CN113111541B - Demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load - Google Patents
Demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load Download PDFInfo
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- ZLNQQNXFFQJAID-UHFFFAOYSA-L magnesium carbonate Chemical compound [Mg+2].[O-]C([O-])=O ZLNQQNXFFQJAID-UHFFFAOYSA-L 0.000 title claims abstract description 134
- 229910000021 magnesium carbonate Inorganic materials 0.000 title claims abstract description 134
- 235000014380 magnesium carbonate Nutrition 0.000 title claims abstract description 134
- 239000001095 magnesium carbonate Substances 0.000 title claims abstract description 134
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 21
- 238000012795 verification Methods 0.000 claims description 16
- 238000002844 melting Methods 0.000 claims description 14
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- 210000000349 chromosome Anatomy 0.000 claims description 9
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- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims description 6
- 101100510602 Mus musculus Lacc1 gene Proteins 0.000 claims description 6
- 238000003723 Smelting Methods 0.000 claims description 6
- 230000005284 excitation Effects 0.000 claims description 6
- 229910052749 magnesium Inorganic materials 0.000 claims description 6
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- 238000005457 optimization Methods 0.000 claims description 6
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Abstract
The invention provides a demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load, and relates to the technical field of demand response and energy efficiency improving. According to the invention, by establishing a demand response model and a waste heat utilization model which consider individual equipment of the magnesite, and optimally controlling demand response of magnesite load and waste heat utilization equipment, the purposes of improving electricity economy, responding to power grid regulation and control and improving energy efficiency of the magnesite technology are simultaneously realized. By introducing demand response modeling, the economic benefit of the magnesite technology is improved, the problem that the stability of the power grid is damaged when the power consumption of the existing magnesite load is high is solved, and certain social benefit is achieved; the waste heat and raw material waste are reduced, the energy utilization rate in the process is improved, and the method plays an important role in improving the energy efficiency of the magnesite process.
Description
Technical Field
The invention relates to the technical field of demand response and energy efficiency improvement, in particular to a demand response modeling and energy efficiency improvement method based on intelligent regulation and control of magnesite load.
Background
China is one of countries with rich magnesite resources in the world, and the magnesite reserves in Liaoning are most abundant. The magnesite is mainly smelted by adopting an arc melting method in the process of China, the existing magnesite process and production equipment are relatively backward, modern control means are lacked in the production process, and the process is operated by experience, so that the problems of energy waste, environmental pollution and the like are caused.
Demand response (DemandResponse), which is an abbreviation for power demand side response, refers to the behavior of a user to respond to price signals or incentive mechanisms to adjust his own electricity plan. Because the electric energy is produced, conveyed and distributed in the electric power system simultaneously, the generated energy and the used electric energy should be balanced at any time, i.e. the load fluctuation is as small as possible. Industrial users have large electricity consumption, are sensitive to electricity consumption cost and have considerable demand response potential. For the magnesite technology with larger electricity demand, if the electricity consumption is not controlled, a great challenge is caused to realize the supply and demand balance of the power system. The demand response regulation mode is adopted, so that the electricity consumption cost of a user can be reduced, the load of the power grid during the electricity consumption peak can be reduced, the overall stability of the power grid is improved, and the method has remarkable economic and social benefits. However, the existing demand response research on industrial loads has the problems of simple production relation modeling, lack of consideration of characteristics of different industrial users and the like, and the real adjustment potential of the industrial users is difficult to discover.
In recent years, the international society has increasingly strengthened environmental protection, and the country has paid great attention to the work of energy conservation and emission reduction year by year. The energy saving policy capability construction is enhanced, so that energy conservation resources are taken as an entry point for economic work, the industrial structure is promoted to be upgraded, and the energy conservation and emission reduction indexes are realized, so that the energy saving and emission reduction method has become an important responsibility of industrial enterprises. The magnesite technology in China has the problems of energy waste, environmental pollution and the like at present, and the energy efficiency improvement of a technology system can promote industrial development and has great economic and environmental significance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load, which aims at improving electricity economy of a magnesite process and improving energy efficiency of the magnesite process by establishing a demand response model and a waste heat utilization model which consider individual equipment of magnesite, and optimally controlling demand response of magnesite load and waste heat utilization equipment.
