CN110543977B - Regional building group mixing optimization method based on multi-element load leveling visual angle - Google Patents
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Abstract
The invention relates to a regional building group mixing optimization method based on a multi-element load leveling visual angle, which is used for optimizing and determining the area occupation ratio of N buildings in a region, and comprises the following steps: (1) Establishing an objective function with minimum comprehensive load fluctuation rate in the region; (2) Taking the area occupation ratio of N buildings in the area as an optimization variable, and establishing constraint conditions based on the optimization variable; (3) And solving the objective function based on the constraint condition to obtain the area occupation ratio of N buildings in the area. Compared with the prior art, the method considers the cooperative requirement of the distributed energy source mainly supplied by combined cooling, heating and power on the stability of the multi-element loads, adopts a genetic algorithm to solve, can realize model optimization with the minimum fluctuation rate of the multi-element loads as a target, improves the stability of the load at the demand side, reduces the fluctuation of the thermoelectric ratio of regional building groups time by time, and improves the application effect of the regional comprehensive energy system.
Description
Technical Field
The invention relates to a regional building group mixing optimization method, in particular to a regional building group mixing optimization method based on a multi-element load leveling view angle.
Background
Currently, the economy of China is changed from a high-speed growth stage to a high-quality development stage. As an economic development sunny and rainy day, the energy consumption structure of China is also in a deep adjustment period. In general, the industrial energy is increased and slowed down or even the total amount is reduced, and the building energy consumption presents a situation of double increase of the total amount and the specific gravity along with the improvement of living standard and the improvement of living requirements. The above development situation has also been documented by the historical evolution of urbanization, industrialization and modernization in developed countries. Therefore, in a quite long time in the future, building energy conservation becomes a main battlefield for energy conservation and emission reduction of China.
Based on the current situations that the annual utilization hours of the existing single building type distributed energy supply project are small, the dynamic unbalance of cold and hot electric loads at two sides of supply and demand is caused, the multi-element load at the demand side is dynamically changed in real time, the thermoelectric ratio is relatively strong in fluctuation, and the like, the supply side is limited by technology, the ratio of the hot output to the electric output is relatively stable, and the dynamic change at the demand side is difficult to adapt to. In order to solve the above-mentioned dilemma, it is conceivable to start from the source, and to level and stabilize the terminal load by the demand side management in the planning period so as to adapt to the supply side energy output rule. Most of the existing researches are to set different building mixed scenes so as to analyze the load change and the influence of the load change on the operation effect of an energy system, but no research is currently conducted on how to reduce the multi-element load fluctuation rate at the demand side.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a regional building group mixing optimization method based on a multi-element load leveling view angle.
The aim of the invention can be achieved by the following technical scheme:
the regional building group mixing optimization method based on the multi-element load leveling visual angle is used for optimizing and determining the area occupation ratio of N buildings in a region, and comprises the following steps of:
(1) Establishing an objective function with minimum comprehensive load fluctuation rate in the region;
(2) Taking the area occupation ratio of N buildings in the area as an optimization variable, and establishing constraint conditions based on the optimization variable;
(3) And solving the objective function based on the constraint condition to obtain the area occupation ratio of N buildings in the area.
The integrated load fluctuation ratio in the step (1) is an integrated load fluctuation ratio based on the average load.
The objective function of the step (1) is:
Min F＝θ e f e +θ h f h +θ c f c ，
wherein F is the integrated load fluctuation rate based on the average load, F e 、f h And f c Average load-based load fluctuation ratios, θ, of electric load, thermal load and cold load, respectively e 、θ h And theta c Load fluctuation ratio weighting factors, θ, of electric load, thermal load and cold load, respectively e +θ h +θ c ＝1。
f e 、f h And f c Obtained by the following steps:
(a) Calculating the time-by-time load sum of the electric load, the heat load and the cold load of N buildings in the area;
(b) According to the time-by-time load sum of various loads, the load fluctuation rate of the corresponding load based on the average load is obtained: f (f) e 、f h And f c 。
The step (a) comprises the following steps:
where T represents the T-th hour, t=1, 2, … …, T represents the total number of hours in the statistical period, j represents the building type, j=1, 2, … …, N represents the total number of building types in the area, k represents the load type, k=e is the electrical load, k=h is the thermal load, k=c is the cooling load,represents the load quantity of the k class load in t hours, x j Representing the area ratio of the j-class building,the unit area load intensity value of the k-type load of the j-type building in t hours is shown.
Step (b) f e 、f h And f c The method comprises the following steps:
the constraint conditions of the step (2) are as follows:
wherein x is j The area ratio of the j-type buildings is represented, and N represents the total number of building types in the area.
And (3) solving by adopting a genetic algorithm to obtain the area occupation ratio of N buildings in the area.
Compared with the prior art, the invention has the following advantages:
(1) According to the regional building group mixing optimization method based on the multi-element load leveling visual angle, which is provided by the invention, the cooperative requirement of the distributed energy source taking combined supply of cooling, heating and heating as a main body on the stability of the multi-element load is considered, and a genetic algorithm is adopted for solving, so that model optimization with minimum multi-element load fluctuation rate as a target can be realized, the load stability at the demand side is improved, the fluctuation of the time-by-time thermoelectric ratio of the regional building group is reduced, and the application effect of the regional comprehensive energy system is improved.
