CN110417061B - Electric-heat combined system scheduling method based on improved leapfrog algorithm - Google Patents
Electric-heat combined system scheduling method based on improved leapfrog algorithm Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses an electric-heat combined system scheduling method based on an improved leapfrog algorithm, which belongs to the technical field of electric-heat combined system economic scheduling, fully considers the uncertainty of wind power predicted output power, enables the wind power predicted output power to participate in scheduling in the form of a wind power output power scene, guides electricity price type load to participate in demand transfer by time-of-use electricity price, simultaneously considers the demand conversion of the load, and utilizes abandoned wind power to convert electricity into heat; the sum of the power generation cost and the power consumption cost of a user of the electric-heat combined system is used as a target function, the improved mixed frog-leaping algorithm is used for solving the model, the defect that the traditional mixed frog-leaping algorithm is easy to fall into a local optimal solution is overcome to a great extent, the convergence speed is increased, the algorithm optimizing capacity can be effectively improved, and a more reasonable electric-heat combined system optimization scheme is obtained.
Description
Technical Field
The invention relates to the technical field of economic dispatching of an electric heating combined system, in particular to an electric heating combined system dispatching method based on an improved leapfrog algorithm.
Background
Although the permeability of wind power is increasingly enhanced in China, the problem of wind abandon is still outstanding. The method is mainly characterized in that the proportion of a cogeneration unit in a generator set in China is large, and the operation mode of the Cogeneration (CHP) unit for fixing the power by heat causes the improvement of the minimum electric output of the unit, so that the electric output adjusting range of the cogeneration unit in a heating season is limited, and based on an electric energy balance principle, a system cannot provide enough online space for wind power; secondly, the uncertainty of the wind power output power also causes the difficulty of wind power on-line. Therefore, optimal scheduling must be carried out, a reasonable scheduling strategy of the electric-heating combined system is formulated, under the condition that the capacity configuration of the electric-heating combined system is not changed, all units of the system run cooperatively, the adjusting capacity of the electric-heating combined system is released to the maximum extent, the fluctuation of wind power output is balanced, and space is provided for wind power to surf the internet.
Optimization algorithms of a scheduling model of the electric heating combined system are various, particle swarm algorithms, artificial ant colony algorithms, wolf colony algorithms and the like are used mostly at present, but the algorithms are low in convergence speed and prone to falling into local optimization. The mixed frog leaping algorithm is developed by simulating the foraging process of the frogs and has better robustness. The basic mixed frog-leaping algorithm is a research hotspot in the field of optimization calculation, is inspired by a natural modular factorial algorithm based on a genetic algorithm, and is combined with some advantages in a particle swarm optimization algorithm to form a new swarm intelligent optimization algorithm with global cooperative search capability. As a new group intelligent optimization algorithm, the algorithm has the characteristics of less parameters, higher calculation speed, simple concept, strong global optimization capability and the like. However, the mixed frog-leaping algorithm is to perform reordering after the partial search of the subgroups is finished, information exchange with other subgroups is not performed in the partial search process, the dependence degree of the search method on excellent individuals of the subgroups is too high, the subgroups are easily trapped into local optimality, the convergence speed is low, the algorithm is used for optimizing the scheduling method of the electric heating combined system, and the obtained optimal scheme may also be local optimality.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an electric-heat combined system scheduling method based on an improved leapfrogging algorithm.
The technical scheme adopted by the invention is an improved frog leaping algorithm-based electric-heat combined system scheduling method, the flow of the method is shown in figure 1, and the method comprises the following steps:
step 1: acquiring a wind speed day-ahead predicted output value, wind speed historical data, a system electric load predicted value and a system heat load predicted value in an electric heating combined system, and technical parameters of an electric heating combined generation unit, a thermal power unit, an electric boiler and a heat storage tank in the system;
step 2: establishing a probability density function of the wind power output power, performing Latin hypercube sampling on the probability density function, and obtaining E samples in the sampling process as shown in figure 3;
step 2-1: wind speed is fitted by Weibull distribution, and a probability density function of the wind speed is established as follows:
wherein v is wind speed; k is a shape parameter, c is a proportion parameter, mu is an average value of the wind speed sample, sigma is a variance of the sample, and Gamma is a Gamma function;
step 2-2: the probability density function of the wind speed is integrated to obtain a distribution function of the wind speed as follows:
step 2-3: combined with fan output power P W Establishing a probability density function of the wind power output power according to the relation between the wind speed and the wind speed;
output power P of fan W The relationship with the wind speed is shown in fig. 2, and the expression is as follows:
wherein the content of the first and second substances,
rated wind power output power v of the fan r Rated wind speed, v, of the fan in For cutting into the wind speed, v out Cutting out the wind speed;
the probability density function of the wind power output power is:
step 2-4: dividing the probability distribution of the wind power output power into N equal probability intervals;
step 2-5: for any one probability interval
Randomly taking a number q i ：
In the formula, rand (0, 1) is random number in (0, 1);
step 2-6: carrying out inverse transformation on Weibull distribution describing wind power output power probability distribution to obtain corresponding probability in a probability interval
The wind power prediction output power sample value is as follows:
P i W ＝f -1 (q i )
step 2-7: and (5) repeatedly executing the steps 2-4 to 2-6 until E required samples are obtained.
