine unit and the storage battery can not meet the electric power requirement of a user, the insufficient electric power can be supplemented by an urban power grid at the moment; the heat storage tank can perform heat storage and release operation according to the needs so as to ensure the heat supply of the system; if the waste heat provided by the gas generator set and the heat output of the heat storage tank can not meet the heat load demand of a user and the refrigeration power output by the absorption refrigerator can not meet the cold load demand, the gas boiler and the electric refrigerator set can supply heat and supplement cold.
Currently, the most widely used operation strategies of the comprehensive energy system are an electric heating operation strategy and a heat heating operation strategy. However, when an electrically-powered and thermally-powered operating strategy is adopted, the system may generate excessive heat, and for an independent co-generation system, the heat is directly discharged to the environment; when the hot fixed-electricity operation strategy is adopted, redundant electric power can be generated by the system, and at present, the small-sized power generation system in China cannot perform grid-connected power generation, so that the waste of electric energy is caused. Furthermore, in the operation scheduling of integrated energy systems, there are different types of uncertainties on both the supply and demand sides, such as uncertainties in renewable energy availability and energy demand. If such uncertainty is not addressed, the system may run off the optimal way of running. Therefore, scholars propose various methods, mainly including a robust optimization method and an interval optimization method using interval prediction information, an opportunity constraint planning method and a scene optimization method using probability prediction information, and a rolling optimization method. The modeling thought of robust optimization requires that decisions are feasible under the worst condition of uncertain variables and the objective function is optimal, however, the actual occurrence probability of the worst condition is extremely low, so that a scheduling plan obtained by optimization has certain conservation; when the interval optimization method is used, the scenes in interval optimization contain the worst scenes, so that the decision result has certain conservation; the probability constraint planning method requires that a random constraint condition is established at least with a certain confidence level, and is essentially that probability constraint is utilized to replace traditional determination constraint, constraint is not met under a smaller probability, so that the condition that the running economy of the system is influenced for coping with extreme wind power deviation with small probability is avoided, but the selection of the confidence level is subjective; the method for controlling rolling scheduling by model prediction has higher requirement on calculation speed and is difficult to combine with the multi-objective problem.
Disclosure of Invention
The invention aims to provide an optimization scheduling method for a comprehensive energy system, which solves the problem that uncertainty errors in the comprehensive energy system are not considered in the prior art to influence the optimization effect.
The technical scheme adopted by the invention is that the comprehensive energy system optimizing and scheduling method comprises the following steps:
step 1, obtaining predicted values of cold, heat, electric loads and photovoltaic output of each day;
step 2, generating an error scene set S according to the predicted error probability distribution of cold, heat, electric load and photovoltaic output;
step 3, reducing the error scene set to obtain a typical error scene;
step 4, overlapping the typical error scene with the predicted values of the cold, hot, electric loads and the photovoltaic output to obtain a typical scene of the cold, hot and electric loads and the photovoltaic output;
step 5, constructing an equipment mathematical model of a comprehensive energy system, wherein the equipment of the comprehensive energy system comprises a gas unit, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device and any combination of a plurality of electric refrigerators;
step 6, constructing an optimized dispatching model of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system, and taking the minimum running cost of the system and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as an objective function;
step 7, inputting the typical scene into an optimized scheduling model of the comprehensive energy system, and solving by adopting an NSGA-II multi-objective algorithm to obtain a pareto optimal solution set;
and 8, selecting an optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
The invention is also characterized in that:
the step 2 is specifically as follows: generating s error scenes by using a Latin hypercube sampling method according to the error probability distribution, wherein the dimension of each scene is p=4×H, and H is a time scale.
The step 2 specifically comprises the following steps:
step 2.1, recording prediction error v u Is F as the probability distribution function of (2) u (v u ) Where u=1, 2, p.
