CN108053069A - A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene - Google Patents

A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene Download PDF

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CN108053069A
CN108053069A CN201711348152.0A CN201711348152A CN108053069A CN 108053069 A CN108053069 A CN 108053069A CN 201711348152 A CN201711348152 A CN 201711348152A CN 108053069 A CN108053069 A CN 108053069A
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integrated energy
energy system
cool
nsga
algorithm
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曾艾东
郝思鹏
孟高军
陈光宇
董亮
刘思亦
周宇
黄堃
吴清
蔡欣灵
张前
刘磊
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Nanjing Institute of Technology
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    • 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
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
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    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention discloses a kind of integrated energy system traffic control methods suitable for multiple-objection optimization scene, according to the method for operation of cool and thermal power integrated energy system, establish the cool and thermal power integrated energy system optimal operation model under multiple target, it is proposed according to model feature based on the progressive parallel algorithm of objective layered for improving NSGA II, it introduces in terms of goal attainment method carries out acceleration convergence and is improved in non-dominant layer sort algorithm and change convergence decision condition, parallel computation scheme is designed according to algorithm characteristic, take into account algorithm search solution it is multifarious under the premise of accelerate the convergence rate of model solution, the scheduling strategy disaggregation being obtained can select the decision-making of optimized operation scheme to provide foundation for user's different demands, the idea and method for the multiple target combined optimization operation for realizing cool and thermal power integrated energy system is provided.

Description

A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene
Technical field
The present invention relates to cool and thermal power integrated energy system traffic control technologies more particularly to one kind to be suitable for multiple-objection optimization The integrated energy system traffic control method of scene.
Background technology
Production of energy and consumption revolution are promoted, increase energy-saving and emission-reduction dynamics and prevents haze under the new situation in country, is carried Efficiency of energy utilization is risen, reduces the common recognition that pollutant emission has become government, enterprise and the common people.Cool and thermal power integrated energy system Comprising hot and cold, the gentle four kinds of energy forms of electricity, united using technology of Internet of things and information technology to all powering devices in region One integrates and implements to dispatch, and optimizes energy supply to the cold and hot electric load in region to reach, promotes the effect of efficiency of energy utilization.It is cold For thermoelectricity integrated energy system with its efficient efficiency of energy utilization, the energy supply pattern of flexibility and reliability, which becomes, realizes energy life Production and consumption transition promote comprehensive utilization rate of energy source and solve the important means of energy environment issues.
At present in terms of the research of cool and thermal power integrated energy system optimization traffic control, it is concentrated mainly on both at home and abroad to cold and hot On the economic Journal of Sex Research of electric integrated energy system, the body matter of research compares convergence, and different research is mainly reflected in difference System architecture, the energy supply of different systems and energy storage device configuration, different system constraints and to being selected according to model characteristics Take the several aspects of improvement of algorithm.The Optimized Operation target of cool and thermal power integrated energy system does not only have economy one kind, in recent years, Environmental Factors are taken into account the scheduling model a few days ago of system by multidigit scholar, such as add in contamination gas in the optimization object function of system Body discharges penalty, considers the factors such as the operating cost of cool and thermal power integrated energy system and whole pollutant emission and establishes Multi-target machine meeting plan model, and Optimized model is asked using the improvement particle cluster algorithm based on local and global memory body Solution；Or greenhouse gas emission and pollutant emission cost are included to the operating cost function of system, it establishes and energy is integrated with cool and thermal power The Model for Multi-Objective Optimization of the minimum object function of source system integrated operation cost, using particle swarm optimization algorithm to above-mentioned model It solves etc..The common feature of these researchs is to carry out the mistake of cool and thermal power integrated energy system multi-target optimum operation research Cheng Zhong concentrates on the starting point in the selection of multiple targets, optimization aim is summarised as economy class, efficiency promotes class and environmentally friendly class Etc. several respects target, and processing method then include by multiple optimization aims be converted to single optimization aim then optimize and Directly two major classes are optimized using intelligent multi-objective Algorithm.When being solved using intelligent multi-objective Algorithm, the mistake of solution Journey is typically more tediously long, it is difficult to incorporate in actual engineer application；And the algorithm for being converted directly into single goal is used to exist and is searched For rope to the diversity deficiency of solution, Pa Lietuo optimal solutions can not be obtained by being easily trapped into local optimum.
The content of the invention
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention provides a kind of suitable for multiple-objection optimization The integrated energy system traffic control method of scene considers that the optimization of cool and thermal power integrated energy system operates in reduction systematic running cost With and control pollutant emission in terms of significance, cool and thermal power integrated energy system a few days ago economic optimization scheduling basis On take the feature of environmental protection target of cool and thermal power integrated energy system into consideration, establish the cool and thermal power integrated energy system optimization under multiple target Moving model；And proposed according to model feature based on the progressive parallel algorithm of objective layered for improving NSGA-II, which exists Non-dominant layer sort algorithm and change convergence decision condition introduce goal attainment method and improved in terms of acceleration convergence, and According to algorithm characteristic design parallel computation scheme, take into account algorithm search solution it is multifarious under the premise of accelerate model solution Convergence rate；The present invention carries cool and thermal power integrated energy system multi-target optimum operation dispatching method and is searching the more of scheduling strategy The various aspects such as sample, iteration time, convergence are respectively provided with preferable effect, can be according to user not in practical engineering application Select optimized operation scheme with demand, provide the thinking of realizing the operation of cool and thermal power integrated energy system multiple target combined optimization and Method.