The technical proposal of the invention is that,
a demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load comprises the following steps:
step 1: establishing a demand response model of the magnesite load based on the magnesite process, wherein the model belongs to a multi-target mixed integer model, and specifically comprises a load model, a price demand response model and an excitation demand response model of each equipment unit of the magnesite process;
step 1.1: establishing a load model of each equipment unit of the magnesite process;
acquiring information of each load device in the magnesite technical process through an intelligent control center, combining the history information of the load devices, dividing the magnesite technical process into three parts of charging, main melting and exhausting, and establishing a load model of each device individual in the magnesite technical process, wherein the information of the load devices is the starting time t of the working process of the ith device i The load equipment history information is equipment working time t z The method comprises the steps of carrying out a first treatment on the surface of the The load model is shown as follows:
wherein P is i (t) is a power-time function of the ith device, U f The arc voltage is calculated by the following formulaU 0 Is the electrode phase voltage; />Is a power factor; i fi (t) is an arc current-time function of the ith device, calculated as:
wherein the equipment charging process duration t j Duration t of the main melting process of the equipment r Duration t of the plant exhaust process p Are included in the load device history information; in which I fai (t)、I fbi (t)、I fci (t) arc current during charging, primary melting and exhausting processes in the magnesite process respectively;
the calculation formula of the total load power P (t) is as follows:
wherein P is base (t) isThe basic power of factory operation, n is the total number of load devices, mu i (t) is a variable of 0-1, when the value is 1, the device is in an operation state, when the value is 0, the device is in a standby state, and the relation between the value and time is as follows:
μ i (t)＝1，t∈[t i ，t i +t z ]
wherein t is z For the equipment operation time length, the calculation formula is as follows:
t z ＝t j +t r +t p
step 1.2: establishing a price demand response model;
the intelligent control center establishes a relation between demand and price according to electricity price information, and the relation is shown in the following formula:
wherein Deltat is the time interval for the intelligent control center to acquire the real-time electricity price, P (t) is the real-time electricity price at the moment t, P (t) is the requirement at the moment t, M is the number of times for the intelligent control center to acquire the real-time electricity price in one control period, k is the moment other than the moment t in the control period M, lambda tt And lambda is tk The price type electric power self-elasticity coefficient and the cross-elasticity coefficient are respectively calculated according to the following formulas:
wherein Δp (t) is the amount of change in demand;
step 1.3: establishing an excitation demand response model;
the intelligent control center obtains dispatching information issued by the power grid, wherein the dispatching information comprises the size and the time period for increasing or reducing the load, and economic compensation is obtained by signing a demand response protocol, and is shown in the following formula:
wherein a is a discount coefficient of electricity price obtained by increasing load, b is a discount coefficient of electricity price obtained by reducing load, T is a period of demand response control, C IBDR For economic compensation, ΔP, obtained by demand response protocol during time T in (t) and ΔP de (t) increasing and decreasing the power of the load at time t, respectively;
step 2: the electric smelting magnesium furnace is covered and sealed, so that the load energy efficiency of magnesite is improved;
step 2.1: the intelligent control center obtains the temperature of the flue gas generated by the electric smelting magnesium furnace;
step 2.2: introducing waste gas generated by magnesite load into waste heat recovery equipment;
step 2.3: the waste gas discharged from the waste heat recovery device is led into a dust recovery device, and the waste gas contains magnesite powder which can be used for a second time. Feeding the recovered magnesite powder into an electric arc furnace;
step 3: the flue gas temperature, flow and environmental temperature of the waste heat boiler are obtained through an intelligent control system, the waste heat recovery efficiency of each equipment of the magnesite process is calculated, and the calculation formula is as follows:
η i improving the energy efficiency of the ith magnesite equipment and W gi The calculation formula is as follows for the energy generated by the flue gas waste heat of the ith magnesite process equipment through the waste heat recovery equipment:
W gi ＝m gi (h 1 -h 2 )η f
wherein the method comprises the steps of，η f For the efficiency of the generator in the waste heat recovery device, m gi The flow of the superheated steam generated by the waste heat boiler after the waste heat utilization of the smoke exhausted by the ith magnesite equipment is calculated according to the following formula:
wherein m is g For the total flow of the superheated steam generated by the waste heat boiler, V gi For the flow of flue gas produced by the ith magnesite equipment, V g The flow rate of the flue gas generated by all magnesite equipment, h 1 And h 2 The enthalpy of superheated steam generated by the waste heat boiler and the enthalpy of steam turbine exhaust are respectively;
step 4: establishing an optimized objective function and constraint conditions of the magnesite load;
step 4.1: establishing an optimized objective function, wherein the objective functions 1-3 are respectively shown in the following formulas:
min f 1 ＝(C Grid -C IBDR )
in objective function 1, f 1 C for electricity consumption Grid For the electricity purchasing expense from the power grid in the magnesite process, the calculation formula is as follows:
in objective function 2, f 2 As a function of the variance of the load curve,for the load average, the calculation formula is as follows:
in objective function 3, f 3 Waste heat recovery efficiency of magnesite equipment with lowest waste heat recovery efficiency, wherein eta i The energy efficiency of the ith magnesite equipment is improved;
step 4.2: establishing constraint conditions, wherein the constraint conditions are as follows:
P(t)≤I max U N
p(t) min ≤p(t)≤p(t) max
0≤t≤T
I famin ≤I fai (t)≤I famax
I fbmin ≤I fb i(t)≤I fbmax
I fcmin ≤I fci (t)≤I fcmax
wherein I is max Maximum current for grid and user tie line passing, U N For rated voltage, p (t) min And p (t) max Respectively the minimum value and the maximum value of electricity price at the moment t, P min And P max Respectively the minimum value and the maximum value of the total electricity consumption of the magnesite process equipment in the control period, I famin 、I fbmin And I fcmin Respectively the minimum value of arc current in the feeding, main melting and exhausting processes in the magnesite technological process, I famax 、I fbmax And I fcmax The maximum value of arc current in the feeding, main melting and exhausting processes in the magnesite technological process is respectively;
step 5: solving the demand response model of the magnesite load in the step 1 by adopting an optimization algorithm, and finally obtaining an optimal control method for the load;
the optimization algorithm is a non-dominant ranking genetic algorithm II (NSGA-II), and comprises the following specific steps:
step 5.1: chromosome coding and initialization;
the chromosome adopts decimal coding, and the decision variables comprise the running start time of each equipment, the arc current value of each equipment and the pressure P of the waste heat boiler in the magnesite process 0 Therefore, chromosome x of the jth individual is set j ＝(t 1 ,t 2 ,…,t i ,…,t n ,I f1 ,I f2 ,…,I fi ,…,I fn ,P 0 ) Randomly generating an initial population P with N individuals 1 ；
Step 5.2: population P i Through crossing and mutation, generating sub-population Q i ；
Step 5.3: will group P i And population Q i Synthetic population R i Calculating the adaptive value of each objective function, and performing non-dominant sorting;
step 5.4: performing elite individual verification;
checking whether the individual meets constraint conditions or not is needed to pass, and in order to reduce the calculated amount, checking only elite individuals at the highest layer of non-dominant order, wherein the checked contents comprise total load power, electricity price, time, electricity consumption of magnesite equipment and arc current constraint; the individuals meeting all constraint conditions, namely the individuals passing the verification, and the verification mark is set to be feasible; otherwise, the verification mark is set to be infeasible, and when the next round of evolution is carried out, the elimination verification mark is selected as an infeasible individual by elite strategy;
step 5.5: calculating the crowding distance of the last non-dominant layer individual in the next generation population;
the congestion distance calculation formula is as follows:
wherein f d (x i+1 ) And f d (x i-1 ) The (d) th objective function value, f, of the (i+1) th and (i-1) th individuals of the non-dominant layer, respectively dmax And f dmin Respectively isMaximum and minimum values of the d-th objective function value; wherein the crowding distance of the individual sorting edge is set to ≡;
step 5.6: selecting individuals entering a new parent population;
first, the population R is eliminated i The scheme check mark is an infeasible individual; second according to non-dominant order i rank Sequentially placing the whole population into the new parent population P from low to high i+1 Until a certain layer F is put in j At the time P i+1 The size exceeds the population size N; finally, according to F j The individual crowding distances in (a) continue to fill P in order from big to small i+1 Ending until the population number reaches N. If the iteration number does not reach the set value, the iteration number is increased by 1, and the steps 3.2 to 3.6 are repeated until the iteration number reaches the set value;
step 5.7: obtaining a Pareto optimal solution;
the iteration times reach a set value, and a Pareto optimal solution is output, wherein the optimal solution comprises the operation starting time of each device, the arc current value of each device and the pressure of the waste heat boiler;
step 6: the intelligent control center issues control signals to each equipment of the magnesite process;
the control signals are the optimal solutions of the operation start time of each device, the arc current value of each device and the pressure of the waste heat boiler obtained in the step 5;
step 7: and each device of the magnesite process controls the running time, the electric arc current value and the pressure of the waste heat boiler according to the control signals, so that the intelligent regulation and control of the magnesite load are realized.
The beneficial effects generated by adopting the technical method are as follows:
the invention provides a demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load, which has the following beneficial effects:
(1) According to the invention, demand response modeling is introduced, so that the economic benefit of the magnesite process is improved, the problem that the stability of a power grid is damaged when the power consumption of the existing magnesite load is high is solved, and certain social benefit is achieved;
(2) The energy efficiency improving method based on intelligent regulation and control of the magnesite load reduces waste heat and raw material waste, improves the energy utilization rate in the process, and plays an important role in improving the energy efficiency of the magnesite process.
Drawings
FIG. 1 is a schematic diagram of the working principle of an intelligent control center in an embodiment of the invention;
fig. 2 is a schematic diagram of an energy efficiency improvement technology according to an embodiment of the present invention.