Step 2.2, the probability distribution function is F u (v u ) Is a range of values of (a)
Dividing into s equal probability intervals;
step 2.3, randomly selecting one q for the jth probability interval [ (j-1)/s, j/s) j Satisfy q j = (j-1+r)/s, where r is [0,1]Random variables uniformly distributed in interval, let
Step 2.4, obtaining samples through inverse transformation of normal distribution
Step 2.5, repeating the steps 2.1-2.4 to obtain a u-th dimension prediction error v u S samples of each probability interval;
step 2.6, repeating the steps 2.1-2.5 to obtain a u-dimensional prediction error v u Then predict the error v from each dimension u Randomly extracting a sample from the sample values of the sequence to form a vector, so as to obtain an error scene;
and 2.7, repeating the steps 2.1-2.6 to obtain an error scene set S comprising S error scenes.
The step 3 specifically comprises the following steps:
step 3.1, taking the clustering number k, wherein k=2, 3,4,5 and …;
step 3.2, randomly initializing k clustering centers L, l= { L 1 ,l 2 ,…,l k }；
Step 3.3, calculating the distances from each error scene V to k clustering centers, and distributing all error scenes V to the clustering centers closest to the error scenes according to the distance minimum principle to form k clusters C i ，i＝1,2,…k；
Step 3.4, recalculating the mass center of each cluster;
step 3.5, repeating the steps 3.3-3.4 until the centroid position is not changed, and recording the final centroid position of each cluster as L 0 ；
Step 3.6, calculate L 0 And when the index is a clustering center, the index is PFS index of the error scene set S.
Step 3.7, repeating the steps 3.2-3.6, and recording the clustering centers L when different k values are recorded 0 PFS index until the value of k is greater than
When in use;
step 3.8, taking the clustering number k corresponding to the maximum PFS index as the reduced scene number k 0 And corresponding cluster center coordinates L 0 A typical set of error scenes is composed.
The step 6 specifically comprises the following steps:
step 6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the running cost of the system in the first objective function and the scheduling period is the smallest:
in the above, f 1 P is the total operation cost of the integrated energy system w Probability of w scene, T is period of scheduling period, k 0 To reduce the number of scenes, F gas (t,w)、F grid (t,w)、F op And (t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation maintenance cost of the t-period comprehensive energy system in a w scene, and the expression is as follows:
in the above, C gas Is the price of natural gas, C grid The price of the commercial power is the price; c (C) mt For the operation and maintenance cost of the gas unit, C pv C is the operation maintenance cost of the photovoltaic power generation device ac For the operation and maintenance cost of the absorption refrigerator, C er The operation and maintenance cost of the electric refrigerator C es For the operation and maintenance cost of the storage battery, C hs For the operation and maintenance cost of the heat storage tank, P grid (t) is the power purchased by the power grid, P es The charge and discharge power of the storage battery; q (Q) hs The charge and discharge power of the heat storage tank;
the total carbon dioxide emission of the objective function II and the comprehensive energy system is minimum:
in the above, f 2 The total carbon dioxide emission of the comprehensive energy system;
carbon dioxide displacement for gas production, +.>
For the equivalent carbon dioxide emission of the electric quantity purchased by the power grid, the calculation formula is as follows:
in the above-mentioned method, the step of,
is the carbon dioxide conversion coefficient of natural gas, +.>
Is the carbon dioxide conversion coefficient of the commercial power.
Step 6.2, constraint conditions are as follows:
in the above, Q load.h (t) is the thermal load of the user for the period t, Q load.c (t) is the cooling load of the user for period t; p (P) op (t) is the operation electric power of the comprehensive energy system; p (P) er (t) is the electricity consumption of the electric refrigerator; p (P) load (t) is electrical load power other than the electric refrigerator; p (P) grid (t) purchasing power for a power grid;
and 6.3, constructing an energy flow calculation model according to constraint conditions and a comprehensive energy system structure, wherein the energy flow calculation model is constructed as follows:
wherein k is op The self-electricity coefficient of the system;
and 6.4, selecting decision variables of all time scales in the scheduling period.
The decision variable comprises the power P of the gas unit in each hour in the cycle mt Waste heat distribution ratio K of gas unit mt Charge-discharge power P of storage battery es Heat storage and release power P of heat storage tank hs 。
The step 8 specifically comprises the following steps:
step 8.1, constructing a set matrix X= (X) by using the Pareto optimal solution set ij ) m×n M is population number, n is target number, and weight w is calculated by entropy method j ；
Step 8.2, combining the target weights w j And selecting an optimal solution by using a TOPSIS method to obtain an optimal operation scheme.