Technical solution：To achieve the above object, the technical solution adopted by the present invention is：
A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene, it is main compared with the prior art It is improved from the following aspects：1. establish cool and thermal power integrated energy system various dimensions optimization operational objective function；2. it establishes Cool and thermal power integrated energy system various dimensions optimization operation constraints；3. using progressive parallel II algorithms of NSGA- of layering to optimization Model is solved.These parts are specifically described separately below.
First, cool and thermal power integrated energy system various dimensions optimization operational objective function is established
Current cool and thermal power integrated energy system optimal operation model only considers the economic index of system operation, and we Scheme also contemplate the feature of environmental protection index (discharge index) of system operation.
The economic index of 1.1 system operations
The economic index of system operation is minimum for day operation cost, mainly includes system fuel expense, is interacted with power grid Power expense and the aspect of system operation maintenance cost 3, object function be：
f1=minKTotal=min (KFuel+KGrid+KMaintain) (1)
In formula：f1For the economic index of system operation, KTotalFor the day operation comprehensive cost of system, KFuel、KGridWith KMaintainRespectively the fuel cost of system, interacted with power grid power expense and operation and maintenance expense.
In formula：For the t periods by when gas price；fCHPiFor the energy dissipation behavior curvilinear function of miniature gas turbine i,For The electrical power output of miniature gas turbine i, nCHPFor the miniature gas turbine total amount included in system；It is gas fired-boiler i in t The energy consumption of period, nGBFor the gas fired-boiler total amount included in system.
In formula：For the t periods by when electricity price；The electricity interacted for system in the t periods with power grid.
In formula：pmEBFor the unit power operation expense of waste heat boiler,For the heating work(of t period waste heat boilers Rate；pmACFor the unit power operation expense of Absorption Refrigerator,For the refrigeration work consumption of t period Absorption Refrigerators； pmECFor the unit power operation expense of compression electric refrigerating machine,For the operation power of t duration compression formula electric refrigerating machines； pmstorRefer to the unit power operation expense of energy storage device,WithRefer to the charge and discharge energy of t period three classes energy storage devices Power；pmAirFor the unit power operation expense of air-conditioning system,Power is run for t periods air-conditioning system；pmCHPiFor The unit power operation expense of miniature gas turbine i,It is exported for the electrical power of t period miniature gas turbines i； pmdistriiFor the unit power operation expense of Distributed-generation equipment i,For the output of Distributed-generation equipment i, ndistriFor the Distributed-generation equipment total amount included in system；pmGBiFor the unit power operation expense of gas fired-boiler i,For the heats power of gas fired-boiler i.
The feature of environmental protection index of 1.2 system operations
The feature of environmental protection target of system is minimum for day operation gross contamination object discharge capacity, mainly includes system nitride, carbide With 3 aspects of discharge capacity of sulfide, object function is：
f2=min (PGTotal)=min (δ1×PGC+δ2×PGS+δ3×PGN) (5)
In formula：f2For the feature of environmental protection index of system operation, PGTotalFor the day operation gross contamination emission of system, PGC、 PGSAnd PGNRespectively system day carbon, day sulfide and day emitting nitride amount, δ1、δ2And δ3Respectively carbon emission, sulfide Discharge and the weight coefficient of emitting nitride.
In formula：WithThe respectively carbon for the electric energy that distributed generation system i is bought to public electric wire net, sulphur Compound and emitting nitride amount,WithThe respectively carbon of the miniature gas turbine of distributed generation system i, vulcanization Object and emitting nitride amount,WithThe respectively carbon of the gas fired-boiler of distributed generation system i, sulfide and nitrogen Compound discharge capacity；N is the distributed generation system total amount included in system, and Δ t is the scheduling interval of scheduling system.
2nd, cool and thermal power integrated energy system various dimensions optimization operation constraints is established
Cool and thermal power integrated energy system joint multiple target operation need to meet three classes constraints, be respectively system operation about Beam, environmental emission constraint and economy constraint.
2.1 system operations constrain
The system operation constraints of cool and thermal power integrated energy system includes electrical power Constraints of Equilibrium, the supply of cold and hot power about Beam, place capacity constraint, equipment operation constraint etc..
In formula：For the battery power of t periods,For the load value of t periods,For user's air-conditioning of t periods Power.
In formula：The calorific value recycled for t period miniature gas turbine i by waste heat boiler；Exist for gas fired-boiler i The heat production value of t periods, COPcondFor the energy efficiency coefficient of air-conditioning equipment,WithRespectively t period systems by space-time Between thermic load and hot water load.
In formula：For the cold that t period miniature gas turbine i are manufactured by absorption refrigeration unit,For t periods electricity The cold water value that refrigeration machine generates,WithIt is power input and the output of t periods cold energy storage device respectively, EERcondFor air-conditioning The refrigeration efficiency ratio of equipment,WithRespectively cool and thermal power integrated energy system by when space refrigeration duty and freezing make Refrigeration duty.
In formula：For the lotus Warm status of t periods heat accumulation (cold) equipment；Subscript t represents the t periods, and subscript min and max are represented Lower limiting value and upper limit value.
