CN106773704B - Multi-system joint optimization scheduling method and device - Google Patents

Multi-system joint optimization scheduling method and device Download PDF

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CN106773704B
CN106773704B CN201710004834.3A CN201710004834A CN106773704B CN 106773704 B CN106773704 B CN 106773704B CN 201710004834 A CN201710004834 A CN 201710004834A CN 106773704 B CN106773704 B CN 106773704B
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肖炘
曾玉娇
曹宏斌
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Institute of Process Engineering of CAS
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Abstract

The invention discloses a multisystem joint optimization scheduling method and device. The method comprises the following steps: acquiring related data of a gas-steam-electric power system of a steel enterprise; determining a performance model of each energy conversion device in the gas-steam-electric power system; determining a media distribution energy pipe network model; setting the time interval number contained in the whole optimized scheduling cycle, and acquiring input data; establishing a combined optimization scheduling model of the gas-steam-electric power system; solving the combined optimization scheduling model by using a chaotic particle swarm algorithm to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system; and finally, generating fuel distribution, steam and power load distribution, energy distribution and outsourcing power transmission optimization schemes of all the production users and optimal comprehensive objective function indexes of all the energy conversion devices. The multisystem joint optimization scheduling method and the device realize joint optimization of the multi-energy medium.

Description

Multi-system joint optimization scheduling method and device
Technical Field
The embodiment of the invention relates to the technical field of energy optimization scheduling, in particular to a multi-system joint optimization scheduling method and device.
Background
The steel industry is one of important basic raw material industries in national economic construction, but is an energy consumer, and the consumed energy accounts for about 15 percent of the total energy consumption in China. High energy consumption not only leads to increased costs of the steel product but also means more pollution and emissions. The purpose of the energy system optimization regulation and control is to reasonably adjust the energy conversion and transmission and distribution links according to the conditions of the energy use link and the energy recovery link in the production process, ensure the energy supply and demand balance in the production process and reduce the secondary energy emission and energy outsourcing to the maximum extent so as to achieve the aims of energy conservation, emission reduction and cost reduction of iron and steel enterprises.
The primary energy and the secondary energy consumed in the production process of the steel enterprises comprise more than 20 kinds of electricity, raw coal, heavy oil, diesel oil, coal gas, water, coke, steam, oxygen, compressed air and the like. Among them, the interaction between three mediums of gas, steam and electric power is the most frequent, and the degree of association is the highest, and the schedulability is big. Fig. 1 shows the overall architecture of a typical steel enterprise gas-electric-power system in the prior art. On one hand, the coal gas can convert heat energy into electric energy through a power generation device, and can also generate steam through steam power equipment to be input into a steam pipe network; on the other hand, some energy conversion devices also take on the task of providing different quality steam to the heat consumer while generating electricity. Therefore, the byproduct gas and steam are closely related to the production of electricity, and both the quantity and quality of the byproduct gas and steam have an indirect or direct influence on the quantity of electricity produced. Because the three energy media have various forms of production consumption, storage, buffering, transmission and distribution and the like, and have complex association relations of conversion, substitution and the like, the energy optimization scheduling becomes a very complex project, the planning configuration and the adjustment operation are carried out only by depending on the experience of field production personnel, the economic operation of the whole system is difficult to ensure, and a large amount of energy is wasted.
At present, most of researches on energy system optimization scheduling of iron and steel enterprises are still focused on a single energy medium subsystem level, such as a gas system, a steam system, an oxygen system and the like, from the perspective of system engineering are not widely developed, multiple energy media are analyzed as a whole, coupling relations among different energy sources are ignored, the scheme that only one energy medium is considered is only locally optimized, the global optimal balance effect cannot be guaranteed, and obviously the requirement that enterprises achieve the minimum energy consumption cost cannot be supported. Therefore, in order to describe the actual operation condition of the energy system of the iron and steel enterprise more accurately and provide a more reasonable optimization scheduling scheme, it is necessary to comprehensively consider the relation of various energy media and realize the joint optimization of the multi-energy media, particularly three important energy media of gas, steam and electric power, so as to realize the efficient, economic and orderly operation of the energy system, reduce the energy cost of the enterprise to the maximum extent and improve the economic benefit.
Disclosure of Invention
In view of the above technical problems, embodiments of the present invention provide a method and an apparatus for analyzing joint optimization scheduling of multiple systems, so as to implement joint optimization of multiple energy mediums.
In one aspect, an embodiment of the present invention provides a multi-system joint optimization scheduling method, where the method includes:
acquiring related data of a gas-steam-electric power system of a steel enterprise, wherein the related data comprises an energy system network topological structure and characteristic parameters of each energy conversion device in the system;
determining a performance model of each energy conversion device in the gas-steam-electric power system;
determining a media distribution energy pipe network model;
setting the number of time segments contained in the whole optimized scheduling period, and acquiring input data required by energy optimized scheduling calculation;
establishing a joint optimization scheduling model of the gas-steam-electric power system, wherein the optimization scheduling model comprises an objective function and constraint conditions;
solving the combined optimization scheduling model by using a chaotic particle swarm algorithm to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system;
and finally, generating fuel distribution, steam and power production, energy distribution and outsourcing power transmission optimization schemes of all production users and optimal comprehensive objective function indexes of all the energy conversion devices.
