CN104392334A - Joint optimized scheduling method for multiple types of generating sets of self-supply power plant of iron and steel enterprise - Google Patents

Abstract

The invention discloses a joint optimized scheduling method for multiple types of generating sets of a self-supply power plant of an iron and steel enterprise, and belongs to the technical field of energy optimized scheduling of the iron and steel enterprise. Influence of fuel types and gas mixed burning amount on energy consumption of the sets is taken into consideration in construction of a set energy consumption characteristic model, fitting is performed under different gas mixed burning, and the accuracy and representativeness of the model are improved; and influence of the fuel cost, time-of-use power price and surplus gas dynamic change on the generating cost is considered comprehensively in construction of an optimized scheduling model, meanwhile, various constraint conditions including power balance constraint, generating set self-running constraint, purchased power quantity constraint, gas supply constraint, variable load rate limit and the like are considered, and the performability of a generation schedule is guaranteed. Optimization solution is performed on the models by adopting the adaptive particle swarm optimization algorithm, the problems of high dimensionality, nonconvexity, nonlinearity and multiple constraints of the power generation scheduling of the self-supply power plant can be well solved, power production optimization and purchasing rationalization are realized, surplus gas is sufficiently used, and the power supply cost is reduced to the greatest extent.

Description

Power plant for self-supply of iron and steel enterprise polymorphic type genset joint optimal operation method

Technical field

The invention belongs to iron and steel enterprise's energy source optimization dispatching technique field, in particular, provide the method for power plant for self-supply of a kind of iron and steel enterprise polymorphic type genset joint optimal operation.

Background technology

Power plant for self-supply is generally all had in large and medium-sized iron and steel enterprise of China, main interruptible customer as iron and steel enterprise's coal gas system regulates and absorbs surplus gas, and provide electric power for enterprise, reduce electric cost, energy-saving and environmental protection, increase economic efficiency on all serve good effect.Give full play to the existing generating capacity of power plant for self-supply, carry out production and the use of electric power, both can reduce the dependence to external electrical network electric energy, can electric cost have been reduced again, great effect is played to the situation alleviating local electrical network shortage of electric power simultaneously.

From power plant for self-supply, the generating of power plant for self-supply of iron and steel enterprise refers to by utilizing secondary energy such as blast furnace gas (BFG), coke-oven gas (COG), coal gas of converter (LDG) and the outsourcing primary energy (as steam coal) reclaimed in steel manufacture process to generate electricity with the form of burning.The private station generation technology of iron and steel enterprise has: boiler-turbine generating is (referred to as BTG, it is divided into again, and coal-burning boiler generates electricity, clean burn gas boiler generates electricity, blending gas boiler generates electricity), integrated gas-steam combined cycle power plant (CCPP), cogeneration of heat and power generating (CHP).The fuel type of these units is different, and buffering coal gas amount is different, and generating capacity is different, and its economic load scope is just very possible different, even if the unit of same model, respective economic benefit also can difference to some extent.How meeting under power grid security, high-quality service condition, carry out science distribution to the burden with power of genset, the cost that enterprise is generated electricity is minimum, and the economic benefit that enterprise obtains is maximum, is the vital task that each enterprise faces.

At present, the research for generation optimization scheduling mainly concentrates on conventional firepower or hydraulic power plant, carries out load optimal distribution to realize reducing coal consumption or cost of electricity-generating object according to the energy consumption model of unit.And being still in the starting stage for the generation optimization scheduling of power plant for self-supply of iron and steel enterprise, the main experience of staff that relies on arranges exerting oneself of unit.The generation schedule rule of thumb set can run into all difficulties usually when reality performs; be difficult to ensure its safety, stable, economical operation; and the changes in demand to electric power such as product demand, processing capacity of constantly change can not be adapted to; obviously cannot adapt to the market competition be growing more intense, also cannot meet the target of industry energy conservation consumption reduction.Therefore, the research of the generation optimization scheduling of power plant for self-supply has important practical significance to iron and steel enterprise's self competitive power of raising.

