CN109345005A - A multi-dimensional optimization method for integrated energy system based on improved whale algorithm - Google Patents

Abstract

The invention discloses a kind of based on the integrated energy system multidimensional optimization method for improving whale algorithm, this method models integrated energy system, with the minimum objective function of overall cost of operation, using electricity, heat, cold equilibrium condition as constraint condition, the globally optimal solution of objective function is acquired using improvement whale algorithm.Wherein, improved whale algorithm is to be obtained on the basis of traditional whale algorithm by introducing self-adaptive weight sum Cauchy function.One aspect of the present invention improves the ability of searching optimum of whale algorithm, avoids and falls into local optimum；On the other hand local search ability is improved, improves convergence rate and precision, the stability having had.

Description

A multi-dimensional optimization method for integrated energy system based on improved whale algorithm

technical field

The invention relates to a multi-dimensional optimization method for an integrated energy system, in particular to a multi-dimensional optimization method for a cold-heat-electric integrated energy system containing distributed new energy power generation based on an improved whale algorithm.

Background technique

In recent years, with the increasingly serious energy crisis and the maturity and improvement of distributed power technology, the integrated energy system of cold-heat-electricity with renewable energy and high energy utilization has also received extensive attention. The integrated energy system not only makes full use of the clean and environmentally friendly characteristics of renewable energy power generation, but also uses natural gas as primary energy on the basis of the concept of energy cascade utilization to generate co-generation and co-supply of cooling, heat and electricity. It is a system with high energy utilization and resource complementarity.

For the optimization problem of the integrated energy system, it is first necessary to establish a system optimization operation model with multiple energy forms. The objective function is to minimize the overall operating cost, and it is necessary to consider the conversion efficiency between different energy forms and various types of power generation and storage. energy constraints. A stable and reliable high-precision optimization algorithm is used to solve the problem, so as to realize the comprehensive utilization and collaborative optimization of energy flow and meet the multi-type load requirements of end users.

The whale algorithm is an optimized search algorithm recently proposed by Australian researchers Mirjalili et al. by imitating the hunting behavior characteristics of humpback whales. However, the traditional whale algorithm adopts the linear inertia weight method, so it is easy to fall into the local optimal solution, the convergence speed is slow and the convergence accuracy is low.

SUMMARY OF THE INVENTION

The purpose of the invention is to provide a multi-dimensional optimization method for an integrated energy system based on an improved whale algorithm, so as to solve the optimization problem of economic operation including renewable energy and a combined cooling-heating-electricity system.

Technical solution: The multi-dimensional optimization method for an integrated energy system provided by the present invention includes the following steps: (1) establishing a model of renewable energy and cooling, heating and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining the comprehensive The calculation method of various operating costs in the energy system and the balance conditions of electricity, heat and cooling; among them, each operating cost includes: the total operating cost of renewable energy units, the total cost of gas, and the The total maintenance cost, transaction cost and the total operating cost of the energy storage equipment; (2) Calculate the overall operating cost of the integrated energy system based on the determined operating costs, take the minimum overall operating cost as the objective function, and use electricity, heat, and cooling as the objective function. The balance condition is used as the constraint condition; (3) The whale algorithm is used to carry out multi-dimensional optimization of the objective function on the one-day time scale based on the constraint condition.

Further, step (3) comprises the following steps:

(31) Randomly generate N solution vectors of each variable in the objective function that satisfy the constraints; regard the number N of solution vectors as the number of whales; regard the number of variables M in the objective function as the dimension of the whale search space; The i-th solution vector is regarded as the position of the i-th whale in the M-dimensional space i=1, 2, ..., N; regard the objective function value as the fitness function value;

(32) Set the maximum number of iterations, record the maximum number of iterations as it ^max ; record the current number of iterations as k and initialize: k=1; record the ordinal number of the current whale as i and initialize: i=1; Whales are regarded as optimal individuals;

(33) Update the optimal individual: Compare the fitness function value of the i-th whale individual with the fitness function value of the optimal individual, and update the whale individual with the smaller function value as the optimal individual. If the two are equal, the optimal individual The optimal individual remains unchanged; record the updated optimal individual and its fitness function value and position X _p ;

