CN111463836A - Optimized scheduling method for comprehensive energy system - Google Patents

Optimized scheduling method for comprehensive energy system Download PDF

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CN111463836A
CN111463836A CN202010404237.1A CN202010404237A CN111463836A CN 111463836 A CN111463836 A CN 111463836A CN 202010404237 A CN202010404237 A CN 202010404237A CN 111463836 A CN111463836 A CN 111463836A
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energy system
error
power
scene
comprehensive energy
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CN111463836B (en
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贺强
施纪卫
王超
李萌
袁杰
张靠社
张刚
解佗
冯培基
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Shaanxi Gas Group Co ltd
Shaanxi Provincial Natural Gas Co ltd
Shaanxi Gas Group New Energy Development Co Ltd
Xian University of Technology
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Shaanxi Gas Group Co ltd
Shaanxi Provincial Natural Gas Co ltd
Shaanxi Gas Group New Energy Development Co Ltd
Xian University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses an optimized scheduling method of a comprehensive energy system, which comprises the following steps: obtaining the predicted values of cold, heat, electric load and photovoltaic output every day; generating an error scene set S according to the prediction error probability distribution of the cold, hot and electric loads and the photovoltaic output; reducing the error scene set to obtain a typical error scene; superposing the typical error scene with the cold, heat and electric loads and the photovoltaic output predicted value to obtain a typical scene of the cold, heat 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 dispatching 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 the optimal solution from the Pareto optimal solution set to obtain the optimal operation scheme.

Description

Optimized scheduling method for comprehensive energy system
Technical Field
The invention belongs to the technical field of energy utilization methods, and relates to an optimal scheduling method of a comprehensive energy system.
Background
With the increasing severity of energy crisis and global warming, research on improving the utilization efficiency of traditional and clean energy has received wide attention from scholars at home and abroad. The comprehensive energy system is a multi-energy comprehensive supply system which takes combined supply equipment (a gas turbine unit and an absorption refrigerator) as a core, comprises a plurality of distributed units (power generation, load, energy storage and the like) and has various energy forms of cold, heat, electricity and the like. The comprehensive energy system is established on the basis of energy gradient utilization, a primary energy source is utilized to drive a generator to generate electricity, and waste heat is recovered through various waste heat utilization devices. The energy utilization rate is improved, and the energy source device has lower energy cost, higher safety and better environmental protection. In addition, aiming at the uncertainty and the intermittence of clean renewable energy sources such as wind power, photovoltaic and the like, the comprehensive energy source system can be combined with the wind power and the photovoltaic, and effective support is provided for the development and the utilization of distributed renewable energy sources.
FIG. 1 is a comprehensive energy system structure model and energy flow diagram. In the figure, the gas turbine unit takes natural gas as fuel and provides power for users, meanwhile, high-temperature flue gas generated by the gas turbine unit and heat carried by cylinder sleeve water can be conveyed to an absorption refrigerator and a heat exchange device to meet the cold and heat load requirements of the users, and the proportion of the waste heat of the gas turbine unit for refrigeration and heating is determined according to a waste heat distribution ratio. In addition, the photovoltaic and the electric energy storage also participate in the supply of electric energy, and if the electric energy provided by the gas turbine set and the storage battery cannot meet the electric power demand of a user, the insufficient electric power can be supplemented by an urban power grid; the heat storage tank can perform heat storage and release operation as required to ensure heat supply of the system; if the waste heat provided by the gas generator set and the heat output of the heat storage tank cannot meet the heat load requirement of a user, and the refrigerating power output by the absorption refrigerator cannot meet the cold load requirement, the gas boiler and the electric refrigerator set can supply heat and cool.