And 3, step 3: e samples are cut down by a scene cutting-down method, and the process is shown in figure 4, so that M wind power output power scenes are obtained;
step 3-1: e sample probabilities of the wind power output power are initialized, namely, the probabilities of any wind power output power sample are equal:
step 3-2: for any 2 wind power output power samples i and j (i is more than or equal to 1 and is less than or equal to j and is less than or equal to E), P is calculated i W And
scene distance between:
step 3-3: for a wind power output power scene i, finding a scene j closest to the scene i:
calculating the probability distance between two scenes with the closest distance:
step 3-4: calculating a probability distance set of each wind power output power scene through the step 3-3, and searching the minimum probability distance in E scenes:
p Ds ＝min{p D |1≤i≤E}
reducing the scene i with the probability closest to the scene j from the scene set;
step 3-5: probability p of updating scene j j ＝p j +p i (ii) a Number of update scenes E = E-E i ；
Step 3-6: and repeating the steps 3-2 to 3-5 until the number of scenes reaches the required number of scenes M.
And 4, step 4: according to maximum value P of electric load max Minimum value P min And mean value P av Dividing the peak-to-valley period of the electric load, and determining the time-of-use electricity price C of each period t ；
Step 4-1: dividing the electric load in a peak-to-valley flat period:
in the formula, t f 、t p 、t g The peak, the plateau and the valley are respectively three periods, alpha and beta are respectively proportional coefficient, P L.t The electric load quantity at the moment t before participating in demand response;
step 4-2: combining the electricity prices of different time periods to obtain the time-of-use electricity price C of each time period in the whole time period T t 。
And 5: according to the characteristic that the electricity price type load participates in demand response, a model of the electricity load participating in demand response is established;
step 5-1: relationship between electric load transfer amount and electricity price:
wherein Δ C is a price change amount when the electric load participating in the demand response is transferred from a period of high electricity prices to a period of low electricity prices; c is the electricity price of the time period when the electricity price is high before participating in demand response; Δ P is an electrical load variation amount when the electrical load participating in the demand response shifts from a period of high electricity prices to a period of low electricity prices; p L An electrical load that is a period of time during which the electricity price is high before participation in demand response; xi is a demand response coefficient;
step 5-2: load amount after the electric load participates in the demand response:
P DRL.t ＝P L.t +P in.t -P out.t
wherein, P DRL.t The electric load quantity at the time t after the participation in demand response; p L.t The electric load quantity at the moment t before participating in demand response; p in.t The electric load transferred at the moment t; p out.t The electric load transferred at the moment t;
step 5-3: in order not to affect the normal life of the resident users, the change of the electricity consumption amount should be within a certain range, and the electricity consumption amount in one day is not changed, namely:
ΔP out ≤ΔP
-P L.t ·γ≤ΔP in.t -ΔP out.t ≤P L.t ·γ
wherein gamma is needed for participation at the time tCalculating the proportion of the maximum load of the response, wherein T is the number of scheduling period time segments in one day, and delta P out Is the amount of load that can be transferred to the rest of the time period.
And 6: establishing an electric boiler start-stop strategy according to the demand response of the electric load:
P tpu +P CHP +P WF ＞P DRL
wherein, P CHP The electric output of the cogeneration unit; p tpu The power is the electric output of the thermal power generating unit; p WF Predicting output for the wind power plant; p is DRL To participate in the electrical load after the demand response.
And 7: establishing an electric-heat combined system scheduling model by taking the minimum system power generation cost and power utilization cost as a target function;
step 7-1: the cost function of the thermal power generating unit is as follows:
wherein the content of the first and second substances,
electric power output by the ith thermal power generating unit at time t, a i 、b i 、c i The method comprises the following steps that (1) a coal-fired cost coefficient of a thermal power generating unit is obtained, and delta t is the length of a scheduling time interval;
step 7-2: the cogeneration unit cost function is:
wherein, the first and the second end of the pipe are connected with each other,
the electric power output by the ith cogeneration unit at the moment t;
the thermal power output by the ith cogeneration unit at the moment t; lambda 0.i 、λ 1.i 、λ 2.i 、λ 3.i 、λ 4.i 、λ 5.i A coal-fired cost coefficient for the ith cogeneration unit;
and 7-3: and (3) constructing an electric-heating combined system scheduling model by combining the cost of the thermal power generating unit, the cost of the cogeneration unit, a wind power output power scene and the power consumption cost of a user:
wherein p is j Probability of occurrence of jth wind power output power scene, C t Electricity price at time t, P DRL.t For participating in the electric load quantity at time t after the demand response, S tpu Set of number of thermal power generating units, S chp The number of the cogeneration units is set, and T is the number of the scheduling period time segments.