Summary of the invention

The object of the present invention is to provide the method for power plant for self-supply of a kind of iron and steel enterprise polymorphic type genset joint optimal operation, scheduling is optimized in order to solve the main genset dissimilar to multiple stage by rule of thumb of dispatcher in prior art, the method of the problem that energy consumption is high and cost is high of the electrical production caused, guarantee at satisfied production electric load needs, unit output ability, under the conditions such as fuel consumption limit value, in conjunction with fuel ratio, the factors such as rate for incorporation into the power network and electrical network electricity price are on the impact of cost of electricity-generating, meritorious the exerting oneself of each unit of power plant for self-supply in the reasonable arrangement following dispatching cycle, gas allocation and the plan of outsourcing power transmission, Shi Quan factory power supply cost is minimum.

The present invention includes following steps:

Step one, obtains the following characterisitic parameter of each unit of power plant for self-supply: meritorious maximal value of exerting oneself, meritorious exert oneself minimum value, maximum loading rate, maximum load down speed, fuel consumption scope and the coal gas mixed-fuel burning proportion upper limit.

Step 2, determines the energy consumption model of power plant for self-supply's unit under different coal gas fuel mixing ratio.

From database server, obtain the history data needed for the energy consumption model building each unit of power plant for self-supply by genset supervisory system, comprise the power gas consumption of each unit, by-product gas consumption and force data of gaining merit.

Pre-service is carried out to above-mentioned data, coal amount or blast furnace coal tolerance is amounted to the mark coal amount of calorific values such as becoming, obtain the energy consumption data group of unit under different load different fuel proportioning, and draw energy consumption characteristics curve.

According to the feature of curve, mix burning amount D for independent variable with meritorious P and the coal gas of exerting oneself of unit, norm-coal consumption B is dependent variable, and adopt the method for quadratic polynomial curve, determine the energy consumption model of each unit, its model expression is as follows:

I-th pure Thermal generation unit: B _i=a _ip _i ²+ b _ip _i+ c _i;

The pure jet dynamic control of jth platform: B _j=a _jp _j ²+ b _jp _j+ c _j;

Kth platform bottle coal multifuel combustion genset: B _k=a _kp _k ²+ b _kp _k+ c _k+ e _kd _k ²+ f _kd _k+ g _kp _kd _k

Wherein, B represents the mark coal consumption of unit, and P is that the meritorious of unit is exerted oneself, and D is respectively the gas consumption amount of unit, and a, b, c, e, f, g are the characterisitic parameter of units consumption model, and the method (as least square method) by parameter identification is determined.

Step 3, set the time hop count that the whole Optimized Operation cycle comprises, obtain set optimization scheduling calculate needed for input data: comprise enterprise's electric load demand forecast curve in dispatching cycle, surplus gas supply curve, production and turnaround plan, the plan of outsourcing power transmission, the start and stop state of unit, upstate, the fixing data such as unit output plan and the plan of adjustable unit output.

Step 4, to set in the period of quantity, minimum for target with whole power plant for self-supply power supply cost, set up power plant for self-supply of iron and steel enterprise polymorphic type genset and combine electrically optimized scheduling model.Described Optimal Operation Model comprises objective function and constraint condition.

Described objective function is: minimum for objective function with power supply cost total in the complete period, specifically comprise fuel cost, by-product gas diffuse rejection penalty, unit operation maintenance cost, the outsourcing electricity charge with and outer power transmission income.

$\begin{array}{c}\mathrm{Min\; J}={\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{i=1}^{N_G}({\mathrm{\Σ}}_g^{N_g}{C}_g\×F_{i,t}^g+C_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}+C_{M,i}\×P_{i,t})\\ +{\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{g=1}^{N_g}{C}_{\mathrm{rel},g}\×R_{g,t}+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{b,t}\×{\mathrm{\δ}}_t+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{s,t}\×(1-{\mathrm{\δ}}_t)\end{array}$