2.2 environmental emissions constrain
Cool and thermal power integrated energy system needs to control the pollutant discharge amount of whole system one during operation Determine in scope, including the total exhaust emission constraint of pollutant in region and each pollutant by when exhaust emission constraint.
0≤PGC≤PGC max (20)
0≤PGS≤PGS max (21)
0≤PGN≤PGN max (22)
In formula：PGC max、PGS maxAnd PGN maxRespectively system day carbon, day sulfide and the day emitting nitride amount upper limit,WithThe respectively carbon of t period systems, sulfide and the emitting nitride amount upper limit
2.3 economy constrain
Cool and thermal power integrated energy system needs to control the operating cost of whole system certain during operation In the range of, economy constraint calculation formula is as follows：
0≤KTotal≤Kmax (26)
3rd, Optimized model is solved using layering progressive parallel II algorithms of NSGA-
Due in Optimized model there are it is a variety of for can equipment, and cold and hot electric energy coupled relation is complicated, causes model Optimized variable it is large number of, the constraint matrix scale of construction is huge, when using traditional global Searching Resolution Space class algorithm such as NSGA- II or when multi-objective particle swarm algorithm, can cause solution procedure to become very very long, can not meet scheduling system rolling optimization to calculating Method calculates the requirement in time limit；And using from the transition of single object optimization algorithm and come traditional multi-objective optimization algorithm such as leash law, When being solved to model, the solution acquired is often confined in local Pa Lietuo optimal solution sets weighting method, it is difficult to be obtained larger The Pa Lietuo optimal solution sets of scope, therefore scheduling system can not be given to provide scheduling scheme various enough and supply the system decision-making.
Present invention utilization is layered progressive II algorithms of parallel NSGA- can both make full use of global solution in solving-optimizing model Advantage of the spatial search algorithm in terms of the harmony and diversity of disaggregation, while can realize that large-scale nonlinear optimization is asked again The rapid solving of topic disclosure satisfy that requirement of the scheduling system rolling optimization to the calculating time limit, therefore the present invention is progressive using being layered Parallel II algorithms of NSGA- solve cool and thermal power integrated energy system joint multi-target optimum operation model, and algorithm is based on NSGA-II has done 3 points of improvement, specific as follows.
3.1 improve non-dominant layer sort algorithm according to model feature realizes fast hierarchical
During multi-target optimum operation model solution is combined to cool and thermal power integrated energy system, it is contemplated that only include Double optimization target, therefore the present invention uses more effective and quick on the basis of NSGA-II algorithms according to model feature Non-dominant layer sort method, to meet the requirement of model rapid solving.
First, merger ranking method is selected to carry out ascending sort to the individual in population according to one of target, when two When individual first aim is identical, ascending sort is carried out according to second target, the result after merger is sorted deposits in array FiIn, at this point, the individual of first aim value minimum must be an individual for non-dominant layer first layer.Then first is searched for The adjacent individual of individual, if the individual second target functional value is less than previous individual, the individual and previous individual Same layer in non-dominant layer, otherwise the individual be not at non-dominant layer same layer, be inserted into array FiAfterbody, successively Analogize, find the non-dominant individual of whole first layers of population.Next from FiFirst element of afterbody starts, and it is non-to find the second layer Individual is dominated, and so on, by population, all layering finishes.
Since next-generation number of individuals is FiThe half of middle individual, therefore incomplete Stratified Strategy is taken in delaminating process, I.e. individual can save many unnecessary layering comparison steps once the strategy that layering is interrupted more than next-generation number of individuals：
First, the average time complexity of merger sequence is O (N × log2N)；Secondly, the total that hierarchical operations are spent Calculating number is：
In formula：N is twice of scale of population, and k is total number of plies after non-dominated ranking is fully completed, TTotalTo be non- Total calculation times that hierarchical operations are spent after the completion of dominated Sorting, TiTo generate the comparison that i-th layer of non-dominant layer at most needs Number.
The time complexity of hierarchical operations is O (kN) i.e. after the completion of non-dominated ranking, and the total time complexity of algorithm is O (N ×log2N+kN).Under normal circumstances, there are a k ＜ ＜ N, therefore the total time complexity of algorithm is O (N × log2N).Compared with NSGA- II, the present invention carries algorithm and time complexity is dropped to line style logarithm rank by a square rank.
3.2 modification convergence decision conditions and combining target reach method and accelerate convergence process
Improvement multi-objective genetic algorithm based on NSGA-II can be arranged soon in the solution procedure to model close to pa Region near forward position is held in the palm, but substantial amounts of iterations and function comparison procedure can be spent in order to reach final convergence effect, This means that the longer calculating time.Therefore, the present invention proposes a kind of comprehensive layering Algorithm of Progressive to accelerate to find Pa Lietuo The process of optimal solution set.First, calculated using the improved adaptive GA-IAGA based on NSGA-II, its end condition is arranged to Limited genetic algebra causes the disaggregation close to Pa Lietuo forward positions.Secondly, application target reaches method progress binary search, will be previous Initial value of the result of calculation of step as goal attainment method carries out local search and obtains Pa Lietuo disaggregation.