The beneficial effects of the invention are as follows:
according to the comprehensive energy system optimal scheduling method, the actual value of the comprehensive energy system is obtained by combining the predicted values of cold, heat, electric load and photovoltaic output with the uncertainty prediction error, and the actual value is input into an optimal model of the comprehensive energy system to obtain an optimal operation scheme; the operation scheme can reduce the operation cost and the carbon dioxide emission of the comprehensive energy system, improve the energy utilization rate of natural gas, promote the new energy consumption of photovoltaics and the like, and fully exert the advantages of the comprehensive energy system in the aspects of energy cascade utilization, economy and environmental protection and promotion of the new energy consumption.
Drawings
FIG. 1 is a composite energy system structural model and energy flow diagram;
FIG. 2 is a flow chart of an integrated energy system optimization scheduling method of the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
An optimized dispatching method of a comprehensive energy system, as shown in fig. 2, comprises the following steps:
step 1, obtaining predicted values of cold, heat and electric loads and photovoltaic output of the next day; in this embodiment, the time scale H is one hour, and a total of 4×24 predicted power data are required.
Step 2, generating an error scene set S by using a Latin hypercube sampling method according to the predicted error probability distribution of cold, hot, electric loads and photovoltaic output;
setting the number s of error scenes contained in the generated error scene set, wherein the value of s is generally larger, such as 1000, in order to ensure the precision;taking one hour as a time scale, the dimension p=4x24 of each scene; the ith scene is denoted as V i ＝[v 1 ,v 2 ,...,v 96 ]Wherein v is 1 -v 24 、v 25 -v 48 、v 49 -v 72 Respectively representing the prediction error of cold, hot and electric loads of each hour, v 73 -v 96 Representing the photovoltaic output prediction error of each hour; the specific error scene generation steps are as follows:
step 2.1, recording prediction error v u Is F as the probability distribution function of (2) u (v u ) Where u=1, 2, p. v u As the u dimension of the scene, according to the difference of u values, the prediction error of cold, heat, electric load or photovoltaic output in a certain hour is represented;
step 2.2, the probability distribution function is F u (v u ) Is a range of values of (a)
Dividing into s equal probability intervals, wherein ∈>
V is u Upper and lower limits of the value;
step 2.3, randomly selecting one q for the jth probability interval [ (j-1)/s, j/s) j Satisfy q j = (j-1+r)/s, where r is [0,1]Random variables uniformly distributed in interval, let
Step 2.4, obtaining samples through inverse transformation of normal distribution
Obtaining the sampling point of the jth probability interval of the ith dimension;
step 2.5, repeating the steps 2.1-2.4 to obtain a u-th dimension prediction error v u S samples of each probability interval;
step 2.6, repeating the steps 2.1-2.5 to obtain samples of the u-dimensional prediction error, and then from eachDimension prediction error v u Randomly extracting one sample from the sample values of (1) to form a vector to obtain an error scene V i ＝[v 1 ,v 2 ,...,v 96 ]；
Step 2.7, repeating the steps 2.1-2.6 to obtain an error scene set S= [ V ] comprising S error scenes 1 ,V 2 ,...,V s ] T 。
Step 3, reducing an error scene set S by using an improved K-means clustering algorithm provided herein to obtain a typical error scene set; the method comprises the following specific steps:
step 3.1, taking the clustering number k, wherein k=2, 3,4,5 and …;
step 3.2, randomly initializing k clustering centers L, l= { L 1 ,l 2 ,…,l k }；
Step 3.3, calculating the distances between each sample point (namely the error scene V) and k clustering centers respectively, and distributing all the error scenes V to the clustering centers closest to the sample point according to the distance minimum principle to form k clusters C i ，i＝1,2,…k；
Step 3.4, recalculating the mass center of each cluster (namely the average value of each cluster of samples);
step 3.5, repeating the steps 3.3-3.4 until the centroid position is not changed, and recording the final centroid position of each cluster as L 0 ；
Step 3.6, calculate L 0 And (3) when the error scene set is a clustering center, the PFS index of the error scene set obtained in the step (2.7).