In formula, the fixed number of T for comprising in the dispatching cycle, N _grepresent the number of power plant for self-supply's unit, N _grepresent the number of by-product gas, C _grepresent the price of by-product gas g, represent the consumption of unit i at t period by-product gas g, C _coalrepresent the price of outsourcing bunker coal, represent that unit i is at t period fuel the consumption of coal, C _m,irepresent the manufacturing expense (comprising equipment amortization, maintenance cost, artificial emolument etc., proportional with the meritorious size of exerting oneself of unit) of the i-th generator, P _i,trepresent that unit i exerts oneself at the meritorious of t period, C _{rel, g}represent that by-product gas g's diffuses penalty price, R _g,trepresent the diffuse amount of by-product gas g in the t period, P _w,trepresent that utility power grid exchanges power at the critical point of t period, C _b,tfor the outsourcing electricity price of t period, δ _tbe that 0,1 scale is levied with or without outer power supply, C _s,telectricity price is sent outside for the t period.

Described constraint condition comprises: the constraint conditions such as the constraint of units consumption model, power balance, unit capacity constraint, the constraint of unit load rate of change, fuel consumption scope, the restriction of coal gas mixed-fuel burning proportion.

1) power balance constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{P}_{i,t}+P_{w,t}=P_{D,t}$

In formula, P _d,trepresent the electric load aggregate demand in the t period;

2) unit capacity constraint

$P_i^{\min }\≤P_{i,t}\≤P_i^{\max },i=\mathrm{1,2},...,N_G$

In formula, with be respectively the minimum of unit i and peak load;

3) unit load rate of change constraint

-UR _i≤P _i,t-P _i,t-1≤DR _i,i＝1,2...N _G

In formula, UR _iand DR _ithe peak load that can increase within a period for unit i and the peak load that can reduce;

4) unit fuel consumption constraint

$F_i^{g,\min }\≤F_{i,t}^g\≤F_i^{g,\max }$

In formula, with be respectively the minimum and maximal value that unit i consumes by-product gas g;

5) coal gas mixed-fuel burning proportion constraint:

$\frac{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g}{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g+h_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}}\≤{\mathrm{\α}}_i$

In formula, α _irepresent that unit i is to the upper limit requirement of coal gas mixed-fuel burning proportion, h _gand h _coalrepresent the calorific value of by-product gas g and outsourcing bunker coal respectively;

6) surplus gas supply constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{F}_{i,t}^g\≤F_t^{g,\max }$

In formula, for by-product gas g is in the consumption upper limit of t period;

7) critical point power constraint:

$-P_w^{\max }\≤P_{w,t}{\≤P}_w^{\max }$

In formula, represent that corporate intranet and outer net critical point exchange the upper limit of power respectively.

Step 5, adopts APSO algorithm to solve described Optimal Operation Model, to obtain the plan of exerting oneself and fuel plan of distribution and the system outsourcing power transmission scheme of described power plant for self-supply each unit under current power workload demand and coal gas supply.Specific implementation process is as follows:

Step 1: optimum configurations.The data such as the upper lower limit value of input generator parameter, inequality constrain and electric load demand, arrange the correlation parameter in particle cluster algorithm, variable range and maximum iteration time.

Step 2: initialization population; In colony, each individuality is a solution of this optimization problem, is exerted oneself, Fuel Consumption and critical point power forms by one group of decision variable and each unit meritorious.A random generation initial population in each decision variable feasible zone variation range, and calculate the current individual optimal value of the current global optimum of whole particle and each particle.

Step 4: the fitness value calculating each individuality of current population, and calculate local optimum and global optimum.

Step 5: self-adaptation is carried out to the controling parameters of particle cluster algorithm and inertia weight and speedup factor and dynamically updates.

Step 6: the speed and the position that upgrade each particle of current population.

Step 7: constraint process.To each individuality in current population, interpretation its whether meet all constraint condition.To obtaining infeasible scheme (namely not meeting the individuality of constraint condition), according to heuristic strategies, it progressively being adjusted, making it meet all constraint condition.And the fitness value of all individualities after upgrading according to objective function evaluates.