And 8: solving the objective function by adopting an improved mixed frog-leaping algorithm, and when the maximum iteration number N is reached max Or when the search precision epsilon is met, outputting optimized values of all control variables of the electric-heat combined system, wherein the process is shown in fig. 5, and the local search of each subgroup is shown in fig. 6;
step 8-1: setting parameters of an improved mixed frog leaping algorithm, determining population scale F and sub-population number m, determining the number n of frogs in each sub-population, F = m multiplied by n, and setting the maximum step length of allowed change of individual frogs as a matrix D max Maximum number of iterations N of population max Subgroup maximum number of iterations in max ；
Step 8-2: initializing parameters, randomly generating a frog position according to the value upper limit and the value lower limit of each control variable to be optimized of the electric-heat combined system, wherein the frog position is coded as follows:
wherein X is a value matrix of the individual control variable of the frog in each time period, P DRL.t To participate in demand response T (T =1,2, 3., T)The electrical load at that moment;
is T (T =1,2, 3.., T) time i (i =1,2, 3.., S) chp ) Electric power output by the station cogeneration unit;
for time T (T =1,2,3, T), the ith (i =1,2,3, S) chp ) Thermal power output by a combined heat and power generation unit;
is T (T =1,2, 3.., T) time i (i =1,2, 3.., S) tpu ) Electric power output by the thermal power generating unit;
thermal power output by the thermal storage tank for time T (T =1,2, 3.., T); p t EB An input electric power of the electric boiler for time T (T =1,2, 3.., T); p t W The electrical power output by the wind turbine at time T (T =1,2, 3.., T); x is the number of t Optimizing a control variable of the electric heating combined system for a randomly generated T (T =1,2, 3.., T) moment;
and 8-3: randomly generating an initial frog position:
X rc ＝X rc,min +rand(0,1)×(X rc,max -X rc,min )，0＜r≤S tpu +2S chp +4，0＜c≤T
wherein, X rc Is a frog position component, X rc,min And X rc,max Lower and upper limits, S, respectively, of the corresponding position component tpu The method comprises the steps of collecting the number of thermal power generating units; s chp The method comprises the steps of (1) collecting the number of cogeneration units;
and 8-4: initial updating distance of frog:
D rc (0)＝-D rc.max +rand(0,1)×2D rc.max
wherein D is rc Distance updated for frog initiation, D rc.max Can be updated when the frog is initially updatedThe maximum distance of (d);
and 8-5: sequencing frogs according to adaptive values and recording global optimal frog positions X g Distributing F frogs to m sub-groups Y by improved frog distribution formula 1 ，Y 2 ，...，Y m In the above example, each group comprises n frogs, and the improved distribution formula is:
wherein X (l) represents the first frog in the frog group, and f (l) represents the objective function value of the first frog;
and 8-6: setting subgroup counter im =0 and local optimization counter in =0, and determining the position X of the best frog and the worst frog in each subgroup b And X w ；
And 8-7: update the worst frog position in the subgroup:
and (3) frog distance updating:
D(in+1)＝w×D(in)+(X avg -X w )·E(r)+(X g -X w )·E(r)
-D max ≤D(in+1)≤D max
and (3) frog position updating: x' w ＝X w +D(in+1)
Wherein w is an inertia factor, D (in) is the updating distance of the number counted by the current counter, D (in + 1) is the frog updating distance of the next counting number of the counter, and X w The worst frog position of subgroup, D max The maximum distance that can be updated when the frog position is updated, in is the number counted by the current counter, E (r) is the T multiplied by T diagonal matrix with the element rand (0, 1), X avg Average of the best frog positions for each subgroup, X b.1 ,X b.2 ,...,X b.m The optimal frog positions from the 1 st subgroup to the m th subgroup, w ini Is an initial inertia weight, w end Iterating to the maximum times of inertia weight;
and 8-8: the updated position X' w And position X before update w Comparing, if the updated position is better than the position before updating, keeping the updated position, if the updated position is not good, using the global optimum frog position X g Replacing;
and 8-9: when the maximum number of iterations in of the subgroup is satisfied max Then, the subgroup iteration is finished, the frogs in each subgroup are reordered and divided after each subgroup is subjected to a round of local optimization iteration, and the current global optimal frog position X is recorded g ；
And 8-10: repeating the steps 8-3 to 8-9 until the maximum iteration number N of the population is met max And stopping the algorithm execution process, and outputting the global optimal frog position, namely the force output value of each component of the electric heating combined system.