At present, the most widely applied operation strategies of the comprehensive energy system are a constant-heat-by-electricity operation strategy and a constant-heat-by-electricity operation strategy. However, when the electric constant heat operation strategy is adopted, the system may generate redundant heat, and for an independent combined supply system, the part of heat is directly discharged to the environment; when the operation strategy of using heat for fixed power is adopted, the system can possibly generate redundant power, and because the small power generation system in China cannot carry out grid-connected power generation at present, the waste of electric energy is caused. Furthermore, in the operational scheduling of the integrated energy system, there are different types of uncertainties in both the supply and demand parties, such as uncertainties in renewable energy availability and energy demand. If such uncertainties are not addressed, the operation of the system may deviate from optimal operation. Therefore, the 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, a rolling optimization method and the like. The modeling idea of robust optimization requires that a decision is feasible and an objective function is optimal under the worst condition of uncertain variables, but the probability of the worst condition is extremely low, so that the scheduling plan obtained by optimization has certain conservatism; when the interval optimization method is used, because the scenes in the interval optimization include the worst scenes, the decision result also has certain conservatism; the opportunity constraint planning method requires that random constraint conditions are at least satisfied with a certain confidence level, and essentially utilizes probability constraints to replace traditional determination constraints, and the constraints are not satisfied under a smaller probability, so that the condition that the system operation economy is influenced for dealing with the extreme wind power deviation with the small probability is avoided, but the selection of the confidence level has subjectivity; the method for controlling rolling scheduling by model prediction has higher requirement on the calculation speed and is difficult to combine with the multi-target problem.
Disclosure of Invention
The invention aims to provide an optimization scheduling method of an integrated energy system, which solves the problem that the optimization effect is influenced by uncertainty errors in the integrated energy system which is not considered in the prior art.
The technical scheme adopted by the invention is that the comprehensive energy system optimal scheduling method comprises the following steps:
step 1, obtaining predicted values of daily cold, hot and electric loads and photovoltaic output;
step 2, generating an error scene set S according to the prediction error probability distribution of the cold, hot and electric loads and the photovoltaic output;
step 3, reducing the error scene set to obtain a typical error scene;
step 4, superposing the typical error scene with the cold, heat and electric loads and the photovoltaic output prediction value to obtain a typical scene of the cold, heat and electric loads and the photovoltaic output;
step 5, constructing an equipment mathematical model of the comprehensive energy system, wherein the equipment of the comprehensive energy system comprises any combination of a gas turbine set, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device and an electric refrigerator;
step 6, constructing an optimized dispatching model of the comprehensive energy system according to an equipment mathematical model of the comprehensive energy system, and taking the minimum system operation cost and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as objective functions;
step 7, inputting the typical scene into an optimized dispatching 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 the optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
The invention is also characterized in that:
the step 2 specifically comprises the following steps: and generating s error scenes by adopting a Latin hypercube sampling method according to the error probability distribution, wherein the dimension of each scene is p-4H, and H is a time scale.
The step 2 specifically comprises the following steps:
step 2.1, recording the prediction error vuHas a probability distribution function of Fu(vu) Wherein u is 1,2,. and p;
step 2.2, the probability distribution function is Fu(vu) Value range of
Figure BDA0002490672000000031
Dividing the data into s equal probability intervals;
step 2.3, for the jth probability interval [ (j-1)/s, j/s, randomly selecting a qjSatisfy qj(j-1+ r)/s, wherein r is [0,1 ]]Random variables evenly distributed within the interval, order
Figure BDA0002490672000000041
Step 2.4, obtaining samples through inverse transformation of normal distribution
Figure BDA0002490672000000042
Step 2.5, repeating the steps 2.1-2.4 to obtain the u dimension prediction error vuS samples of each probability interval of (1);
step 2.6, repeating the step 2.1-2.5 to obtain u-dimensional prediction error vuThen predict the error v from each dimensionuRandomly extracting a sample from the sample values to form a vector 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 is 2,3,4,5 and …;
step 3.2, randomly initializing k cluster centers L ═ l1,l2,…,lk}；
Step 3.3, calculating the distance from each error scene V to k clustering centers respectively, distributing all the error scenes V to the clustering centers closest to the error scenes V according to the minimum distance principle, and forming k clusters Ci，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 any more, and recording the final centroid position of each cluster as L0；
Step 3.6, calculate to L0And when the cluster center is adopted, the PFS index of the error scene set S is adopted.