Step 8: variation: in order to increase the diversity of population, adopts TSP question mechanism.First, to each individuality in population, produce two different variation vectors according to differential variation and Gaussian mutation two kinds of Different Variation operators.Then, the adaptive value corresponding to these two variation vectors and the new adaptive value of current individual are compared, select the most the superior of fitness as the next generation according to Greedy principle.

Step 9: according to the current individual after renewal, calculate the global extremum of its local extremum and whole population.

Step 10: judge whether to reach iteration stopping condition, as met, then in the global value of last iteration, the weights of every one dimension are required; If do not meet, turn to Step 5, algorithm continues iteration, until satisfy condition.

Step 6, generates the generation schedule of final each unit, gas allocation plan and the plan of outsourcing power transmission and optimal synthesis objective function index.

The invention has the beneficial effects as follows:

The present invention considers fuel type in structure units consumption characteristic model, coal gas mixes the impact of burning amount on units consumption, under different coal gas mixes burning, carry out matching, improves the accuracy of model with representative; Fuel price, tou power price and the surplus gas dynamic change impact for cost of electricity-generating has been considered in structure Optimal Operation Model, consider the various constraint conditions such as power balance constraint, the constraint of genset self-operating, outsourcing Constraint, coal gas supply constraint and Changing load-acceleration restriction simultaneously, ensure that the enforceability of generation schedule; Simultaneously, the present invention adopts APSO algorithm to carry out solving described model optimization, the problem that power plant for self-supply's multicomputer power generation dispatching has high dimension, non-convex, non-linear, multiple constraint can be solved very well, and standard particle group algorithm can be overcome and be easily absorbed in local convergence and precocious shortcoming, most economical generation schedule, gas allocation and outsourcing power transmission scheme can be sought for power plant for self-supply, achieve the rationalization of electrical production optimization and outsourcing and making full use of of surplus gas, reduce full factory power supply cost to greatest extent, improve its economic benefit.

Accompanying drawing explanation

Fig. 1 is the flow chart element of power plant for self-supply of iron and steel enterprise of the present invention polymorphic type genset joint optimal operation method.

Fig. 2 is of the present invention based on APSO algorithm acquisition generation optimization scheduling scheme Technology Roadmap.

Embodiment

The technical scheme that the present invention proposes can adopt computer software technology to realize automatic operational scheme.Below in conjunction with accompanying drawing the present invention done and walk explanation in detail into one.

Refer to Fig. 1, Fig. 1 is the process flow diagram of the method side's embodiment giving power plant for self-supply of a kind of iron and steel enterprise of the application polymorphic type genset joint optimal operation, and it comprises the following steps:

Step one, obtains the following characterisitic parameter of each unit of power plant for self-supply: meritorious maximal value of exerting oneself, meritorious exert oneself minimum value, maximum loading rate, maximum load down speed, fuel consumption scope and the coal gas mixed-fuel burning proportion upper limit.

Step 2, determines the energy consumption model of power plant for self-supply's unit under different coal gas fuel mixing ratio.

From database server, obtain the history data needed for the energy consumption model building each unit of power plant for self-supply by genset supervisory system, comprise the power gas consumption of each unit, by-product gas consumption and force data of gaining merit.

Pre-service is carried out to above-mentioned data, coal amount or blast furnace coal tolerance is amounted to the mark coal amount of calorific values such as becoming, obtain the energy consumption data group of unit under different load different fuel proportioning, and draw energy consumption characteristics curve.

According to the feature of curve, mix burning amount D for independent variable with meritorious P and the coal gas of exerting oneself of unit, norm-coal consumption B is dependent variable, and adopt the method for quadratic polynomial curve, determine the energy consumption model of each unit, its model expression is as follows:

I-th pure Thermal generation unit: B _i=a _ip _i ²+ b _ip _i+ c _i;

The pure jet dynamic control of jth platform: B _j=a _jp _j ²+ b _jp _j+ c _j;

Kth platform bottle coal multifuel combustion genset: B _k=a _kp _k ²+ b _kp _k+ c _k+ e _kd _k ²+ f _kd _k+ g _kp _kd _k

Wherein, B represents the mark coal consumption of unit, and P is that the meritorious of unit is exerted oneself, and D is respectively the gas consumption amount of unit, and a, b, c, e, f, g are the characterisitic parameter of units consumption model, and the method (as least square method) by parameter identification is determined.