FIG. 3 is a flow chart of a demand response model solution in an embodiment of the present invention;
FIG. 4 is a diagram of a control architecture of an intelligent control center in an embodiment of the present invention;
FIG. 5 is a graph showing the result of controlling magnesite load in the embodiment of the present invention;
FIG. 6 is a graph of a wind curtailment curve in an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Step 1: establishing a demand response model of the magnesite load based on the magnesite process, wherein the model belongs to a multi-target mixed integer model, and specifically comprises a load model, a price demand response model and an excitation demand response model of each equipment unit of the magnesite process;
step 1.1: establishing a load model of each equipment unit of the magnesite process;
based on the characteristics that the magnesite technology has the characteristics that the working process can not be interrupted, the working time can be adjusted at will, and the arc current in each time period in the working process is different, the intelligent control center acquires information of each load device, and the working principle of the intelligent control center is shown in figure 1. Combining historical information of load equipment, dividing the magnesite technical process into three parts of charging, main melting and exhausting, and establishing a load model of each equipment unit of the magnesite technical process, wherein the load equipment information is the starting time t of the working process of the ith equipment i The load equipment history information is equipment working time t z The method comprises the steps of carrying out a first treatment on the surface of the The load model is shown as follows:
wherein P is i (t) is a power-time function of the ith device, U f The arc voltage is calculated by the following formulaU 0 Is the electrode phase voltage; />Is a power factor; i fi (t) is an arc current-time function of the ith device, calculated as:
wherein the equipment charging process duration t j Duration t of the main melting process of the equipment r Duration t of the plant exhaust process p Are included in the load device history information; in which I fai (t)、I fbi (t)、I fci (t) arc current during charging, primary melting and exhausting processes in the magnesite process respectively;
the charging process and the exhausting process of the magnesite technology are short in time, and in the charging process, the resistance in the electric furnace is changed by the added materials, so that the process has the characteristic of frequent current fluctuation; the electrode is repeatedly lifted and dropped in the exhaust process, so that the process has the characteristic of large current fluctuation range; the main melting process is the process of melting materials, continuous high-power input of electric energy is needed, and current fluctuation is stable in the process;
the calculation formula of the total load power P (t) is as follows:
wherein P is base (t) is the plant operating base powerN is the total number of load devices, mu i (t) is a variable of 0-1, wherein the value of the variable is 1, the variable indicates that the equipment is in an operation state, the value of the variable is 0, the variable indicates that the equipment is in a standby state, and the relation between the value and time is as follows:
μ i (t)＝1，t∈[t i ，t i +t z ]
wherein t is z For the equipment operation time length, the calculation formula is as follows:
t z ＝t j +t r +t p
step 1.2: establishing a price demand response model;
the intelligent control center establishes a relation between demand and price according to electricity price information by a price rule in economics, and the relation is shown in the following formula:
wherein Deltat is the time interval for the intelligent control center to acquire the real-time electricity price, P (t) is the real-time electricity price at the moment t, P (t) is the requirement at the moment t, M is the number of times for the intelligent control center to acquire the real-time electricity price in one control period, k is the moment other than the moment t in the control period M, lambda tt And lambda is tk The price type electric power self-elasticity coefficient and the cross-elasticity coefficient are respectively calculated according to the following formulas:
wherein Δp (t) is the amount of change in demand;
step 1.3: establishing an excitation demand response model;
the intelligent control center obtains dispatching information issued by the power grid, the dispatching information comprises the size and the time period for increasing or reducing the load, and economic compensation is obtained by signing a demand response protocol and is shown in the following formula:
wherein a is a discount coefficient of electricity price obtained by increasing load, b is a discount coefficient of electricity price obtained by reducing load, T is a period of demand response control, C IBDR For economic compensation, ΔP, obtained by demand response protocol during time T in (t) and ΔP de (t) increasing and decreasing the power of the load at time t, respectively;
step 2: the electric smelting magnesium furnace is covered and sealed, the magnesite load energy efficiency is improved, and the technical principle of the energy efficiency improving system is shown in figure 4;
step 2.1: the intelligent control center obtains the temperature of the flue gas generated by the electric smelting magnesium furnace;
step 2.2: introducing waste gas generated by magnesite load into waste heat recovery equipment;
