CN112949942A - Intelligent building load optimization scheduling method based on multi-objective multi-universe optimization algorithm - Google Patents
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Abstract
The invention discloses an intelligent building load optimization scheduling method based on a multi-objective multi-universe optimization algorithm. The method comprises the steps of carrying out feature extraction on predicted running time data of various building loads to obtain the predicted running time and peak power utilization running data of each load in one day, classifying the load features of the building householder loads through a clustering algorithm, obtaining controllable time periods of building controllable loads according to power utilization habits of the building householders on various loads, establishing a building load power utilization scheduling model, and improving a multi-objective multi-element universe optimization algorithm to be used for building power utilization load scheduling to achieve the aims of lowest total power utilization cost of a user side, lowest power utilization amount of a power grid during stable running and peak power utilization time and lowest electric energy discarding rate of new energy.
Description
Technical Field
The invention relates to the field of automatic demand response of power utilization of an intelligent power grid, and discloses an intelligent building load optimization scheduling method based on a multi-objective multi-universe optimization algorithm.
Background
The intelligent building is the development direction of urban residents in the future, and realizes automatic load control and automatic demand response. Therefore, the construction load scheduling problem is very important. The more the housing aspect of the power situation is developed in the future, and the housing aspect of the power situation is developed in recent years. And implementing a demand response strategy to limit the power consumption of the user terminal so as to change the consumption habit of the user.
The demand response mechanism is currently the most important and effective demand-side management approach, and demand responses can be divided into price-based demand response policies and incentive-based demand response policies in different ways to guide users into participating in demand responses. Because the price mechanism is the basis of the market mechanism, the user adjusts the original electricity utilization mode according to the market price, reduces or transfers the price-based demand response strategy of the electricity utilization demand to formulate the electricity utilization strategy meeting the self economic and comfort targets.
The multivariate universe optimization algorithm is based on the principle that substances in the universe are transferred from white holes to black holes through wormholes for simulation. The main performance parameters are the wormhole existence probability and wormhole travel distance rate, the parameters are relatively few, and low-dimensional numerical experiments show relatively excellent performance.
Disclosure of Invention
The invention mainly aims at the problems of energy waste, stable operation of a power grid and utilization rate of new energy caused by unreasonable electricity utilization habits of building residents, and provides an intelligent building load optimal scheduling method based on a multi-objective multi-universe optimization algorithm to improve the electricity utilization habits of the building residents. On the premise of considering the comfort level of household electricity consumption, the household electricity consumption is reduced, the operation of a power grid is stabilized, and the utilization rate of new energy is improved through the optimized scheduling of the household energy management system.
The method comprises the steps of extracting the characteristics of the predicted running time data of various building loads to obtain the one-day predicted running time and peak power utilization running data of each load, classifying the load characteristics of the building householder loads through a clustering algorithm, obtaining the controllable time period of the building controllable loads according to the power utilization habits of the building householder on various loads, establishing a building load power utilization scheduling model, improving a multi-objective multi-element universe optimization algorithm, and using the improved multi-objective multi-element universe optimization algorithm in building power utilization load scheduling to achieve a better expected effect.
In order to solve the problems, the invention adopts the following technical scheme that the method comprises the following steps:
step 1: according to the electricity utilization habits and the electricity utilization satisfaction degrees of users, building loads are divided into two major categories, namely controllable loads and uncontrollable loads, the uncontrollable loads do not directly participate in the building household load electricity utilization scheduling, but indirectly influence the schedulable loads in the building load scheduling, and therefore renewable energy sources and additional storage batteries are added in a building load scheduling model. The controllable loads are divided into three types of translatable continuous workload, interruptible workload, charging/discharging load and the like,
step 2: and (3) defining a target function of building load scheduling and constraint conditions of each power load, and building a building load power utilization scheduling model.
And step 3: the multi-universe algorithm is subjected to multi-objective improvement by adopting a non-dominated method, intelligent building load scheduling is an integer programming problem, and variables are required to be guaranteed to be integers in the iterative optimization process of the algorithm, so that the original population updating mechanism is improved, but the diversity of the population is not changed; and the multi-objective multi-universe optimization algorithm is used for optimizing scheduling.
And 4, step 4: and (5) making an experimental conclusion, and analyzing the conclusion.