Step 3, set the time hop count that the whole Optimized Operation cycle comprises, obtain set optimization scheduling calculate needed for input data: comprise enterprise's electric load demand forecast curve in dispatching cycle, surplus gas supply curve, production and turnaround plan, the plan of outsourcing power transmission, the start and stop state of unit, upstate, the fixing data such as unit output plan and the plan of adjustable unit output.

Step 4, to set in the period of quantity, minimum for target with whole power plant for self-supply power supply cost, set up power plant for self-supply of iron and steel enterprise polymorphic type genset and combine electrically optimized scheduling model.Described Optimal Operation Model comprises objective function and constraint condition.

Described objective function is: minimum for objective function with power supply cost total in the complete period, specifically comprise fuel cost, by-product gas diffuse rejection penalty, unit operation maintenance cost, the outsourcing electricity charge with and outer power transmission income.

$\begin{array}{c}\mathrm{Min\; J}={\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{i=1}^{N_G}({\mathrm{\Σ}}_g^{N_g}{C}_g\×F_{i,t}^g+C_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}+C_{M,i}\×P_{i,t})\\ +{\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{g=1}^{N_g}{C}_{\mathrm{rel},g}\×R_{g,t}+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{b,t}\×{\mathrm{\δ}}_t+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{s,t}\×(1-{\mathrm{\δ}}_t)\end{array}$

In formula, the fixed number of T for comprising in the dispatching cycle, N _grepresent the number of power plant for self-supply's unit, N _grepresent the number of by-product gas, C _grepresent the price of by-product gas g, represent the consumption of unit i at t period by-product gas g, C _coalrepresent the price of outsourcing bunker coal, represent that unit i is at t period fuel the consumption of coal, C _m,irepresent the manufacturing expense (comprising equipment amortization, maintenance cost, artificial emolument etc., proportional with the meritorious size of exerting oneself of unit) of the i-th generator, P _i,trepresent that unit i exerts oneself at the meritorious of t period, C _{rel, g}represent that by-product gas g's diffuses penalty price, R _g,trepresent the diffuse amount of by-product gas g in the t period, P _w,trepresent that utility power grid exchanges power at the critical point of t period, C _b,tfor the outsourcing electricity price of t period, δ _tbe that 0,1 scale is levied with or without outer power supply, C _s,telectricity price is sent outside for the t period.

Described constraint condition comprises: the constraint conditions such as the constraint of units consumption model, power balance, unit capacity constraint, the constraint of unit load rate of change, fuel consumption scope, the restriction of coal gas mixed-fuel burning proportion.

1) power balance constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{P}_{i,t}+P_{w,t}=P_{D,t}$

In formula, P _d,trepresent the electric load aggregate demand in the t period;

2) unit capacity constraint

$P_i^{\min }\≤P_{i,t}\≤P_i^{\max },i=\mathrm{1,2},...,N_G$

In formula, with be respectively the minimum of unit i and peak load;

3) unit load rate of change constraint

-UR _i≤P _i,t-P _i,t-1≤DR _i,i＝1,2...N _G

In formula, UR _iand DR _ithe peak load that can increase within a period for unit i and the peak load that can reduce;

4) unit fuel consumption constraint

$F_i^{g,\min }\≤F_{i,t}^g\≤F_i^{g,\max }$

In formula, with be respectively the minimum and maximal value that unit i consumes by-product gas g;

5) coal gas mixed-fuel burning proportion constraint:

$\frac{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g}{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g+h_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}}\≤{\mathrm{\α}}_i$

In formula, α _irepresent that unit i is to the upper limit requirement of coal gas mixed-fuel burning proportion, h _gand h _coalrepresent the calorific value of by-product gas g and outsourcing bunker coal respectively;

6) surplus gas supply constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{F}_{i,t}^g\≤F_t^{g,\max }$

In formula, for by-product gas g is in the consumption upper limit of t period;

7) critical point power constraint:

$-P_w^{\max }\≤P_{w,t}{\≤P}_w^{\max }$

In formula, represent that corporate intranet and outer net critical point exchange the upper limit of power respectively.