And step 9: and adjusting the output power of a thermal power unit of the electric-heat combined system, the electricity/heat output power of a cogeneration unit, the storage/heat release power of a heat storage tank, the output thermal power of an electric boiler and the electric load value of the system after each time interval participates in demand response according to the optimized value of each control variable of the electric-heat combined system.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1. according to the method, the uncertainty of the wind power predicted output power is considered, the wind power predicted output power participates in scheduling in the form of a wind power output power scene, the randomness characteristic of wind power output is fitted to the maximum extent, and the accuracy of the wind power predicted output power is improved;
2. the invention considers the action of demand response, and makes the electricity price type load in the electric heating combination system participate in the dispatching of the electric heating combination system by calling the demand side resource, thereby realizing the peak clipping and valley filling of the system electric load, and improving the system stability while ensuring the economical efficiency of the operation of the electric heating combination system;
3. according to the invention, when the time-of-use electricity price is used for guiding the electricity price type load to participate in the demand transfer, the demand conversion of the load is also considered, electricity is converted into heat by utilizing the abandoned wind power, the heat supply is provided for the electric heating combined system, and the consumption of the abandoned wind power is promoted;
4. the method takes the sum of the power generation cost and the power consumption cost of the user of the electric heating combined system as a target function, and promotes wind power consumption while ensuring the economical efficiency of the operation of the electric heating combined system;
5. according to the invention, by improving the frog distribution formula, the frogs can be uniformly distributed into each subgroup according to the adaptive values of the frogs, and the overall levels of the adaptive values of the subgroups are ensured to be similar; the situation that the adaptive value level of the first subgroup is the highest and the adaptive value level of the last subgroup is the lowest does not occur, so that the optimization speed of the algorithm is improved;
6. according to the method, the frog position updating formula is improved, so that the information exchange can be carried out on the frogs of each subgroup in the position updating process, the defect that the traditional mixed frog leaping algorithm is easy to fall into a local optimal solution is greatly improved, and the optimization accuracy of the algorithm is improved;
7. the invention uses the leapfrog algorithm in the optimal scheduling problem of the electric heating combined system for the first time, improves the leapfrog algorithm, improves the convergence rate, can effectively improve the optimization capability of the algorithm and obtains a more reasonable optimization scheme of the electric heating combined system.
Drawings
FIG. 1 is a flow chart of an electric-heat combined system scheduling method based on an improved leapfrog algorithm of the invention;
FIG. 2 is a graph of the relationship between wind speed and wind power according to the present invention;
FIG. 3 is a flowchart of Latin hypercube sampling according to the present invention;
FIG. 4 is a flowchart illustrating scene cuts according to the present invention;
FIG. 5 is a flow chart of the improved leapfrog algorithm solution of the present invention;
FIG. 6 is a flow chart of the local search of the improved leapfrog algorithm of the present invention;
FIG. 7 is a structural diagram of an electric heating combination system in an embodiment of the invention;
FIG. 8 is a diagram of the predicted power output and system load of the wind power generation at the present day.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The structure of the electric-heat combined system of the embodiment is shown in fig. 7 and comprises 2 thermal power generating units, 3 cogeneration units, an electric boiler, a heat storage tank and a wind power generating unit.
As shown in fig. 1, a process of the electric-thermal combination system scheduling method based on the improved leapfrog algorithm is as follows:
step 1: acquiring a predicted wind speed value, historical wind speed data, a predicted system electrical load value and a predicted system thermal load value in an electric heating combined system in the day ahead; technical parameters of a cogeneration unit, a thermal power unit, an electric boiler and a heat storage tank in the system are shown in table 1, specific parameters of the thermal power unit are shown in table 2, the maximum power of the electric boiler is 50MW, the maximum heat storage capacity of the heat storage tank is 100MW & h, the maximum charging and discharging thermal power is 100MW, the installed capacity of a wind power unit is 150MW, and a daily wind power predicted output and system load graph is shown in fig. 8;
TABLE 1 Cogeneration Unit parameters
TABLE 2 thermal power generating unit parameters
And 2, step: establishing a probability density function of the wind power output power, performing Latin hypercube sampling on the probability density function, and obtaining 1000 samples in the sampling process as shown in figure 3;
and step 3: cutting 1000 samples by using a scene cutting subtraction method, wherein the process is shown in fig. 4, 5 wind power output power scenes are obtained, and the probability of each scene is shown in table 3;
TABLE 3 probability of occurrence of each scene
| Scene one | Scene two | Scene three | Scene four | Scene five |
| 24.7％ | 24.5％ | 16.7％ | 13.3％ | 20.8％ |
And 4, step 4: according to maximum value P of electric load max =582.57MWH, minimum value P min =421.07MWH and mean value P av =510.10MWH, the electric load is divided into peak-to-valley periods, and the time-of-use electricity rate C of each period is determined t As shown in table 4;
TABLE 4 timesharing electricity rate table for each period
| Time interval division | Peak period | Flat time period | In the valley period |
| The time interval of each time t | 9～10 | 6～8&20～21 | 0～5&22～23 |
| Electricity price (Yuan/kilowatt hour) | 1.0323 | 0.6362 | 0.3315 |
And 5: according to the characteristic that the electricity price type load participates in demand response, a model of the electricity load participating in demand response is established;
step 6: establishing an electric boiler start-stop strategy according to the demand response of the electric load;
and 7: establishing an electric heating combined system scheduling model by taking the minimum system power generation cost and power utilization cost as a target function;
and 8: solving the objective function by adopting an improved mixed frog-leaping algorithm, and when the maximum iteration number N is reached max Or when the searching precision epsilon is met, outputting optimized values of all control variables of the electric heating combined system;
step 8-1: setting parameters of an improved mixed frog leaping algorithm, determining the population scale 200, the number of sub-populations 10, the number of frogs in each sub-population 20, and the maximum step length of allowed change of individual frogs as a matrix D max Maximum iteration count of population 100, maximum iteration count of subgroup 15.