Step 3.7, repeating the steps 3.2-3.7, and recording the clustering centers L when different k values are recorded0PFS index until k is greater than
Figure BDA0002490672000000043
When the current is over;
step 3.8, the cluster number k corresponding to the maximum PFS index is taken as the reduced scene number k0And corresponding cluster center coordinates L0A typical set of error scenes is composed.
The step 6 specifically comprises the following steps:
6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the first objective function and the minimum system operation cost in the scheduling period are as follows:
Figure BDA0002490672000000051
in the above formula, f1For the total operating cost of the integrated energy system, PwIs the probability of the w scene, T is the period of the scheduling period, k0To reduce the number of scenes, Fgas(t,w)、Fgrid(t,w)、Fop(t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation and maintenance cost of the comprehensive energy system in the period of t under the scene of w, and the expression is as follows:
Figure BDA0002490672000000052
in the above formula, CgasFor the price of natural gas, CgridIs the commercial power price; cmtFor operating and maintenance costs of gas units, CpvFor operating and maintenance costs of photovoltaic power plants, CacFor the operating maintenance costs of absorption chillers, CerCost of operation and maintenance of electric refrigerators, CesFor operating and maintenance costs of the accumulator, ChsFor the operating maintenance costs of the heat storage tank, Pgrid(t) power purchasing power of the grid, PesIs the charge and discharge power of the storage battery; qhsIs the charge and discharge power of the heat storage tank;
and the target function II is that the total carbon dioxide emission of the comprehensive energy system is minimum:
Figure BDA0002490672000000053
in the above formula, f2The total carbon dioxide emission of the comprehensive energy system;
Figure BDA0002490672000000054
is the discharge amount of carbon dioxide generated by fuel gas,
Figure BDA0002490672000000055
the calculation formula of the equivalent carbon dioxide emission of the electric quantity purchased by the power grid is as follows:
Figure BDA0002490672000000061
in the above formula, the first and second carbon atoms are,
Figure BDA0002490672000000064
is the carbon dioxide conversion coefficient of the natural gas,
Figure BDA0002490672000000065
the carbon dioxide conversion coefficient of the commercial power.
And 6.2, the constraint conditions are as follows:
Figure BDA0002490672000000062
in the above formula, Pload(t) electric load of the user, Q, for a period of tload.h(t) thermal load of the user during t periods, Qload.c(t) the cooling load of the user for a period of t; pop(t) the running power of the comprehensive energy system; per(t) is the electricity consumption of the electric refrigerator; pload(t) is the electrical load power excluding the electrical refrigerator; pgrid(t) purchasing power for the power grid;
6.3, constructing an energy flow calculation model according to the constraint conditions and the comprehensive energy system structure as follows:
Figure BDA0002490672000000063
wherein k isopIs the self-power utilization coefficient of the system;
and 6.4, selecting decision variables of each time scale in the scheduling period.
The decision variables comprise the gas turbine unit generating power P of each hour in the degree periodmtWaste heat distribution ratio K of gas turbine unitmtAnd the charging and discharging power P of the storage batteryesAnd heat storage and discharge power P of heat storage tankhs。
The step 8 specifically comprises:
step 8.1, utilizing Pareto optimalitySolution set construction set matrix X ═ Xij)m×nM is the population number, n is the target number, and the weight w is calculated by an entropy methodj；
Step 8.2, combining the weights w of the targetsjAnd selecting the optimal solution by using a TOPSIS method to obtain an optimal operation scheme.
The invention has the beneficial effects that:
the invention relates to an optimized dispatching method of an integrated energy system, which is characterized in that the actual value of the predicted values of cold, heat and electric loads and photovoltaic output are combined with the prediction error of uncertainty to obtain the actual value, and the actual value is input into an optimized model of the integrated 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 utilization rate of natural gas energy, promote the consumption of new energy such as photovoltaic and the like, and fully exert the advantages of the comprehensive energy system in the aspects of energy cascade utilization, economy, environmental protection and new energy consumption promotion.