Specifically, the error scene set S and the clustering center L 0 ＝{l 1 ,l 2 ,…,l k Carry into the following equation, calculate PFS index:
in the above-mentioned method, the step of,
for matrix->
Track of->
For matrix->
S is the number of sample values, i.e. the number of error scenes generated, k is the number of clusters, < ->
Inter-class, +.>
The intra-class scatter matrix for the p-dimensional variable samples is expressed as follows:
in the above, V j For one sample vector, i.e. the j-th error scene V in the set of error scenes S j ＝[v 1 ,v 2 ,...,v 96 ]，l i For the ith cluster C i Is used for the clustering center of the (c),
representation l i Transpose of mu ij The expression of (2) is as follows:
step 3.7, repeating the steps 3.2-3.6, and recording the clustering centers L when different k values are recorded 0 PFS index until the value of k is greater than
Step 3.8, taking the corresponding cluster number k when the PFS index is maximum as the reduced scene number k 0 And will shrinkSubtracted cluster center coordinate L 0 A typical set of error scenes is composed.
Step 4, overlapping the typical error scene with the predicted values of the cold, hot and electric loads and the photovoltaic output to obtain a typical cold, hot and electric load and a photovoltaic output scene;
step 5, constructing an equipment mathematical model of a comprehensive energy system, wherein the equipment of the comprehensive energy system comprises a gas internal combustion unit, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device, an electric refrigerator and a gas boiler, which can be selected according to actual conditions;
step 5.1, constructing a mathematical model of the internal combustion engine of the combustion engine:
in the above, eta mtP (t) is the power generation efficiency, eta of the gas turbine set in the period of t mtQ (t) is the waste heat efficiency of the gas turbine set in the period t, f is the load rate of the internal combustion engine of the gas turbine, a 1 、a 2 、a 3 、b 1 、b 2 、b 3 Is the efficiency coefficient of the internal combustion engine of the combustion engine, V mt Is the natural gas consumption of the gas unit, m 3 ；P mt For generating power of gas unit, Q mt The waste heat power of the gas unit is kW; l (L) gas Is the heat value of natural gas, (kW.h)/m 3 ；
Step 5.2, constructing a mathematical model of the storage battery and the heat storage tank:
in the above, S ES (t)、S HS (t) is the residual energy of the storage battery and the heat storage tank in the period t, kW.h; p (P) es (t)、P es (t) storage battery and storage battery for t periodCharging and discharging power of the hot tank, kW, a negative value represents energy storage, and a positive value represents energy discharging. τ ES Is the loss coefficient of energy storage; Δt is the unit scheduling time; η (eta) ES.chr 、η HS.chr Energy input conversion efficiency of storage battery and heat storage tank, eta ES.dis 、η HS.dis The energy output conversion efficiency of the storage battery and the heat storage tank is achieved;
step 5.3, constructing a mathematical model of the absorption refrigerator:
in the above, Q ac The refrigerating power of the bromine cooler is kW; COP of ac The refrigerating coefficient of the bromine cooler;
step 5.4, constructing a mathematical model of the gas boiler and the heat exchange device:
in the above, Q gb Heating power of the gas boiler is kW; v (V) gb For gas consumption, m 3 ；η gb Efficiency of gas boiler, eta ex For heat exchanger efficiency, Q ex The heat output of the heat exchanger is kW;
step 5.5, constructing a mathematical model of the electric refrigerator:
in the above, Q er Is the refrigerating power, kW and P of the electric refrigerator er Is the power consumption, kW and COP of the electric refrigerator er Is the energy efficiency ratio of the electric refrigerator.