Step 8-2: initializing parameters, and randomly generating a frog position according to the upper value limit and the lower value limit of each control variable to be optimized of the electric heating combined system, wherein the frog position code is as follows:
wherein X is a value matrix of the individual control variables of the frogs in each period, P DRL,t For the load quantity after the electric load participates in the demand response at the time t,
for the electric power and the thermal power output by the 1 st cogeneration unit at the time t,
for the electric power and the thermal power output by the 2 nd cogeneration unit at time t,
for the electric power and the thermal power output by the 3 rd cogeneration unit at time t,
respectively the electric power output by 2 thermal power generating units at the time t,
the thermal power output by the thermal storage tank at the moment t; p t EB The input electric power of the electric boiler at the time t; x is the number of t Optimizing a control variable of the electric heating combined system for a randomly generated T (T =1,2, 3.., T) moment; p t W T =1,2, a, T for the electrical power output by the wind turbine at time T;
step 8-3: randomly generating an initial frog position:
X rc ＝X rc,min +rand(0,1)×(X rc,max -X rc,min )，0＜r≤S tpu +2S chp +4，0＜c≤T
wherein, X rc Is a frog position component, X rc,min And X rc,max Lower and upper limits, S, respectively, of the corresponding position component tpu The method comprises the steps of collecting the number of thermal power generating units; s chp The method comprises the steps of (1) collecting the number of cogeneration units;
step 8-4: initial updating distance of frog:
D rc (0)＝-D rc.max +rand(0,1)×2D rc.max
wherein D is rc Distance initially updated for frog, D rc.max The maximum distance which can be updated when the frog is initially updated;
and 8-5: sequencing frogs according to adaptive values and recording global optimal frog positions X g Distributing F frogs to m sub-groups Y by improved frog distribution formula 1 ，Y 2 ，...，Y m In the middle, each group comprises n frogs, and the improved distribution formula is as follows:
wherein X (l) represents the first frog in the frog group, and f (l) represents the objective function value of the first frog;
and 8-6: setting subgroup counter im =0 and local optimization counter in =0, and determining the position X of the best frog and the worst frog in each subgroup b And X w ；
And 8-7: update the worst frog position in the subgroup:
and (3) updating the frog distance:
D(in+1)＝w×D(in)+(X avg -X w )·E(r)+(X g -X w )·E(r)
-D max ≤D(in+1)≤D max
and (3) frog position updating: x' w ＝X w +D(in+1)
Wherein w is an inertia factor; d (in) is the updating distance of the number counted by the current counter; d (in + 1) is the frog updating distance of the next counting number of the counter; x w The worst frog position of the subgroup; d max The maximum distance which can be updated when the frog position is updated; in is the number counted by the current counter; e (r) is a T multiplied by T diagonal matrix with elements rand (0, 1); x avg The average value of the best frog positions of each subgroup is obtained; x b.1 ,X b.2 ,...,X b.m The positions of the best frogs from the 1 st subgroup to the m th subgroup respectively; w is a ini Is the initial inertia weight; w is a end Iterating to the maximum times of inertia weight;
and 8-8: the updated position X' w And position X before update w Comparing, if the updated position is better than the position before updating, keeping the updated position, if the updated position is not good, using the global optimum frog position X g Replacing;
and 8-9: when in is more than 10, the subgroup iteration is finished, after each subgroup is subjected to one round of local optimization iteration, the frogs in each subgroup are reordered and divided, and the current global optimal frog position X is recorded g ；
And 8-10: and (5) repeatedly executing the steps 8-3 to 8-9 until the maximum iteration times of the population is 100, stopping the algorithm execution process, and outputting the global optimal frog position, namely the output value of each component of the electric heating combined system.