Drawings
FIG. 1 is a comprehensive energy system structural model and energy flow diagram;
fig. 2 is a flowchart of an optimal scheduling method of an integrated energy system according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
An optimized scheduling method of an integrated energy system, as shown in fig. 2, includes the following steps:
step 1, obtaining predicted values of cold, heat and electric loads and photovoltaic output of the next day; in this embodiment, a total of 4 × 24 predicted power data is required, taking one hour as the time scale H.
Step 2, generating an error scene set S by using a Latin hypercube sampling method according to the prediction error probability distribution of cold, heat, electric load and photovoltaic output;
setting the number s of error scenes contained in the generated error scene set, wherein the value of s is generally large to ensure the precision, such as 1000; on an hour time scale, the dimension p of each scene is 4 x 24; the ith scene is denoted as Vi＝[v1,v2,...,v96]Wherein v is1-v24、v25-v48、v49-v72Respectively represents the prediction errors of cold, heat and electric loads in each hour, v73-v96Representing the photovoltaic output prediction error in each hour; the specific error scene generation steps are as follows:
step 2.1, recording the prediction error vuHas a probability distribution function of Fu(vu) Wherein u is 1,2,. and p; v. ofuThe prediction error of cold, heat, electric load or photovoltaic output in a certain hour is expressed according to the difference of u values in the u dimension of the scene;
step 2.2, the probability distribution function is Fu(vu) Value range of
Figure BDA0002490672000000081
Divided into s equal probability intervals, in which
Figure BDA0002490672000000082
Is v isuUpper and lower limits of value;
step 2.3, for the jth probability interval [ (j-1)/s, j/s, randomly selecting a qjSatisfy qj(j-1+ r)/s, wherein r is [0,1 ]]Random variables evenly distributed within the interval, order
Figure BDA0002490672000000083
Step 2.4, obtaining samples through inverse transformation of normal distribution
Figure BDA0002490672000000084
Obtaining a sampling point of the jth probability interval of the u dimension;
step 2.5, repeating the steps 2.1-2.4 to obtain the u dimension prediction error vuS samples of each probability interval of (1);
step 2.6, repeating the steps 2.1-2.5 to obtain a u-dimension prediction error sample, and then obtaining a v from each dimension prediction erroruRandomly extracting a sample from the sample values to form a vector to obtain an error scene Vi＝[v1,v2,...,v96]；
Step 2.7, repeating steps 2.1-2.6 to obtain an error scene set S ═ V including S error scenes1,V2,...,Vs]T。
Step 3, reducing the error scene set S by using the improved K-means clustering algorithm provided by the text to obtain a typical error scene set; the method comprises the following specific steps:
step 3.1, taking the clustering number k, wherein k is 2,3,4,5 and …;
step 3.2, randomly initializing k cluster centers L ═ l1,l2,…,lk}；
Step 3.3, calculating the distance from each sample point (namely the error scene V) to k clustering centers respectively, and distributing all the error scenes V to the clustering centers closest to the error scenes according to the minimum distance principle to form k clusters Ci，i＝1,2,…k；
Step 3.4, recalculating the centroid of each cluster (namely the mean value of each cluster of samples);
step 3.5, repeating the steps 3.3-3.4 until the centroid position is not changed any more, and recording the final centroid position of each cluster as L0；
Step 3.6, calculate to L0And (3) when the cluster center is obtained, obtaining the PFS index of the error scene set in the step 2.7.
Specifically, the error scene set S is combined with the cluster center L0＝{l1,l2,…,lkSubstituting into the following equation, the PFS index is calculated:
Figure BDA0002490672000000091
in the above formula, the first and second carbon atoms are,
Figure BDA0002490672000000092
is a matrix
Figure BDA0002490672000000093
The trace of (a) is determined,
Figure BDA0002490672000000094
is a matrix
Figure BDA0002490672000000095
S is the number of sample values, i.e. the number of error scenes generated, k is the number of clusters,
Figure BDA0002490672000000096
for the inter-class of the p-dimensional variable samples,
Figure BDA0002490672000000097
for an intra-class scatter matrix of p-dimensional variable samples, the expression is as follows:
Figure BDA0002490672000000098
in the above formula, VjIs a sample vector, i.e. the jth error scene V in the set S of error scenesj＝[v1,v2,...,v96]，liIs the ith cluster CiThe center of the cluster of (a) is,
Figure BDA00024906720000000911
is represented byiTransposition of, muijThe expression of (a) is as follows:
Figure BDA0002490672000000099
step 3.7, repeating the steps 3.2-3.7, and recording the clustering centers L when different k values are recorded0PFS index until k is greater than
Figure BDA00024906720000000910
Step 3.8, taking the corresponding clustering number k when the PFS index is maximum as the reduced scene number k0And the reduced cluster center coordinates L0A typical set of error scenes is composed.