step 2.3: the waste gas discharged from the waste heat recovery device is led into a dust recovery device, and the waste gas contains magnesite powder which can be used for a second time. Feeding the recovered magnesite powder into an electric arc furnace;
step 3: the flue gas temperature, flow and environmental temperature of the waste heat boiler are obtained through an intelligent control system, the waste heat recovery efficiency of each equipment of the magnesite process is calculated, and the calculation formula is as follows:
η i improving the energy efficiency of the ith magnesite equipment and W gi The calculation formula is as follows for the energy generated by the flue gas waste heat of the ith magnesite process equipment through the waste heat recovery equipment:
W gi ＝m gi (h 1 -h 2 )η f
wherein eta f For the efficiency of the generator in the waste heat recovery device, m gi The flow of the superheated steam generated by the waste heat boiler after the waste heat utilization of the smoke exhausted by the ith magnesite equipment is calculated according to the following formula:
wherein m is g For the total flow of the superheated steam generated by the waste heat boiler, V gi For the flow of flue gas produced by the ith magnesite equipment, V g The flow rate of the flue gas generated by all magnesite equipment, h 1 And h 2 The enthalpy of superheated steam and the exhaust enthalpy of a steam turbine generated by the waste heat boiler are respectively calculated by the pressure P of the waste heat boiler 0 And the temperature T of the superheated steam generated by the waste heat boiler cs The enthalpy value h of the superheated steam discharged from the superheater of the waste heat boiler can be obtained by consulting the enthalpy entropy diagram of the heat medium 1 And entropy value S 1 The formula is as follows;
temperature T of superheated steam generated by waste heat boiler cs The calculation formula is as follows:
T cs ＝T gin -ΔT sd
wherein DeltaT sd Is the temperature difference of the upper end of the waste heat boiler, T gin The temperature of the flue gas entering the waste heat recovery device;
steam turbine exhaust enthalpy h 2 The calculation formula of (2) is as follows:
h 2 ＝h 1 -η q (h 1 -h 3 )
wherein eta q For turbine efficiency, h 3 Isentropic enthalpy of a steam discharge point of the steam turbine is isentropic, and the isentropic enthalpy h of the steam discharge point of the steam turbine is obtained because superheated steam enters the steam turbine to be processed into isentropic process 3 Can pass through the entropy value S 1 And steam turbine discharge pressure P pq The method comprises the steps of obtaining by consulting a thermal working substance property table;
E gi with means for recovering waste heat from flue gases collected by the apparatusThe calculation formula is as follows:
E gi ＝αQ gi
wherein alpha is the content of the flue gas collected by the waste heat recovery equipmentThe conversion coefficient of the heat energy is calculated as follows:
wherein C is g Specific heat capacity of the flue gas collected by the waste heat recovery equipment; delta T g Ambient temperature T for waste heat recovery device env With the temperature T of the flue gas entering the waste heat recovery device gin Is the difference between (1); q (Q) gi The calculation formula of the heat energy of the flue gas generated by the ith magnesite equipment and collected by the waste heat recovery equipment is as follows:
Q gi ＝βQ i
wherein beta is the proportion coefficient of the heat energy of the flue gas to the total heat energy, and can be obtained by calculation of heat balance; q (Q) i In the magnesite technology, the electric heat energy generated by the electric arc of the ith magnesite equipment has the following calculation formula:
wherein R is i For arc resistance, the calculation formula is as follows:
wherein ρ is a proportionality coefficient determined by the nature of the feedstock; d, d i Is the diameter of the electrode;
step 4: establishing an optimized objective function and constraint conditions of the magnesite load;
step 4.1: establishing an optimized objective function, wherein the objective functions 1-3 are respectively shown in the following formulas:
min f 1 ＝(C Grid -C IBDR )
in objective function 1, f 1 C for electricity consumption Grid For the electricity purchasing expense from the power grid in the magnesite process, the calculation formula is as follows:
in objective function 2, f 2 As a function of the variance of the load curve,for the load average, the calculation formula is as follows:
in objective function 3, f 3 Waste heat recovery efficiency of magnesite equipment with lowest waste heat recovery efficiency, wherein eta i The energy efficiency of the ith magnesite equipment is improved;
step 4.2: establishing constraint conditions, wherein the constraint conditions are as follows:
P(t)≤I max U N
p(t) in ≤p(t)≤p(t) max
0≤t≤T
I famin ≤I fai (t)≤I famax
I fbmin ≤I fbi (t)≤I fbmax
I fcmin ≤I fci (t)≤I fcmax
wherein I is max Maximum current for grid and user tie line passing, U N For rated voltage, p (t) min And p (t) max Respectively the minimum value and the maximum value of electricity price at the moment t, P min And P max Respectively the minimum value and the maximum value of the total electricity consumption of the magnesite process equipment in the control period, I famin 、I fbmin And I fcmin Respectively the minimum value of arc current in the feeding, main melting and exhausting processes in the magnesite technological process, I famax 、I fbmax And I fcmax The maximum value of arc current in the feeding, main melting and exhausting processes in the magnesite technological process is respectively;