And step 9: adjusting the output power of a thermal power unit of the electric-heat combined system, the electricity/heat output power of a cogeneration unit, the storage/heat release power of a heat storage tank, the output thermal power of an electric boiler and the electric load value of the system after each time interval participates in demand response according to the optimized value of each control variable of the electric-heat combined system, as shown in table 5;
TABLE 5 optimization scheduling result graph after each time interval participates in demand response
The method of the invention is used for optimizing and adjusting each control variable of the electric heating combined system, fully considers the uncertainty of the wind power prediction output power, enables the wind power prediction output power to participate in the dispatching in the form of a wind power output power scene, combines the action of demand response, and enables the electricity price type load in the electric heating combined system to participate in the dispatching of the electric heating combined system by calling demand side resources, thereby realizing the peak clipping and valley filling of the system electric load, ensuring the running economy of the electric heating combined system and simultaneously improving the system stability; the method adopts the improved mixed frog-leaping algorithm to solve the model, greatly overcomes the defect that the traditional mixed frog-leaping algorithm is easy to fall into the local optimal solution by improving the frog allocation formula and the position updating formula, improves the convergence speed, can effectively improve the algorithm optimizing capability, and obtains a more reasonable electric-thermal combined system optimization scheme.
Claims (8)
1. An electric-heat combined system scheduling method based on an improved leapfrog algorithm is characterized by comprising the following steps:
step 1: acquiring a wind speed day-ahead predicted output value, wind speed historical data, a system electric load predicted value and a system heat load predicted value in an electric heating combined system, and technical parameters of an electric heating combined generation unit, a thermal power unit, an electric boiler and a heat storage tank in the system;
step 2: establishing a probability density function of the wind power output power, and performing Latin hypercube sampling on the probability density function to obtain E samples;
and step 3: e samples are cut by a scene cutting subtraction method to obtain M wind power output power scenes;
and 4, step 4: according to maximum value P of electric load max Minimum value P min And mean value P av Dividing the electric load into peak-to-valley periods, and determining the time-of-use electricity price C of each period t ；
And 5: according to the characteristic that the electricity price type load participates in demand response, a model of the electricity load participating in demand response is established;
and 6: establishing an electric boiler start-stop strategy according to the demand response of the electric load;
and 7: establishing an electric-heat combined system scheduling model by taking the minimum system power generation cost and power utilization cost as a target function;
and 8: solving the objective function by adopting an improved mixed frog-leaping algorithm, and when the maximum iteration number N is reached max Or when the searching precision epsilon is met, outputting optimized values of all control variables of the electric heating combined system;
and step 9: and adjusting the output power of a thermal power unit of the electric-heat combined system, the electricity/heat output power of a cogeneration unit, the storage/heat release power of a heat storage tank, the output thermal power of an electric boiler and the electric load value of the system after each time interval participates in demand response according to the optimized value of each control variable of the electric-heat combined system.
2. The electric-heat combined system scheduling method based on the improved frog-leaping algorithm as claimed in claim 1, wherein the probability density function of the wind power output power is established in step 2, and the Latin hypercube sampling process is as follows:
step 2-1: wind speed is fitted by Weibull distribution, and a probability density function of the wind speed is established as follows:
wherein v is the wind speed, k is the shape parameter, c is the proportion parameter, mu is the average value of the wind speed sample, sigma is the variance of the sample, and Gamma is the Gamma function;
step 2-2: the probability density function of the wind speed is integrated to obtain a distribution function of the wind speed as follows:
step 2-3: combined with fan output power P W Establishing a probability density function of the wind power output power according to the relation between the wind speed and the wind speed;
output power P of fan W The relationship to wind speed is as follows:
wherein the content of the first and second substances,
rated wind power output power v of the fan r Rated wind speed, v, of the fan in For cutting into the wind speed, v out Cutting out the wind speed;
the probability density function of the wind power output power is:
step 2-4: dividing the wind power output power probability distribution into N equal probability intervals;
step 2-5: for any one probability interval
Randomly taking a number q i ：
Wherein rand (0, 1) is a random number in (0, 1);
step 2-6: to describeCarrying out inverse transformation on Weibull distribution of wind power output power probability distribution to obtain corresponding probability in a probability interval
The wind power prediction output power sample value is as follows:
P i W ＝f -1 (q i )
step 2-7: and (5) repeatedly executing the steps 2-4 to 2-6 until E required samples are obtained.