Step 4, superposing the typical error scene with the cold, heat and electric loads and the photovoltaic output prediction value to obtain a typical cold, heat and electric load and photovoltaic output scene;
step 5, constructing an equipment mathematical model of the comprehensive energy system, wherein the equipment of the comprehensive energy system comprises any combination of a gas internal combustion engine set, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device, an electric refrigerator and a gas boiler, and can be selected according to actual conditions;
step 5.1, constructing a mathematical model of the internal combustion engine of the gas turbine:
Figure BDA0002490672000000101
in the above formula, ηmtP(t) Power Generation efficiency of gas turbine Unit during time t, ηmtQ(t) is the residual heat efficiency of the gas turbine unit in the period of t, f is the load factor of the internal combustion engine of the gas turbine, and a1、a2、a3、b1、b2、b3Is the efficiency coefficient, V, of an internal combustion engine of a combustion enginemtIs the natural gas consumption of the gas turbine unit, m3；PmtFor the generated power of gas-turbine units, QmtL is the waste heat power of gas turbine set, kWgasIs the heat value of natural gas (kW.h)/m3；
Step 5.2, constructing a mathematical model of the storage battery and the heat storage tank:
Figure BDA0002490672000000102
Figure BDA0002490672000000103
in the above formula, SES(t)、SHS(t) the residual energy of the storage battery and the heat storage tank in the period of t, kW.h; pes(t)、PesAnd (t) is the charging and discharging power of the storage battery and the heat storage tank in a period of t, kW is obtained, a negative value represents energy storage, and a positive value represents energy discharging. Tau isESCoefficient of loss of stored energy, delta t unit scheduling time ηES.chr、ηHS.chrConversion efficiency of energy input into storage battery and heat storage tank, ηES.dis、ηHS.disFor accumulators and reservoirsThe energy output conversion efficiency of the hot tank;
and 5.3, constructing a mathematical model of the absorption refrigerator:
Figure BDA0002490672000000111
in the above formula, QacThe refrigerating power of the bromine refrigerator is kW; COPacThe refrigeration coefficient of the bromine refrigerator is shown;
step 5.4, establishing a mathematical model of the gas boiler and the heat exchange device:
Figure BDA0002490672000000112
in the above formula, QgbThe heating power of the gas boiler is kW; vgbFor gas consumption, m3；ηgbFor the efficiency of gas boilers, ηexFor the efficiency of the heat exchanger, QexkW is the output heat of the heat exchanger;
step 5.5, constructing a mathematical model of the electric refrigerator:
Figure BDA0002490672000000113
in the above formula, QerIs the refrigerating power of the electric refrigerator, kW, PerIs the power consumed by the electric refrigerator, kW, COPerIs the energy efficiency ratio of the electric refrigerator.