In another aspect, an embodiment of the present invention further provides a multi-system joint optimization scheduling apparatus, where the apparatus includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring related data of a gas-steam-electric power system of a steel enterprise, and the related data comprises a network topology structure of an energy system and characteristic parameters of energy conversion equipment in the system;
the performance model determining module is used for determining performance models of all energy conversion equipment in the gas-steam-electric power system;
the pipe network model determining module is used for determining a medium distribution energy pipe network model;
the time segment number setting module is used for setting the time segment number contained in the whole optimized dispatching cycle and acquiring input data required by energy optimized dispatching calculation;
the scheduling model establishing module is used for establishing a combined optimization scheduling model of the gas-steam-power system, wherein the optimization scheduling model comprises an objective function and constraint conditions;
the model solving module is used for solving the combined optimization scheduling model by adopting a chaotic particle swarm algorithm so as to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system;
and the scheme generation module is used for generating fuel distribution, steam and power production, energy distribution and outsourcing power transmission optimization schemes of all production users and optimal comprehensive objective function indexes of all the energy conversion devices.
According to the multi-system joint optimization scheduling method and device provided by the embodiment of the invention, the influence of the change of fuel, load and operating conditions on efficiency is considered in the construction of the performance model of each energy conversion device, and the nonlinear characteristics contained in the actual production of each device are reflected by adopting a data multi-parameter nonlinear fitting processing technology, so that the accuracy and the representativeness of the model are improved; the influence of fuel price, time-of-use electricity price, coal gas emission punishment, coal gas cabinet fluctuation and boiler nozzle switch change on system energy cost is comprehensively considered in the construction of an optimized scheduling model, and various constraint conditions such as energy demand constraint, equipment capacity constraint, gateway power constraint, boiler nozzle constraint, coal gas cabinet safety constraint, variable load rate limitation and the like of each production unit are considered at the same time, so that the performability of an energy optimized scheduling scheme is ensured; meanwhile, the chaotic particle swarm algorithm is used for solving the established optimization model, the problem that multi-energy medium integrated optimization scheduling is high in dimension, non-convex, non-linear and multi-constraint can be well solved, the defect that a standard particle swarm algorithm is prone to being trapped in local convergence and prematurity can be overcome, the most economical coal gas distribution, steam and power production and outsourcing power transmission schemes can be found for an energy system, integrated coordination and optimized distribution of coal gas, steam and power in the whole process of each link of energy use, generation, conversion and transmission and distribution are achieved, accordingly, energy consumption and energy cost of enterprises are reduced, and economic benefits of the enterprises are improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is a block diagram of the overall architecture of a gas-steam-power system provided by the prior art;
FIG. 2 is a flowchart of a multi-system joint optimization scheduling method according to a first embodiment of the present invention;
FIG. 3 is a flowchart of performance model determination in a multi-system joint optimization scheduling method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating model solution in a multi-system joint optimization scheduling method according to a third embodiment of the present invention;
fig. 5 is a block diagram of a multi-system joint optimization scheduling apparatus according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
First embodiment
The embodiment provides a technical scheme of a multisystem joint optimization scheduling method.
Referring to fig. 2, the multi-system joint optimization scheduling method includes:
s21, acquiring relevant data of the coal gas-steam-electric power system of the iron and steel enterprise, including the network topology structure of the energy system and the characteristic parameters of each energy conversion device in the system. The characteristic parameters of each energy conversion device comprise parameters such as a maximum load value, a minimum load value, a maximum load increasing speed, a maximum load decreasing speed, a normal operation range of the device, an upper limit of a coal gas blending combustion ratio, a lower limit of a mixed coal gas heat value and the like, and an initial operation state of the device.
And S22, determining the performance model of each energy conversion device in the coal gas-steam-electric power system of the iron and steel enterprise.
And S23, determining the media distribution energy pipe network model. The method comprises the steps of obtaining generation, conversion and transmission and distribution information of various energy media of coal gas, steam and electric power, numbering main pipe networks of the various energy media respectively, adopting directed graph theory of graph theory according to the flowing direction of the energy media, carrying out data transformation on information of a pipe network graph (a branch matrix and a branch matrix are mixed with a ring network) through a correlation matrix and a basic loop matrix, and correlating the information with energy production and consumption nodes to construct a medium-divided energy pipe network model.
S24, setting the number of time segments contained in the whole optimized dispatching cycle, and acquiring input data required by energy optimized dispatching calculation: the method comprises data such as supply and demand prediction curves of various energy media in a scheduling period, production and maintenance plans, energy demand prediction curves of various production users, power peak-valley time periods and prices, outsourcing power transmission plans and the like.
And S25, establishing a coal gas-steam-electric power system combined optimization scheduling model of the iron and steel enterprise. The optimized scheduling model includes an objective function and a constraint condition.
The objective function is: the method takes the minimum total operation cost of the whole steel enterprise gas-steam-electric power system in a whole period as an objective function, and specifically comprises outsourcing fuel cost, boiler water supply cost, gas cabinet position fluctuation cost (penalty of gas cabinet capacity overrun and normal capacity deviation), gas diffusion penalty cost, boiler igniter switch change cost, equipment maintenance cost, outsourcing electricity cost when the electric energy supply is insufficient and outsourcing electricity delivery income when the electric energy is surplus.