(2) The invention breaks through the limitation of single building, plays the complementarity of the energy consumption behaviors of different industrial buildings through the clustered management of a plurality of buildings in the area, and realizes the leveling of the load on the area level.
Drawings
FIG. 1 is a flow diagram of a method for regional building group hybrid optimization based on a multiple load leveling perspective of the present invention;
FIG. 2 is a graph of typical day-by-day electrical, thermal, and cold loads for 5 typical buildings in an example, where 2 (a) is electrical, 2 (b) is thermal, and 2 (c) is cold;
FIG. 3 is a graph of building mix ratio and corresponding load fluctuation ratio for different load weights, where 3 (a) is building mix ratio for different load weights and 3 (b) is a graph of load fluctuation ratio;
fig. 4 is a graph showing the typical solar heat level leveling effect in summer after optimization in this embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. Note that the following description of the embodiments is merely an example, and the present invention is not intended to be limited to the applications and uses thereof, and is not intended to be limited to the following embodiments.
Examples
A regional building group mixed optimization method based on a multi-element load leveling visual angle is used for optimizing and determining the area occupation ratio of N buildings in a region, and 5 typical buildings in hotels, houses, offices, restaurants and shops in Shanghai are selected for analysis.
As shown in fig. 1, the method comprises the steps of:
(1) Establishing an objective function with minimum comprehensive load fluctuation rate in the region;
(2) Taking the area occupation ratio of N buildings in the area as an optimization variable, and establishing constraint conditions based on the optimization variable;
(3) And solving the objective function based on the constraint condition to obtain the area occupation ratio of N buildings in the area.
Specifically, 2 indexes of the load fluctuation ratio based on the maximum load and the load fluctuation ratio based on the average load are selected for research in the research process: taking one year as a statistical period, the load fluctuation rate based on the maximum load can be defined as the ratio of the difference value between the time-by-time load maximum value and the time-by-time load average value to the maximum value; based on the definition of the load fluctuation rate of the average load, the absolute value of the difference value between the average load value and the gradual time value is accumulated all the year round, and then the ratio of the average load value to the total annual load is calculated. Based on the method, 2 corresponding objective functions are obtained, and finally, the area occupation ratios of the corresponding N buildings are obtained by respectively solving the 2 objective functions, and the comparison analysis shows that the area occupation ratio of the N buildings obtained when the comprehensive load fluctuation ratio based on the average load is the minimum objective function is the most reasonable, so that the comprehensive load fluctuation ratio in the step (1) is the comprehensive load fluctuation ratio based on the average load, and the objective function is as follows:
Min F＝θ e f e +θ h f h +θ c f c ，
wherein F is the integrated load fluctuation rate based on the average load, F e 、f h And f c Average load-based load fluctuation ratios, θ, of electric load, thermal load and cold load, respectively e 、θ h And theta c Load fluctuation ratio weighting factors, θ, of electric load, thermal load and cold load, respectively e +θ h +θ c ＝1。
f e 、f h And f c Obtained by the following steps:
(a) The total time-by-time load of the electric load, the heat load and the cold load of N buildings in the area is calculated, and the method specifically comprises the following steps:
where T represents the T-th hour, t=1, 2, … …, T represents the total number of hours in the statistical period, j represents the building type, j=1, 2, … …, N represents the total number of building types in the area, k represents the load type, k=e is the electrical load, k=h is the thermal load, k=c is the cooling load,represents the load quantity of the k class load in t hours, x j Representing the area ratio of the j-class building,a unit area load intensity value of a kth load of the j-type building in t hours is represented;
(b) According to the time-by-time load sum of various loads, the load fluctuation rate of the corresponding load based on the average load is obtained: f (f) e 、f h And f c ：
In this embodiment, the statistical time period is 1 year, i.e., t=8760.
The constraint conditions of the step (2) are as follows:
wherein x is j The area ratio of the j-type buildings is represented, and N represents the total number of building types in the area.
And (3) solving by adopting a genetic algorithm to obtain the area occupation ratio of N buildings in the area, wherein the area occupation ratio is specifically as follows:
initializing a population: inputting initial values into the model, generating random populations of n chromosomes, setting the maximum genetic algebra, and randomly generating initial populations. To ensure result reliability and population diversity, the population number was set to 100 and the genetic algebra was set to 150.
Assessing fitness of an individual: and evaluating the goodness of the individual solutions, wherein the minimum value of the regional load fluctuation index is solved, the objective function is calculated as the fitness, and the finally solved optimal fitness value is the final objective function value.
Selection operation: and selecting excellent individuals of the population according to the individual fitness, wherein the lower the fitness is, the smaller the corresponding load fluctuation rate is, and the easier the individuals are inherited.