Step 5, adopts APSO algorithm to solve described Optimal Operation Model, to obtain the plan of exerting oneself and fuel plan of distribution and the system outsourcing power transmission scheme of described power plant for self-supply each unit under current power workload demand and coal gas supply.Refer to Fig. 2, specific implementation process is as follows:

Step 1: optimum configurations.The data such as the upper lower limit value of input generator parameter, inequality constrain and electric load demand, arrange the correlation parameter in particle cluster algorithm, variable range and maximum iteration time.

Step 2: initialization population; In colony, each individuality is a solution of this optimization problem, is exerted oneself, Fuel Consumption and critical point power forms by one group of decision variable and each unit meritorious.A random generation initial population in each decision variable feasible zone variation range, and calculate the current individual optimal value of the current global optimum of whole particle and each particle.

Step 4: the fitness value calculating each individuality of current population, and calculate local optimum and global optimum.

Step 5: self-adaptation is carried out to the controling parameters of particle cluster algorithm and inertia weight and speedup factor and dynamically updates.

Step 6: the speed and the position that upgrade each particle of current population.

Step 7: constraint process.To each individuality in current population, interpretation its whether meet all constraint condition.To obtaining infeasible scheme (namely not meeting the individuality of constraint condition), according to heuristic strategies, it progressively being adjusted, making it meet all constraint condition.And the fitness value of all individualities after upgrading according to objective function evaluates.

Step 8: variation: in order to increase the diversity of population, adopts TSP question mechanism.First, to each individuality in population, produce two different variation vectors according to differential variation and Gaussian mutation two kinds of Different Variation operators.Then, the adaptive value corresponding to these two variation vectors and the new adaptive value of current individual are compared, select the most the superior of fitness as the next generation according to Greedy principle.

Step 9: according to the current individual after renewal, calculate the global extremum of its local extremum and whole population.

Step 10: judge whether to reach iteration stopping condition, as met, then in the global value of last iteration, the weights of every one dimension are required; If do not meet, turn to Step 5, algorithm continues iteration, until satisfy condition.

Step 6, generates the generation schedule of final each unit, gas allocation plan and the plan of outsourcing power transmission and optimal synthesis objective function index.

Adopt power plant for self-supply of the iron and steel enterprise polymorphic type genset joint optimal operation method that the present invention proposes, can the generation schedule of each unit of the following power plant for self-supply of arranged rational, gas allocation plan and outsourcing power transmission scheme; The present invention has considered fuel price, tou power price and the surplus gas dynamic change impact for cost of electricity-generating, minimum for objective function with complete period whole power plant for self-supply power supply cost, each constraint conditions such as restriction, the restriction of unit climbing capacity are used with unit generation capacity, fuel, establish power plant for self-supply of iron and steel enterprise generation optimization scheduling mathematic model, and adopting intelligent optimization algorithm iterative to obtain, unit is meritorious exerts oneself and gas allocation, efficiently solves industrial power plant's multicomputer generation optimization scheduling problem.

Above embodiment is used for illustrative purposes only, it is not limitation of the present invention, person skilled in the relevant technique, without departing from the spirit and scope of the present invention, various conversion or modification can be made, therefore, all equivalent technical schemes also should belong to category of the present invention, should be limited by claim.

Claims (1)

1. a method for power plant for self-supply of iron and steel enterprise polymorphic type genset joint optimal operation, it is characterized in that, processing step is as follows:

Step one, obtains the following characterisitic parameter of each unit of power plant for self-supply: meritorious maximal value of exerting oneself, meritorious exert oneself minimum value, maximum loading rate, maximum load down speed, fuel consumption scope and the coal gas mixed-fuel burning proportion upper limit.