According to the intelligent building load optimization scheduling method based on the multi-objective multi-universe optimization algorithm, the translational continuous working load in the step 1 is free in operation time interval, but needs to be electrified continuously to work for a certain time, such as loads of a washing machine, an electric cooker, a dish washing machine and the like, the loads can meet power consumption requirements within a period of time, and the intelligent building load optimization scheduling method has a certain scheduling potential; the interruptible workload refers to short-time interruption in the operation process, and does not affect the normal life of users, such as air conditioners, water heaters and the like, but the load has certain limitation on the interruption time, and the use habits and comfort of the users need to be comprehensively considered; the charge/discharge load refers to an electric vehicle and an energy storage battery, and the new energy power generation load generally refers to photovoltaic power generation or wind power generation equipment.
In the intelligent building load optimal scheduling method of the multi-objective multi-universe optimization algorithm, in the step 2, the building load scheduling model, the objective function and the constraint condition are respectively set up as follows:
a. objective function for building load scheduling optimization
The method comprises the following steps of establishing an objective function model by using the lowest total electricity consumption cost of a user side, the lowest electricity consumption of a power grid in stable operation and at the peak electricity price moment, the lowest electricity energy discarding rate of new energy and the like, wherein the objective function model is as follows:
minF＝[C,D,U] (1)
in the formula, C, D and U are the total electricity charge (taking RMB as a unit) of all families in one day, the deviation value (kilowatt-hour) of electricity consumption and the new energy discarding rate respectively. T isi,j、Pi,jAnd ci,jRespectively representing the j-th electricity consumption time period, the power and the time-of-use price of the load ith, wherein n is the total amount of the load, H is the number of the time periods divided by 24 hours, and wj,gen、wj,genRespectively representing the electric quantity generated and the electric quantity consumed by the new energy device in the j time period. EjAnd E represents the power consumption amount at the j-th time and the average power consumption amount for one day, respectively.
b. Translatable continuous workload model
The load of the initial running time can be changed within the controllable time of the load by the translatable continuous workload, the starting time can be changed within a short time, the starting time can be translated forwards or backwards for a certain time from the original starting time within the controllable time, and the optimal selection is to translate the running at the time of the peak electricity price to the running at the time of the low peak electricity price. As shown in fig. 3. The method comprises the following steps that a certain controllable time period of the translatable continuous workload has three moments, the earliest starting moment of the controllable time period is the latest starting moment, the starting moment of the running and starting of the load, the load cannot be interrupted in the running time period, and the load is the continuous workload. Where Ton and Toff represent the start and end times of the load in a controlled time period T1-Tn during which the load may be translated, and Tturn represents the time during which the load needs to continue operating during the controlled time period.
c. Continuous interruptible workload model
The interruptible workload model can interrupt the load operation within a controllable working time in a short time under the condition of meeting the permission of residents, and the load returns to the working state when the residents are not dissatisfied, the working time of the interruptible working load indicates the maximum interruptible time of the load and the minimum continuous working time of the load, in the process of load operation, the maximum interruptible time period cannot be exceeded when the load is interrupted, and the load can be interrupted after the minimum continuous working time of the load. As shown in fig. 4, T1, T2, T3, …, Tm indicates a load-controllable continuous operation state, the continuous operation time reaches an interruptible load minimum operation time Tmin, and the operation state can be exited or the operation can be continued at the next time Tm + 1; tm +1, …, Tm + n indicates that the interruptible load continuous dead time cannot exceed the maximum discontinuous operating time, and if Tn is Tmax, the next time Tn +1 must enter the operating state.
d. Charge/discharge load model
A charge/discharge load such as an electric vehicle charges during a low-electricity-rate period, feeds electricity to a power grid or a home energy management system during a peak-electricity-rate or peak-electricity-rate period, charges at night during a day, and feeds electricity to the energy management system during a peak-electricity-rate period during a day, as shown in fig. 5. The load needs to be restrained by increasing the electric quantity of the storage battery, the electric quantity is calculated according to the electricity price of the power grid during charging, and the loss of the electric automobile needs to be subsidized when the electric automobile is transmitted to a home energy management system.
Constraint conditions of charge/discharge load:
wherein, XjFor the load state (1: charging, 0: suspended, -1: discharging) of the charging/discharging load at the j-th time period, Cappre、ηch、PjAnd Cap respectively represent initial electric quantity, charging efficiency, charging power and maximum chargeable quantity of charging load, TonAnd ToffRespectively showing the charging start time and the charging end time.
e. New energy additional energy storage load model
Because new energy such as photovoltaic and wind power is unstable, a storage battery is required to buffer energy and stably output voltage, and the voltage is directly supplied to building residents for use, as shown in fig. 6. The storage battery of the new energy additional energy storage load (the storage battery is selected herein) is suspended in other time except for finishing the power storage and the power supply, and the storage battery is mainly used for storing the multiple power of the new energy and supplying power to the building residents at the next power consumption peak or the peak power price, as shown in figure 7.