And (3) performing crossover operation: new individuals are obtained through parent individual gene exchange recombination, and the crossover rate is set to be 0.4 in order to ensure high fitness and searching efficiency.
And (3) mutation operation: and (3) changing a certain individual gene into a corresponding allele through an algorithm, preventing the individual gene from falling into local optimum, and setting the mutation rate to be 0.2.
Judging termination: and stopping the algorithm when the maximum genetic algebra is reached, selecting the individual with the minimum fitness as the optimal solution output, stopping the algorithm, and otherwise, continuing to evaluate the fitness of the individual.
The optimized model constructed by the invention is a multidimensional nonlinear problem, a genetic algorithm is to be adopted, 5 typical buildings of hotels, houses, offices, restaurants and shops located in Shanghai are selected for analysis according to the current typical block building composition, and the time sequence difference of the loads of all types of buildings is expected to be utilized, so that the leveling of the demand side is realized by exerting the time complementation of the time sequence difference.
Parameters required by simulation such as an enclosure structure, indoor design parameters, internal disturbance (personnel, equipment, illumination) and the like are set according to related national standards and specifications, and a cold-heat-electricity load curve diagram of 5 buildings on a typical day is specifically simulated, as shown in fig. 2.
And iterating for multiple times to obtain a converged iteration number-fitness curve, namely obtaining an optimal solution under the minimum value of the objective function.
In the optimization process, the cold, hot and electric loads are set with the same weight, the building mixing ratio under the load fluctuation rate based on the maximum load and the load fluctuation rate based on the average load is analyzed, and the analysis of which objective function is more reasonable. (load fluctuation ratio based on average load is more excellent)
And analyzing the change trend of the fluctuation rate by taking the electric load weight as a parameter and the cold and hot load weights as the consistency. Fig. 3 is a graph of building mix ratios and corresponding load fluctuation ratios under different load weights, wherein the building mix ratios are the area occupation ratios of various buildings. By reducing the number of building types in an area, the impact of reducing the building type area on the optimization results was discussed and analyzed. The building mixing ratio and the load fluctuation rate optimization results of 4 kinds of buildings, 3 kinds of buildings and 2 kinds of buildings are considered respectively.
The optimized building mixing ratio is set for the regional building groups, and the practicability and feasibility of the method are verified by comparing the optimized load leveling rates with the load leveling rates of single building types.
For the combined cooling, heating and power system, the electric heat balance of the two sides of supply and demand is mainly embodied in the matching of the thermoelectric ratio. Therefore, the load leveling effect of each unit of the invention is more remarkable than the load leveling effect of each unit of the cold, heat and electricity, the influence of the optimization model on the fluctuation of the thermoelectric ratio of the regional building group time by time is analyzed, and the graph of the leveling effect of the thermoelectric ratio in the typical summer after optimization is shown in fig. 4.
The above embodiments are merely examples, and do not limit the scope of the present invention. These embodiments may be implemented in various other ways, and various omissions, substitutions, and changes may be made without departing from the scope of the technical idea of the present invention.
Claims (1)
1. The regional building group mixing optimization method based on the multi-element load leveling visual angle is characterized by being used for optimizing and determining the area occupation ratio of N buildings in a region and comprising the following steps of:
(1) Establishing an objective function with minimum comprehensive load fluctuation rate in the region;
(2) Taking the area occupation ratio of N buildings in the area as an optimization variable, and establishing constraint conditions based on the optimization variable;
(3) Solving an objective function based on constraint conditions to obtain the area occupation ratio of N buildings in the area;
the comprehensive load fluctuation rate in the step (1) is the comprehensive load fluctuation rate based on the average load;
the objective function of the step (1) is:
Min F＝θ e f e +θ h f h +θ c f c ，
wherein F is the integrated load fluctuation rate based on the average load, F e 、f h And f c Average load-based load fluctuation ratios, θ, of electric load, thermal load and cold load, respectively e 、θ h And theta c Load fluctuation ratio weighting factors, θ, of electric load, thermal load and cold load, respectively e +θ h +θ c ＝1；
f e 、f h And f c Obtained by the following steps:
(a) Calculating the time-by-time load sum of the electric load, the heat load and the cold load of N buildings in the area;
(b) According to the time-by-time load sum of various loads, the load fluctuation rate of the corresponding load based on the average load is obtained: f (f) e 、f h And f c ；
The step (a) comprises the following steps:
where T represents the T-th hour, t=1, 2, … …, T represents the total number of hours in the statistical period, j represents the building type, j=1, 2, … …, N represents the total number of building types in the area, k represents the load type, k=e is the electrical load, k=h is the thermal load, k=c is the cooling load,represents the load quantity of the k class load in t hours, x j Represents the area ratio of the j-type building, +.>A unit area load intensity value of a kth load of the j-type building in t hours is represented;
step (b) f e 、f h And f c The method comprises the following steps:
the constraint conditions of the step (2) are as follows:
wherein x is j Represents the area ratio of the j-type building, and N represents the areaTotal number of building types;
and (3) solving by adopting a genetic algorithm to obtain the area occupation ratio of N buildings in the area.
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