CN110137938A - Optimization Scheduling based on the wind fire storage association system for improving bat algorithm - Google Patents

Abstract

The invention discloses a kind of Optimization Schedulings based on the wind fire storage association system for improving bat algorithm, which is characterized in that specifically according to the objective function for establishing system total operating cost minimum operating cost a few days ago under the premise of wind energy consumption beam is maximum；The constraint condition of wind fire storage association system；Based on bat algorithm is improved, on the basis of meeting the constraint condition, the minimum value of calculating target function obtains the step of wind fire stores up minimum operating cost of association system under the premise of wind electricity digestion amount is maximum.The economic performance of analysis wind fire storage association system comprehensively, wind fire storage association system Optimized Operation can more accurately be carried out, the digestion capability of wind-powered electricity generation can be improved in the case where guaranteeing the normal operation of wind fire storage association system, reduce thermal power output, coal consumption cost is saved, wind fire is saved and stores up association system cost.

Description

Wind, fire and storage combined system optimized scheduling method based on improved bat algorithm

Technical Field

The invention belongs to the technical field of power dispatching, and relates to an optimized dispatching method of a wind-fire-storage combined system based on an improved bat algorithm.

Background

Energy shortage and environmental pollution are two important problems facing the world, the breakthrough for solving the two problems is to reasonably utilize renewable energy, and wind energy is the most widely applied renewable energy at present and has the greatest significance in the future energy development strategy. The energy storage device has great help to solve the problems of intermittence and volatility of renewable energy sources and improve the power supply reliability of the microgrid by participating in energy management of the microgrid. Therefore, the wind-fire-storage combined system can improve the running performance of the micro-grid from many aspects, not only can save the output of the thermal power generating unit, but also can reduce the adverse effect on the grid when the wind power is connected to the grid. In addition, the energy storage can not only enable the micro-grid belt to bring more profits through storing and consuming the wind power, but also can increase the absorption capacity of renewable energy.

Aiming at the research of a wind storage combined system, the optimal scheduling problem of the power distribution network is difficult to comprehensively analyze and solve from the aspect of economy or renewable energy utilization rate.

Disclosure of Invention

The invention aims to provide an optimized dispatching method of a wind, fire and storage combined system based on an improved bat algorithm, which can improve the wind power consumption capability, reduce the thermal power output and save the cost of the wind, fire and storage combined system under the condition of ensuring the normal operation of the wind, fire and storage combined system.

The technical scheme adopted by the invention is that the optimal scheduling method of the wind-fire-storage combined system based on the improved bat algorithm is specifically carried out according to the following steps:

step 1, establishing a daily running cost objective function with the lowest total running cost of a system on the premise of the maximum wind energy absorption beam;

constraint conditions of the wind-fire-storage combined system;

and 2, calculating the minimum value of the objective function on the basis of satisfying the constraint condition based on the improved bat algorithm to obtain the minimum running cost of the wind-fire storage combined system on the premise of the maximum wind power consumption.

The invention is also characterized in that:

the step 1 of establishing the objective function of the day-ahead operation cost is specifically carried out according to the following steps:

step 1.1, establishing a lowest total operation cost objective function of the wind, fire and storage combined system:

min F₁＝f₁+f₂+f₃(1)

wherein f is₁Cost of the fire-electric unit in the combined wind-fire-storage system, f₂Interaction cost, f, for wind-fire-storage combined system₃The energy storage cost in the wind-fire energy storage combined system is saved;

establishing a maximum wind energy absorption objective function of the wind-fire-storage combined system:

wherein, tP_w,i,tActive scheduling processing of the ith fan in the wind, fire and storage combined system in the period t, N_wThe total number of the fans is N, the total time period number is represented by N, and delta t is a unit time period;

step 1.2, obtaining an objective function of the day-ahead running cost according to the minimum total running cost objective function and the maximum wind energy consumption objective function:

1.1, calculating the cost of the thermoelectric generator set according to the following steps:

step 1.1.1, calculating the fuel cost of the thermal power generating unit:

wherein, a_i、b_iAnd is c_iCoefficient of operating costs, P, of thermal power generating units_G,i,tThe active output of the ith conventional thermal power generating unit at the moment t is represented;

calculating the maintenance cost of the thermal power generating unit:

f_main,i,t＝d_iP_G,i,t·Δt (5)

wherein d is_iIs the coefficient of the operating cost of the thermal power generating unit;

calculating the starting and stopping cost of the thermal power generating unit:

f_state,i,t＝(U_on,G,i·B_on,G,i,t+U_off,G,i·B_off,G,i,t)·Δt (6)

wherein, U_on,G,iRepresents the opening state of the ith conventional thermal power generating unit in a time period t, B_on,G,i,tTo represent the stop state of the ith conventional thermal power generating unit in the time period t, U_off,G,iRepresents the starting cost of the ith conventional thermal power generating unit in the time period t, B_off,G,i,tRepresenting the stopping cost of the ith conventional thermal power generating unit in the time period t;

step 1.1.2, calculating the cost of the thermal power generating unit according to the fuel cost, the maintenance cost and the starting and stopping cost:

step 1.1 the interaction cost of the wind-fire-storage combined system is specifically carried out according to the following steps:

step a, calculating the cost of the wind-fire-storage combined system for purchasing electric energy from a large power grid:

f_buy,t＝U_buy·B_buy,t·P_buy,t·Δt (8)