(34) Update parameters ω, A, C and l:

A=2ω·r-ω,

C=2·r,

Among them, ω is the linear convergence factor, t is the current number of iterations, it ^max is the maximum number of iterations, r is a random number between [0, 1], and l is a random number between [-1, 1];

(35) Utilize the updated parameters to update the whale’s position:

Generate a random number p, and if p ≥ 0.5, update the position using the following formula:

in, represents the distance between the i-th whale and its prey, and b is a constant used to define the logarithmic spiral shape;

If p<0.5 and |A|<1, update the position with:

D ₌ |C·Xp(t)-X(t)|,

X(t+1)=ω(t)·X _p (t)-A·D,

t is the current number of iterations, it ^max is the maximum number of iterations, and A·D is the bracketing step size;

If p < 0.5 and |A| ≥ 1, then update the position using:

In the formula, X _ij (t) is the j-th position of the i-th whale before mutation, that is, the solution of the j-th independent variable in the i-th solution vector randomly generated in step (31);

(36) make i=i+1, judge whether i reaches N; if it does not reach N, then repeat steps (33) to (35); otherwise, go to step (37);

(37) make k=k+1, judge whether k reaches the maximum iteration number it ^max ; if not, repeat steps (33) to (36); otherwise, end the algorithm to obtain the updated optimal position of the whale X _p is the global optimal solution of the objective function.

Beneficial effects: Compared with the prior art, the present invention introduces adaptive weight and Cauchy variation for calculation based on the traditional whale algorithm when solving the global optimal solution of the integrated energy system. The introduction of adaptive inertia weight improves the local search ability of the traditional whale algorithm and improves the convergence accuracy; the position of the whale is mutated by the Cauchy mutation operator, which improves the global search ability of the whale algorithm and avoids getting caught in the local area during the solution process. optimal.

Description of drawings

FIG. 1 is a flowchart of an improved whale algorithm provided by an embodiment of the present invention.

Detailed ways

The following is a detailed description of the present invention with reference to the accompanying drawings.

The multi-dimensional optimization method of the integrated energy system based on the improved whale algorithm of the present invention aims to solve the problem that the integrated energy system of cold-heat-electricity combined supply meets the constraints of cooling, heating and electricity balance, energy storage equipment constraints and electric heating, electric cooling conversion equipment constraints Under the conditions of , the optimization problem that achieves the goal of minimizing the total operating cost. Its implementation steps are as follows:

Step S1: Establish models of renewable energy and cooling, heating and power loads, system operation models, energy storage equipment models and energy conversion equipment models, and determine the calculation methods of various operating costs in the integrated energy system and the balance conditions of electricity, heat and cold ; Among them, various operating costs include: total operating cost of renewable energy units, total gas cost, total maintenance cost of CCHP units of the combined cooling, heating and power system, transaction cost and total operating cost of energy storage equipment;

(1) Mathematical model of renewable energy and cooling, heating and power load:

In the formula, km ( _t ) is a 0-1 variable, which is used to control whether the renewable energy is input. C _g is the power generation cost parameter, C _om is the operation and maintenance cost parameter, and C _dm is the downtime maintenance cost. C _m (t) is the cost of the renewable energy unit m. is the output of the renewable energy unit m in period t, and it satisfies the power constraint:

in, and are the upper and lower limits of the output of the renewable energy unit m, respectively.

(2) System operation model:

in, and are the upper limit and lower limit of the output power of the k-electricity of the CCHP unit, respectively; and are the downward ramp rate and the upward ramp rate of the electrical output power, respectively.

The operating constraints of the waste heat boiler in CCHP unit k are as follows:

H _k (t)/η _HEX +L _k (t)/η _COP ≤η _FH F _k (t)+η _b F _kb (t),

Wherein, η _FH is the waste heat recovery coefficient of the internal combustion engine in the CCHP; η _b is the boiler supplementary combustion heat efficiency; η _HEX is the heat exchanger efficiency; η _COP is the refrigeration coefficient of the refrigerator; is the maximum operating power of the waste heat boiler in the CCHP unit k; F _k (t) is the natural gas flow consumed by the internal combustion engine in the CCHP unit k in the t period; F _kb (t) is the natural gas flow for the compensation consumption of the waste heat boiler in the CCHP unit k in the n period; H _k (t) is the heat output power of the waste heat boiler regenerator in CCHP unit k; L _k (t) is the cooling output power of the refrigeration equipment in CCHP unit k. F _k (t) and F _kb (t) are measured by heat.