Step 6, constructing an optimized dispatching model of the comprehensive energy system according to an equipment mathematical model of the comprehensive energy system, and taking the minimum system operation cost and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as objective functions;
6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the first objective function and the minimum system operation cost in the scheduling period are as follows:
Figure BDA0002490672000000121
in the above formula, f1For the total operating cost of the integrated energy system, PwIs the probability of the w scene, T is the period of the scheduling period, k0To reduce the number of scenes, Fgas(t,w)、Fgrid(t,w)、Fop(t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation and maintenance cost of the comprehensive energy system in the period of t under the scene of w, wherein the expression is as follows:
Figure BDA0002490672000000122
in the above formula, CgasIs the price of natural gas, yuan/m3，CgridIs the commercial power price, yuan/kW.h; cmtFor operating and maintenance costs of gas units, CpvFor operating and maintenance costs of photovoltaic power plants, CacFor the operating maintenance costs of absorption chillers, CerCost of operation and maintenance of electric refrigerators, CesFor operating and maintenance costs of the accumulator, ChsFor the operation and maintenance cost of the heat storage tank, yuan/kW, Pgrid(t) the power purchasing power of the power grid at the time period t, kW.h; pesThe charging and discharging power of the storage battery is kW; qhsThe energy storage capacity is the charge and discharge power of the heat storage tank, kW;
and the target function II is that the total carbon dioxide emission of the comprehensive energy system is minimum:
Figure BDA0002490672000000123
in the above formula, f2The total carbon dioxide emission of the comprehensive energy system;
Figure BDA0002490672000000124
is the discharge amount of carbon dioxide generated by fuel gas,
Figure BDA0002490672000000125
the equivalent carbon dioxide emission of the electric quantity purchased for the power grid, kg, is calculated according to the following formula:
Figure BDA0002490672000000126
in the above formula, the first and second carbon atoms are,
Figure BDA0002490672000000127
is the carbon dioxide conversion coefficient of natural gas, kg/m3,
Figure BDA0002490672000000128
The carbon dioxide conversion coefficient of the commercial power is kg/kWh.
And 6.2, the constraint conditions are as follows:
Figure BDA0002490672000000131
in the above formula, Pload(t) electric load of the user, Q, for a period of tload.h(t) thermal load of the user during t periods, Qload.c(t) is the cooling load, kW, of the user during the period t; pop(t) the running power consumption of the comprehensive energy system, kW; per (t) is the power consumption of the electric refrigerator, kW; pload(t) is the electrical load power, kW, excluding the electrical refrigerator; pgridAnd (t) is the power purchasing power, kW, of the power grid.
6.3, constructing an energy flow calculation model according to the constraint conditions and the comprehensive energy system structure as follows:
Figure BDA0002490672000000132
wherein k isopIs the self-power utilization coefficient of the system;
and 6.4, searching an operation scheme which enables the daily operation cost of the system to be minimum, namely the operation condition of each hour of each device of the system in the scheduling period under the condition of meeting the load requirement and other constraints. For the purpose of reducing the solving difficulty, the power generation power P of the gas turbine set in each hour in the scheduling period is selected by taking the structural characteristics of the system shown in the figure I into considerationmtWaste heat distribution ratio (namely valve opening) K of gas turbine unitmtAnd the charging and discharging power P of the storage batteryesAnd heat storage and discharge power P of heat storage tankhsAs a decision variable; once the decision variables are determined, the operating scheme for each plant can be obtained by the energy flow calculation model of equation (15).
Step 7, inputting the typical scene into an optimization scheduling model of the comprehensive energy system, solving by adopting an NSGA-II multi-target algorithm, setting the population size m to be 200 and the iteration number to be 2000, and obtaining a pareto optimal solution set;
and 8, selecting the optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
Step 8.1, constructing a solution set matrix X ═ X (X) by using Pareto optimal solution setij)m×nM is the number of alternatives (i.e. the number of populations), the number of targets n is 2, and the weight w is calculated by using an entropy methodjThe method comprises the following steps:
step 8.1.1, calculate normalized matrix R ═ R (R)ij)m×nWherein:
Figure BDA0002490672000000141
step 8.1.2, calculating entropy value e of each target informationj
Figure BDA0002490672000000142
In the above formula, k is 1/ln (m) and h isj＝1-ej；
Step 8.1.3, then each target weight wj：
Figure BDA0002490672000000143
Step 8.2, combining the weights w of the targetsjSelecting the optimal solution by using a TOPSIS method to obtain an optimal operation scheme, and specifically comprising the following steps of:
step 8.2.1, obtaining a normalized decision matrix Y (Y) by adopting a vector normalization methodij)m×nWherein:
Figure BDA0002490672000000144
step 8.2.2, construct weighted normalized matrix z ═ z (z)ij)m×nWherein:
zij＝wjyij,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
Figure BDA0002490672000000145
Wherein the content of the first and second substances,
Figure BDA0002490672000000146
step 8.2.4, calculating Euclidean distances between each scheme and positive ideal solution and negative ideal solution respectively
Figure BDA0002490672000000147
And
Figure BDA0002490672000000148
Figure BDA0002490672000000151
step 8.2.5, calculating the comprehensive evaluation index of each scheme
Figure BDA0002490672000000152
Figure BDA0002490672000000153
Step 8.2.6, according to
Figure BDA0002490672000000154
Selecting the best solution to obtain the best operation by arranging the order of the merits of the scheme from big to smallAnd (4) scheme.