wherein, U_buyPurchase of electricity prices for wind, fire and storage combined systems to large grids, B_buy,tThe variable is 0-1 for power purchase, 0 represents no power purchase, and 1 represents power purchase P_buy,tPurchasing power of electric energy from a large power grid for the wind-fire-storage combined system;

calculating the cost of the wind-fire-storage combined system for selling electric energy to a large power grid:

f_sell,t＝U_sell·B_sell,t·P_sell,t·Δt (9)

wherein, U_sellSelling electricity prices for large grids for combined wind, fire and storage systems, B_sell,tWhether electricity is sold or not is a variable of 0-1, 0 represents that electricity is not sold, 1 represents that electricity is sold, and P_sell,tSelling the power of electric energy to a large power grid for the wind-fire-storage combined system;

step b, calculating the interaction cost of the wind-fire-storage combined system according to the cost of the wind-fire-storage combined system for purchasing electric energy from the large power grid and the cost obtained by the wind-fire-storage combined system for selling the electric energy from the large power grid:

the energy storage cost in the wind-fire energy storage combined system is calculated according to the following method:

step A, calculating the maintenance cost of the wind-fire-storage combined system:

f_omb,t＝(K_omb·|B_ch,t·P_ch,t+B_dis,tP_dis,t|)·Δt (11)

in the formula, K_ombRepresenting a maintenance cost factor of the stored energy, B_ch,tRepresents the charging state of the stored energy in the period t, and is a variable from 0 to 1, 0 represents no charging, 1 represents charging, and P_ch,tCharging power for storing energy for a period of t, B_dis,tThe discharge state of energy storage in t period is represented as 0-1 variable, 0 represents no discharge, 1 represents discharge, and P_dis,tThe discharge power for storing energy in the t period;

calculating depreciation cost of the fire storage combined system:

in the formula, E_essFor a rated energy storage capacity, N_life,tIs a cycle life representing the stored energy over time t;

calculating the electric energy loss cost of the fire storage combined system:

in the formula of U_marketIndicating the price of electricity in the market unit, P_esslossFor electric energy consuming power, N_essIndicating the number of energy storage devices in the distribution network, B_ess,iThe dimension of the change of state of the first energy storage device in a certain time period compared with the previous time period is 1 if the state is changed, otherwise, the change of state is 0, and delta P_esslo,iIndicating the loss generated by the ith energy storage device during state switching, η_in，η_outRespectively representing the charge and discharge efficiency of stored energy;

and B, calculating the energy storage cost in the wind-fire-storage combined system according to the maintenance cost, depreciation cost and electric energy loss cost of the wind-fire-storage combined system:

the constraint conditions in step 1 include:

(1) and power balance constraint:

in the formulaRepresenting the output of all thermal power generating units in the period t,represents the output of all fans in the period of t, P_lRepresenting the active load of the system in the period t;

(2) the operation constraint of the thermal power generating unit specifically comprises the following steps:

1) and (3) unit power constraint:

P_G，i，min≤P_G，i，t≤P_G，i，max(16)

wherein, P_G,i,minUpper limit of output power, P, of the ith thermal power generating unit_G,i,maxRepresenting the lower limit of the output power of the ith thermal power generating unit;

2) minimum start-stop time constraint:

(B_on,G,i,t-1-B_on,G,i,t)·(T_on,i,t-T_on,i,min)≥0 (17)

(B_on,G,i,t-B_on,G,i,t-1)·(T_off,i,t-T_off,i,min)≥0 (18)

wherein, T_on,i,tIndicating the starting duration time T of the ith thermal power generating unit in the period T_off,i,tShutdown duration of i thermal power generating units in T period, T_on,i,minIndicating the minimum operating time, T, of the thermal power generating unit_off,i,minRepresenting a minimum shutdown time of the thermal power generating unit;

3) and (3) climbing restraint:

-r_d,i·Δt≤P_G,i,t-P_G,i,t-1≤r_u,i·Δt (19)

wherein r is_u,iRepresents the maximum upward climbing speed r of the ith thermal power generating unit in unit time_d,iThe maximum downward climbing speed of the ith thermal power generating unit in unit time is represented;

(3) and (3) interactive power flow limit constraint between the micro-grid and the large power grid:

0≤P_buy,t≤P_tr,max(20)

0≤P_sell,t≤P_tr,max(21)

wherein, P_tr,maxRepresenting an interaction limit power;

(4) the energy storage related constraints specifically include:

1) charge and discharge power constraint:

0≤P_ch,i,t≤P_ch,max(22)

0≤P_dis,i,t≤P_dis,max(23)

in order to ensure the working quality of the energy storage system, the instantaneous charge-discharge power of the stored energy in unit time is generally controlled to be 0.2E_essThe following steps:

P_ch，max＝P_dis，max＝0.2E_ess(24)

2) and (3) state of charge constraint:

SOC_min≤SOC_i,t≤SOC_max(25)

in the formula, SOC_i,tRepresenting the ratio of the residual capacity of the stored energy at the current moment to the capacity of the stored energy in the full-charge state,SOC_minrepresenting the upper limit of the state of charge, SOC, of the energy storage system_maxRepresents a lower limit of the energy storage system state of charge;

3) restraint of charging and discharging modes:

0≤B_dis,t≤1 (27)

0≤B_ch,t≤1 (28)

B_dis,t+B_ch,t≤1 (29)

B_dis,t,B_ch,t∈Z (30)；

4) conservation of energy in the energy storage device:

wherein E is_ess,0Which represents the initial energy of the stored energy,representing the residual energy of the stored energy at the end of the scheduling period;

5) and (3) discharge depth constraint:

D_od,min≤D_od,t≤D_od,max(32)

wherein D is_od,minLower limit of depth of discharge, D_od,maxAn upper bit discharge depth limit;

(5) operation constraints of the wind turbine:

0≤P_w,i,t≤P_wfore,i,t(34)

wherein, P_wfore,i,tAnd the active predicted output of the ith fan in the t period is shown.