Figure BDA0001202764230000061
In the formula, T is the number of time periods contained in one scheduling cycle, I, J, K and M respectively represent the number of boilers, steam turbines, cogeneration equipment and waste heat and energy power generation equipment, G represents the number of byproduct gas, and tau represents the working time of each operation time period; cng，Ccoal，CoilAnd CwRespectively representing the unit prices of outsourcing natural gas, power coal, heavy oil and boiler feed water;andrespectively representing the natural gas consumption, the power coal consumption, the heavy oil consumption and the water consumption of the device i in a period t;
Figure BDA0001202764230000071
the weight of the penalty for the gas g in the gas holder,
Figure BDA0001202764230000072
representing the diffusion quantity of the gas g in the gas tank in the t period;
Figure BDA0001202764230000073
and
Figure BDA0001202764230000074
g is the penalty weight of the gas cabinet for the cabinet level exceeding the upper limit and the lower limit of the normal fluctuation range,
Figure BDA0001202764230000075
and
Figure BDA0001202764230000076
respectively representing the quantity of the gas tank level exceeding the upper limit and the lower limit of the normal fluctuation range;and
Figure BDA0001202764230000078
respectively represents the penalty weight of the deviation of the tank level of the g gas tank from the optimal position upper limit and the optimal position lower limit,
Figure BDA0001202764230000079
and
Figure BDA00012027642300000710
respectively representing the amount of deviation of the tank level of the g gas tank from the upper limit and the lower limit of the optimal position;penalty weight, Δ N, representing boiler igniter switch changei,g,tIndicating the number of switch changes of all igniters in the boiler;
Figure BDA00012027642300000712
and
Figure BDA00012027642300000713
respectively representing the penalty weight of simultaneous on-off conversion of two and three igniters of gas g in the same boiler;
Figure BDA00012027642300000715
the two igniters are binary variables and respectively represent the states that two igniters of the coal gas g in the same boiler are opened and closed at the same time and three igniters are opened and closed at the same time in the period t;
Figure BDA00012027642300000716
and Fi,tRespectively represent the equipment operation and maintenance cost (including equipment depreciation, maintenance cost, manual compensation and the like) of the steam generating equipment i and the steam yield in the period t,
Figure BDA00012027642300000717
and Pj,tRespectively representing the equipment operation and maintenance cost (including equipment depreciation, maintenance cost, artificial compensation and the like) and the power generation amount in the time period t of the power generation equipment j;
Figure BDA00012027642300000718
represents the gateway transmission power, delta, of the enterprise intranet and the large power grid in the period of ttIndicating the power supply status of the external network during the time period t,δtis 0, the quantity 1 represents the presence or absence of external power supply,
Figure BDA00012027642300000719
is the price of the outsourcing electricity in the period t,
Figure BDA00012027642300000720
is the outgoing electricity rate for the period t.
The constraint conditions include: the system comprises constraint conditions such as an equipment performance model, system energy balance constraint, equipment capacity constraint, unit load change rate constraint, fuel consumption range, mixed gas heat value lower limit requirement and the like.
1) System energy balance constraints
And (3) power balance constraint:
Figure BDA00012027642300000721
steam balance constraint:
Figure BDA0001202764230000081
and (3) coal gas balance constraint:
Figure BDA0001202764230000082
in the formula, r represents steam grade number, upper marks in and out represent material flow entering and flowing out respectively, F represents steam flow or coal gas flow, P represents power generation power,
Figure BDA0001202764230000083
respectively representing the power demand and the r-level steam demand, V, during a period tg,tRepresenting the level of the g gas tank during the time period t,
Figure BDA0001202764230000084
denotes the total amount of by-product gas g generated in the period t, Fu,g,tThe consumption of the byproduct gas g in the t time period for the production user u.
2) Device capability constraints
Boiler capacity constraint:
Figure BDA0001202764230000086
and (3) steam turbine capacity constraint:
Figure BDA0001202764230000087
Figure BDA0001202764230000088
Figure BDA0001202764230000089
capacity constraint of cogeneration plant:
Figure BDA00012027642300000810
Figure BDA00012027642300000811
Figure BDA00012027642300000812
and (3) restraining the capacity of the waste heat and complementary energy power generation equipment:
Figure BDA00012027642300000813
Figure BDA0001202764230000091
in the formula, min and max represent the minimum value and the maximum value, respectively.
3) Boiler nozzle restraint
Figure BDA0001202764230000094
Figure BDA0001202764230000096
Figure BDA0001202764230000097
In the formula (I), the compound is shown in the specification,
Figure BDA0001202764230000099
representing the consumption change value of the coal gas g in the boiler i in the t period;
Figure BDA00012027642300000910
representing the unit variation of the igniter of the coal gas g in the boiler i;
Figure BDA00012027642300000911
representing the number of igniter switches that boiler i is on during a period t;
Figure BDA00012027642300000912
indicating the number of igniter switches that boiler i is off during the t period.
4) Gas holder operation restraint
Figure BDA00012027642300000913
Figure BDA00012027642300000914
Figure BDA00012027642300000915
Figure BDA00012027642300000916
In the formula (I), the compound is shown in the specification,
Figure BDA00012027642300000917
and
Figure BDA00012027642300000918
respectively representing the lower limit, the upper limit, the lower limit and the upper limit of the tank level of the g gas tank.
