1. A multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm is characterized by comprising the following steps:

step 1: establishing a multi-energy heat supply model aiming at a household multi-target electricity, gas and heat heating method;

step 1.1: the input and discharge of electricity, gas and heat are represented by electricity as P₁And P₂；

Step 1.2: collecting a relevant temperature variable T, a relevant unit power price variable A and a relevant emission variable B;

step 1.3: establishing a lowest price model of a multi-target electricity, gas and heat heating method;

A_{general assembly}＝A_{Electric power}P_{Electricity 1}t_{Electric power}+A_{Qi (Qi)}P_{Gas 1}t_{Qi (Qi)}+A_{Heat generation}P_{Heat 1}t_{Heat generation}

In the formula: a. the_{General assembly}The sum of prices required for 24-hour heating; a. the_{Electric power}、A_{Qi (Qi)}、A_{Heat generation}Prices of real-time unit power for electric heating, gas heating, heating company heating, and the like, respectively; p_{Electricity 1}、P_{Gas 1}、P_{Heat 1}Respectively inputting electric heating, gas heating, heating of a heating company and other different time periods; t is t_{Electric power}、t_{Qi (Qi)}、t_{Heat generation}Supplying power, gas and heat in one day;

step 1.4: establishing a minimum discharge model of a multi-target electricity, gas and heat heating method:

B_{general assembly}＝B_{Electric power}P_{Electric 2}t_{Electric power}+B_{Qi (Qi)}P_{Gas 2}t_{Qi (Qi)}+B_{Heat generation}P_{Heat 2}t_{Heat generation}

In the formula: b is_{General assembly}The sum of prices required for 24-hour heating; b is_{Electric power}、B_{Qi (Qi)}、B_{Heat generation}Real-time unit power emission rates of electric heating, gas heating, heating company heating and the like are respectively set; p_{Electric 2}、P_{Gas 2}、P_{Heat 2}The water is discharged in different time periods of electric heating, gas heating, heating of heating companies and the like; t is t_{Electric power}、t_{Qi (Qi)}、t_{Heat generation}Supplying power, gas and heat in one day;

step 2: optimizing a heat supply model, summarizing a coordination method into an optimization problem based on lowest price and lowest emission, and establishing related constraint conditions;

step 2.1: establishing a multi-objective optimization constraint condition;

heat exchange restraint:

Q_{heat generation}＝cG_{Water (W)}ΔT₁

In the formula: q_{Heat generation}Heat required for heating; g_{Water (W)}Is the mass flow rate of water; delta T₁The temperature difference between the water supply temperature and the water return temperature of the heat supply device;

and (3) heat dissipation capacity constraint:

in the formula: q_{Powder medicine}Indoor heat dissipation capacity; k is a heat dissipation coefficient; f is the heat dissipation area; delta T₂The sum of the water supply temperature and the return water temperature of the heat supply device; t is_{Temperature of}Is the current indoor temperature;

the thermodynamic characteristics constrain:

in the formula: c. C_{Heat generation}The specific heat capacity of the building;indicating an increase in heat, building heat storage;indicating a reduction in heat, building heat dissipation; q_{Powder medicine}The heat dissipation power of the heat sink; q_{Consumption unit}The total heat consumption of the building is achieved;

building constant temperature restraint:

T_min＜T_chamber＜T_max

In the formula: t is_minAnd T_maxRespectively the highest and lowest temperatures of the building;

electric force restraint:

P_d,min＜P_d＜P_d,max

in the formula: p_d,minAnd P_d,maxThe maximum value and the minimum value of the electric power output are obtained;

step 2.2: optimizing a lowest price model of the multi-target electricity, gas and heat heating method;

in the formula: a. the₁、A₂、A₃The prices of real-time unit power of electric heating, gas heating, heating company heating and the like in each time period within 24 hours are respectively; p₁(t)、P₂(t)、P₃(t) the output of electric heating, gas heating and heating company in different time periods;

in the formula: b is₁、B₂、B₃The real-time unit power discharge amount of electric heating, gas heating, heating company heating and the like in each time period within 24 hours is respectively;

step 2.3: optimizing a minimum discharge model of a multi-target electricity, gas and heat heating method;

and step 3: aiming at the generalized correlation model, performing minimum solution optimization based on a CLS-PSO algorithm to obtain a minimum price scheme and an emission minimum scheme;

step 3.1: randomly initializing the positions and speeds of various particles in the population;

step 3.2: evaluating the fitness of each particle, storing the fitness in pbest of each particle, searching an individual with the best fitness value in pbest, and storing the position and the fitness value in gbest;

step 3.3: updating the speed and the position of the particles, respectively solving the solution of each particle objective function, and then selecting the best 20% of the particles to be reserved;

step 3.4: performing chaotic local search on the optimal particles in the population, and updating pbest and gbest;

step 3.5: if the preset condition is met, stopping the algorithm, stopping searching and outputting the result, and if the preset condition is not met, turning to the step 3.6;

step 3.6: contracting the search area according to the range of the related variable set;

x_min,j＝max{x_min,j,x_g,j-r^*(x_max,j-x_min,j)}，0＜r＜1

x_max,j＝min{x_max,j,x_g,j-r^*(x_max,j-x_min,j)}，0＜r＜1

in the formula: x is the number of_g,jA value of a variable of dimension j representing a current pbest;

step 3.7: randomly generating the remaining 80% of the population to the shrunk space, and then turning to step 3.2;

step 3.8: and stopping when the maximum iteration times are reached.