Step 2, determines the energy consumption model of power plant for self-supply's unit under different coal gas fuel mixing ratio.

From database server, obtain the history data needed for the energy consumption model building each unit of power plant for self-supply by genset supervisory system, comprise the power gas consumption of each unit, by-product gas consumption and force data of gaining merit.

Pre-service is carried out to above-mentioned data, coal amount or blast furnace coal tolerance is amounted to the mark coal amount of calorific values such as becoming, obtain the energy consumption data group of unit under different load different fuel proportioning, and draw energy consumption characteristics curve.

According to the feature of curve, mix burning amount D for independent variable with meritorious P and the coal gas of exerting oneself of unit, norm-coal consumption B is dependent variable, and adopt the method for quadratic polynomial curve, determine the energy consumption model of each unit, its model expression is as follows:

I-th pure Thermal generation unit: B _i=a _ip _i ²+ b _ip _i+ c _i;

The pure jet dynamic control of jth platform: B _j=a _jp _j ²+ b _jp _j+ c _j;

Kth platform bottle coal multifuel combustion genset: B _k=a _kp _k ²+ b _kp _k+ c _k+ e _kd _k ²+ f _kd _k+ g _kp _kd _k

Wherein, B represents the mark coal consumption of unit, and P is that the meritorious of unit is exerted oneself, and D is respectively the gas consumption amount of unit, and a, b, c, e, f, g are the characterisitic parameter of units consumption model, and the method (as least square method) by parameter identification is determined.

Step 3, set the time hop count that the whole Optimized Operation cycle comprises, obtain set optimization scheduling calculate needed for input data: comprise enterprise's electric load demand forecast curve in dispatching cycle, surplus gas supply curve, production and turnaround plan, the plan of outsourcing power transmission, the start and stop state of unit, upstate, the fixing data such as unit output plan and the plan of adjustable unit output.

Step 4, to set in the period of quantity, minimum for target with whole power plant for self-supply power supply cost, set up power plant for self-supply of iron and steel enterprise polymorphic type genset and combine electrically optimized scheduling model.Described Optimal Operation Model comprises objective function and constraint condition.

Described objective function is: minimum for objective function with power supply cost total in the complete period, specifically comprise fuel cost, by-product gas diffuse rejection penalty, unit operation maintenance cost, the outsourcing electricity charge with and outer power transmission income.

$\begin{array}{c}\mathrm{MinJ}={\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{i=1}^{N_G}({\mathrm{\Σ}}_g^{N_g}{C}_g\×F_{i,t}^g+C_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}+C_{M,i}\×P_{i,t})\\ +{\mathrm{\Σ}}_{t=1}^T{\mathrm{\Σ}}_{g=1}^{N_g}{C}_{\mathrm{rel},g}\×R_{g,t}+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{b,t}\×{\mathrm{\δ}}_t+{\mathrm{\Σ}}_{t=1}^T{P}_{w,t}\×C_{s,t}\×(1-{\mathrm{\δ}}_1)\end{array}$

In formula, the fixed number of T for comprising in the dispatching cycle, N _grepresent the number of power plant for self-supply's unit, N _grepresent the number of by-product gas, C _grepresent the price of by-product gas g, represent the consumption of unit i at t period by-product gas g, C _coalrepresent the price of outsourcing bunker coal, represent that unit i is at t period fuel the consumption of coal, C _m,irepresent the manufacturing expense (comprising equipment amortization, maintenance cost, artificial emolument etc., proportional with the meritorious size of exerting oneself of unit) of the i-th generator, P _i,trepresent that unit i exerts oneself at the meritorious of t period, C _{rel, g}represent that by-product gas g's diffuses penalty price, R _g,trepresent the diffuse amount of by-product gas g in the t period, P _w,trepresent that utility power grid exchanges power at the critical point of t period, C _b,tfor the outsourcing electricity price of t period, δ _tbe that 0,1 scale is levied with or without outer power supply, C _s,telectricity price is sent outside for the t period.