(3) Mathematical model of energy storage equipment:

The operating constraints of electric energy storage are as follows:

0≤P _dis (t)≤P ^max ,

0≤P _char (t)≤P ^max ,

SOC(t)=SOC(t-1)+η _char P _char (t)-P _dis (t)/η _dis ,

SOC ^min ≤SOC(t)≤SOC ^max ,

Among them, P _char (t) and P _dis (t) are the charging and discharging power of the electric energy storage, respectively, and P ^max is the maximum value; SOC (t) is the capacity of the electric energy storage in the t period; η _char and η _dis are respectively is the charging and discharging efficiency of the electric energy storage; SOC ^min and SOC ^max are the minimum and maximum capacity of the electric energy storage, respectively.

The operational constraints of thermal energy storage are as follows:

H _T (t)=n _T H _T (t-1)+n _TD H _TD (t)-H _TC (t)/n _TC ,

Among them, H _TD (t) and H _TC (t) are the charging and discharging power of the heat storage device, respectively, is the maximum value of the charging and discharging power of the heat storage device; H _T (t) is the heat storage level of the heat storage device in the t period; η _TD and η _TC are the charging and discharging efficiencies of the heat storage device, respectively; 1-η _T is the loss rate of the heat storage device after unit time; is the heat storage capacity of the heat storage device. The operating costs of energy storage equipment are expressed as follows:

C _bat (t)=C _EES (P _char (t)+P _dis (t))+C _TES (H _TD (t)+H _TC (t))+C _CES (L _TD (t)+L _TC ( t)),

Among them, C _EES , C _TES and C _CES are the cycle loss costs of electricity, heat and cold energy storage, respectively; L _TD (t) and L _TC (t) are the charging and discharging power of cold energy storage, respectively.

(4) The mathematical model of the electric heating and electric cooling energy conversion equipment is as follows:

H _EH (t)=η _EH P _EH (t),

L _EC (t) = η _EC P _EC (t),

Among them, the subscripts EH and EC represent the conversion of electricity to heat and electricity, respectively; η _EH and η _EC represent the conversion efficiencies of electricity to heat and electricity, respectively. The running costs are as follows:

C _conv (t) = C _EH P _EH (t) + C _EC P _EC (t).

Step S2: Calculate the overall operating cost of the integrated energy system based on the determined operating costs, take the minimum overall operating cost as the objective function, and take the electricity, heat, and cooling balance conditions as the constraints.

Objective function:

Among them, C _gas is the price of unit gas; C _CCHP,l is the maintenance cost of CCHP unit l; N _CCHP is the number of CCHP units.

Restrictions:

Among them, P _buy (t) and P _sell (t) are the power purchased and sold by the park to the upper power grid, respectively; E _load (t), H _load (t), and L _load (t) are the total electricity and heat of the park, respectively. and cooling load.

Step S3: The whale algorithm is used to perform multi-dimensional optimization on the objective function within a one-day time scale based on constraints.

As shown in FIG. 1 , the whale algorithm adopted in the present invention is an improved whale algorithm. The general idea is as follows: first, calculate the fitness function value of each individual whale, and record the optimal individual and its position X _p . Then, update the parameters ω, A, C, l. Next, generate a random number p, if p < 0.5, continue to judge, if |A| < 1, then the whale swims with adaptive inertia weight, otherwise, it swims with Cauchy variation. If p ≥ 0.5, the whale swims in a spiral. Determine whether the number of whales has reached the maximum value and the maximum number of iterations has been reached. If so, end the algorithm and get the optimal position of the whale.