Background

In recent years, the environmental pollution problem caused by central heating is a lot of attention due to frequent occurrence of haze weather in the north, and meanwhile, the haze weather in some areas has poor heating quality and low temperature and is also subject to a lot of troubles. Due to the limitation of economic cost, the centralized coal-fired heating still occupies a great amount of proportion in China, and is polluted more and poor in quality, so that clean heating is urgent. The cleaning and heating should follow the principle of 'gas and electricity when gas is suitable', and a plurality of targets are flexibly selected according to local conditions.

Disclosure of Invention

In order to solve the problems, the invention provides a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm. The method has the advantages that three heating modes are combined for use, distributed development is achieved, an original heating system is split into three system coordination optimization problems, on one hand, heating cost paid by a user can be saved, and on the other hand, pollution emission caused by heating can be reduced. Therefore, the research object of the present invention is: and a reasonable coordination scheduling scheme of the three heating modes is accurately found, and a minimum heating cost and a minimum heating pollution emission are obtained.

The technical scheme adopted by the invention is as follows:

a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm comprises the following steps:

step 1: establishing a multi-energy heat supply model aiming at a household multi-target electricity, gas and heat heating method;

step 1.1: the input and discharge of electricity, gas and heat are represented by electricity as P₁And P₂；

Step 1.2: collecting a relevant temperature variable T, a relevant unit power price variable A and a relevant emission variable B;

step 1.3: establishing a lowest price model of a multi-target electricity, gas and heat heating method;

A_{general assembly}＝A_{Electric power}P_{Electricity 1}t_{Electric power}+A_{Qi (Qi)}P_{Gas 1}t_{Qi (Qi)}+A_{Heat generation}P_{Heat 1}t_{Heat generation}

In the formula: a. the_{General assembly}The sum of prices required for 24-hour heating; a. the_{Electric power}、A_{Qi (Qi)}、A_{Heat generation}Prices of real-time unit power for electric heating, gas heating, heating company heating, and the like, respectively; p_{Electricity 1}、P_{Gas 1}、P_{Heat 1}Respectively inputting electric heating, gas heating, heating of a heating company and other different time periods; t is t_{Electric power}、t_{Qi (Qi)}、t_{Heat generation}Supplying power, gas and heat in one day;

step 1.4: establishing a minimum discharge model of a multi-target electricity, gas and heat heating method:

B_{general assembly}＝B_{Electric power}P_{Electric 2}t_{Electric power}+B_{Qi (Qi)}P_{Gas 2}t_{Qi (Qi)}+B_{Heat generation}P_{Heat 2}t_{Heat generation}

In the formula: b is_{General assembly}The sum of prices required for 24-hour heating; b is_{Electric power}、B_{Qi (Qi)}、B_{Heat generation}Real-time unit power emission rates of electric heating, gas heating, heating company heating and the like are respectively set; p_{Electric 2}、P_{Gas 2}、P_{Heat 2}The water is discharged in different time periods of electric heating, gas heating, heating of heating companies and the like; t is t_{Electric power}、t_{Qi (Qi)}、t_{Heat generation}The power, gas and heat supply time in one day.

Step 2: optimizing a heat supply model, summarizing a coordination method into an optimization problem based on lowest price and lowest emission, and establishing related constraint conditions;

step 2.1: establishing a multi-objective optimization constraint condition;

heat exchange restraint:

Q_{heat generation}＝cG_{Water (W)}ΔT₁

In the formula: q_{Heat generation}Heat required for heating; g_{Water (W)}Is the mass flow rate of water; delta T₁The temperature difference between the water supply temperature and the water return temperature of the heat supply device;

and (3) heat dissipation capacity constraint:

in the formula: q_{Powder medicine}Indoor heat dissipation capacity; k is a heat dissipation coefficient; f is the heat dissipation area; delta T₂The sum of the water supply temperature and the return water temperature of the heat supply device; t is_{Temperature of}Is the current indoor temperature;

the thermodynamic characteristics constrain:

in the formula: c. C_{Heat generation}For construction purposesSpecific heat capacity;indicating an increase in heat, building heat storage;indicating a reduction in heat, building heat dissipation; q_{Powder medicine}The heat dissipation power of the heat sink; q_{Consumption unit}The total heat consumption of the building is realized.