Described constraint condition comprises: the constraint conditions such as the constraint of units consumption model, power balance, unit capacity constraint, the constraint of unit load rate of change, fuel consumption scope, the restriction of coal gas mixed-fuel burning proportion.

1) power balance constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{P}_{i,t}=P_{i,t}+P_{w,t}=P_{D,t}$

In formula, P _d,trepresent the electric load aggregate demand in the t period;

2) unit capacity constraint

$P_i^{\min }\≤P_{i,t}\≤P_i^{\max },i=1,2,...,N_G$

In formula, with be respectively the minimum of unit i and peak load;

3) unit load rate of change constraint

-UR _i≤P _i,t-P _i,t-1≤DR _i,i＝1,2...N _G

In formula, UR _iand DR _ithe peak load that can increase within a period for unit i and the peak load that can reduce;

4) unit fuel consumption constraint

$F_i^{g,\min }\≤F_{i,t}^g\≤F_i^{g,\max }$

In formula, with be respectively the minimum and maximal value that unit i consumes by-product gas g;

5) coal gas mixed-fuel burning proportion constraint:

$\frac{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g}{{\mathrm{\Σ}}_{g=1}^{N_g}{h}_g\×F_{i,t}^g+h_{\mathrm{coal}}\×F_{i,t}^{\mathrm{coal}}}\≤{\mathrm{\α}}_i$

In formula, α _irepresent that unit i is to the upper limit requirement of coal gas mixed-fuel burning proportion, h _gand h _coalrepresent the calorific value of by-product gas g and outsourcing bunker coal respectively;

6) surplus gas supply constraint

${\mathrm{\Σ}}_{i=1}^{N_G}{F}_{i,t}^g\≤F_t^{g,\max }$

In formula, for by-product gas g is in the consumption upper limit of t period;

7) critical point power constraint:

${-P}_w^{\max }\≤P_{w,t}\≤P_w^{\max }$

In formula, represent that corporate intranet and outer net critical point exchange the upper limit of power respectively.

Step 5, adopts APSO algorithm to solve described Optimal Operation Model, to obtain the plan of exerting oneself and fuel plan of distribution and the system outsourcing power transmission scheme of described power plant for self-supply each unit under current power workload demand and coal gas supply.Specific implementation process is as follows:

Step1: optimum configurations.The data such as the upper lower limit value of input generator parameter, inequality constrain and electric load demand, arrange the correlation parameter in particle cluster algorithm, variable range and maximum iteration time.

Step2: initialization population; In colony, each individuality is a solution of this optimization problem, is exerted oneself, Fuel Consumption and critical point power forms by one group of decision variable and each unit meritorious.A random generation initial population in each decision variable feasible zone variation range, and calculate the current individual optimal value of the current global optimum of whole particle and each particle.

Step4: the fitness value calculating each individuality of current population, and calculate local optimum and global optimum.

Step5: self-adaptation is carried out to the controling parameters of particle cluster algorithm and inertia weight and speedup factor and dynamically updates.

Step6: the speed and the position that upgrade each particle of current population.

Step7: constraint process.To each individuality in current population, interpretation its whether meet all constraint condition.To obtaining infeasible scheme (namely not meeting the individuality of constraint condition), according to heuristic strategies, it progressively being adjusted, making it meet all constraint condition.And the fitness value of all individualities after upgrading according to objective function evaluates.

Step8: variation: in order to increase the diversity of population, adopts TSP question mechanism.First, to each individuality in population, produce two different variation vectors according to differential variation and Gaussian mutation two kinds of Different Variation operators.Then, the adaptive value corresponding to these two variation vectors and the new adaptive value of current individual are compared, select the most the superior of fitness as the next generation according to Greedy principle.

Step9: according to the current individual after renewal, calculate the global extremum of its local extremum and whole population.

Step10: judge whether to reach iteration stopping condition, as met, then in the global value of last iteration, the weights of every one dimension are required; If do not meet, turn to Step5, algorithm continues iteration, until satisfy condition.

Step 6, generates the generation schedule of final each unit, gas allocation plan and the plan of outsourcing power transmission and optimal synthesis objective function index.