The step 2 is specifically carried out according to the following method:

step 2.1, setting initial parameters:

setting the population scale by taking the output of each thermal power machine and the output of each wind power machine in the wind-fire-storage combined system as an individual in a population;

setting the strength, pulse rate and pulse frequency of the sound equipment;

setting the number of iterations t_max；

Calculating a bat algorithm search dimension;

step 2.2, obtaining the position of the initial optimal individual through a Halton sequence, setting the moving boundary of the optimal individual according to a constraint condition, and enabling the position of the initial optimal individual to be the position of the optimal individual, wherein t is 1, and t is the current iteration number;

step 2.3, judging whether the current iteration times meet t ═ t_maxIf yes, outputting the position of the optimal individual and the cost corresponding to the optimal individual;

otherwise, updating the position of the optimal individual to obtain the position of the optimal individual for one-time updating, and calculating the cost F of the optimal individual after one-time updating according to the position of the optimal individual for one-time updating and the objective function_a；

Step 2.4, randomly generating a rand if rand is more than r_iThen, the optimal individual position is updated again, and the cost F of the updated optimal individual is calculated according to the updated optimal individual position and the objective function_b；

Otherwise, repeating the step 2.4 until rand > r_i；

Step 2.5, judging whether rand satisfies rand < A_iAnd F is_a＞F_bIf so, the updated optimal individuals are set as the optimal individuals, and the optimal cost of the optimal individuals and the optimal cost of the last iteration are sequenced; simultaneously, when t is t +1, repeating the steps 2.2-2.4 until t is t_max；

Otherwise, repeating the step 2.4-2.5 until whether the rand satisfies that rand < A_iAnd F is₁＞F₂。

In the step 2.1, the population scale is 10-25, the sound intensity is 0-1, the pulse rate is 0-1, the pulse frequency is 0-2, and the iteration frequency is 1000.

In step 2.3, the position of the updated optimal individual is obtained specifically according to the following steps:

step 2.3.1, updating the pulse frequency of the optimal individual once:

f₁＝f_min+(f_max-f_min)·β (35)

wherein f is₁Updated pulse frequency of the optimal individual, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.3.2, updating the pulse speed of the optimal individual at one time:

wherein,is the pulse speed of the optimal individual after one update,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x_*The position of the current optimal individual;

step 2.3.3, updating the position of the optimal individual once:

step 2.3.4, judging whether the position of the optimal individual after the primary updating meets the constraint condition, and if so, taking the position of the individual after the primary updating as the position of the optimal individual after the primary updating; otherwise, repeating the steps 2.3.1-2.3.3 until the updated position of the optimal individual meets the constraint condition.

In step 2.4, the position of the optimal individual is updated again, specifically according to the following steps:

step 2.4.1, update the pulse frequency of the optimal individual again:

f₂＝f_min+(f_max-f_min)·β (35)

wherein f is₂The pulse frequency of the optimum individual after the re-update, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.4.2, the pulse speed of the optimal individual is updated again:

wherein,is the pulse speed of the optimum individual after being updated again,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x_*The position of the current optimal individual;

step 2.4.3, update the position of the optimal individual again:

step 2.4.4, judging whether the position of the updated optimal individual meets the constraint condition, and if so, taking the position of the updated individual as the position of the updated optimal individual; otherwise, repeating the steps 2.4.1-2.4.3 until the updated position of the optimal individual meets the constraint condition.

In step 2.4 rand is randomly generated by means of a rand function.

The invention has the beneficial effects that:

the wind, fire and storage combined system optimizing and scheduling method based on the improved bat algorithm comprehensively analyzes the economic performance of the wind, fire and storage combined system, can more accurately perform wind, fire and storage combined system optimizing and scheduling, can improve the wind power consumption capacity under the condition of ensuring the normal operation of the wind, fire and storage combined system, reduces the thermal power output, saves the coal consumption cost and saves the wind, fire and storage combined system cost.

Drawings

FIG. 1 is a daily load graph in an embodiment of an optimized scheduling method of a combined wind, fire and storage system based on an improved bat algorithm;

FIG. 2 is a wind power prediction curve diagram in an embodiment of an optimized scheduling method of a wind, fire and storage combined system based on an improved bat algorithm;

fig. 3 is a flow chart of the minimum operation cost on the premise of calculating the maximum wind power consumption based on the improved bat algorithm in step 2 of the optimal scheduling method of the wind-fire-storage combined system based on the improved bat algorithm.