In order to prolong the service life of the storage battery, the additional energy storage load is used for increasing the electric quantity Cap of the battery at the h momenthAnd maximum and minimum charge/discharge degrees alphamax、αminConstraint conditions are as follows:
wherein, capiThe electric quantity of the energy storage load at the moment h is added to the new energy,
is the initial electric quantity in one day，ηch、ηdis、xjAnd pjAnd dividing and charging power.
In the intelligent building load optimal scheduling method based on the multi-objective multi-universe optimization algorithm, in step 3, the multi-objective multi-universe optimization algorithm is used for building load optimal scheduling, the flow chart of the algorithm is shown in fig. 2, and the specific steps are as follows:
(1) and importing the data of the predicted electricity consumption of the residential load of the building.
(2) Defining an objective function and a constraint condition in the step 2; and initializing, namely, definitely searching space dimensions, initializing according to the upper limit and the lower limit of each variable, and setting an iteration number parameter N and a population number P of the algorithm. The generation of the initialization population positions are as follows:
in the formula: d is the number of variables; n is the number of cosmic populations (candidate solutions);
(3) calculating the expansion rate (fitness) of each universe according to the objective function;
(4) finding the best universe and the optimum universe expansion rate. After sorting the groups according to non-dominant methods, the optimal cosmic and cosmic expansion ratios are selected.
(5) And (4) updating the population. And excavating wormholes to finish the updating iteration of the population while exploring the black holes and the white holes. The population updating mechanism of the multi-target multi-universe optimization algorithm is as follows:
WEP＝WEP,min+l×(WEP,max-WEP,min)/L (10)
TDR＝(L1/c-l1/c)/L1/c (11)
in the above formula:
representing the optimal universe, TDRIs the trip distance rate, WEPIt is the possibility that a wormhole exists,
and
respectively representing the maximum and minimum values of each variable. r isa,rb,rcAnd rdIs a random number between 0 and 1. n isj(Pj) Is the standard expansion ratio of the j-th universe, WEP,minAnd WEP,maxThe minimum value and the maximum value of the probability of existence of wormholes; l is the current iteration number, L is the maximum iteration number, and c defines the detection speed that varies with the iteration number.
Intelligent building load scheduling is an integer programming problem requiring a change in MOMVO initialization, with each variable of the population representing the time at which the controllable load begins within a controlled time. In the iterative optimization process of the algorithm, it is required to ensure that variables are integers. Applying the original updating method, the load code needs to be modified twice, and the modification of the population updating mechanism is as follows: the method can automatically meet the upper and lower boundaries, and the diversity of the population in the excavation process cannot be reduced.
(6) And (4) judging whether the iteration condition is met, whether the output result is met and the iteration condition is not met, and returning to the step 3.
The intelligent building load optimization scheduling beneficial effects based on the multi-objective multi-universe optimization algorithm are as follows: and classifying the building loads, and building a load scheduling model and a load constraint condition. And establishing an objective function model according to the lowest total electricity consumption cost of the user side, the lowest electricity consumption at the moment when the power grid operates stably and has the peak electricity price, the lowest electricity energy discarding rate of new energy and the like. And (3) adopting a multi-objective multi-universe optimization algorithm, improving a population updating mechanism of the multi-objective multi-universe optimization algorithm, and applying the improved population updating mechanism to scheduling to obtain a Pareto solution set, thereby providing a power utilization scheme for users. The invention is beneficial to promoting building users to participate in automatic demand response of power utilization, and achieves the purpose of saving electric energy.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a flow chart of a multi-objective multi-universe optimization algorithm
FIG. 3 is a schematic diagram of translatable load scheduling
FIG. 4 is a schematic diagram of translatable load scheduling
FIG. 5 is a schematic diagram of charge/discharge load scheduling
FIG. 6 is a schematic diagram of new energy power delivery and scheduling
FIG. 7 is a schematic diagram of energy storage load and energy and scheduling
Detailed Description
The present invention will be described in detail below with reference to a specific embodiment, in which the electricity load of the residents of a certain cell 120 is used in the experiment.
In this example, an intelligent building load optimization scheduling method based on a multi-objective multi-universe optimization algorithm includes the following steps:
step 1: the building load is divided into two major types of controllable load and uncontrollable load, and renewable energy sources and additional storage batteries are added in a building load scheduling model. The controllable loads are divided into three categories, namely, a translatable continuous workload, an interruptible workload, and a charge/discharge load.