The canonical form of goal attainment method solving model is：
In formula：Optimized variable x ties up variable for n, contributes including gas turbine co-feeding system by hour cool and thermal power, auxiliary type The contributing by hour of powering device, the contributing by hour of all kinds of energy storage devices, the SOC states of energy storage and the purchase interacted with power grid Electricity sales amount；fi(x) it is i-th of optimization object function, ωiFor the weight coefficient of i-th of object function,It is expected i-th of mesh Mark the optimization target values reached；γ reaches coefficient for target, reaches situation for predeterminated target for characterizing；c(x)、ceq(x) The various nonlinear inequalities and Nonlinear Equality Constrained met for optimized variable；A, b is the various linear of optimized variable satisfaction The coefficient matrix of inequality constraints；Aeqx、beqFor the coefficient matrix for the various linear equality constraints that optimized variable meets；Lb and ub The respectively lower and upper limit of optimized variable x.
Multi-objective optimization question is converted by single object optimization by using dummy variable γ, and then using interior point method to excellent Change model to be solved, interior point method constructs during solution meets Caro need-Ku En-Plutarch (Karush-Kuhn- Tucker) the Lagrangian auxiliary function of condition：
Second order derived function Hessian matrixes in its update equation formula are：
In formula, λc,i、λA,i、λceq,iAnd λAeq,iFor Lagrange multiplier, dimension is identical with respective constraints number； X, lb, ub are n dimensional vectors, and b is p dimensional vectors, beqFor q dimensional vectors, A ties up matrix, A for p × neqSquare is tieed up for q × n Battle array, Hessian matrixes tie up matrix for n × n.
By being converted to matrix in update equation group, LDLTDecomposition and back substitution are simultaneously corrected, and can realize that iteration is asked Solution, iteration is automatically stopped after meeting the condition of convergence, is found distance in feasible zone and is expected the nearest solution of optimization aim as a pa Tired support optimal solution.
3.3 design parallel computation scheme according to model feature accelerates solution procedure
Due to there is the loop iteration processes of a large amount of repeatability in the objective layered Algorithm of Progressive based on improvement NSGA-II, And each process each other without parameter transfer, each process that do not interdepend as a result, therefore introduce parallel computing come The convergence process of accelerating algorithm.Wherein, being applicable in the part of parallel computation mainly has non-dominant layer merger sort sections and calls mesh Mark reaches method and realizes Fast Convergent part.In non-dominant layer merger sort sections, carried out first on multiple parallel processing elements The fitness of individual calculates, and then population at individual to be sorted is evenly distributed on m parallel processing element, and when parallel processing returns And the complexity to sort is reduced to O (N/m × log2N), algorithm processing speed quickening with increasing for parallel processing element core.It adjusts When carrying out binary search with goal attainment method, due to from different initial values, transferring without parameter, therefore also fitting each other Close multiple parallel processing elements that the calculating task using parallel processing technique, is averagely allocated to computer.
Further, since GPU possesses more arithmetic elements compared with CPU, more towards concurrent operation during design, for abundant profit It handled with GPU in extensive array, contain multiple computing that complex logic judges and the little repetition of data dependence Population in algorithm is intersected and transfers to GPU with mutation operation task to undertake by the advantage in terms of computing.GPU is used in Matlab The mode accelerated parallel, which mainly has, to be called GPU directly in matlab and carries out comprehensive two kinds of programming based on CUDA, and the present invention is main To be based on the former.Since GPU is limited to hardware structure, the calculating of single precision is far faster than double precision, therefore the present invention is being planted It is all calculated during all kinds of computings of group using single precision (single types).
Advantageous effect：Integrated energy system traffic control method provided by the invention suitable for multiple-objection optimization scene, Energy resource system optimal operation model is closed by establishing the synthesis of the cool and thermal power under multiple target, takes into full account and considers cool and thermal power comprehensive energy Significance of the system optimized operation in terms of reducing systematic running cost and using and control pollutant emission, can significantly shorten To the calculating time of Pareto disaggregation to meet the needs of scheduling system rolling optimization, at the same can ensure solution be uniformly distributed and The diversity of population provides flexile scheduling scheme and is made choice for decision-maker.
Description of the drawings
Fig. 1 is that the present invention is based on the signals of the non-dominant layer hierarchical algorithm after II algorithm improvements of NSGA-；
Fig. 2 is the solution flow based on the progressive parallel algorithm of objective layered for improving NSGA-II；
Fig. 3 energizes structure for cool and thermal power integrated energy system；
Fig. 4 is representative heat-cool electricity integrated energy system day electric heating cooling load prediction curve in example；
Fig. 5 is the Pa Lietuo optimal solution sets border acquired using weighting method；
Fig. 6 is the Pa Lietuo optimal solution sets border acquired using NSGA-II algorithms；
Fig. 7 is using the Pa Lietuo optimal solution sets acquired based on the progressive parallel algorithm of objective layered for improving NSGA-II Border；
Fig. 8 is once the situation of the non-dominant layer hierarchy number variation of typical evolution solution procedure neutron parent mixed population；
The Optimized Operation electric load profile of equilibrium a few days ago in the case of Fig. 9 takes into account for the economy feature of environmental protection；
Optimized Operation space thermic load supply curve a few days ago in the case of Figure 10 takes into account for the economy feature of environmental protection；
Optimized Operation hot water load supply curve a few days ago in the case of Figure 11 takes into account for the economy feature of environmental protection；
Optimized Operation space refrigeration duty supply curve a few days ago in the case of Figure 12 takes into account for the economy feature of environmental protection；
The cooling load supply curve of Optimized Operation freezing a few days ago in the case of Figure 13 takes into account for the economy feature of environmental protection；
Optimized Operation energy storage device operating status a few days ago in the case of Figure 14 takes into account for the economy feature of environmental protection；
All kinds of powering device pollutant discharge amounts in the case of Figure 15 takes into account for the economy feature of environmental protection.