step 5: solving the demand response model of the magnesite load in the step 1 by adopting an optimization algorithm, wherein the specific flow is shown in a figure 2, and finally obtaining an optimal control method for the load;
the optimization algorithm is a non-dominant ranking genetic algorithm II (NSGA-II), and comprises the following specific steps:
step 5.1: chromosome coding and initialization;
the chromosome adopts decimal coding, and the decision variables comprise the running start time of each equipment, the arc current value of each equipment and the pressure P of the waste heat boiler in the magnesite process 0 Therefore, chromosome x of the jth individual is set j ＝(t 1 ,t 2 ,…,t i ,…,t n ,I f1 ,I f2 ,…,I fi ,…,I fn ,P 0 ) Randomly generating an initial population P with N individuals 1 ；
Step 5.2: population P i Through crossing and mutation, generating sub-population Q i ；
Step 5.3: will group P i And population Q i Synthetic population R i And calculate the adaptive value of each objective functionNon-dominant ordering is performed;
first find the non-dominant solution in the group, form the non-dominant solution set, marked as F 1 All individuals thereof are given a non-dominant order i rank =1 and is removed from the whole population, and then the non-dominant solution set in the remaining population is continued to be found, denoted as F 2 ，F 2 Is assigned to i rank =2, proceeding until the whole population is stratified, F i The non-dominant order values in the layers are the same;
the judging method of the non-dominant solution is shown as the following formula:
where d is the sequence number of the objective function. Satisfying the above two formulas, we call solution x j Is a non-dominant solution;
step 5.4: performing elite individual verification;
whether the individual meets the constraint condition needs to pass the verification. In order to reduce the calculated amount, checking only elite individuals at the highest layer of non-dominant order, wherein the checked contents comprise total load power, electricity price, time, electricity consumption of magnesite equipment and arc current constraint; the individuals meeting all constraint conditions, namely the individuals passing the verification, and the verification mark is set to be feasible; otherwise, the verification mark is set to be infeasible, and when the next round of evolution is carried out, the elimination verification mark is selected as an infeasible individual by elite strategy;
step 5.5: calculating the crowding distance of the last non-dominant layer individual in the next generation population;
the congestion distance calculation formula is as follows:
wherein f d (x i+1 ) And f d (x i-1 ) The (d) th objective function value, f, of the (i+1) th and (i-1) th individuals of the non-dominant layer, respectively dmax And f dmin Respectively the maximum value and the minimum value of the d objective function value; wherein the crowding distance of the individual sorting edge is set to ≡;
step 5.6: selecting individuals entering a new parent population;
first, the population R is eliminated i The scheme check mark is an infeasible individual; second according to non-dominant order i rank Sequentially placing the whole population into the new parent population P from low to high i+1 Until a certain layer F is put in j At the time P i+1 The size exceeds the population size N; finally, according to F j The individual crowding distances in (a) continue to fill P in order from big to small i+1 Ending until the population number reaches N. If the iteration number does not reach the set value, the iteration number is increased by 1, and the steps 3.2 to 3.6 are repeated until the iteration number reaches the set value;
step 5.7: obtaining a Pareto optimal solution;
the iteration times reach a set value, and a Pareto optimal solution is output, wherein the optimal solution comprises the operation starting time of each device, the arc current value of each device and the pressure of the waste heat boiler;
step 6: the intelligent control center issues control signals to each equipment of the magnesite technology, and the control structure of the intelligent control center is shown in fig. 3;
the control signals are the optimal solutions of the operation start time of each device, the arc current value of each device and the pressure of the waste heat boiler obtained in the step 5;
step 7: and controlling the running time, the electric arc current value and the pressure of the waste heat boiler by each device of the magnesite process according to the control signals, and finally completing the demand response and the energy efficiency improvement of the magnesite load.
For the wind abandoning problem of the wind turbine generator, the magnesite load is regulated and controlled according to the operation starting time of each device, the arc current value of each device and the pressure of the waste heat boiler, which are obtained in the demand response modeling and energy efficiency lifting method, at intervals of 16:30 to 21:45 in 15 minutes. The output of each device of the magnesite process in the time period of 16:30 to 21:45 after being regulated is shown in figure 5. As shown in FIG. 6, comparing the two curves can effectively reduce the power loss caused by the wind curtailment.
In conclusion, the demand response modeling and energy efficiency improving method based on intelligent regulation and control of the magnesite load has effectiveness and rationality.