Step 2: and (3) defining a target function of building load scheduling and constraint conditions of each power load, and building a building load power utilization scheduling model.
The building load scheduling model, the objective function and the constraint condition are respectively set up as follows:
a. objective function for building load scheduling optimization
The method comprises the following steps of establishing an objective function model by using the lowest total electricity consumption cost of a user side, the lowest electricity consumption of a power grid in stable operation and at the peak electricity price moment, the lowest electricity energy discarding rate of new energy and the like, wherein the objective function model is as follows:
minF＝[C,D,U] (1)
in the formula, C, D and U are the total electricity charge (taking RMB as a unit) of all families in one day, the deviation value (kilowatt-hour) of electricity consumption and the new energy discarding rate respectively. T isi,j、Pi,jAnd ci,jRespectively representing the j-th electricity utilization time period, the power and the time-of-use price of the load ith, wherein n is the total number of the loads, H is the number of time periods divided by 24 hours, and H is 96, wherein w isj,gen、wj,genRespectively representing the electric quantity generated and the electric quantity consumed by the new energy device in the j time period. EjAnd E represents the power consumption amount at the j-th time and the average power consumption amount for one day, respectively.
b. Translatable continuous workload model
As shown in fig. 3; the method comprises the following steps that a certain controllable time period of the translatable continuous workload has three moments, the earliest starting moment of the controllable time period is the latest starting moment, the starting moment of the running and starting of the load, the load cannot be interrupted in the running time period, and the load is the continuous workload. Where Ton and Toff represent the start and end times of the load in a controlled time period T1-Tn during which the load may be translated, and Tturn represents the time during which the load needs to continue operating during the controlled time period.
c. Continuous interruptible workload model
As shown in fig. 4, T1, T2, T3, …, Tm indicates a load-controllable continuous operation state, the continuous operation time reaches an interruptible load minimum operation time Tmin, and the operation state can be exited or the operation can be continued at the next time Tm + 1;
tm +1, …, Tm + n indicates that the interruptible load continuous dead time cannot exceed the maximum discontinuous operating time, and if Tn is Tmax, the next time Tn +1 must enter the operating state.
d. Charge/discharge load model
As shown in fig. 5, such loads require a constraint of increasing the electric quantity of the storage battery, and when charging is performed, the loss of the electric vehicle needs to be compensated when transmitting power to the home energy management system according to the power rate calculation of the power grid.
Constraint conditions of charge/discharge load:
wherein, XjFor the load state (1: charging, 0: suspended, -1: discharging) of the charging/discharging load at the j-th time period, Cappre、ηch、PjAnd Cap respectively represent initial electric quantity, charging efficiency, charging power and maximum chargeable quantity of charging load, TonAnd ToffRespectively showing the charging start time and the charging end time.
e. New energy additional energy storage load model
As shown in fig. 6. The storage battery of the new energy additional energy storage load (the storage battery is selected herein) is suspended in other time except for finishing the power storage and the power supply, and the storage battery is mainly used for storing the multiple power of the new energy and supplying power to the building residents at the next power consumption peak or the peak power price, as shown in figure 7.
In order to prolong the service life of the storage battery, the additional energy storage load is used for increasing the electric quantity Cap of the battery at the h momenthAnd maximum and minimum charge/discharge degrees alphamax、αminConstraint conditions are as follows:
wherein, capiAdditional storage for new energyThe electric quantity at the moment h can be loaded,
is the initial electric quantity of the day, etach、ηdis、xjAnd pjAnd dividing and charging power.
And step 3: and performing multi-objective improvement on the multi-objective multi-universe algorithm by adopting a non-dominant method, improving the original population updating mechanism, and using the multi-objective multi-universe optimization algorithm in optimized scheduling.
The multi-objective multi-universe optimization algorithm is used for building load optimization scheduling, and the method specifically comprises the following steps:
(1) and importing the data of the predicted electricity consumption of the residential load of the building.
(2) Defining an objective function and a constraint condition in the step 2; and initializing, namely explicitly searching the space dimension, initializing according to the upper limit and the lower limit of each variable, and setting the iteration number parameter N of the algorithm to be 200 and the population number P to be 300. The generation of the initialization population positions are as follows:
in the formula: d is the number of variables; n is the number of cosmic populations (candidate solutions);
(3) calculating the expansion rate (fitness) of each universe according to the objective function;
(4) finding the best universe and the optimum universe expansion rate. After sorting the groups according to non-dominant methods, the optimal cosmic and cosmic expansion ratios are selected.