Step 6, constructing an optimized dispatching model of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system, and taking the minimum running cost of the system and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as an objective function;
step 6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the running cost of the system in the first objective function and the scheduling period is the smallest:
in the above, f1 is the total operation cost of the integrated energy system, P w Probability of w scene, T is period of scheduling period, k 0 To reduce the number of scenes, F gas (t,w)、F grid (t,w)、F op And (t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation and maintenance cost of the t-period comprehensive energy system in a w scene, wherein the meta/kW is expressed as follows:
in the above, C gas For natural gas price, yuan/m 3 ，C grid Is the price of commercial power, yuan/kW.h; c (C) mt For the operation and maintenance cost of the gas unit, C pv C is the operation maintenance cost of the photovoltaic power generation device ac For the operation and maintenance cost of the absorption refrigerator, C er The operation and maintenance cost of the electric refrigerator C es For the operation and maintenance cost of the storage battery, C hs For the operation and maintenance cost, yuan/kW, P of the heat storage tank grid (t) the power purchased by the power grid in the period t, wherein kW.h; p (P) es Charging and discharging power of the storage battery is kW; q (Q) hs Charging and discharging power of the heat storage tank, kW;
the total carbon dioxide emission of the objective function II and the comprehensive energy system is minimum:
in the above, f 2 The total carbon dioxide emission of the comprehensive energy system;
carbon dioxide displacement for gas production, +.>
The equivalent carbon dioxide emission quantity for the electric quantity purchased by the power grid is kg, and the calculation formula is as follows: />
In the above-mentioned method, the step of,
is the carbon dioxide conversion coefficient of natural gas, kg/m 3 ,
Is the carbon dioxide conversion coefficient of commercial power, kg/kWh.
Step 6.2, constraint conditions are as follows:
in the above, Q load.h (t) is the thermal load of the user for the period t, Q load.c (t) is the cooling load of the user for period t, kW; p (P) op (t) is the operation electric power of the comprehensive energy system, kW; p (P) er (t) is the electricity consumption of the electric refrigerator, kW; p (P) load (t) is electric load power other than the electric refrigerator, kW; p (P) grid And (t) the power purchased by the power grid, and kW.
And 6.3, constructing an energy flow calculation model according to constraint conditions and a comprehensive energy system structure, wherein the energy flow calculation model is constructed as follows:
wherein k is op Is self-contained in the systemA power consumption number;
the purpose of step 6.4, the method, is to find the operating scheme that minimizes the daily operating costs of the system, i.e. the operating conditions of the system for each hour of each device in the scheduling period, while meeting the load demands and other constraints. For the purpose of reducing solving difficulty, taking the structural characteristics of the system shown in the figure I into consideration, the power P of the gas turbine generator set in each hour in the scheduling period is selected mt Waste heat distribution ratio (namely valve opening) K of gas unit mt Charge-discharge power P of storage battery es Heat storage and release power P of heat storage tank hs As a decision variable; once the decision variables are determined, the operating scheme for each device can be obtained by the energy flow calculation model of equation (15).
Step 7, inputting a typical scene into an optimized scheduling model of the comprehensive energy system, solving by adopting an NSGA-II multi-objective algorithm, setting the population size m=200 and the iteration number as 2000, and obtaining a pareto optimal solution set;
and 8, selecting an optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
Step 8.1, constructing a solution set matrix X= (X) by utilizing the Pareto optimal solution set ij ) m×n M is the number of alternatives (i.e. population number), the target number n=2, and the weight w is calculated by using the entropy method j The method comprises the following steps:
step 8.1.1, calculating a normalized matrix r= (R) ij ) m×n Wherein:
step 8.1.2, calculating entropy value e of each target information j
In the above formula, k=1/ln (m), and h is recorded j ＝1-e j ；
Step 8.1.3, then each target weight w j ：
Step 8.2, combining the target weights w j The best solution is selected by using a TOPSIS method, and the best operation scheme is obtained, and the specific steps are as follows:
step 8.2.1, obtaining a normalized decision matrix Y= (Y) by adopting a vector normalization method ij ) m×n Wherein:
step 8.2.2, constructing a weighted normalized matrix z= (z) ij ) m×n Wherein:
z ij ＝w j y ij ,i＝1,2...,m；j＝1,2...,n (20)；
step 8.2.3 determining the Positive ideal solution A + And negative ideal solution A - ：
Definition of the definition
Wherein,,
step 8.2.4, calculating Euclidean distance of each scheme to positive ideal solution and negative ideal solution respectively
And->
Step 8.2.5, calculating the schemesComprehensive evaluation index
Step 8.2.6 according to
And selecting the optimal solution from the good and bad sequences of the arrangement schemes from big to small to obtain the optimal operation scheme.