Specific embodiment
The present invention is further described below in conjunction with the accompanying drawings.
To realize that the cool and thermal power integrated energy system traffic control under multiple-objection optimization scene needs to pay close attention in practical application Below the problem of three aspects：(1) whether algorithm result of calculation can search relatively uniform pa row under definite scene Hold in the palm border；(2) whether algorithm calculating speed disclosure satisfy that the needs of practical application；(3) whether result of calculation can be directly used for Distribution scheduling system is performed.
In order to answer three problems above, structure conduct is energized using a typical cool and thermal power integrated energy system Capital equipment in example scenario, wherein cool and thermal power integrated energy system has miniature gas turbine, waste heat boiler, absorption refrigeration Mechanical, electrical refrigeration machine, gas fired-boiler, accumulator, accumulation of heat (cold) device, family air-conditioning and photovoltaic cell, the system pass through centralization Power bus-bar and public electric wire net Change Power in systematic electricity insufficient supply to bulk power grid power purchase, are supplied rich in systematic electricity To bulk power grid sale of electricity when remaining.Simultaneously, it is believed that without combustion-gas jet test inside cool and thermal power integrated energy system, required whole combustion gas is by outside Gas company supplies, and the relevant parameter of powering device and energy storage device in example mainly fires in system as shown in Table 1 and Table 2 Gas and fuel oil equipment pollutant emission index are as shown in table 3.
1 powering device parameter of table
2 energy storage device parameter of table
Major gas and fuel oil equipment pollutant emission index in 3 system of table
Wherein gas turbine, internal combustion engine, fuel cell, public electric wire net are on the basis of producing 1kWh electricity；Gas fired-boiler with On the basis of production 1kWh heats；Direct fired absorption refrigeration unit is on the basis of producing 1kWh colds.
Fig. 4 is certain electric heating cooling load prediction curve in example representative heat-cool electricity integrated energy system day, this area using point When electricity price pricing mode, the crest segment time be 8 points to 11 points, 18 points to 23 points；The flat section time is 7 points to 8 points, 11 points to 18 points； The paddy section time is 23 points to 7 points；Electricity price is as shown in table 4 at times, while Gas Prices are set as 3.25 yuan/cubic metre, recognize It is 8571 kilocalories/cubic meter for its higher calorific value, by gas price conversion for 0.325 yuan/kWh, carbon emission, sulphur emissions and nitrogen discharged Weight coefficient δ1、δ2And δ31,100,100 are taken respectively.
4 tou power price of table
The cool and thermal power integrated energy system traffic control needs under multiple-objection optimization scene to be realized are paid close attention to below three The problem of a aspect, is analyzed.
(1) system optimized operation Pa Lietuo borders compare under algorithms of different
Three kinds of different algorithms have been used respectively to cool and thermal power integrated energy system joint multi-target optimum operation and scheduling Model is solved, and has asked for the Pa Lietuo optimal solution sets under different solutions respectively.
Initial solution is generated at random as initial value in the range of feasible solution when a) using weighting method, and the weight of two targets is being advised 49 groups of weights is taken to be iterated after formatting altogether, the Pa Lietuo optimal solution sets border acquired is as shown in figure 5, it can be seen that weighting The Pa Lietuo optimal solution sets border that method acquires more is concentrated in subregion, be not on entire disaggregation forward position it is very continuous, It is short of in terms of the diversity of solution.
B) initial population scale is arranged to 2000 using during NSGA-II algorithms, it is a using single precision real numbers coding mode Each of body is the electricity of all kinds of powering devices, hot and cold energy output valve, and crossover operator uses two-point crossover, adjacent two Body intersects, and crossing-over rate selection 0.8, mutation probability takes 0.04.Greatest iteration algebraically is arranged to two in 300, Pa Lietuo optimal solution sets 1e-1 is arranged to for the opposite variation criterion between population at individual, iteration is terminated if two generation individual difference in change are less than criterion, Greatest iteration algebraically under this stopping criterion for iteration was arranged to for 200 generations, the Pa Lietuo optimal solution sets border such as Fig. 6 institutes acquired Show.
It can be seen that the Pa Lietuo optimal solution sets border that NSGA-II algorithms acquire is more dispersed, in entire disaggregation forward position On it is more continuous, have more prominent advantage in terms of the diversity of solution, i.e., have found more Paretos as much as possible most Excellent solution, but the drawback is that the calculating time is very very long, up to a few hours, this is an optimization process in actual engineer application It can not be received.