The foregoing embodiments are only for illustrating the technical aspects of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the scope of the present invention is not limited to the technical aspects of the specific combination of the foregoing technical features, and other technical aspects formed by any combination of the foregoing technical features or the equivalent thereof are also contemplated without departing from the spirit of the present invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (5)
1. The demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load is characterized by comprising the following steps:
step 1: establishing a demand response model of the magnesite load based on the magnesite process, wherein the model belongs to a multi-target mixed integer model, and specifically comprises a load model, a price demand response model and an excitation demand response model of each equipment unit of the magnesite process;
step 1.1: establishing a load model of each equipment unit of the magnesite process;
acquiring information of each load device in the magnesite technical process through an intelligent control center, combining the history information of the load devices, dividing the magnesite technical process into three parts of charging, main melting and exhausting, and establishing a load model of each device individual in the magnesite technical process, wherein the information of the load devices is the starting time t of the working process of the ith device i The load equipment history information is equipment working time t z The method comprises the steps of carrying out a first treatment on the surface of the The load model is shown as follows:
wherein P is i (t) is a power-time function of the ith device, U f The arc voltage is calculated by the following formulaU 0 Is the electrode phase voltage; />Is a power factor; i fi (t) is an arc current-time function of the ith device, calculated as:
wherein the equipment charging process duration t j Duration t of the main melting process of the equipment r Duration t of the plant exhaust process p Are included in the load device history information; in which I fai (t)、I fbi (t)、I fci (t) arc current during charging, primary melting and exhausting processes in the magnesite process respectively;
the calculation formula of the total load power P (t) is as follows:
wherein P is base (t) is the basic power of plant operation, n is the total number of load devices, mu i (t) is a variable of 0-1, wherein the value of the variable is 1, the variable indicates that the equipment is in an operation state, the value of the variable is 0, the variable indicates that the equipment is in a standby state, and the relation between the value and time is as follows:
μ i (t)＝1,t∈[t i ,t i +t z ]
wherein t is z For the equipment operation time length, the calculation formula is as follows:
t z ＝t j +t r +t p
step 1.2: establishing a price demand response model;
the intelligent control center establishes a relation between demand and price according to electricity price information, and the relation is shown in the following formula:
wherein Deltat is the time interval for the intelligent control center to acquire the real-time electricity price, P (t) is the real-time electricity price at the moment t, P (t) is the requirement at the moment t, M is the number of times for the intelligent control center to acquire the real-time electricity price in one control period, k is the moment other than the moment t in the control period M, lambda tt And lambda is tk The price type electric power self-elasticity coefficient and the cross-elasticity coefficient are respectively calculated according to the following formulas:
wherein Δp (t) is the amount of change in demand;
step 1.3: establishing an excitation demand response model;
the intelligent control center obtains dispatching information issued by the power grid, wherein the dispatching information comprises the size and the time period for increasing or reducing the load, and economic compensation is obtained by signing a demand response protocol, and is shown in the following formula:
wherein a is a discount coefficient of electricity price obtained by increasing load, b is a discount coefficient of electricity price obtained by reducing load, T is a period of demand response control, C IBDR For economic compensation, ΔP, obtained by demand response protocol during time T in (t) and ΔP de (t) increasing and decreasing the power of the load at time t, respectively;
step 2: the electric smelting magnesium furnace is covered and sealed, so that the load energy efficiency of magnesite is improved;
step 3: acquiring the temperature, flow and environmental temperature of the flue gas fed into the waste heat boiler through an intelligent control system, and calculating the waste heat recovery efficiency of each equipment of the magnesite process;
step 4: establishing an optimized objective function and constraint conditions of the magnesite load;
step 5: solving the demand response model of the magnesite load in the step 1 by adopting an optimization algorithm, and finally obtaining an optimal control method for the load;
step 6: the intelligent control center issues control signals to each equipment of the magnesite process; the control signals are the optimal solutions of the operation start time of each device, the arc current value of each device and the pressure of the waste heat boiler obtained in the step 5;
step 7: and each device of the magnesite process controls the running time, the electric arc current value and the pressure of the waste heat boiler according to the control signals, so that the intelligent regulation and control of the magnesite load are realized.
2. The method for modeling and improving energy efficiency based on intelligent regulation and control of magnesite load according to claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1: the intelligent control center obtains the temperature of the flue gas generated by the electric smelting magnesium furnace;
step 2.2: introducing waste gas generated by magnesite load into waste heat recovery equipment;
step 2.3: waste gas discharged from the waste heat recovery device is led into a dust recovery device, the waste gas contains magnesite powder which can be used for a second time, and the recovered magnesite powder is led into an electric arc furnace.
3. The demand response modeling and energy efficiency improving method based on intelligent regulation and control of magnesite load according to claim 1, wherein the efficiency calculation formula of waste heat recovery of each equipment of the magnesite process in step 3 is as follows:
η i improving the energy efficiency of the ith magnesite equipment and W gi The calculation formula is as follows for the energy generated by the flue gas waste heat of the ith magnesite process equipment through the waste heat recovery equipment:
W gi ＝m gi (h 1 -h 2 )η f
wherein eta f For the efficiency of the generator in the waste heat recovery device, m gi The flow of the superheated steam generated by the waste heat boiler after the waste heat utilization of the smoke exhausted by the ith magnesite equipment is calculated according to the following formula:
wherein m is g For the total flow of the superheated steam generated by the waste heat boiler, V gi For the flow of flue gas produced by the ith magnesite equipment, V g The flow rate of the flue gas generated by all magnesite equipment, h 1 And h 2 The enthalpy of superheated steam generated by the waste heat boiler and the enthalpy of steam turbine exhaust are respectively.