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

The optimized dispatching method of the wind-fire-storage combined system based on the improved bat algorithm is specifically carried out according to the following steps:

step 1, establishing a day-ahead operation cost objective function with the lowest total system operation cost on the premise of the maximum wind energy consumption, wherein the scheduling cycle is 1 day, the scheduling cycle is divided into 24 time periods, and each hour is 1 time period, and the method specifically comprises the following steps:

the method specifically comprises the following steps of establishing an objective function of the day-ahead operation cost:

step 1.1, establishing a lowest total operation cost objective function of the wind, fire and storage combined system:

min F₁＝f₁+f₂+f₃(1)

wherein f is₁Cost of the fire-electric unit in the combined wind-fire-storage system, f₂Interaction cost, f, for wind-fire-storage combined system₃The energy storage cost in the wind-fire energy storage combined system is saved;

the cost of the thermal power generating unit is calculated according to the following steps:

step 1.1.1, calculating the fuel cost of the thermal power generating unit:

wherein, a_i、b_iAnd is c_iCoefficient of operating costs, P, of thermal power generating units_G,i,tThe active output of the ith conventional thermal power generating unit at the moment t is represented;

calculating the maintenance cost of the thermal power generating unit:

f_main,i,t＝d_iP_G,i,t·Δt (5)

wherein d is_iIs the coefficient of the operating cost of the thermal power generating unit;

calculating the starting and stopping cost of the thermal power generating unit:

f_state,i,t＝(U_on,G,i·B_on,G,i,t+U_off,G,i·B_off,G,i,t)·Δt (6)

wherein, U_on,G,iRepresents the opening state of the ith conventional thermal power generating unit in a time period t, B_on,G,i,tTo represent the stop state of the ith conventional thermal power generating unit in the time period t, U_off,G,iRepresents the starting cost of the ith conventional thermal power generating unit in the time period t, B_off,G,i,tRepresenting the stopping cost of the ith conventional thermal power generating unit in the time period t;

step 1.1.2, calculating the cost of the thermal power generating unit according to the fuel cost, the maintenance cost and the starting and stopping cost:

the interaction cost of the wind-fire-storage combined system is specifically carried out according to the following steps:

step a, calculating the cost of the wind-fire-storage combined system for purchasing electric energy from a large power grid:

f_buy,t＝U_buy·B_buy,t·P_buy,t·Δt (8)

wherein, U_buyFor wind, fire and storage combined systemPrice of electricity purchased from the large grid, B_buy,tThe variable is 0-1 for power purchase, 0 represents no power purchase, and 1 represents power purchase P_buy,tPurchasing power of electric energy from a large power grid for the wind-fire-storage combined system;

calculating the cost of the wind-fire-storage combined system for selling electric energy to a large power grid:

f_sell,t＝U_sell·B_sell,t·P_sell,t·Δt (9)

wherein, U_sellSelling electricity prices for large grids for combined wind, fire and storage systems, B_sell,tWhether electricity is sold or not is a variable of 0-1, 0 represents that electricity is not sold, 1 represents that electricity is sold, and P_sell,tSelling the power of electric energy to a large power grid for the wind-fire-storage combined system;

step b, calculating the interaction cost of the wind-fire-storage combined system according to the cost of the wind-fire-storage combined system for purchasing electric energy from the large power grid and the cost obtained by the wind-fire-storage combined system for selling the electric energy from the large power grid:

the energy storage cost in the wind-fire energy storage combined system is calculated according to the following method:

step A, calculating the maintenance cost of the wind-fire-storage combined system:

f_omb,t＝(K_omb·|B_ch,t·P_ch,t+B_dis,tP_dis,t|)·Δt (11)

in the formula, K_ombRepresenting a maintenance cost factor of the stored energy, B_ch,tRepresents the charging state of the stored energy in the period t, and is a variable from 0 to 1, 0 represents no charging, 1 represents charging, and P_ch,tCharging power for storing energy for a period of t, B_dis,tThe discharge state of energy storage in t period is represented as 0-1 variable, 0 represents no discharge, 1 represents discharge, and P_dis,tThe discharge power for storing energy in the t period;

calculating depreciation cost of the fire storage combined system:

in the formula, E_essFor a rated energy storage capacity, N_life,tIs a cycle life representing the stored energy over time t;

calculating the electric energy loss cost of the fire storage combined system:

in the formula of U_marketIndicating the price of electricity in the market unit, P_esslossFor electric energy consuming power, N_essIndicating the number of energy storage devices in the distribution network, B_ess,iThe dimension of the change of state of the first energy storage device in a certain time period compared with the previous time period is 1 if the state is changed, otherwise, the change of state is 0, and delta P_esslo,iIndicating the loss generated by the ith energy storage device during state switching, η_in，η_outRespectively representing the charge and discharge efficiency of stored energy;

and B, calculating the energy storage cost in the wind-fire-storage combined system according to the maintenance cost, depreciation cost and electric energy loss cost of the wind-fire-storage combined system:

establishing a maximum wind energy absorption objective function of the wind-fire-storage combined system:

wherein, tP_w,i,tFor wind and fire storageActive scheduling processing of the ith fan in the combined system in the period t, N_wThe total number of the fans is N, the total time period number is 24h, and delta t is a unit time period;

step 1.2, obtaining an objective function of the day-ahead running cost according to the minimum total running cost objective function and the maximum wind energy consumption objective function:

establishing a constraint condition of a wind-fire-storage combined system:

(1) and power balance constraint:

in the formulaRepresenting the output of all thermal power generating units in the period t,represents the output of all fans in the period of t, P_lRepresenting the active load of the system in the period t;

(2) the operation constraint of the thermal power generating unit specifically comprises the following steps:

1) and (3) unit power constraint:

P_G，i，min≤P_G，i，t≤P_G，i，max(16)

wherein, P_G,i,minUpper limit of output power, P, of the ith thermal power generating unit_G,i,maxRepresenting the lower limit of the output power of the ith thermal power generating unit;

2) minimum start-stop time constraint:

(B_on,G,i,t-1-B_on,G,i,t)·(T_on,i,t-T_on,i,min)≥0 (17)

(B_on,G,i,t-B_on,G,i,t-1)·(T_off,i,t-T_off,i,min)≥0 (18)

wherein, T_on,i,tIndicating the starting duration time T of the ith thermal power generating unit in the period T_off,i,tShutdown duration of i thermal power generating units in T period, T_on,i,minIndicating the minimum operating time, T, of the thermal power generating unit_off,i,minRepresenting a minimum shutdown time of the thermal power generating unit;

3) and (3) climbing restraint:

-r_d,i·Δt≤P_G,i,t-P_G,i,t-1≤r_u,i·Δt (19)

wherein r is_u,iRepresents the maximum upward climbing speed r of the ith thermal power generating unit in unit time_d,iThe maximum downward climbing speed of the ith thermal power generating unit in unit time is represented;

(3) and (3) interactive power flow limit constraint between the micro-grid and the large power grid:

0≤P_buy,t≤P_tr,max(20)

0≤P_sell,t≤P_tr,max(21)

wherein, P_tr,maxRepresenting an interaction limit power;

(4) the energy storage related constraints specifically include:

1) charge and discharge power constraint:

0≤P_ch,i,t≤P_ch,max(22)

0≤P_dis,i,t≤P_dis,max(23)

in order to ensure the working quality of the energy storage system, the instantaneous charge-discharge power of the stored energy in unit time is generally controlled to be 0.2E_essWithin, i.e.：

P_ch，max＝P_dis，max＝0.2E_ess(24)

2) And (3) state of charge constraint:

SOC_min≤SOC_i,t≤SOC_max(25)

in the formula, SOC_i,tRepresenting the ratio of the residual capacity of the stored energy at the current moment to the capacity of the stored energy in the full-charge state,SOC_minrepresenting the upper limit of the state of charge, SOC, of the energy storage system_maxRepresents a lower limit of the energy storage system state of charge;

3) restraint of charging and discharging modes:

0≤B_dis,t≤1 (27)

0≤B_ch,t≤1 (28)

B_dis,t+B_ch,t≤1 (29)

B_dis,t,B_ch,t∈Z (30)；

4) conservation of energy in the energy storage device:

wherein E is_ess,0Which represents the initial energy of the stored energy,representing the residual energy of the stored energy at the end of the scheduling period;

5) and (3) discharge depth constraint:

D_od,min≤D_od,t≤D_od,max(32)

wherein D is_od,minLower limit of depth of discharge, D_od,maxAn upper bit discharge depth limit;

(5) operation constraints of the wind turbine:

0≤P_w,i,t≤P_wfore,i,t(34)

wherein, P_wfore,i,tAnd the active predicted output of the ith fan in the t period is shown.

Step 2, based on the improved bat algorithm, on the basis of satisfying the constraint condition, calculating the minimum value of the objective function to obtain the minimum operating cost of the wind-fire-storage combined system on the premise of the maximum wind power consumption, as shown in fig. 3, specifically according to the following steps:

step 2.1, setting initial parameters:

taking the output of each thermal power machine and the output of each wind power machine in the wind-fire-storage combined system as an individual in a population, and setting the population scale to be 10-25;

setting the sound intensity to be 0-1, the pulse rate to be 0-1 and the pulse frequency to be 0-2;

setting the iteration number to be 1000;

calculating a bat algorithm search dimension;

step 2.2, obtaining the position of the initial optimal individual through a Halton sequence, setting the moving boundary of the optimal individual according to a constraint condition, and enabling the position of the initial optimal individual to be the position of the optimal individual, wherein t is 1, and t is the current iteration number;

step 2.3, judging whether the current iteration times meet t ═ t_maxIf yes, outputting the position of the optimal individual and the cost corresponding to the optimal individual;

if not, then,updating the position of the optimal individual to obtain the position of the optimal individual with one updating, and calculating the cost F of the optimal individual with one updating according to the position of the optimal individual with one updating and the objective function_aWherein, updating the optimal individual position at one time is specifically obtained according to the following steps:

step 2.3.1, updating the pulse frequency of the optimal individual once:

f₁＝f_min+(f_max-f_min)·β (35)

wherein f is₁Updated pulse frequency of the optimal individual, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.3.2, updating the pulse speed of the optimal individual at one time:

wherein,is the updated pulse velocity of the optimal individual,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x_*The position of the current optimal individual;

step 2.3.3, updating the position of the optimal individual once:

step 2.3.4, judging whether the position of the optimal individual after the primary updating meets the constraint condition, and if so, taking the position of the updated individual as the position of the optimal individual after the primary updating; otherwise, repeating the steps 2.3.1-2.3.3 until the position of the optimal individual after one-time updating meets the constraint condition.