C) by initial population during parallel algorithm progressive using the objective layered proposed by the present invention based on improvement NSGA-II Scale is arranged to 2000, and using single precision real numbers coding mode, each individual is the electricity of all kinds of powering devices, hot and cold energy Output valve, crossover operator use two-point crossover, and adjacent two individual intersections, crossing-over rate selection 0.8, mutation probability takes 0.04. Greatest iteration algebraically is arranged to 200, and invocation target reaches method and carries out last time calculating during more than greatest iteration algebraically.Pa Lietuo Opposite variation criterion in optimal solution set between two generation population at individual is arranged to 1e-1, if two generation individual difference in change are less than criterion Iteration is then terminated, the greatest iteration algebraically under this stopping criterion for iteration was arranged to for 100 generations.The miniature PC of test is configured with Intel third generation Duo Processor i5-3230M, can carry out the calculating of four thread parallel of double-core, and the dominant frequency of each cores of CPU is 2.60GHz；The GPU of configuration is Nvidia GeForce 610M, video memory 1G, acquires that the results are shown in Figure 7.
As can be known from Fig. 7, the Pa Lietuo optimal solutions acquired based on the progressive parallel algorithm of objective layered for improving NSGAII It is also more dispersed to collect border, it is more continuous on entire disaggregation forward position, but since the goal attainment method of final step is not noted in itself The multifarious feature solved again, it will sacrifice the diversity of certain solution to exchange calculating convergence time for, entire algorithm is in solution Advantage in terms of diversity protrudes not as NSGA-II algorithms, but the advantage is that and combine the quick of goal attainment method Advantage in terms of the diversity of convergence and NSGA-II algorithm search solutions so that find as much as possible in the short period of time More more dispersed Pareto optimal solutions, one time optimization process can be down in several minutes on miniature PC, greatly improve The engineering practicability of algorithm.
(2) algorithm performance comparative analysis
A) time-consuming comparison of the algorithms of different in the non-dominant layer sequence of single
Combine multi-target optimum operation model for cool and thermal power integrated energy system, proposed using NSGA-II and this patent Based on improve NSGAII the progressive parallel algorithm of objective layered solved, record both algorithms respectively and once asking Average individual number of comparisons in solution preocess in single delaminating process, non-dominant forward position is averaged the number of plies and single layering expend it is flat The equal CPU time.The miniature PC of test is configured with Intel third generation Duo Processor i5-3230M, can carry out four line of double-core Journey parallel computation, the dominant frequency of each cores of CPU is 2.60GHz；The GPU of configuration be Nvidia GeForce610M, video memory 1G, Programmed environment is matlab R2012a.The parameter setting of example：Initial population scale is arranged to 2000, and progeny population scale is 1000, using single precision real numbers coding mode, each individual is the electricity of all kinds of powering devices, hot and cold energy output valve, is handed over Operator is pitched using two-point crossover, adjacent two individual intersections, crossing-over rate selection 0.8, mutation probability takes 0.04.Greatest iteration generation The opposite variation criterion that number is arranged in 300, Pa Lietuo optimal solution sets between two generation population at individual is arranged to 1e-1, if two generations Individual difference in change then terminates iteration less than criterion, and the greatest iteration algebraically under this stopping criterion for iteration was arranged to for 200 generations.Test When, experiment is all repeated 20 times for every-a algorithm, and takes the average value of each test data.NSGA-II and this patent are carried The results are shown in Table 5 for test comparison of the algorithm when constructing non-dominant forward position collection every time.
1 NSGA of table-II and this patent put forward test comparison result of the algorithm when constructing non-dominant forward position collection every time
As shown in Table 5, this patent puies forward average individual number of comparisons of the algorithm in single evolution delaminating process, non-dominant Obviously advantage is suffered from terms of forward position average mark three indexs of average CPU time that number and single layering expend layer by layer, The decline of wherein average individual number of comparisons is primarily due to employ the non-dominant frontal conformation based on Bi-objective merger sequence Method, number reduces the way for being primarily due to take individual full i.e. stopping layering, single point to non-dominant forward position average mark layer by layer Layer expend the average CPU time be greatly decreased mainly due to number of comparisons reduction and introduce CPU double-core parallel computations.
Fig. 8 lists once the feelings of the non-dominant layer hierarchy number variation of typical evolution solution procedure neutron parent mixed population Condition, as seen from the figure, based on improving the progressive parallel algorithm of objective layered of NSGA-II compared to NSGA- II, in single during solution Number is layered in evolutionary process to greatly reduce, and computational efficiency is greatly improved in the process for performing layering selection operation.
B) algorithm totally calculates and takes comparison under the different conditions of convergence
Although having carried out multinomial improvement to NSGA-II algorithms, due to evolutionary generation is more, number of comparisons mostly and calculation amount Greatly, total evaluation time is still longer, and algorithm performs efficiency is lower than traditional optimization method.By this patent proposition based on receipts Hold back the improved mixing layering Algorithm of Progressive of condition criterion, using NSGA-II scalabilities it is strong the characteristics of, pass through and certain cut be set Disconnected evolutionary generation, the convergence that method progress final step is reached with reference to conventional target calculate, and accelerate the convergence progress of entire algorithm, tool The body measuring and calculation time is as shown in the table.