The specific steps of using the improved whale algorithm to solve the optimal solution of the objective function based on the constraints in step S2 are as follows:

(1) Randomly generate N solution vectors of each variable in the objective function that satisfy the constraints; regard the number N of solution vectors as the number of whales; regard the number of variables M in the objective function as the dimension of the whale search space; The i-th solution vector is regarded as the position of the i-th whale in the M-dimensional space i=1, 2, ..., N; regard the objective function value as the fitness function value;

(2) Set the maximum number of iterations, and record the maximum number of iterations as it ^max ; record the current number of iterations as k and initialize: k=1; record the ordinal number of the current whale as i and initialize: i=1; Whales are regarded as optimal individuals;

(3) Update the optimal individual: compare the fitness function value of the i-th whale individual with the fitness function value of the optimal individual, and update the whale individual with the smaller function value as the optimal individual. The optimal individual remains unchanged; record the updated optimal individual and its fitness function value and position X _p ;

(4) Update parameters ω, A, C and l:

A=2ω·r-ω,

C=2·r,

Among them, ω is a linear convergence factor, which decreases linearly from 1 to 0 as the number of iterations increases; t is the current number of iterations, it ^max is the maximum number of iterations, r is a random number between [0, 1], and l is [-1 , a random number between 1];

(5) Use the updated parameters to update the whale's position:

A random number p is generated. If p≥0.5, the whale swims in a spiral, which is used to simulate the whale's spiral motion to capture prey. At this point, the position is updated using the following formula:

in, represents the distance between the i-th whale and its prey, and b is a constant used to define the logarithmic spiral shape;

If p<0.5 and |A|<1, the whale swims with adaptive inertial weights. At this point, the position is updated using the following formula:

D ₌ |C·Xp(t)-X(t)|,

X(t+1)=ω(t)·X _p (t)-A·D,

t is the current number of iterations, it ^max is the maximum number of iterations, and A·D is the bracketing step size. The mathematical description method of the whale's bubble net prey behavior includes the shrinkage and encirclement mechanism, which is realized with the decrease of the convergence factor ω; after the convergence factor ω is introduced as the adaptive inertia weight, the linear inertia weight of the traditional whale algorithm is carried out. Improvements have been made to improve the local search ability and convergence accuracy;

If p<0.5 and |A|≥1, then the Cauchy whale swims with variation, which is used to simulate the behavior of individual whales randomly searching for food according to each other's positions. At this point, the position is updated using the following formula:

In the formula, X _ij (t) is the j-th position of the i-th whale before mutation, that is, the solution of the j-th independent variable in the i-th solution vector randomly generated in step (1);

(6) make i=i+1, judge whether i reaches N; if it does not reach N, then repeat steps (3) to (5); otherwise, go to step (7);

(7) Let k=k+1, judge whether k reaches the maximum iteration number it ^max ; if not, repeat steps (3) to (6); otherwise, end the algorithm to obtain the updated optimal position of the whale X _p is the global optimal solution of the objective function.

Claims (7)

1. A multi-dimensional optimization method for an integrated energy system is characterized by comprising the following steps:

(1) establishing a model of renewable energy and cold, heat and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining a calculation mode of each operation cost and balanced conditions of electricity, heat and cold in a comprehensive energy system; wherein, each item of operation cost includes: the method comprises the following steps of (1) total operation cost of a renewable energy source unit, total gas cost, total maintenance cost of a combined cooling heating and power system (CCHP) unit, transaction cost and total operation cost of energy storage equipment;

(2) determining the overall operation cost of the comprehensive energy system based on each operation cost, taking the minimum overall operation cost as a target function, and taking the balance conditions of electricity, heat and cold as constraint conditions;

(3) and performing multi-dimensional optimization on the objective function within a time scale of one day by adopting a whale algorithm based on a constraint condition.

2. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), based on the established mathematical models of renewable energy and cooling, heating and power loads,

in the formula, C_m(t) the cost of the renewable energy unit m at the time period t; c_gIs a power generation cost parameter;generating capacity of the renewable energy source unit m at a time period t; c_omA cost parameter for operation and maintenance; k is a radical of_m(t) is a variable of 0 to 1 for controlling whether renewable energy is input; c_dmMaintenance costs for shutdown;andrespectively is the upper limit and the lower limit of the output of the renewable energy unit m.