Through the mode, the comprehensive energy system optimizing and scheduling method obtains the actual value by combining the predicted values of the cold, heat, electric load and photovoltaic output and the uncertainty prediction error, and inputs the actual value into an optimizing model of the comprehensive energy system to obtain the optimal operation scheme; the operation scheme can reduce the operation cost and the carbon dioxide emission of the comprehensive energy system, improve the energy utilization rate of natural gas, promote the new energy consumption of photovoltaics and the like, and fully exert the advantages of the comprehensive energy system in the aspects of energy cascade utilization, economy and environmental protection and promotion of the new energy consumption.
Claims (7)
1. The comprehensive energy system optimal scheduling method is characterized by comprising the following steps of:
step 1, obtaining predicted values of cold, heat, electric loads and photovoltaic output of each day;
step 2, generating an error scene set S according to the predicted error probability distribution of cold, heat, electric load and photovoltaic output;
step 3, reducing the error scene set to obtain a typical error scene;
step 4, superposing the typical error scene with the predicted values of the cold, hot, electric loads and the photovoltaic output to obtain a typical scene of the cold, hot and electric loads and the photovoltaic output;
step 5, constructing an equipment mathematical model of a comprehensive energy system, wherein the equipment of the comprehensive energy system comprises a gas unit, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device and any combination of a plurality of electric refrigerators;
step 6, constructing an optimized dispatching model of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system, and taking the minimum running cost of the system and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as an objective function;
step 7, inputting the typical scene into an optimized scheduling model of the comprehensive energy system, and solving by adopting an NSGA-II multi-objective algorithm to obtain a pareto optimal solution set;
and 8, selecting an optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
2. The optimal scheduling method for the comprehensive energy system according to claim 1, wherein the step 2 is specifically: generating s error scenes by using a Latin hypercube sampling method according to the error probability distribution, wherein the dimension of each scene is p=4×H, and H is a time scale.
3. The method for optimizing and scheduling an integrated energy system according to claim 2, wherein step 2 specifically comprises:
step 2.1, recording prediction error v u Is F as the probability distribution function of (2) u (v u ) Where u=1, 2, p.
Step 2.2, the probability distribution function is F u (v u ) Is a range of values of (a)
Dividing into s equal probability intervals;
step 2.3, for the jth probability interval [ (j-1)/s, j/s ]]Randomly select a q j Satisfy q j = (j-1+r)/s, where r is [0,1]Random variables uniformly distributed in interval, let
Step 2.4 inverse of the normal distributionTransforming to obtain a sample
Step 2.5, repeating the steps 2.1-2.4 to obtain a u-th dimension prediction error v u S samples of each probability interval;
step 2.6, repeating the steps 2.1-2.5 to obtain a u-dimensional prediction error v u Then predict the error v from each dimension u Randomly extracting a sample from the sample values of the sequence to form a vector, so as to obtain an error scene;
and 2.7, repeating the steps 2.1-2.6 to obtain an error scene set S comprising S error scenes.
4. The method for optimizing and scheduling an integrated energy system according to claim 1, wherein step 3 specifically comprises:
step 3.1, taking the clustering number k, wherein k=2, 3,4,5 and …;
step 3.2, randomly initializing k clustering centers L, l= { L 1 ,l 2 ,…,l k }；
Step 3.3, calculating the distances from each error scene V to k clustering centers, and distributing all error scenes V to the clustering centers closest to the error scenes according to the distance minimum principle to form k clusters C i ，i＝1,2,…k；
Step 3.4, recalculating the mass center of each cluster;
step 3.5, repeating the steps 3.3-3.4 until the centroid position is not changed, and recording the final centroid position of each cluster as L 0 ；
Step 3.6, calculate L 0 When the error scene set S is a clustering center, the PFS index of the error scene set S is obtained;
step 3.7, repeating the steps 3.2-3.6, and recording the clustering centers L when different k values are recorded 0 PFS index until the value of k is greater than
When in use; />
Step 3.8, taking the maximum PFS indexThe clustering number k corresponding to the time is used as the reduced scene number k 0 And corresponding cluster center coordinates L 0 A typical set of error scenes is composed.