All kinds of algorithms totally calculate time-consuming test comparison result under the different conditions of convergence of table 6
As seen from the above table, when will block algebraically and being arranged to for 100 generation, the technology time of entire algorithm can be controlled at 10 minutes It is interior, it is much fast to cross NSGA-II (up to a few hours, listing due to the different no averages of the order of magnitude therefore not).It is fastest Traditional algorithm due to needing specified weight and initial position, cause single optimal speed fast, but global convergence situation is not It is good, it may be necessary to very multiple exploration can just find satisfied Pa Lietuo forward positions, therefore on the whole not as good as this patent is carried Algorithm.Judge that improved mixing layering Algorithm of Progressive is calculated by NSGA-II and tradition based on the condition of convergence as seen from the above table simultaneously When method combines, accelerate and calculate convergent progress, but this is to sacrifice the diversity of a part of Pa Lietuo disaggregation as cost , in terms of this is not only embodied in the number of the solution searched, but also it is embodied in final step and reaches method progress using conventional target The degree of scatter of solution can be reduced during calculating, diversity is a bit weaker compared with improved NSGA-II, but is reached due to the use of target Initial value when finally being calculated to method obtains after several generations that have been the NSGA-II algorithm iterations after, these initial values have opposite Good dispersiveness, therefore the diversity that algorithm solves on the whole will in general calculated much stronger than traditional multi-objective calculation method Two aspect of diversity of time reconciliation has stronger COMPREHENSIVE CALCULATING ability and calculates advantage.
C) situation analysis is accelerated using the algorithm after parallel computing
In based on the objective layered Algorithm of Progressive for improving NSGA-II, parallel computation accelerates mainly to include CPU double-cores simultaneously Row accelerates and GPU+CPU collaboration processing accelerates two parts.Wherein, the parallel accelerating part of CPU double-cores mainly has non-dominant layer merger Sort sections and last invocation target reach method and realize Fast Convergent part.The two are partially due to be by calculation amount average mark Into two cores of CPU, preferable speed-up ratio is 2, but is set to integer and data fractionation and integration since decomposition computation amount differs Factor, speed-up ratio will be less than 2 in actual test, and the results are shown in table below for specific average test.
Table 7 employs the algorithm acceleration situation test result after parallel computing
On theoretic due to GPU compared with CPU possess more arithmetic elements (integer, floating-point multiplicaton addition unit, special computing Unit etc.), more towards concurrent operation during design, therefore GPU is handled in extensive array, multiple simple computation sum number There is larger advantage compared with CPU in terms of according to the little repetitive operation of correlation；At specifically execution level, GPU+CPU collaborations Reason, which accelerates the population intersection in single cycle to take aspect with mutation operation, has advantage, but is used due to counting always calculating When need loss time plus GPU-CPU data transfer communications, speed-up ratio is only capable of reaching average 1.08 or so.
(3) result of calculation
Model is asked based on the objective layered progressive parallel algorithm for improving NSGA-II using what this patent proposed Solution concentrates the Pa Lietuo solutions chosen a typical economy feature of environmental protection and taken into account, pollutant emission from the Pa Lietuo solutions acquired It is 42771.23268kg/ days to be worth summation, and operating cost is 25674.2 yuan/day, lists all kinds of balancing the loads and equipment a few days ago Dispatch situation, a few days ago the electric load profile of equilibrium of Optimized Operation see Fig. 9, as seen from the figure, system is in the electricity price relatively low period 23: 00—8:00 buys electric power to meet the needs of electric load from public electric wire net, and the amount of buying is less than the optimal situation of economy, is more than The optimal situation of the feature of environmental protection.When entering crest segment peace section electricity price, miniature gas turbine enter rated operation completely send out with Meet electrical load requirement, gross generation is between economy optimal situation and feature of environmental protection optimal situation.
The economy feature of environmental protection take into account in the case of a few days ago Optimized Operation space thermic load supply curve see Figure 10, Space Thermal in figure Load is by air-conditioning system, gas fired-boiler and the system combined supply of miniature gas turbine, and thermic load is main between low ebb electricity price space-time It is provided by air-conditioning, the crest segment electricity price moment adds waste heat boiler and gas fired-boiler energy supply in miniature gas turbine system.
The economy feature of environmental protection take into account in the case of a few days ago Optimized Operation hot water load supply curve see Figure 11, as seen from the figure, should The hot water load of cool and thermal power integrated energy system is met by waste heat boiler and hot energy storage device, the supply situation of hot water load and Feature of environmental protection index optimal situation is only embodied in the scheduling of hot energy storage, and population differences are little.
The economy feature of environmental protection take into account in the case of a few days ago Optimized Operation space refrigeration duty supply curve see Figure 12, in figure, space Refrigeration duty is by air-conditioning system and miniature gas turbine system-lithium bromide absorption refrigerating set joint supply, in low ebb electricity price Space refrigeration duty is mainly provided by air-conditioning, remaining moment is mainly by the lithium-bromide absorption-type refrigerating machine in miniature gas turbine system Group provides, and when lithium bromide absorption refrigerating set refrigerating capacity deficiency, space refrigeration peak regulation equipment is served as in family with air-conditioning system.
The cooling load supply curve of Optimized Operation freezing a few days ago is shown in Figure 13 in the case of the economy feature of environmental protection is taken into account, can by figure Know, the freezing cooling load of the cool and thermal power integrated energy system in the region is by lithium bromide absorption refrigerating set, electric refrigerating machine Combine with cold energy storage device and meet, three classes powering device is contributed more uniform.