5) Unit load change rate constraints
-DRj≤Pj,t-Pj,t-1≤URj(26)
-DRk≤Pk,t-Pk,t-1≤URk(27)
-DRm≤Pm,t-Pm,t-1≤URm(28)
In the formula, UR and DR are the maximum load that can be increased and the maximum load that can be decreased in a period of time by the unit.
6) Mixed gas combustion heat value restriction
In the formula (I), the compound is shown in the specification,
Figure BDA0001202764230000102
represents i pairs of boilersLower limit of calorific value of the mixed gas, HgIndicating the calorific value of the by-product gas g.
7) Energy requirement for production user
Figure BDA0001202764230000103
Figure BDA0001202764230000104
Figure BDA0001202764230000106
In the formula, Hu,tIndicating the heating value of the mixed gas used by the production user u during the period t,
Figure BDA0001202764230000107
to produce the energy demand of user u during time t.
8) Gateway switching power constraints
Figure BDA0001202764230000108
In the formula, Ptie,minAnd Ptie,maxRespectively representing the lower limit and the upper limit of the exchange power of the intranet and the extranet gateway.
And S26, solving the optimized scheduling model established in the previous step by adopting a chaotic particle swarm algorithm to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system.
And S27, generating fuel distribution, steam and power production, energy distribution and outsourcing power transmission optimization schemes of various production users and optimal comprehensive objective function indexes of various energy conversion devices.
By adopting the multi-energy system joint optimization scheduling method provided by the invention, a coal gas-steam-electric power system joint optimization scheduling scheme in a plurality of scheduling periods in the future can be provided for iron and steel enterprises, and the scheme comprises a coal gas distribution scheme in the whole production system and an energy system, production scheduling of steam among energy conversion equipment, and production scheduling and outsourcing strategies of electric power among power generation equipment and an external network; the invention comprehensively considers the influence of fuel price, time-of-use electricity price, coal gas emission punishment, coal gas cabinet fluctuation and boiler nozzle switch change on the operation cost of the energy system, establishes a coal gas-steam-electric power system combined optimization scheduling model of the steel enterprise by taking the minimum total operation cost of the whole integrated energy system in a whole period as an objective function and taking energy demand constraint, equipment capability constraint, gateway power constraint, boiler nozzle constraint, coal gas cabinet safety constraint, variable load rate constraint and the like of each production unit as constraint conditions, adopts an intelligent optimization algorithm to iteratively solve a whole process optimization allocation scheme of obtaining three media of coal gas, steam and electric power, and effectively solves the problem of multi-energy medium integrated optimization scheduling of the steel enterprise.
Second embodiment
The present embodiment further provides a technical solution for determining a performance model in a multi-system joint optimization scheduling method based on the foregoing embodiments of the present invention. In the technical scheme, the determining the performance model of each energy conversion device in the gas-steam-power system comprises the following steps: acquiring initial data required for establishing the performance model of the energy conversion equipment from a database server of a discrete information system; preprocessing the initial data, and drawing a working condition operation characteristic curve of the energy conversion equipment by combining an equipment design working condition diagram and thermal test data; and constructing a performance model of the energy conversion equipment by adopting a data-based multi-parameter nonlinear fitting processing technology according to the characteristics of the drawn characteristic curve.
Referring to fig. 3, determining a performance model of each energy conversion device in the gas-steam-power system includes:
s31, acquiring initial data required for establishing performance models of energy conversion equipment in the energy system of the iron and steel enterprise from a system database server such as DCS, EMS and the like, wherein the initial data comprises historical data of power gas consumption, byproduct gas consumption and steam production of a boiler, steam intake, power generation and steam extraction of a steam turbine, fuel consumption, power generation and steam extraction of cogeneration equipment, and steam intake (or heat energy recovery), power generation and steam extraction of waste heat power generation equipment; the calorific value of the byproduct gas, the temperature and pressure of steam of each grade, and the calorific value and price of outsourcing fuel.
And S32, preprocessing the initial data (preprocessing methods include but are not limited to an outlier detection algorithm, a linear smoothing algorithm or a standardization algorithm), and drawing operation characteristic curves (energy consumption characteristic curves or steam consumption characteristic curves) of various equipment under different working conditions by combining an equipment design working condition diagram and thermal test data.
S33, according to the characteristics of the drawn characteristic curve, adopting a data-based multi-parameter nonlinear fitting processing technology to construct a performance model of each device of the energy system, wherein the model expression is as follows:
the ith multi-fuel co-fired boiler:
Figure BDA0001202764230000121
j, a turbonator (steam extraction back pressure type or steam extraction condensing type):
back pressure model:
Figure BDA0001202764230000122
an extraction type model:
Figure BDA0001202764230000123
the kth heat and power cogeneration equipment (a coal-gas-mixed coal-fired boiler-steam turbine generator set or a gas-steam combined cycle generator set):
Figure BDA0001202764230000132
the mth residual heat and energy power generation equipment (such as dry quenching power generation, sintering residual heat power generation and blast furnace top residual pressure power generation):
Figure BDA0001202764230000133
in the formula, q represents a fuel type number, r represents a steam grade number, superscripts in and out represent the entry and exit of a material flow, respectively, F represents the fuel consumption or the steam flow, P represents the power generation power, H represents the low calorific value of the fuel or the enthalpy value of the steam, E represents the energy available in the recovered steam or residual energy, and α, γ, μ, λ, ν, Φ, and σ represent regression coefficients of an equipment model.