4. The method for modeling and improving energy efficiency based on intelligent regulation and control of magnesite load according to claim 1, wherein the step 4 specifically comprises the following steps:
step 4.1: establishing an optimized objective function, wherein the objective functions 1-3 are respectively shown in the following formulas:
min f 1 ＝(C Grid -C IBDR )
in objective function 1, f 1 C for electricity consumption Grid For the electricity purchasing expense from the power grid in the magnesite process, the calculation formula is as follows:
in objective function 2, f 2 As a function of the variance of the load curve,for the load average, the calculation formula is as follows:
in objective function 3, f 3 Waste heat recovery efficiency of magnesite equipment with lowest waste heat recovery efficiency, wherein eta i The energy efficiency of the ith magnesite equipment is improved;
step 4.2: establishing constraint conditions, wherein the constraint conditions are as follows:
P(t)≤I max U N
p(t) min ≤p(t)≤p(t) max
I famin ≤I fai (t)≤I famax
I fbmin ≤I fbi (t)≤I fbmax
I fcmin ≤I fci (t)≤I fcmax
wherein I is max Maximum current for grid and user tie line passing, U N For rated voltage, p (t) min And p (t) max Respectively the minimum value and the maximum value of electricity price at the moment t, P min And P max Respectively the minimum value and the maximum value of the total electricity consumption of the magnesite process equipment in the control period, I famin 、I fbmin And I fcmin Respectively the minimum value of arc current in the feeding, main melting and exhausting processes in the magnesite technological process, I famax 、I fbmax And I fcmax The maximum arc current values in the feeding, main melting and exhausting processes in the magnesite technological process are respectively.
5. The method for modeling demand response and improving energy efficiency based on intelligent regulation and control of magnesite load according to claim 1, wherein the optimization algorithm in step 5 is a non-dominant ranking genetic algorithm II, namely NSGA-II, and the specific steps are as follows:
step 5.1: chromosome coding and initialization;
the chromosome adopts decimal coding, and the decision variables comprise the running start time of each equipment, the arc current value of each equipment and the pressure P of the waste heat boiler in the magnesite process 0 Therefore, chromosome x of the jth individual is set j ＝(t 1 ,t 2 ,…,t i ,…,t n ,I f1 ,I f2 ,…,I fi ,…,I fn ,P 0 ) Randomly generating an initial population P with N individuals 1 ；
Step 5.2: population P i Through crossing and mutation, generating sub-population Q i ；
Step 5.3: will group P i And population Q i Synthetic population R i Calculating the adaptive value of each objective function, and performing non-dominant sorting;
step 5.4: performing elite individual verification;
checking whether the individual meets constraint conditions or not is needed to pass, and in order to reduce the calculated amount, checking only elite individuals at the highest layer of non-dominant order, wherein the checked contents comprise total load power, electricity price, time, electricity consumption of magnesite equipment and arc current constraint; the individuals meeting all constraint conditions, namely the individuals passing the verification, and the verification mark is set to be feasible; otherwise, the verification mark is set to be infeasible, and when the next round of evolution is carried out, the elimination verification mark is selected as an infeasible individual by elite strategy;
step 5.5: calculating the crowding distance of the last non-dominant layer individual in the next generation population;
the congestion distance calculation formula is as follows:
wherein f d (x i+1 ) And f d (x i-1 ) The (d) th objective function value, f, of the (i+1) th and (i-1) th individuals of the non-dominant layer, respectively dmax And f dmin Respectively the maximum value and the minimum value of the d objective function value; wherein the crowding distance of the individual sorting edge is set to ≡;
step 5.6: selecting individuals entering a new parent population;
first, the population R is eliminated i The scheme check mark is an infeasible individual; second according to non-dominant order i rank Sequentially placing the whole population into the new parent population P from low to high i+1 Until a certain layer F is put in j At the time P i+1 The size exceeds the population size N; finally, according to F j The individual crowding distances in (a) continue to fill P in order from big to small i+1 Ending until the population number reaches N, increasing the iteration number by 1 if the iteration number does not reach the set value, and repeating the steps 3.2 to 3.6 until the iteration number reaches the set value;
step 5.7: obtaining a Pareto optimal solution;
and (3) when the iteration times reach a set value, outputting a Pareto optimal solution, wherein the optimal solution comprises the operation starting time of each device, the arc current value of each device and the pressure of the waste heat boiler.
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