3. The electric-thermal combination scheduling method based on the improved frog-leaping algorithm as claimed in claim 1, wherein the process of clipping E samples by scene clipping in step 3 is as follows:
step 3-1: e sample probabilities of the wind power output power are initialized, namely, the probabilities of any wind power output power sample are equal:
step 3-2: for any 2 wind power output power samples i and j (i is more than or equal to 1 and less than or equal to j and less than or equal to E), P is calculated i W And
scene distance between:
step 3-3: for a wind power output power scene i, finding a scene j closest to the scene i:
calculating the probability distance between two scenes with the closest distance:
step 3-4: calculating a probability distance set of each wind power output power scene through the step 3-3, and searching the minimum probability distance in E scenes:
p Ds ＝min{p D |1≤i≤E}
reducing the scene i with the probability closest to the scene j from the scene set;
step 3-5: probability p of updating scene j j ＝p j +p i (ii) a Number of update scenes E = E-E i ；
Step 3-6: and repeating the steps 3-2 to 3-5 until the scene number reaches the required scene number M.
4. The electric-thermal combination system dispatching method based on the improved leapfrog algorithm as claimed in claim 1, wherein the step 4 is based on the maximum value P of the electric load max Minimum value P min And mean value P av Dividing the peak-to-valley period of the electric load, and determining the time-of-use electricity price C of each period t The process of (2) is as follows:
step 4-1: dividing the peak-to-valley period of the electric load:
in the formula, t f 、t p 、t g The three time periods of peak, flat and valley are respectively, alpha and beta are respectively proportional coefficient, P L.t The electric load quantity at the moment t before participating in demand response;
step 4-2: combining the electricity prices of different time periods to obtain the time-of-use electricity price C of each time period in the whole time period T t 。
5. The electric-heat combined system dispatching method based on the improved leapfrog algorithm as claimed in claim 1, wherein the process of establishing the model of the electric load participation demand response according to the characteristic of the electric price type load participation demand response in step 5 is as follows:
step 5-1: relationship between electric load transfer amount and electricity price:
wherein Δ C is a price change amount when the electric load participating in the demand response is transferred from a period of high electricity prices to a period of low electricity prices; c is the electricity price of the time period with high electricity price before participating in demand response; Δ P is an electrical load variation amount when the electrical load participating in the demand response shifts from a period of high electricity prices to a period of low electricity prices; p L An electrical load that is a period of time during which the electricity price is high before participation in demand response; xi is a demand response coefficient;
step 5-2: load amount after the electric load participates in the demand response:
P DRL.t ＝P L.t +P in.t -P out.t
wherein, P DRL.t The electric load quantity at the time t after the participation in demand response; p L.t The electric load quantity at the moment t before participation in demand response; p in.t The electric load transferred at the time t; p out.t The electric load transferred at the moment t;
step 5-3: in order not to affect the normal life of the residential users, the change of the power consumption should be within a certain range, and the power consumption in one day is not changed, namely:
ΔP out ≤ΔP
-P L.t ·γ≤ΔP in.t -ΔP out.t ≤P L.t ·γ
wherein gamma is the ratio of the maximum load quantity participating in demand response at the time T, T is the number of scheduling period time segments in one day, and delta P out Is the amount of load that can be transferred to the rest of the time period.
6. The electric-heat combined system scheduling method based on the improved leapfrog algorithm according to claim 1, wherein in step 6, according to the demand response of the electric load, an electric boiler start-stop strategy is established to meet the following conditions:
P tpu +P CHP +P WF ＞P DRL
wherein, P CHP The electric output of the cogeneration unit; p tpu The power is the electric output of the thermal power generating unit; p is WF Predicting output for the wind power plant; p is DRL To participate in the electrical load after the demand response.
7. The electric-heat combined system dispatching method based on the improved leapfrog algorithm as claimed in claim 1, wherein the process of establishing the electric-heat combined system dispatching model with the minimum system power generation cost and power consumption cost as the objective function in step 7 is as follows:
step 7-1: the cost function of the thermal power generating unit is as follows:
wherein, the first and the second end of the pipe are connected with each other,
electric power output by the ith thermal power generating unit at the time t, a i 、b i 、c i The method comprises the following steps that (1) a coal-fired cost coefficient of a thermal power generating unit is obtained, and delta t is the length of a scheduling time interval;
step 7-2: the cost function of the cogeneration unit is:
wherein the content of the first and second substances,
the electric power output by the ith cogeneration unit at the moment t;
the thermal power output by the ith combined heat and power generation unit at the moment t; lambda [ alpha ] 0.i 、λ 1.i 、λ 2.i 、λ 3.i 、λ 4.i 、λ 5.i A coal-fired cost coefficient for the ith cogeneration unit;
and 7-3: and (3) constructing an electric-heating combined system scheduling model by combining the cost of the thermal power generating unit, the cost of the cogeneration unit, a wind power output power scene and the power consumption cost of a user:
wherein p is j Probability of occurrence of the jth wind power output power scene, C t Electricity price at time t, P DRL.t For participating in the electric load quantity at time t after the demand response, S tpu Is a collection of the number of thermal power generating units, S chp The number of the cogeneration units is set, and T is the number of the scheduling period time segments.