According to the embodiment, the performance model determination in the multi-system joint optimization scheduling method is realized through initial data acquisition, data preprocessing, curve drawing and model fitting.
Third embodiment
The embodiment of the invention is based on the above embodiments, and further provides a technical scheme for model solution in a multi-system joint optimization scheduling method. In the technical scheme, the chaos particle swarm algorithm is adopted to solve the joint optimization scheduling model so as to obtain the optimization scheme of the coal gas distribution, the steam and power production and the outsourcing power transmission of the whole coal gas-steam-power system, and the optimization scheme comprises the following steps: setting the particle population scale, the iteration times, the control parameters and the optimization variable range; initializing a particle population; constructing a fitness function; calculating the fitness value of each particle of the current population, and determining the individual extreme value of each particle and the global extreme value of the whole particle swarm; updating control parameters, wherein the control parameters comprise inertia weight and acceleration factor; updating the speed and the position of each particle of the current population; judging whether the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint; if the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint, judging whether the population individual meets the climbing rate constraint of the unit; if the population individuals do not meet the climbing rate constraint, restoring the unit climbing constraint by adopting a heuristic method; if the population individual meets the climbing rate constraint, performing population variation by adopting a self-adaptive variation mechanism; updating the individual extremum and the global extremum; and judging whether an iteration stop condition is reached, if so, determining the weight of each dimension in the global extreme value of the last iteration as the result.
Referring to fig. 4, solving the joint optimization scheduling model by using a chaotic particle swarm algorithm to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system comprises:
s401, particle population scale, iteration times, control parameters and optimization variable range are set.
S402, initializing the population. Each particle in the population is a solution of the optimization problem, namely, the particle is composed of a set of decision variables, specifically comprising input quantity and output quantity (comprising fuel consumption, steam inlet quantity, steam production quantity, steam extraction quantity, power generation quantity and the like) of each energy device, gateway power and energy distribution quantity of each production user. And (4) initializing the population, namely randomly generating an initial population within the variable range of each decision variable feasible region.
And S403, constructing a fitness function. The optimization goal of the particle swarm algorithm is to find an individual with an optimal fitness value, and in order to realize the optimization selection of the individual and enable the optimization result to meet the equality constraint of the equipment performance model, the fitness function is defined as the sum of a target function and a penalty function. Wherein, the penalty function for processing the equation constraint of the equipment performance model is defined as follows:
Figure BDA0001202764230000141
s404, calculating the fitness value of each particle of the current population, and determining the individual extreme value of each particle and the global extreme value of the whole particle swarm.
And S405, updating control parameters, namely inertia weight and acceleration factor. For enhancing global convergence of algorithm, reducing trapping of local extremaPossibly, the inertia weight is adjusted by using chaotic ergodic search in each iteration process, that is, the inertia weight W will gradually decrease according to the chaotic model as the iteration number increases. Furthermore, the acceleration factor c1Using a linear decrease, c2A linear incremental strategy, which further enhances the convergence of the particle to the global optimum.
And S406, updating the speed and the position of each particle of the current population.
S407, judging whether the population individuals meet equipment capacity constraint, production user energy demand constraint and gateway power constraint, and if so, turning to operation S408; if not, the following pseudo code is adopted to process the obtained infeasible scheme (namely the individuals not meeting the constraint condition):
IF Y<YminTHEN
IF
Figure BDA0001202764230000152
THEN
Figure BDA0001202764230000153
wherein, YiRepresenting the ith decision variable.
S408, judging whether the population individuals meet the climbing rate constraint of the unit, and if so, turning to operation S409; and if the set climbing constraint does not meet the requirements, restoring the unit climbing constraint by adopting a heuristic method for the obtained infeasible scheme (the individuals not meeting the constraint conditions).
The heuristic modification strategy processing extreme value climbing rate constraint process comprises the following steps:
1) the initial genset number j is 1.
2) Determining the active output upper limit of the generator j in the time period t
Figure BDA0001202764230000154
And lower limit
Figure BDA0001202764230000155
If t is equal to 1, the process is repeated,and
Figure BDA0001202764230000157
if not, then,
Figure BDA0001202764230000158
and
Figure BDA0001202764230000159
3) judging active power P of generator j in time period tj,tWhether the capacity and climbing constraints of the generator are met. If not, then further correction is made according to the following equation.
Figure BDA0001202764230000161
4) If j is less than or equal to NG(wherein, N isGRepresenting the number of generators), j equals j +1 and jumps to step 2).
5) And finishing the heuristic correction process.
And S409, mutation. In order to increase the diversity of the population, an adaptive mutation mechanism is adopted. First, for each individual in the population, two different mutation vectors are generated according to two different mutation operators, differential mutation and gaussian mutation. Then, the adaptive values corresponding to the two variation vectors are compared with the new adaptive value of the current individual, and the most suitable adaptive value is selected as the next generation according to a greedy principle.
And S410, updating the individual extremum and the global extremum. In order to select the individual with the optimal fitness as the next generation and meet the energy balance constraint, the energy balance constraint is processed by a method based on a feasible rule, the domination relationship judgment is carried out, and the updating of the individual extreme value and the global extreme value is realized.