Building constant temperature restraint:

T_min＜T_chamber＜T_max

In the formula: t is_minAnd T_maxRespectively the highest and lowest temperatures of the building;

electric force restraint:

P_d,min＜P_d＜P_d,max

in the formula: p_d,minAnd P_d,maxThe maximum value and the minimum value of the electric power output are obtained;

step 2.2: optimizing a lowest price model of the multi-target electricity, gas and heat heating method;

in the formula: a. the₁、A₂、A₃The prices of real-time unit power of electric heating, gas heating, heating company heating and the like in each time period within 24 hours are respectively; p₁(t)、P₂(t)、P₃(t) the output of electric heating, gas heating and heating company in different time periods;

in the formula: b is₁、B₂、B₃The real-time unit power discharge amount of electric heating, gas heating, heating company heating and the like in each time period within 24 hours is respectively;

step 2.3: and optimizing the minimum discharge model of the multi-objective electric, gas and heat heating method.

And step 3: and aiming at the generalized correlation model, performing minimum solution optimization based on a CLS-PSO algorithm to obtain a minimum price scheme and an emission minimum scheme.

Step 3.1: randomly initializing the positions and speeds of various particles in the population;

step 3.2: evaluating the fitness of each particle, storing the fitness in pbest of each particle, searching an individual with the best fitness value in pbest, and storing the position and the fitness value in gbest;

step 3.3: updating the speed and the position of the particles, respectively solving the solution of each particle objective function, and then selecting the best 20% of the particles to be reserved;

step 3.4: performing chaotic local search on the optimal particles in the population, and updating pbest and gbest;

step 3.5: if the preset condition is met, stopping the algorithm, stopping searching and outputting the result, and if the preset condition is not met, turning to the step 3.6;

step 3.6: contracting the search area according to the range of the related variable set;

x_min,j＝max{x_min,j,x_g,j-r^*(x_max,j-x_min,j)}，0＜r＜1

x_max,j＝min{x_max,j,x_g,j-r^*(x_max,j-x_min,j)}，0＜r＜1

in the formula: x is the number of_g,jA value of a variable of dimension j representing a current pbest;

step 3.7: randomly generating the remaining 80% of the population to the shrunk space, and then turning to step 3.2;

step 3.8: and stopping when the maximum iteration times are reached.

Drawings

FIG. 1 is a flow chart of a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm.

FIG. 2 is an iterative graph of a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm.

FIG. 3 is a price coordination diagram of a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm.

FIG. 4 is an emission coordination diagram of a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm.

Detailed Description

In order to further illustrate the technical method adopted by the invention, the following detailed description is made on the multi-target electric, gas and thermal heating coordination method based on the CLS-PSO algorithm according to the invention by combining the attached drawings and the embodiment.

In this embodiment, a multi-target electricity, gas and heat heating coordination method based on a CLS-PSO algorithm is provided, as shown in fig. 1, the multi-target electricity, gas and heat coordination method includes the following steps:

step 1: establishing a multi-energy heat supply model aiming at a household multi-target electricity, gas and heat heating method;

step 1.1: collecting a relevant temperature variable T, a relevant unit power price variable A and a relevant emission variable B;

step 1.2: calculating the output force required for reaching the optimal indoor temperature, and electrically expressing the input and discharge of electricity, gas and heat as P₁、P₂And P₃；

Step 1.3: establishing a lowest price model of a multi-target electricity, gas and heat heating method;

step 1.4: and establishing a minimum discharge model of the multi-target electricity, gas and heat heating method.

Step 2: optimizing a heat supply model, summarizing a coordination method into an optimization problem based on lowest price and lowest emission, and establishing related constraint conditions;

step 2.1: establishing a multi-objective optimization constraint condition;

step 2.2: optimizing a lowest price model of the multi-target electricity, gas and heat heating method;

step 2.3: and optimizing the minimum discharge model of the multi-objective electric, gas and heat heating method.

And step 3: and aiming at the generalized correlation model, performing minimum solution optimization based on a CLS-PSO algorithm to obtain a minimum price scheme and an emission minimum scheme.

Step 3.1: randomly initializing the positions and speeds of various particles in the population;

step 3.2: evaluating the fitness of each particle, storing the fitness in pbest of each particle, searching an individual with the best fitness value in pbest, and storing the position and the fitness value in gbest;

step 3.3: updating the speed and the position of the particles, respectively solving the solution of each particle objective function, and then selecting the best 20% of the particles to be reserved;

step 3.4: performing chaotic local search on the optimal particles in the population, and updating pbest and gbest;

step 3.5: if the preset condition is met, stopping the algorithm, stopping searching and outputting the result, and if the preset condition is not met, turning to the step 3.6;

step 3.6: contracting the search area according to the range of the related variable set;

step 3.7: randomly generating the remaining 80% of the population to the shrunk space, and then turning to step 3.2;

examples

In order to verify the effectiveness and the applicability of the multi-target electricity, gas and heat heating coordination method based on the CLS-PSO algorithm, the method is taken into an example for analysis. Wherein, data such as 24-hour relevant air temperature, step electricity price, step gas price, emission and the like in a certain place are selected for relevant calculation.

Bring the relevant variables into the price model:and an emission model:the optimization is carried out by using a CLS-PSO algorithm, an iteration curve is shown in figure 2, and price coordination and emission coordination are shown in figures 3 and 4.