According to the method, the actual value of the comprehensive energy system is obtained by combining the predicted values of the cold, heat and electric loads and the photovoltaic output with the prediction error of uncertainty, and the actual value is input into the optimization 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 utilization rate of natural gas energy, promote the consumption of new energy such as photovoltaic and the like, and fully exert the advantages of the comprehensive energy system in the aspects of energy cascade utilization, economy, environmental protection and new energy consumption promotion.

Claims (7)

1. An optimal scheduling method for an integrated energy system is characterized by comprising the following steps:
step 1, obtaining predicted values of daily cold, hot and electric loads and photovoltaic output;
step 2, generating an error scene set S according to the prediction error probability distribution of the cold, hot and electric loads and the photovoltaic output;
step 3, reducing the error scene set to obtain a typical error scene;
step 4, superposing the typical error scene with the cold, heat and electric loads and the photovoltaic output prediction value to obtain a typical scene of the cold, heat and electric loads and the photovoltaic output;
step 5, constructing an equipment mathematical model of the comprehensive energy system, wherein the equipment of the comprehensive energy system comprises any combination of a gas turbine set, a storage battery, a heat storage tank, an absorption refrigerator, a heat exchange device and an electric refrigerator;
step 6, constructing an optimized dispatching model of the comprehensive energy system according to an equipment mathematical model of the comprehensive energy system, and taking the minimum system operation cost and the minimum total carbon dioxide emission of the comprehensive energy system in a dispatching period as objective functions;
step 7, inputting the typical scene into an optimization 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 the optimal solution from the Pareto optimal solution set to obtain an optimal operation scheme.
2. The optimal scheduling method of the integrated energy system according to claim 1, wherein the step 2 specifically comprises: and generating s error scenes by adopting a Latin hypercube sampling method according to the error probability distribution, wherein the dimension of each scene is p-4H, and H is a time scale.
3. The optimal scheduling method of the integrated energy system according to claim 2, wherein the step 2 specifically comprises:
step 2.1, recording the prediction error vuHas a probability distribution function of Fu(vu) Wherein u is 1,2,. and p;
step 2.2, taking the probability distribution function as Fu(vu) Value range of
Figure FDA0002490671990000021
Dividing the data into s equal probability intervals;
step 2.3, for the jth probability interval [ (j-1)/s, j/s]Randomly selecting a qjSatisfy qj(j-1+ r)/s, wherein r is [0,1 ]]Random variables evenly distributed within the interval, order
Figure FDA0002490671990000022
Step 2.4, obtaining samples through inverse transformation of normal distribution
Figure FDA0002490671990000023
Step 2.5, repeating the steps 2.1-2.4 to obtain the u dimension prediction error vuS samples of each probability interval of (1);
step 2.6, repeating the step 2.1-2.5 to obtain u-dimensional prediction error vuThen predict the error v from each dimensionuRandomly extracting a sample from the sample values to form a vector 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 optimal scheduling method of the integrated energy system according to claim 1, wherein the step 3 specifically comprises:
step 3.1, taking the clustering number k, wherein k is 2,3,4,5 and …;
step 3.2, randomly initializing k cluster centers L ═ l1,l2,…,lk}；
Step 3.3, calculating the distance from each error scene V to k clustering centers respectively, distributing all the error scenes V to the clustering centers closest to the error scenes V according to the minimum distance principle, and forming k clusters Ci，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 any more, and recording the final centroid position of each cluster as L0；
Step 3.6, calculate to L0And when the cluster center is obtained, the PFS index of the error scene set S is obtained.