The constraint processing and extreme value updating process based on the feasibility rule comprises the following steps:
1) setting t as the iteration number of the particle swarm algorithm, and enabling t to be 1;
2) setting i as an individual serial number, wherein i is 1;
3) setting Pbesti(t) representing the best individual position of the population individual i in the t iteration, and judging Pbesti(t) whether the constraint condition of the system energy balance is satisfied, if not, calculating the constraint violation quantity. Wherein, the constraint violation quantity calculation function is defined as follows:
Figure BDA0001202764230000171
4) setting of YiAnd (t +1) representing the individual position of the population individual i in the t +1 th iteration, and judging whether the population individual i meets the energy balance constraint. If the system energy balance constraint is met, marking as a feasible solution; otherwise, marking as an infeasible solution and calculating the violation value F of the system energy balance constraint conditionV(Yi(t+1))。
5) According to feasibility rules, limiting individual value Pbesti(t) and Yi(t +1) and updating Pbest if one of the following three conditions is meti(t), i.e. Pbesti(t+1)＝Yi(t + 1); otherwise Pbesti(t+1)＝Pbesti(t) of (d). The specific conditions are as follows:
①Pbesti(t) and Yi(t +1) are all feasible solutions, and YiThe fitness value of (t +1) is better than that of Pbesti(t) fitness value;
②Xi(t +1) is a feasible solution, Pbesti(t) is infeasible to solve;
③ if Pbesti(t) and Yi(t +1) are all infeasible solutions, and YiThe violation quantity of the constraint condition of (t +1) is less than PbestiViolation of (t), i.e. FV(Yi(t+1))<FV(Pbesti(t))。
6) Supposing Gbest (t) to represent the best position of the population in the t iteration, judging whether Gbest (t) meets the energy balance constraint condition, if not, calculating the constraint regulation violationInverse FV(Gbest(t))。
7) Also, the individual extreme value Pbest is determined according to the feasibility rulei(t) comparing with the population extreme value Gbest (t), if one of the following three conditions is met, updating Gbest (t), namely Gbest (t +1) ═ Pbesti(t), otherwise Gbest (t +1) ═ Gbesti(t) of (d). The specific conditions are as follows:
① Gbest (t) and Pbesti(t) are all feasible solutions, and Pbesti(t) a fitness value better than that of gbest (t);
②Pbesti(t) is feasible solution, Gbest (t) is infeasible solution;
③ if Gbest (t) and Pbesti(t) are all infeasible solutions, and PbestiThe violation of the constraint of (t) is smaller than the violation of Gbest (t), i.e. FV(Pbesti(t))<FV(Gbest(t))。
8) And i +1, judging whether the population is traversed, if so, turning to the step 9), and otherwise, turning to the step 3).
9) t is t + 1. Judging whether the particle swarm algorithm reaches the maximum iteration times, if so, turning to the step 10), otherwise, turning to the step 3).
S411, judging whether an iteration stop condition is reached, if so, determining the weight of each dimension in the global extreme value of the last iteration as the weight; if not, the process moves to operation S405 and the algorithm continues to iterate until the condition is met.
In the embodiment, the model solving operation is realized by solving the constructed joint optimization scheduling model according to the chaotic particle swarm optimization.
Fourth embodiment
The embodiment provides a technical scheme of a multisystem joint optimization scheduling device. Referring to fig. 5, the multi-system joint optimization scheduling apparatus includes: the system comprises a data acquisition module 51, a performance model determination module 52, a pipe network model determination module 53, a time period number setting module 54, a scheduling model establishing module 55, a model solving module 56 and a scheme generating module 57.
The data acquisition module 51 is configured to acquire relevant data of a gas-steam-power system of a steel enterprise, where the relevant data includes a network topology structure of an energy system and characteristic parameters of each energy conversion device in the system.
The performance model determining module 52 is configured to determine a performance model of each energy conversion device in the gas-steam-electric power system.
The pipe network model determining module 53 is configured to determine a medium distribution energy pipe network model.
The time segment number setting module 54 is configured to set the number of time segments included in the entire optimal scheduling period, and acquire input data required by the energy optimal scheduling calculation.
The scheduling model establishing module 55 is configured to establish a joint optimization scheduling model of the gas-steam-power system, where the optimization scheduling model includes an objective function and a constraint condition.
The model solving module 56 is configured to solve the joint optimization scheduling model by using a chaotic particle swarm algorithm, so as to obtain a gas distribution, steam and power production, and outsourcing power transmission optimization scheme of the whole gas-steam-power system.
The scheme generating module 57 is configured to generate a final fuel distribution, steam and power generation, an energy distribution and outsourcing power transmission optimization scheme for each production user, and an optimal comprehensive objective function index for each energy conversion device.
Further, the performance model determining module 52 includes: the device comprises an initial data acquisition unit, a curve drawing unit and a model building unit.
The initial data acquisition unit is used for acquiring initial data required for establishing the energy conversion equipment performance model from a database server of a discrete information system.
And the curve drawing unit is used for preprocessing the initial data and drawing a working condition operation characteristic curve of the energy conversion equipment by combining an equipment design working condition diagram and thermal test data.
And the model construction unit is used for constructing the performance model of the energy conversion equipment by adopting a data-based multi-parameter nonlinear fitting processing technology according to the characteristics of the drawn characteristic curve.