(5) And (4) updating the population. Excavating wormholes to complete update iteration of the population while exploring black holes and white holes, and the related parameter W of the wormholesEP,min＝0.2，W EP,max1, c 6, the population updating of the multi-target multivariate universe optimization algorithm adopts an improved updating mechanism as follows:
(6) and (4) judging whether the iteration condition is met, whether the output result is met and the iteration condition is not met, and returning to the step 3. And 4, step 4: and obtaining Pareto frontier solution and providing a power utilization scheme for building residents.
Claims (3)
1. The intelligent building load optimization scheduling method based on the multi-objective multi-universe optimization algorithm is characterized by comprising the following steps of:
(a) the building load is divided into two major types of controllable load and uncontrollable load, the uncontrollable load does not directly participate in the building household load power utilization scheduling, and renewable energy sources and additional storage batteries are added in a building load scheduling model. The controllable load is divided into three types of translational continuous working load, interruptible working load, charging/discharging load and the like;
(b) defining an objective function and a constraint condition of building load power utilization optimized scheduling, and building a building load power utilization scheduling model;
(c) the multi-objective multi-universe optimization algorithm is used for optimizing and scheduling the electricity load of the intelligent building.
2. The intelligent building load optimization scheduling method based on the multi-objective multi-universe optimization algorithm as claimed in claim 1, wherein: in the step (b), an objective function model is established according to the lowest total electricity cost of a user side, the lowest electricity consumption of a power grid in stable operation and at the time of peak electricity price and the lowest electricity energy discarding rate of new energy, and a translational continuous working load model, an interruptible working load model, a charging/discharging load and a new energy additional energy storage load model are established.
3. The intelligent building load optimization scheduling method based on the multi-objective multi-universe optimization algorithm as claimed in claim 1, wherein: in the step (c), a non-dominated method is adopted for multi-objective improvement on the multi-objective multi-universe optimization algorithm, building load scheduling is an integer programming problem, variables are required to be guaranteed to be integers in the iterative optimization process of the algorithm, and a population updating mechanism of the multi-objective multi-universe optimization algorithm is improved; the update mechanism is as follows:
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| CN113313324A (en) * | 2021-06-18 | 2021-08-27 | 杭州电力设备制造有限公司 | Power utilization scheme determining method and power utilization scheme determining method device |
| CN114037354A (en) * | 2021-12-06 | 2022-02-11 | 河北师范大学 | Method for capacity optimization configuration of combined cooling heating and power system |
| CN114355955A (en) * | 2022-03-21 | 2022-04-15 | 武汉理工大学 | Path planning method of multi-element universe electric vehicle group inspired by ant colony algorithm |
| CN115239028A (en) * | 2022-09-22 | 2022-10-25 | 北京邮电大学 | Comprehensive energy scheduling method, device, equipment and storage medium |
| CN115249094A (en) * | 2022-09-26 | 2022-10-28 | 烟台东方智能技术有限公司 | Building energy efficiency management and optimization method based on big data |
| WO2024060521A1 (en) * | 2022-09-21 | 2024-03-28 | 国网河北省电力有限公司电力科学研究院 | Calculation method for schedulable potential of active loads, terminal, and storage medium |
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| CN114037354A (en) * | 2021-12-06 | 2022-02-11 | 河北师范大学 | Method for capacity optimization configuration of combined cooling heating and power system |
| CN114037354B (en) * | 2021-12-06 | 2024-06-04 | 河北师范大学 | Capacity optimization configuration method for combined cooling heating power system |
| CN114355955A (en) * | 2022-03-21 | 2022-04-15 | 武汉理工大学 | Path planning method of multi-element universe electric vehicle group inspired by ant colony algorithm |
| WO2024060521A1 (en) * | 2022-09-21 | 2024-03-28 | 国网河北省电力有限公司电力科学研究院 | Calculation method for schedulable potential of active loads, terminal, and storage medium |
| CN115239028A (en) * | 2022-09-22 | 2022-10-25 | 北京邮电大学 | Comprehensive energy scheduling method, device, equipment and storage medium |
| CN115239028B (en) * | 2022-09-22 | 2022-12-09 | 北京邮电大学 | Comprehensive energy scheduling method, device, equipment and storage medium |
| CN115249094A (en) * | 2022-09-26 | 2022-10-28 | 烟台东方智能技术有限公司 | Building energy efficiency management and optimization method based on big data |
| CN115249094B (en) * | 2022-09-26 | 2022-12-09 | 烟台东方智能技术有限公司 | Building energy efficiency management and optimization method based on big data |
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