3. The multi-dimensional optimization method of integrated energy system according to claim 1, wherein in step (1), based on the established system operation model,

wherein,andrespectively setting the upper limit and the lower limit of the k electric output power of the CCHP unit;andthe downward climbing rate and the upward climbing rate of the electrical output power are respectively;

the waste heat boiler operation constraints in the CCHP unit k are as follows:

H_k(t)/η_HEX+L_k(t)/η_COP≤η_FHF_k(t)+η_bF_k-b(t)，

wherein H_k(t) is the heat output power of a waste heat boiler regenerator in a CCHP unit k, η_HEXTo the heat exchanger efficiency; l is_k(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit k, η_COPη being the refrigeration coefficient of the refrigerator_FHThe waste heat recovery coefficient of the internal combustion engine in CCHP; f_k(t) natural gas flow consumed by the internal combustion engine in the CCHP unit k during the period t η_bThe thermal efficiency is complemented-burning for the boiler; f_k-b(t) compensating the consumed natural gas flow rate of the waste heat boiler in the CCHP unit k at a time period t;the maximum operation power of a waste heat boiler in a CCHP unit k; f_k(t) and F_k-b(t) is measured by heat.

4. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein in the step (1), the operation of the electrical energy storage is constrained according to the established mathematical model of the energy storage device as follows:

SOC(t)＝SOC(t-1)+η_charP_char(t)-P_dis(t)/η_dis，

SOC^min≤SOC(t)≤SOC^max，

0≤P_dis(t)≤P^max，

0≤P_char(t)≤P^max，

wherein, SOC (t) is the capacity of the electric energy storage in the period t; p_char(t) and P_dis(t) charging and discharging power, P, for electrical energy storage, respectively^maxFor maximum value of charge-discharge power of electric energy storage η_charAnd η_disCharging and discharging efficiencies for electrical energy storage, respectively; SOC^minAnd SOC^maxThe minimum value and the maximum value of the capacity of the electric energy storage are respectively;

the operating constraints of thermal energy storage are as follows:

H_T(t)＝η_TH_T(t-1)+η_TCH_TC(t)-H_TD(t)/η_TD，

wherein H_T(t) the heat storage level of the heat storage device is 1- η_Tη is the loss rate of the heat storage device after unit time_TDAnd η_TCThe heat charging efficiency and the heat discharging efficiency of the heat storage device are respectively; h_TD(t) and H_TC(t) the charging and discharging powers of the heat storage device are respectively;is the heat storage capacity of the heat storage device;the maximum value of the charging power and the discharging power of the heat storage device;

the operating cost of the energy storage device is expressed as follows:

C_bat(t)＝C_EES(P_char(t)+P_dis(t))+C_TES(H_TD(t)+H_TC(t))+C_CES(L_TD(t)+L_TC(t))，

wherein, C_bat(t) is the total operating cost of the energy storage device during the period t; c_EES、C_TESAnd C_CESThe cycle loss costs of electricity, heat, and cold stored energy, respectively; l is_TD(t) and L_TCAnd (t) cold charging power and cold discharging power of cold energy storage in a time period t respectively.

5. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), according to the mathematical model of the electric heating and cooling energy conversion equipment,

H_EH(t)＝η_EHP_EH(t)，

L_EC(t)＝η_ECP_EC(t)，

wherein H_EH(t) heat quantity converted at time t, L_EH(t) the cold quantity converted at the moment t; p_EH(t) and P_EC(t) the amount of electricity converted into heat and cold at time t, η_EHAnd η_ECThe electric heating conversion efficiency and the electric cooling conversion efficiency are respectively obtained;andupper limits of the amount of electricity that can be converted into heat and cold, respectively;

the operating costs of the electric heat and electric cold energy conversion equipment are as follows:

C_conv(t)＝C_EHP_EH(t)+C_ECP_EC(t)，

wherein, C_conv(t) is the total operating cost of the electric heating and cooling energy conversion equipment at the moment t; c_EHAnd C_ECThe cost of unit electric heat and electric cold conversion respectively.