Further, the model solving module 56 includes: the system comprises a parameter setting unit, a population initializing unit, a function constructing unit, a fitness calculating unit, a parameter updating unit, a speed position updating unit, a first constraint judging unit, a second constraint judging unit, a repairing unit, a variation unit, an extreme value updating unit and a calculation stopping unit.
The parameter setting unit is used for setting the particle population scale, the iteration times, the control parameters and the optimization variable range; the population initialization unit is used for initializing a particle population.
The function construction unit is used for constructing a fitness function.
The fitness calculating unit is used for calculating the fitness value of each particle of the current population and determining the individual extreme value of each particle and the global extreme value of the whole particle swarm.
The parameter updating unit is used for updating control parameters, wherein the control parameters comprise inertia weight and acceleration factor.
The speed and position updating unit is used for updating the speed and position of each particle of the current population.
The first constraint judging unit is used for judging whether the population individuals meet equipment capacity constraint, production user energy demand constraint and gateway power constraint.
And the second constraint judging unit is used for judging whether the population individual meets the climbing rate constraint of the unit or not when the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint.
And the repairing unit is used for repairing the unit climbing constraint by adopting a heuristic method when the population individuals do not meet the climbing rate constraint.
And the variation unit is used for performing population variation by adopting a self-adaptive variation mechanism if the population individuals meet the climbing rate constraint.
The extreme value updating unit is used for updating the individual extreme value and the global extreme value.
The calculation stopping unit is used for judging whether an iteration stopping condition is met, and if the iteration stopping condition is met, the weight of each dimension in the global extreme value of the last iteration is the required result.
Further, the repair unit is specifically configured to: initializing a generator set number; determining the upper limit and the lower limit of active output of the generator in a determined period; and judging whether the active output of the generator in the determined time period meets the capacity and climbing constraint of the generator.
It will be understood by those skilled in the art that the modules or steps of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or it may be separately fabricated into various integrated circuit modules, or it may be fabricated by fabricating a plurality of modules or steps thereof into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A multi-system joint optimization scheduling method is characterized by comprising the following steps:
acquiring related data of a gas-steam-electric power system of a steel enterprise, wherein the related data comprises an energy system network topological structure and characteristic parameters of each energy conversion device in the system;
acquiring initial data required for establishing the energy conversion equipment performance model from a database server of a discrete information system, wherein the initial data comprises the power gas consumption, the byproduct gas consumption and the steam production of a boiler, the steam inlet, the power generation and the steam extraction of a steam turbine, the fuel consumption, the power generation and the steam extraction of cogeneration equipment, and historical data of the steam inlet or the recovered heat energy, the power generation and the steam extraction of waste heat power generation equipment; the calorific value of the byproduct gas, the temperature and pressure of steam of each grade, and the calorific value and price of outsourcing fuel;
preprocessing the initial data, and drawing a working condition operation characteristic curve of the energy conversion equipment by combining an equipment design working condition diagram and thermal test data;
according to the characteristics of the drawn characteristic curve, a performance model of the energy conversion equipment is constructed by adopting a data-based multi-parameter nonlinear fitting processing technology, and the model expression is as follows:
the ith multi-fuel co-fired boiler:
Figure FDA0002190573530000011
j, a turbonator (steam extraction back pressure type or steam extraction condensing type):
back pressure model:
Figure FDA0002190573530000012
an extraction type model:
Figure FDA0002190573530000013
the kth heat and power cogeneration equipment (a coal-gas-mixed coal-fired boiler-steam turbine generator set or a gas-steam combined cycle generator set):
Figure FDA0002190573530000014
Figure FDA0002190573530000021
the mth residual heat and energy power generation equipment:
Figure FDA0002190573530000022
in the formula, q represents a fuel type number, r represents a steam grade number, superscripts in and out represent the entry and exit of a material flow respectively, F represents the fuel consumption or the steam flow, P represents the power generation power, H represents the low-order heating value of the fuel or the enthalpy value of the steam, E represents the available energy in the recovered steam or residual energy, and α, gamma, mu, lambda, ν, phi and sigma are regression coefficients of an equipment model;
determining a media distribution energy pipe network model;
setting the number of time segments contained in the whole optimized scheduling period, and acquiring input data required by energy optimized scheduling calculation;
establishing a joint optimization scheduling model of the gas-steam-electric power system, wherein the optimization scheduling model comprises an objective function and constraint conditions;
solving the combined optimization scheduling model by using a chaotic particle swarm algorithm to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system;
and finally, generating fuel distribution, steam and power production, energy distribution and outsourcing power transmission optimization schemes of all production users and optimal comprehensive objective function indexes of all the energy conversion devices.
2. The method of claim 1, wherein solving the joint optimization scheduling model by using a chaotic particle swarm algorithm to obtain a gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole gas-steam-power system comprises:
setting the particle population scale, the iteration times, the initial value of the control parameter and the optimization variable range;
initializing a particle population;
constructing a fitness function;
calculating the fitness value of each particle of the current population, and determining the individual extreme value of each particle and the global extreme value of the whole particle swarm;
updating control parameters, wherein the control parameters comprise inertia weight and acceleration factor;
updating the speed and the position of each particle of the current population;
judging whether the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint;
if the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint, judging whether the population individual meets the climbing rate constraint of the unit;
if the population individuals do not meet the climbing rate constraint, restoring the unit climbing constraint by adopting a heuristic method;
if the population individual meets the climbing rate constraint, performing population variation by adopting a self-adaptive variation mechanism;
updating the individual extremum and the global extremum;
and judging whether an iteration stop condition is reached, if so, determining the weight of each dimension in the global extreme value of the last iteration as the result.