Step 3.7, repeating the steps 3.2-3.7, and recording the clustering centers L when different k values are recorded0PFS index until k is greater than
Figure FDA0002490671990000031
When the current is over;
step 3.8, the cluster number k corresponding to the maximum PFS index is taken as the reduced scene number k0And corresponding cluster center coordinates L0A typical set of error scenes is composed.
5. The optimal scheduling method of the integrated energy system according to claim 1, wherein the step 6 specifically comprises:
6.1, constructing an objective function of the comprehensive energy system according to the equipment mathematical model of the comprehensive energy system:
the first objective function and the minimum system operation cost in the scheduling period are as follows:
Figure FDA0002490671990000032
in the above formula, f1For the total operating cost of the integrated energy system, PwIs the probability of the w scene, T is the period of the scheduling period, k0To reduce the number of scenes, Fgas(t,w)、Fgrid(t,w)、Fop(t, w) are respectively the fuel cost, the power grid electricity purchasing cost and the operation and maintenance cost of the comprehensive energy system in the period of t under the scene of w, and the expression is as follows:
Figure FDA0002490671990000033
in the above formula, CgasFor the price of natural gas, CgridIs the commercial power price; cmtFor operating and maintenance costs of gas units, CpvFor operating and maintenance costs of photovoltaic power plants, CacFor the operating maintenance costs of absorption chillers, CerCost of operation and maintenance of electric refrigerators, CesFor operating and maintenance costs of the accumulator, ChsFor the operating maintenance costs of the heat storage tank, Pgrid(t) power purchasing in the power grid for a period of t, PesIs the charge and discharge power of the storage battery; qhsIs the charge and discharge power of the heat storage tank;
and the target function II is that the total carbon dioxide emission of the comprehensive energy system is minimum:
Figure FDA0002490671990000034
in the above formula, f2The total carbon dioxide emission of the comprehensive energy system;
Figure FDA0002490671990000041
is the discharge amount of carbon dioxide generated by fuel gas,
Figure FDA0002490671990000042
equivalent carbon dioxide emission for power purchased by power gridThe discharge quantity is calculated according to the following formula:
Figure FDA0002490671990000043
in the above formula, the first and second carbon atoms are,
Figure FDA0002490671990000044
is the carbon dioxide conversion coefficient of the natural gas,
Figure FDA0002490671990000045
the carbon dioxide conversion coefficient of the commercial power.
And 6.2, the constraint conditions are as follows:
Figure FDA0002490671990000046
in the above formula, Pload(t) electric load of the user, Q, for a period of tload.h(t) thermal load of the user during t periods, Qload.c(t) the cooling load of the user for a period of t; pop(t) the running power of the comprehensive energy system; per(t) is the electricity consumption of the electric refrigerator; pload(t) is the electrical load power excluding the electrical refrigerator; pgrid(t) purchasing power for the power grid;
6.3, constructing an energy flow calculation model according to the constraint conditions and the comprehensive energy system structure as follows:
Figure FDA0002490671990000047
wherein k isopIs the self-power utilization coefficient of the system;
and 6.4, selecting decision variables of each time scale in the scheduling period.
6. The optimal scheduling method for integrated energy system according to claim 5, wherein the decision variables comprise gas turbine generator power P for each hour of a degree periodmtGas engineComponent waste heat distribution ratio KmtAnd the charging and discharging power P of the storage batteryesAnd heat storage and discharge power P of heat storage tankhs。
7. The optimal scheduling method of the integrated energy system according to claim 1, wherein step 8 specifically comprises:
step 8.1, constructing a set matrix X ═ X (X) by using the Pareto optimal solution setij)m×nM is the population number, n is the target number, and the weight w is calculated by an entropy methodj；
Step 8.2, combining the weights w of the targetsjAnd selecting the optimal solution by using a TOPSIS method to obtain an optimal operation scheme.
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