6. The method according to claim 1, wherein in step (2), if the objective function is denoted as f, then there is

Wherein, C (t) is the whole operation cost in the period t; c_m(t) the cost of the renewable energy unit m at the time period t; n is a radical of_reThe total number of the renewable energy units; c_gasIs the unit price of the gas; n is a radical of_CCHPThe number of units of a combined cooling heating and power system CCHP; f_l(t) is the natural gas flow consumed by the internal combustion engine in the CCHP unit l during a period t; f_l-b(t) compensating the consumption of natural gas flow by a waste heat boiler in a CCHP unit l at a time period t; c_CCHP,lThe maintenance cost of the CCHP unit l in the period t; c_trade(t) transaction cost for t period；C_bat(t) is the total operating cost of the energy storage device during the period t; c_conv(t) is the operating cost of the electric heating and cooling energy conversion equipment;

the constraint conditions are as follows:

wherein,generating capacity of the renewable energy source unit m at a time period t; p_l ^CCHP(t) is the generated energy of the CCHP unit l in a period t; p_buy(t) and P_sell(t) purchasing electricity and selling electric power to the upper-level power grid in the park respectively; p_char(t) and P_dis(t) charging and discharging power for electrical energy storage, respectively; p_EH(t) and P_EC(t) the electric quantities converted into heat and cold at time t, respectively; e_load(t)、H_load(t) and L_load(t) total electrical, thermal and cold load for the park, respectively; h_TI(t) and H_TO(t) the heat storage absorption amount and the discharge amount of the heat storage device in the period of t are respectively; l is_TI(t) and L_TO(t) the cold storage absorption amount and the discharge amount of the cold storage device in the period of t are respectively; h_l(t) is the heat output power of a waste heat boiler heat regenerator in the CCHP unit l; l is_l(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit l; h_EH(t) heat quantity converted at time t, L_EHAnd (t) is the cold energy converted at the moment t.

7. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein the step (3) comprises the steps of:

(31) randomly generating solution vectors of variables in N objective functions meeting constraint conditions; considering the number of solution vectors, N, as the number of whales; taking the variable number M in the objective function as the dimension of a whale search space; regarding the ith solution vector as the position of the ith whale in the M-dimensional space Taking the objective function value as a fitness function value;

(32) setting the maximum iteration times, and recording the maximum iteration times as it^max(ii) a Recording the current iteration number as k and initializing: k is 1; the ordinal number of the current whale is recorded as i and initialized: i is 1; the 1 st whale was considered as the optimal individual;

(33) updating the optimal individual: comparing the fitness function value of the ith whale individual with the fitness function value of the optimal individual, updating the whale individual with a small function value into the optimal individual, and keeping the optimal individual unchanged if the two are equal; recording updated optimal individual and fitness function value and position X thereof_p；

(34) Update parameters ω, A, C, and l:

A＝2ω·r-ω，

C＝2·r，

where ω is a linear convergence factor, t is the current iteration number, it^maxIs the maximum number of iterations, r is [0, 1 ]]Is a random number of [ -1, 1 ]]A random number in between;

(35) updating the location of the whale with the updated parameters:

generating a random number p, if p ≧ 0.5, updating the location using the following equation:

wherein,represents the distance between the ith whale and the prey, b is a constant defining the shape of a logarithmic spiral;

if p <0.5 and | A | <1, then the location is updated using:

D＝|C·X_p(t)-X(t)|，

X(t+1)＝ω(t)·X_p(t)-A·D，

t is the current iteration number it^maxIs the maximum iteration number, and A.D is the surrounding step length;

if p <0.5 and | A | ≧ 1, the location is updated using the following equation:

in the formula, X_ij(t) is the jth position point of the ith whale before mutation, namely the solution of the jth independent variable in the ith solution vector randomly generated in the step (31);

(36) making i equal to i +1, and judging whether i reaches N; if not, repeating the steps (33) to (35); otherwise, go to step (37);

(37) let k equal to k +1, judge whether k reaches the maximum iteration number it^max(ii) a If not, repeating the steps (33) to (36); otherwise, the algorithm is ended to obtain the updated optimal position X of the whale_pWhich is the global optimal solution of the objective function.