Step 2.4, the rand function randomly generates a rand, if the rand is more than r_iThen, the position of the optimum individual is updated again, and the cost F of the updated optimum individual is calculated based on the position of the updated optimum individual and the objective function_bWherein, updating the optimal individual position again is specifically obtained according to the following steps:

step 2.4.1, update the pulse frequency of the optimal individual again:

f₂＝f_min+(f_max-f_min)·β (35)

wherein f is₂The pulse frequency of the optimum individual after the re-update, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.4.2, the pulse speed of the optimal individual is updated again:

wherein,is the pulse speed of the optimum individual after being updated again,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x is the current maximumThe location of the preferred individual;

step 2.4.3, update the position of the optimal individual again:

step 2.4.4, judging whether the position of the updated optimal individual meets the constraint condition, and if so, taking the position of the updated individual as the position of the updated optimal individual; otherwise, repeating the steps 2.4.1-2.4.3 until the updated position of the optimal individual meets the constraint condition.

Otherwise, repeating the step 2.4 until rand > r_i；

Step 2.5, judging whether rand satisfies rand < A_iAnd F is_a＞F_bIf so, the updated optimal individuals are set as the optimal individuals, and the optimal cost of the optimal individuals and the optimal cost of the last iteration are sequenced; simultaneously, when t is t +1, repeating the steps 2.2-2.4 until t is t_max；

Otherwise, repeating the step 2.4-2.5 until whether the rand satisfies that rand < A_iAnd F is₁＞F₂。

Example 1

An improved IEEE 30 node system is adopted for simulation analysis, nodes 1, 2, 5, 8, 11 and 13 are thermal power generating unit nodes, and the economic dispatching parameters of the conventional thermal power generating unit are shown in a table 1; 3 wind power plants are respectively added at the node 3, the node 7 and the node 21, and the installed capacity of a single fan is 70 MW. The energy storage system is configured according to 10% of installed capacity of wind power according to certain wind power storage demonstration project in China. The daily load curve is shown in fig. 1. The wind power prediction refers to an actual wind power plant of a power grid in a certain region of China, as shown in FIG. 2. Interaction with the large grid limits power to 60 MW. The 24h day is divided into three periods of peak, valley and average, and each period has different electricity purchasing prices as shown in table 2.

TABLE 1 conventional thermal power plant parameters

TABLE 2 Peak-valley flat time period electricity purchase and sale price

Analysis of scheduling results before and after algorithm improvement:

in order to verify the effectiveness of the improved algorithm, a simulation experiment is performed on a representative one-dimensional univariate model, the initial population number is 10, 60 sampling periods are total, and 50 times of simulation are performed, and the result is shown in table 3.

TABLE 3 Bat Algorithm front and rear Performance comparison

From the simulation result, the improved bat algorithm is better in precision than the original algorithm, because the improved algorithm is more uniform and random compared with the unmodified algorithm in the initial population, and the correlation generated under the high-dimensional condition is avoided.

Claims (9)

1. The wind-fire-storage combined system optimal scheduling method based on the improved bat algorithm is characterized by comprising the following steps:

step 1, establishing a daily running cost objective function with the lowest total running cost of a system on the premise of the maximum wind energy absorption beam;

constraint conditions of the wind-fire-storage combined system;

and 2, based on an improved bat algorithm, calculating the minimum value of the objective function on the basis of meeting the constraint condition to obtain the minimum running cost of the wind-fire storage combined system on the premise of the maximum wind power consumption.

2. The optimal scheduling method of the wind, fire and storage combined system based on the improved bat algorithm as claimed in claim 1, wherein the step 1 of establishing the objective function of the day-ahead operation cost is specifically performed according to the following steps:

step 1.1, establishing a lowest total operation cost objective function of the wind, fire and storage combined system:

minF₁＝f₁+f₂+f₃(1)

wherein f is₁Cost of the fire-electric unit in the combined wind-fire-storage system, f₂Interaction cost, f, for wind-fire-storage combined system₃The energy storage cost in the wind-fire energy storage combined system is saved;

establishing a maximum wind energy absorption objective function of the wind-fire-storage combined system:

wherein, tP_w,i,tActive scheduling processing of the ith fan in the wind, fire and storage combined system in the period t, N_wThe total number of the fans is N, the total time period number is represented by N, and delta t is a unit time period;

step 1.2, obtaining an objective function of the day-ahead running cost according to the minimum total running cost objective function and the maximum wind energy consumption objective function:

3. the optimal scheduling method of the wind, fire and storage combined system based on the improved bat algorithm as claimed in claim 2, wherein the cost of the fire-electric generating set in the step 1.1 is calculated according to the following steps:

step 1.1.1, calculating the fuel cost of the thermal power generating unit:

wherein, a_i、b_iAnd is c_iCoefficient of operating costs, P, of thermal power generating units_G,i,tThe active output of the ith conventional thermal power generating unit at the moment t is represented;

calculating the maintenance cost of the thermal power generating unit:

f_main,i,t＝d_iP_G,i,t·Δt (5)

wherein d is_iIs the coefficient of the operating cost of the thermal power generating unit;

calculating the starting and stopping cost of the thermal power generating unit:

f_state,i,t＝(U_on,G,i·B_on,G,i,t+U_off,G,i·B_off,G,i,t)·Δt (6)

wherein, U_on,G,iRepresents the opening state of the ith conventional thermal power generating unit in a time period t, B_on,G,i,tTo represent the stop state of the ith conventional thermal power generating unit in the time period t, U_off,G,iRepresents the starting cost of the ith conventional thermal power generating unit in the time period t, B_off,G,i,tRepresenting the stopping cost of the ith conventional thermal power generating unit in the time period t;