3. The method of claim 2, wherein the step of using a heuristic method to perform unit ramp constraint restoration comprises:
initializing a generator set number;
determining the upper limit and the lower limit of active output of the generator in a determined period;
and judging whether the active output of the generator in the determined time period meets the capacity and climbing constraint of the generator.
4. A multi-system joint optimization scheduling device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring related data of a gas-steam-electric power system of a steel enterprise, and the related data comprises a network topology structure of an energy system and characteristic parameters of energy conversion equipment in the system;
a performance model determination module for determining a performance model of each energy conversion device in the gas-steam-electric power system, the performance model determination module comprising: the system comprises an initial data acquisition unit, a data analysis unit and a data analysis unit, wherein the initial data acquisition unit is used for acquiring initial data required for establishing a performance model of the energy conversion equipment from a database server of a discrete information system, and the initial data comprises the power gas consumption, the byproduct gas consumption and the steam production of a boiler, the steam intake, the power generation and the steam extraction of a steam turbine, the fuel consumption, the power generation and the steam extraction of cogeneration equipment and historical data of the steam intake or the recovered heat energy, the power generation and the steam extraction of waste heat power generation equipment; the calorific value of the byproduct gas, the temperature and pressure of steam of each grade, and the calorific value and price of outsourcing fuel;
the curve drawing unit is used for preprocessing the initial data and drawing a working condition operation characteristic curve of the energy conversion equipment by combining an equipment design working condition diagram and thermal test data;
the model construction unit is used for constructing a performance model of the energy conversion equipment by adopting a data-based multi-parameter nonlinear fitting processing technology according to the characteristics of the drawn characteristic curve, and the model expression is as follows:
the ith multi-fuel co-fired boiler:
j, a turbonator (steam extraction back pressure type or steam extraction condensing type):
back pressure model:
Figure FDA0002190573530000042
an extraction type model:
Figure FDA0002190573530000043
the kth heat and power cogeneration equipment (a coal-gas-mixed coal-fired boiler-steam turbine generator set or a gas-steam combined cycle generator set):
Figure FDA0002190573530000044
the mth residual heat and energy power generation equipment:
in the formula, q represents a fuel type number, r represents a steam grade number, superscripts in and out represent the entry and exit of a material flow respectively, F represents the fuel consumption or the steam flow, P represents the power generation power, H represents the low-order heating value of the fuel or the enthalpy value of the steam, E represents the available energy in the recovered steam or residual energy, and α, gamma, mu, lambda, ν, phi and sigma are regression coefficients of an equipment model;
the pipe network model determining module is used for determining a medium distribution energy pipe network model;
the time segment number setting module is used for setting the time segment number contained in the whole optimized dispatching cycle and acquiring input data required by energy optimized dispatching calculation;
the scheduling model establishing module is used for establishing a combined optimization scheduling model of the gas-steam-power system, wherein the optimization scheduling model comprises an objective function and constraint conditions;
the model solving module is used for solving the combined optimization scheduling model by adopting a chaotic particle swarm algorithm so as to obtain a coal gas distribution, steam and power production and outsourcing power transmission optimization scheme of the whole coal gas-steam-power system;
and the scheme generation module is used for generating fuel distribution, steam and power production, energy distribution and outsourcing power transmission optimization schemes of all production users and optimal comprehensive objective function indexes of all the energy conversion devices.
5. The apparatus of claim 4, wherein the model solution module comprises:
the parameter setting unit is used for setting the particle population scale, the iteration times, the initial value of the control parameter and the optimization variable range;
a population initializing unit for initializing a particle population;
the function construction unit is used for constructing a fitness function;
the fitness calculation unit is used for calculating the fitness value of each particle of the current population and determining the individual extreme value of each particle and the global extreme value of the whole particle swarm;
the parameter updating unit is used for updating control parameters, wherein the control parameters comprise inertia weight and acceleration factors;
the speed and position updating unit is used for updating the speed and position of each particle of the current population;
the first constraint judging unit is used for judging whether the population individuals meet equipment capacity constraint, energy demand constraint of production users and gateway power constraint;
the second constraint judging unit is used for judging whether the population individual meets the climbing rate constraint of the unit or not when the population individual meets the equipment capacity constraint, the energy demand constraint of the production user and the gateway power constraint;
the restoring unit is used for restoring the unit climbing constraint by adopting a heuristic method when the population individuals do not meet the climbing rate constraint;
the variation unit is used for performing population variation by adopting a self-adaptive variation mechanism when the population individuals meet the climbing rate constraint;
the extreme value updating unit is used for updating the individual extreme value and the global extreme value;
and the calculation stopping unit is used for judging whether the iteration stopping condition is met, and if the iteration stopping condition is met, the weight of each dimension in the global extreme value of the last iteration is the required result.
6. The apparatus according to claim 5, wherein the repair unit is specifically configured to:
initializing a generator set number;
determining the upper limit and the lower limit of active output of the generator in a determined period;
and judging whether the active output of the generator in the determined time period meets the capacity and climbing constraint of the generator.
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