The economy feature of environmental protection take into account in the case of a few days ago Optimized Operation energy storage device operating status curve see Figure 14, can by figure Know, energy storage device operating status is less than the average lotus energy state of first two situation in the case of the economy feature of environmental protection is taken into account, because not Need the limit for reaching economy or feature of environmental protection target, the two in the case of taking into account whole energy storage device operation can leave it is certain Nargin.
The pollutant discharge amount of all kinds of powering devices and public electric wire net is shown in figure in system in the case of the economy feature of environmental protection is taken into account 15, as seen from the figure, in the case of the economy feature of environmental protection is taken into account the discharge of pollutant be mainly derived from public electric wire net energy-consuming, secondly It is discharged for gas turbine and its corollary system and gas fired-boiler.The discharge of pollutant is between warp in the case of the economy feature of environmental protection is taken into account Between the Ji property optimal two kinds of situations of the optimal and feature of environmental protection, pollutant emission largely had not only been caused, but also will be entire cold The operating cost control of thermoelectricity integrated energy system is a kind of scheduling strategy more balanced within the specific limits, the type solution Result can be directly used for distribution scheduling system and performed, meanwhile, can be different according to user in practical engineering application Demand selects optimized operation scheme, such as economic optimum, optimal, the economic and environment-friendly equilibrium of environmental protection.
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (5)

  1. A kind of 1. integrated energy system traffic control method suitable for multiple-objection optimization scene, it is characterised in that：According to cold and hot The method of operation of electric integrated energy system is established the cool and thermal power integrated energy system optimal operation model under multiple target, is not only examined Consider the economic index of system operation, it is also contemplated that the feature of environmental protection index of system operation, the object function of feature of environmental protection index are：
    F=min (PGTotal)=min (δ1×PGC+δ2×PGS+δ3×PGN)
    Wherein：F be system operation feature of environmental protection index, PGTotalFor the day operation gross contamination emission of system, PGC、PGSWith PGNRespectively system day carbon, day sulfide and day emitting nitride amount, δ1、δ2And δ3Respectively carbon emission, sulfide emission With the weight coefficient of emitting nitride.
  2. 2. the integrated energy system traffic control method according to claim 1 suitable for multiple-objection optimization scene, special Sign is：Include the total exhaust emission constraint of pollutant in region and all kinds of dirts for the bound for objective function of feature of environmental protection index Contaminate object by when exhaust emission constraint, then constraints be specially：
    Total carbon emissions constraint in region：0≤PGC≤PGC max
    Overall sulfur emissions constraint in region：0≤PGS≤PGS max
    Total nitrogen exhaust emission constraint in region：0≤PGN≤PGN max
    Being constrained by moment carbon emission in region：
    Being constrained by moment carbon emission in region：
    Being constrained by moment carbon emission in region：
    Wherein：N is the distributed generation system total amount that includes in system, PGC max、PGS maxAnd PGN maxRespectively system day carbon, Day sulfide and the day emitting nitride amount upper limit,WithRespectively carbon, sulfide and the nitridation of t period systems The object discharge capacity upper limit,WithThe respectively carbon for the electric energy that distributed generation system i is bought to public electric wire net, sulphur Compound and emitting nitride amount,WithThe respectively carbon of the miniature gas turbine of distributed generation system i, vulcanization Object and emitting nitride amount,WithRespectively the carbon of the gas fired-boiler of distributed generation system i, sulfide and Emitting nitride amount, i=1,2 ..., n.
  3. 3. the integrated energy system traffic control method according to claim 1 suitable for multiple-objection optimization scene, special Sign is：Using progressive parallel II algorithms of NSGA- of layering to being solved to cool and thermal power integrated energy system optimal operation model, Progressive II algorithms of parallel NSGA- are layered on the basis of NSGA-II algorithms, operation mould is optimized according to cool and thermal power integrated energy system The characteristics of type, quick non-dominant layer merger sort method realized fast hierarchical using more effective, will solve time complexity Line style logarithm rank is dropped to by a square rank.
  4. 4. the integrated energy system traffic control method according to claim 3 suitable for multiple-objection optimization scene, special Sign is：Progressive II algorithms of parallel NSGA- are layered on the basis of NSGA-II algorithms, end condition is arranged to limited something lost Passage number causes disaggregation close to Pa Lietuo forward positions, and subsequent application target reaches method and carries out binary search, by the calculating of back As a result the initial value as goal attainment method carries out local search and obtains Pa Lietuo disaggregation.
  5. 5. the integrated energy system traffic control method according to claim 3 suitable for multiple-objection optimization scene, special Sign is：Progressive II algorithms of parallel NSGA- are layered on the basis of NSGA-II algorithms, it is excellent according to cool and thermal power integrated energy system The characteristics of changing moving model designs the solution procedure that parallel computation scheme accelerates object function, using the part of parallel computation scheme Reach method including non-dominant layer merger sort sections and application target and realize Fast Convergent part.
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CN109670639A (en) * 2018-12-17 2019-04-23 邓茂云 Regional complex energy resource system real-time optimization method based on multiple target grey wolf algorithm
CN109919474A (en) * 2019-02-28 2019-06-21 新奥数能科技有限公司 It is a kind of based on internet platform comprehensive energy quantization select and purchase optimization method and system
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Application publication date: 20180518