step 1.1.2, calculating the cost of the thermal power generating unit according to the fuel cost, the maintenance cost and the starting and stopping cost:

step 1.1 the interaction cost of the wind-fire-storage combined system is specifically carried out according to the following steps:

step a, calculating the cost of the wind-fire-storage combined system for purchasing electric energy from a large power grid:

f_buy,t＝U_buy·B_buy,t·P_buy,t·Δt (8)

wherein, U_buyPurchase of electricity prices for wind, fire and storage combined systems to large grids, B_buy,tThe variable is 0-1 for power purchase, 0 represents no power purchase, and 1 represents power purchase P_buy,tPurchasing from large power grid for wind-fire-storage combined systemThe power of the electrical energy;

calculating the cost of the wind-fire-storage combined system for selling electric energy to a large power grid:

f_sell,t＝U_sell·B_sell,t·P_sell,t·Δt (9)

wherein, U_sellSelling electricity prices for large grids for combined wind, fire and storage systems, B_sell,tWhether electricity is sold or not is a variable of 0-1, 0 represents that electricity is not sold, 1 represents that electricity is sold, and P_sell,tSelling the power of electric energy to a large power grid for the wind-fire-storage combined system;

step b, calculating the interaction cost of the wind-fire-storage combined system according to the cost of the wind-fire-storage combined system for purchasing electric energy from the large power grid and the cost obtained by the wind-fire-storage combined system for selling the electric energy from the large power grid:

the energy storage cost in the wind-fire-storage combined system is calculated according to the following method:

step A, calculating the maintenance cost of the wind-fire-storage combined system:

f_omb,t＝(K_omb·|B_ch,t·P_ch,t+B_dis,tP_dis,t|)·Δt (11)

in the formula, K_ombRepresenting a maintenance cost factor of the stored energy, B_ch,tRepresents the charging state of the stored energy in the period t, and is a variable from 0 to 1, 0 represents no charging, 1 represents charging, and P_ch,tCharging power for storing energy for a period of t, B_dis,tThe discharge state of energy storage in t period is represented as 0-1 variable, 0 represents no discharge, 1 represents discharge, and P_dis,tThe discharge power for storing energy in the t period;

calculating depreciation cost of the fire storage combined system:

in the formula, E_essFor a rated energy storage capacity, N_life,tIs shown asCycle life of energy storage at time t;

calculating the electric energy loss cost of the fire storage combined system:

in the formula of U_marketIndicating the price of electricity in the market unit, P_esslossFor electric energy consuming power, N_essIndicating the number of energy storage devices in the distribution network, B_ess,iThe dimension of the change of state of the first energy storage device in a certain time period compared with the previous time period is 1 if the state is changed, otherwise, the change of state is 0, and delta P_esslo,iIndicating the loss generated by the ith energy storage device during state switching, η_in，η_outRespectively representing the charge and discharge efficiency of stored energy;

and B, calculating the energy storage cost in the wind-fire-storage combined system according to the maintenance cost, depreciation cost and electric energy loss cost of the wind-fire-storage combined system:

4. the optimal scheduling method of a combined wind, fire and storage system based on an improved bat algorithm as claimed in claim 1, wherein the constraint conditions in step 1 comprise:

(1) and power balance constraint:

in the formulaRepresenting the output of all thermal power generating units in the period t,represents the output of all fans in the period of t, P_lRepresenting the active load of the system in the period t;

(2) the operation constraint of the thermal power generating unit specifically comprises the following steps:

1) and (3) unit power constraint:

P_G，i，min≤P_G，i，t≤P_G，i，max(16)

wherein, P_G,i,minUpper limit of output power, P, of the ith thermal power generating unit_G,i,maxRepresenting the lower limit of the output power of the ith thermal power generating unit;

2) minimum start-stop time constraint:

(B_on,G,i,t-1-B_on,G,i,t)·(T_on,i,t-T_on,i,min)≥0 (17)

(B_on,G,i,t-B_on,G,i,t-1)·(T_off,i,t-T_off,i,min)≥0 (18)

wherein, T_on,i,tIndicating the starting duration time T of the ith thermal power generating unit in the period T_off,i,tShutdown duration of i thermal power generating units in T period, T_on,i,minIndicating the minimum operating time, T, of the thermal power generating unit_off,i,minRepresenting a minimum shutdown time of the thermal power generating unit;

3) and (3) climbing restraint:

-r_d,i·Δt≤P_G,i,t-P_G,i,t-1≤r_u,i·Δt (19)

wherein r is_u,iRepresents the maximum upward climbing speed r of the ith thermal power generating unit in unit time_d,iThe maximum downward climbing speed of the ith thermal power generating unit in unit time is represented;

(3) and (3) interactive power flow limit constraint between the micro-grid and the large power grid:

0≤P_buy,t≤P_tr,max(20)

0≤P_sell,t≤P_tr,max(21)

wherein, P_tr,maxRepresenting an interaction limit power;

(4) the energy storage related constraints specifically include:

1) charge and discharge power constraint:

0≤P_ch,i,t≤P_ch,max(22)

0≤P_dis,i,t≤P_dis,max(23)

in order to ensure the working quality of the energy storage system, the instantaneous charge-discharge power of the stored energy in unit time is generally controlled to be 0.2E_essThe following steps:

P_ch，max＝P_dis，max＝0.2E_ess(24)

2) and (3) state of charge constraint:

SOC_min≤SOC_i,t≤SOC_max(25)

in the formula, SOC_i,tRepresenting the ratio of the residual capacity of the stored energy at the current moment to the capacity of the stored energy in the full-charge state,SOC_minrepresenting the upper limit of the state of charge, SOC, of the energy storage system_maxRepresents a lower limit of the energy storage system state of charge;

3) restraint of charging and discharging modes:

0≤B_dis,t≤1 (27)

0≤B_ch,t≤1 (28)

B_dis,t+B_ch,t≤1 (29)

B_dis,t,B_ch,t∈Z (30)；

4) conservation of energy in the energy storage device:

wherein E is_ess,0Which represents the initial energy of the stored energy,representing the residual energy of the stored energy at the end of the scheduling period;

5) and (3) discharge depth constraint:

D_od,min≤D_od,t≤D_od,max(32)

wherein D is_od,minLower limit of depth of discharge, D_od,maxAn upper bit discharge depth limit;

(5) operation constraints of the wind turbine:

0≤P_w,i,t≤P_wfore,i,t(34)

wherein, P_wfore,i,tAnd the active predicted output of the ith fan in the t period is shown.

5. The optimal scheduling method of the combined wind, fire and storage system based on the improved bat algorithm as claimed in claim 1, wherein the step 2 is specifically performed according to the following method:

step 2.1, setting initial parameters:

setting the population scale by taking the output of each thermal power machine and the output of each wind power machine in the wind-fire-storage combined system as an individual in a population;

setting the strength, pulse rate and pulse frequency of the sound equipment;

setting the number of iterations t_max；

Calculating a bat algorithm search dimension;

step 2.2, obtaining the position of the initial optimal individual through a Halton sequence, setting the moving boundary of the optimal individual according to a constraint condition, and enabling the position of the initial optimal individual to be the position of the optimal individual, wherein t is 1, and t is the current iteration number;

step 2.3, judging whether the current iteration times meet t ═ t_maxIf yes, outputting the position of the optimal individual and the cost corresponding to the optimal individual;

otherwise, updating the position of the optimal individual to obtain the position of the optimal individual for one-time updating, and calculating the cost F of the optimal individual after one-time updating according to the position of the optimal individual for one-time updating and the objective function_a；

Step 2.4, randomly generating a rand if rand is more than r_iThen, the optimal individual position is updated again, and the cost F of the updated optimal individual is calculated according to the updated optimal individual position and the objective function_b；

Otherwise, repeating the step 2.4 until rand > r_i；

Step 2.5, judging whether rand satisfies rand < A_iAnd F is_a＞F_bIf so, the updated optimal individuals are set as the optimal individuals, and the optimal cost of the optimal individuals and the optimal cost of the last iteration are sequenced; simultaneously, when t is t +1, repeating the steps 2.2-2.4 until t is t_max；

Otherwise, repeating the step 2.4-2.5 until whether the rand satisfies that rand < A_iAnd F is₁＞F₂。

6. The optimal scheduling method of wind, fire and storage combined system based on the improved bat algorithm as claimed in claim 5, wherein the population size in step 2.1 is 10-25, the sound intensity is 0-1, the pulse rate is 0-1, the pulse frequency is 0-2, and the number of iterations is 1000.

7. The optimal scheduling method of a wind, fire and storage combined system based on the improved bat algorithm as claimed in claim 5, wherein the position of the optimal individual is updated in the step 2.3 specifically according to the following steps:

step 2.3.1, updating the pulse frequency of the optimal individual once:

f₁＝f_min+(f_max-f_min)·β (35)

wherein f is₁Updated pulse frequency of the optimal individual, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.3.2, updating the pulse speed of the optimal individual at one time:

wherein,is the pulse speed of the optimal individual after one update,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x_*The position of the current optimal individual;

step 2.3.3, updating the position of the optimal individual once:

step 2.3.4, judging whether the position of the optimal individual after the primary updating meets the constraint condition, and if so, taking the position of the individual after the primary updating as the position of the optimal individual after the primary updating; otherwise, repeating the steps 2.3.1-2.3.3 until the updated position of the optimal individual meets the constraint condition.

8. The optimal scheduling method of the combined wind, fire and storage system based on the improved bat algorithm as claimed in claim 5, wherein the location of the optimal individual is updated again in the step 2.4, specifically according to the following steps:

step 2.4.1, update the pulse frequency of the optimal individual again:

f₂＝f_min+(f_max-f_min)·β (35)

wherein f is₂The pulse frequency of the optimum individual after the re-update, [ f [ ]_min,f_max]For the frequency range, β is a random perturbation at [0,1 ]]Uniformly distributing the upper layer;

step 2.4.2, the pulse speed of the optimal individual is updated again:

wherein,is the pulse speed of the optimum individual after being updated again,is the pulse velocity of the optimal individual for the last iteration,for the position of the optimal individual at the last iteration, x_*The position of the current optimal individual;

step 2.4.3, update the position of the optimal individual again:

step 2.4.4, judging whether the position of the updated optimal individual meets the constraint condition, and if so, taking the position of the updated individual as the position of the updated optimal individual; otherwise, repeating the steps 2.4.1-2.4.3 until the updated position of the optimal individual meets the constraint condition.

9. The optimal scheduling method of wind, fire and storage combined system based on improved bat algorithm as claimed in claim 5, wherein said rand in step 2.4 is randomly generated by rand function.