5. The method for optimizing and scheduling an integrated energy system according to claim 1, wherein step 6 specifically comprises:
step 6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the running cost of the system in the first objective function and the scheduling period is the smallest:
in the above, f1 is the total operation cost of the integrated energy system, P w Probability of w scene, T is period of scheduling period, k 0 To reduce the number of scenes, F gas (t,w)、F grid (t,w)、F op And (t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation maintenance cost of the t-period comprehensive energy system in a w scene, and the expression is as follows:
in the above, C gas Is the price of natural gas, C grid The price of the commercial power is the price; c (C) mt For the operation and maintenance cost of the gas unit, C pv C is the operation maintenance cost of the photovoltaic power generation device ac For the operation and maintenance cost of the absorption refrigerator, C er The operation and maintenance cost of the electric refrigerator C es For the operation and maintenance cost of the storage battery, C hs For the operation and maintenance cost of the heat storage tank, P grid (t) is the electricity purchasing quantity of the power grid in the period t, P es The charge and discharge power of the storage battery; q (Q) hs Is the charge and discharge power of the heat storage tank, V mt (t) is the natural gas consumption of the gas unit in the period t; p (P) mt (t) is t period gas unitV of (2) gb (t) is the air consumption in t period, P pv (t) is the generated power of the photovoltaic power generation device in the t period;
the total carbon dioxide emission of the objective function II and the comprehensive energy system is minimum:
in the above, f 2 The total carbon dioxide emission of the comprehensive energy system; f (F) co2,gas (t, w) is the carbon dioxide displacement generated by the fuel gas, F co2,grid And (t, w) is the equivalent carbon dioxide emission of the purchased electric quantity of the power grid, and the calculation formula is as follows:
in the above, K co2,gas Is the carbon dioxide conversion coefficient, K of natural gas co2,grid The carbon dioxide conversion coefficient of the commercial power;
step 6.2, constraint conditions are as follows:
in the above, Q load.h (t) is the thermal load of the user for the period t, Q load.c (t) is the cooling load of the user for period t; p (P) op (t) is the operation electric power of the comprehensive energy system; p (P) er (t) is the electricity consumption of the electric refrigerator; p (P) load (t) is electrical load power other than the electric refrigerator; p (P) grid (t) purchasing power for a power grid; and 6.3, constructing an energy flow calculation model according to constraint conditions and a comprehensive energy system structure, wherein the energy flow calculation model is constructed as follows:
wherein k is op Is the self-electricity coefficient of the system, eta mtP Is the power generation efficiency eta of the gas unit mtQ Waste heat efficiency, COP of gas unit ac Is the refrigerating coefficient eta of the bromine cooler ex For efficiency of heat exchanger, COP er Is the energy efficiency ratio, k of the electric refrigerator mt The waste heat distribution ratio of the gas unit is that of the gas unit;
and 6.4, selecting decision variables of all time scales in the scheduling period.
6. The optimal scheduling method for an integrated energy system according to claim 5, wherein the decision variables include power P generated by a gas turbine unit for each hour in a scheduling period mt Waste heat distribution ratio K of gas unit mt Charge-discharge power P of storage battery es Heat storage and release power P of heat storage tank hs 。
7. The method for optimizing and scheduling an integrated energy system according to claim 1, wherein step 8 specifically comprises:
step 8.1, constructing a set matrix X= (X) by using the Pareto optimal solution set ij ) m×n M is population number, n is target number, and weight w is calculated by entropy method j ；
Step 8.2, combining the target weights w j And selecting an optimal solution by using a TOPSIS method to obtain an optimal operation scheme.
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