8. The electric-heat combined system scheduling method based on the improved frog-leaping algorithm as claimed in claim 1, wherein the improved mixed frog-leaping algorithm is used to solve the objective function in step 8, and when the maximum number of iterations N is reached max Or when the search precision epsilon is met, the process of outputting the optimized values of all the control variables of the electric heating combined system is as follows:
step 8-1: setting parameters of an improved mixed frog leaping algorithm, determining population scale F and sub-population number m, determining the number n of frogs in each sub-population, F = m multiplied by n, and setting the maximum step length of allowed change of individual frogs as a matrix D max Maximum number of iterations N of population max Maximum number of iterations in of subgroup max ；
Step 8-2: initializing parameters, and randomly generating a frog position according to the upper value limit and the lower value limit of each control variable to be optimized of the electric heating combined system, wherein the frog position code is as follows:
wherein X is a value matrix of the individual control variable of the frog in each time period, P DRL.t Is the amount of electrical load at time T (T =1,2, 3.., T) after participation in the demand response;
is T (T =1,2, 3.., T) time i (i =1,2, 3.., S) chp ) Electric power output by the cogeneration unit;
for time T (T =1,2,3, T), the ith (i =1,2,3, S) chp ) The thermal power output by the cogeneration unit;
for time T (T =1,2,3, T), the ith (i =1,2,3, S) tpu ) Electric power output by the thermal power generating unit;
thermal power output by the thermal storage tank at time T (T =1,2, 3.., T); p t EB An input electric power of the electric boiler for time T (T =1,2, 3.., T); p is t W The electrical power output by the wind turbine at time T (T =1,2, 3.., T); x is a radical of a fluorine atom t A control variable to be optimized for the electric-heat combined system at a randomly generated T (T =1,2, 3.., T) time;
step 8-3: randomly generating an initial frog position:
X rc ＝X rc,min +rand(0,1)×(X rc,max -X rc,min )，0＜r≤S tpu +2S chp +4，0＜c≤T
wherein, X rc Is the frog position component, X rc,min And X rc,max Lower and upper limits, S, respectively, of the corresponding position component tpu The method comprises the steps of collecting the number of thermal power generating units; s chp The method comprises the steps of (1) collecting the number of cogeneration units;
step 8-4: initial updating distance of frog:
D rc (0)＝-D rc.max +rand(0,1)×2D rc.max
wherein D is rc Distance initially updated for frog, D rc.max The maximum distance which can be updated when the frog is initially updated;
and 8-5: sequencing frogs according to adaptive values and recording global optimal frog position X g Distributing F frogs to m sub-groups Y by improved frog distribution formula 1 ，Y 2 ，...，Y m In the middle, each group comprises n frogs, and the improved distribution formula is as follows:
wherein X (l) represents the first frog in the frog group, and f (l) represents the objective function value of the first frog;
and 8-6: setting subgroup counter im =0 and local optimization counter in =0, and determining the position X of the best frog and the worst frog in each subgroup b And X w ；
And 8-7: update the worst frog position in the subgroup:
and (3) updating the frog distance:
D(in+1)＝w×D(in)+(X avg -X w )·E(r)+(X g -X w )·E(r)
-D max ≤D(in+1)≤D max
and (3) frog position updating: x' w ＝X w +D(in+1)
Wherein w is an inertia factor, D (in) is the updating distance of the number counted by the current counter, D (in + 1) is the frog updating distance of the next counting number of the counter, and X w The worst frog position of subgroup, D max The maximum distance that can be updated when the frog position is updated, in is the number counted by the current counter, E (r) is the T multiplied by T diagonal matrix with the element rand (0, 1), X avg Average of the best frog positions for each subgroup, X b.1 ,X b.2 ,...,X b.m The optimal frog positions from the 1 st subgroup to the m th subgroup, w ini Is an initial inertia weight, w end Iterating to the maximum times of inertia weight;
and 8-8: updated position X' w And position X before update w Comparing, if the updated position is better than the position before updating, keeping the updated position, if the updated position is not good, using the global optimum frog position X g Replacing;
and 8-9: when the maximum iteration number in of subgroup is satisfied max Then, the subgroup iteration is finished, the frogs in each subgroup are reordered and divided after each subgroup is subjected to a round of local optimization iteration, and the current global optimal frog position X is recorded g ；
And 8-10: repeating the steps 8-3 to 8-9 until the maximum iteration number N of the population is met max And stopping the algorithm execution process, and outputting the global optimal frog position, namely the force output value of each component of the electric heating combined system.
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